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‍When you've got thousands of events and millions of potential customers, how do you match the right ticket to the right fan at exactly the right moment? 

In this candid conversation, Jason Davis, Founder and CEO of Simon AI, sits down with Steve Mastrocola, Senior Director of Audience Marketing at SeatGeek, to unpack how AI is transforming audience targeting, eliminating marketing guesswork, and helping fans never miss the events they'll love. Note: This interview has been edited for clarity and length. You can watch the full interview below.

How do you tackle customer segmentation at SeatGeek? What are some of the challenges?

Steve: Well, like many e-commerce companies, we face unique challenges with lifecycle marketing. But we also have the added dynamic of what's happening in the live event landscape — and how to personalize our marketing for customers.

Our biggest challenge right now is figuring out which events to promote to which person at the right time, with so many events to choose from and so many dynamics going on in the live event industry. For example, if Taylor Swift is touring and you're not a huge Swift fan, it doesn’t make sense for us to promote her tour to you.

Jason: I love the Taylor Swift example. When she is touring, the audiences you want to market to aren't always obvious. If I'm a Taylor Swift fan in New York, you'll probably promote her show at Madison Square Garden. But many people will travel to see Taylor Swift as well. Who are those people, and how do you think about them?

Steve: There are so many aspects to consider. How do we know if you like Taylor Swift? Maybe you've shown interest in her or in similar artists. But how do we determine which artists are similar? Where is she playing? Where do you live? Are you willing to travel? How far?

We have access to all this customer data, but my team isn't composed of data scientists. We use tools like Simon AI to generate audiences and build them at scale, but our challenge is in managing all these data points effectively without someone having to make arbitrary decisions or write code that might not be scalable or transparent.

How are you thinking about using AI to unlock the next generation of data?

Steve: Much of our prioritization today is dictated by scale and popularity. So, NFL, MLB, NFL, or major concert artists where there's significant audience interest. But one challenge is that there are thousands of smaller artists and sports teams that collectively add up to a lot of potential revenue. Managing all of that simultaneously is much more difficult than managing just the NFL.

We also face daily decisions like: "We have an NFL campaign going out today, but Beyoncé tickets are going on sale. How do we prioritize?" These decisions are often made manually right now, and we'd love more automated, machine-driven processes for both operational efficiency and greater accuracy. Essentially, how do we remove human biases in favor of data-driven decisions?

You said AI reduces the need for humans to make arbitrary decisions. Can you expand on that?

Steve: When we choose to promote a music artist, we're using our best judgment based on what worked at scale in the past. But as we get more data, you lose economies of scale in how much you can manually evaluate. 

People also bring their own biases, such as, "This worked before, so I'll use it again," or, "I personally like these two artists, so they must be similar," when that might not be true for everyone.

By having machines make these decisions using the best possible data, it frees my team to focus more on strategy: when to send something, what creative aspects to test, and frequency of messages. It reduces the burden of making potentially poor decisions while opening operational doors to focus on areas where human creativity and judgment are more valuable.

Speaking of data points, we just built this cool weather AI agent into our platform.

How does a weather AI agent help your marketing?

Steve: Weather plays a huge role in event attendance! For baseball events, a 74-degree sunny day is much more appealing than a rainy one. Arena events have higher attendance when the weather's bad because people want to be inside.

Having access to weather data helps us improve our marketing messages. A push notification saying, "It's going to be 75 and sunny tomorrow – perfect for a day at Citi Field!" is more compelling than a generic message.

The power of third-party platforms serving this data broadly is efficiency. If I wanted to leverage weather data internally, I'd need several teams at SeatGeek to do a bunch of work. But weather is universal, and if a platform like Simon AI brings that capability to the table, it saves me advocating internally and allows all clients to leverage it at once.

As you know, we recently launched Simon AI. But our approach to AI is different: Marketers start by inputting their goal and letting the AI find the right audience to achieve it

How do you see AI changing the way your team works?

Steve: Currently, we think: "We have this campaign, how do we find an audience?" But it should be flipped to start with a goal: "We want to sell last-minute baseball tickets", and then having an AI agent identify people who buy last-minute MLB tickets.

The AI could even define what "last-minute" means for different people. For some, it might be buying an hour before the game. For me, with children, I can't make that decision in an hour – it might be 3-4 days out. Unlocking these insights at scale lets us market to someone based on how they view "last-minute" versus how we view “last-minute.”

What advice would you give to marketing leaders who are hesitant about AI?

Steve: The biggest thing is keeping an open mind. I've been at SeatGeek for eight years, and in my tenure, I've been involved with CRM in some capacity. I could have an ego and say, "I know what works," but being here that long  doing the same thing is also a detriment, right? It means I might have wrong biases as things have changed.

So, having that open-mindedness that machines can calculate and do certain tasks better is incredibly helpful. Stress to your team the benefits of AI – it's not replacing anybody; it's adding bandwidth so you can focus on more strategic, career-rewarding aspects versus execution and operations.

Also, think about how you use AI in your daily life and how it could apply to your work. We've seen many people start with copy testing by having AI generate subject lines. That was basic but helpful, but AI goes much further. Creating audiences and effective targeting are far more complicated, take more bandwidth, and often have more impact on your company.

My brother got married last year, and he literally wrote his vows by going to ChatGPT. They were good! We would never've known that he did it that way. The breadth and quality of what's out there are pretty cool.

Let's fast forward a year from now. What does success with AI look like?

Steve: Success looks like eliminating those constant decisions and back-and-forth discussions my team has around, "Should this campaign go today or tomorrow?" or, "Who should we include here?"

When we first started CRM at SeatGeek, we literally had a Google calendar for campaign scheduling. That evolved into trigger-based campaigns and recurring newsletters, but we still debate things like: "It's Selection Sunday for March Madness, but there's also a concert on sale that day – which should we prioritize?"

Success is those questions just going away. We're not having a person make decisions based on what we think is best from past experience. Instead, AI is actually crunching the data, and it's not an either/or question. The machines decide which people go where, maximizing response and revenue while helping our users get the best experience and recommendations.

It is fascinating how efficiency and operating leverage drive better results for both the bottom and top lines. When I think about AI's potential, it's allowing your team to do more in a more informed way than today while using it to drive personalization that makes everything they do smarter.

Steve: Absolutely — especially in the CRM world where people get so many emails and push notifications, and it's getting easier to opt out. Having quality messages in the right amounts to the right people is more important than ever. It becomes more of a one-to-one relationship that builds trust and prevents unsubscribes.

Lifecycle marketing is a marathon, not a sprint. While it would be nice to win over customers with a shining first impression and call it a day, truly effective marketing takes a consistent, sustained effort to win over consumers.

This is where lifecycle marketing comes in.Below you’ll find the definition and benefits of lifecycle marketing, plus some strategies marketing teams can use to improve their own processes.

What is lifecycle marketing?

Lifecycle marketing is the process of nurturing your customers throughout the customer journey. Its main goals are to grow your customer base, foster repeat buying, and nurture long-term loyalty. Achieving these goals isn’t as easy as setting up a rigid sales funnel and praying for the best—lifecycle marketing involves building deeply ingrained relationships with your customers.

You build these relationships by delivering what your customers need, tailored to where they’re at on their buyer journey.To help visualize the often involved process of selling to consumers, we like to look at the lifecycle marketing process as a sandwich. It has three categories that represent the three drivers of a business:

• The marketing (or sales funnel) lifecycle: This brand-focused lifecycle approach deals primarily with acquiring customers.
• The customer lifecycle: With a view of customer satisfaction, this approach defines how often customers buy from you and how they use your product.
• The shared loyalty lifecycle: This lifecycle deals with how the customer–brand relationship is developed over time through several customer touchpoints. The goal is to retain and grow customers and help them develop positive attitudes and behaviors toward the brand.

Aligning your lifecycle marketing strategy across all three layers of the sandwich will help you maintain high conversion rates and happy customers. This is an effort that goes well beyond the walls of your marketing department—the strategy will work best if your entire business is involved in each category. Though challenging, there are plenty of upsides to using a comprehensive lifecycle approach.

The benefits of lifecycle marketing

These are just some of the benefits you can expect from a well-executed lifecycle marketing plan:

Better alignment with the customer journey

A holistic lifecycle approach enables you to coordinate every one of your sales channels with the customer journey. With the right data on hand, marketing teams can more closely tailor their strategies to the nuances of each customer’s path.

Better targeted campaign outcomes

You probably already know that retaining customers costs a lot less than acquiring new ones. Focusing on the whole customer journey helps you increase overall profit and margins, offsetting your customer acquisition cost (CAC). Instead of investing resources only in customers whom you haven’t yet built relationships with, lifecycle marketing focuses its efforts across the board. Yes, new customer acquisition is important—but so is selling to your existing customers with a continuous lifecycle strategy.

Higher customer lifetime value

Nurturing customers increases their customer lifetime value (CLV). Along with lowering costs, lifecycle marketing increases retention and accelerates purchase frequency if done right.

Better ROI on resource usage

Rather than using a traditional sales funnel, a lifecycle approach to marketing allocates your resources where they’ll be most effective. Focusing on the needs and wants of your existing customers means you will get more value from the dollars, time, manpower, and skill sets you invest.

Increased efficiency with more useful data

Thanks to tech like predictive analytics, cookies, and automation, brands today can take a more data-driven approach to how they sell. Using data ensures you are basing your marketing decisions on actual metrics instead of spending your resources on guesswork. For example, a retargeting strategy informed by behavioral data is one of the many ways marketing teams can optimize their conversion efforts for higher revenue.

Stages of lifecycle marketing

The process of lifecycle marketing can be broken down into four stages. These stages align with the three lifecycles that are business drivers:

1. Awareness

The awareness stage—at the top of the sales funnel—is where potential customers learn about your offer. In this stage, you get to wow them with the value you bring and the problems you solve. Top-of-funnel awareness activities include:

• Content marketing — blogs, social media, influencers, etc.
• Paid advertising in spaces your ideal customer persona visits
• Engaging social media presence
• Presence at relevant industry events

This is where all customers begin, so it’s important to makes this first impression positive.

2. Consideration

Did you catch their attention? At the consideration stage, would-be customers are intentionally browsing your website, following you on social media, or maybe even subscribing to your emails. This is the point where potential customers start warming up to you and learning more about your products and brand.

Automation will be your best ally here. With it, you can tailor your approach to keep your target customers’ attention based on the actions they take or interests they express. This might involve:

• Engaging customers through a website chat form
• Offering a discount or shipping promotion
• Sending SMS messages
• Personalizing your email marketing
• Retargeting paid ads based on behaviors you’ve tracked on your site.

3. Decision

The decision stage is where it gets serious. Customers have learned enough about who you are, what you do, and if they want what you’re selling. At this stage, your potential customers decide whether to make a purchase. This stage requires proof that you’re the best solution for your customer:

• Testimonials and social proof
• Product comparisons

With lifecycle marketing, it’s important to remember that selling continues well beyond the decision phase. If your customer ultimately decides to go with your offer, you can now start the loyalty stage.

4. Loyalty

Post-purchase loyalty is your best path to stable revenue. There are many ways to nurture loyalty. Once you gain a paying customer, you can nurture them by: 

• Upselling or cross-selling products they might like
• Follow-up customer support ensuring the customer can use their product
• Offer a rewards or loyalty program
• Give customer-only discounts

In addition to encouraging more purchases from them in the future, this will also make them more likely to spread the word about your brand.

If your product or service is something customers need more than once, this stage is to promote retention so you’ll be top-of-mind when they’re ready to purchase again.

5. Advocacy

Word of mouth is one of the most powerful ways to generate new leads. Offer incentives to get your customers on board as brand evangelists, and consider including a referral program in your greater marketing plan. Throwing some love back to the people who send you more customers is money well spent. You should have “always-on” campaigns targeting an audience segment of loyal customers. These campaigns can look like:

• Requests to leave product reviews
• Exclusive first access to sales or new releases
• Referral perks for inviting new customers
• Invitations to leave feedback that fuels product improvements

How to get started with lifecycle marketing

Breaking down your customer lifecycle into the phases above will help make the process less overwhelming. From there, it’s a matter of breaking each phase down even further to pinpoint your marketing objectives. Once you‘re clear on what objectives determine success for each phase, you can map out campaigns to reach them.

During the campaign ideation phase, it helps to start with the end in mind and work backward from there. There are a few essential elements to factor into your lifecycle marketing process:

Lead generation

To market to prospects, you have to get their attention first. Define how you’ll generate leads. Quizzes, email subscriptions, free trials, or contests are some ways you can capture contact information that will be invaluable throughout the lifecycle process. Ideally, your approach to capturing leads will be evergreen.

Once you’ve tested different approaches and found the highest-performing lead capture strategies, automate them and keep them running in the background to continue funneling new leads into your marketing system.

Customer definition and segmentation

Where and how your campaign is executed will depend on the customer segments you want to reach. Once interested leads start to come in, segment them into groups by key identifiers. These could be interests, familiarity with your brand, or even by lead generation strategy. Segmentation helps you create targeted messaging that’s defined by the more specific needs of each segment, rather than marketing to every customer with the exact same approach. As customers go through their lifecycle, the messaging will change according to their needs at whatever stage they’re in. With segmentation, you can develop more personalized messaging that positions you to strengthen customer relationships.

Creating a nurturing strategy

Once you win customers, it’s vital to continue doing the work to keep them. This is where a nurturing strategy comes into play. Your nurturing strategy will include things like building engagement, creating more customer touchpoints, and ramping up your newsletter strategy.

All these efforts may be small individually, yet they are critical to the entirety of the lifecycle. After you’ve tested several campaigns to get a sense of what your ideal customer responds to, you can start automating nurturing workflows. This will mean one less marketing task you need to manage in real time, so you can focus your efforts on monitoring performance and usage data to tailor future campaigns.

Tracking and assessing campaign data

Without a strong data component, data-driven decisions can't be made. Retargeting is harder, reaching warm leads is more challenging, and any digital marketing efforts can fall flat. In other words, campaign data is the fuel that keeps your lifecycle marketing engine going. This is often upheld by a customer relationship management platform (CRM)—but a CRM alone doesn’t always come with the luxury of built-in data-capturing features to keep you informed along the buyer’s journey.

Thankfully, a customer data platform (CDP) is perfect for that. With tools like Simon CDP, you can create omnichannel customer experiences without needing to know code. They enable you to consolidate historical and recent customer data into a single view, so you can create customer touchpoints that are real value drivers.

Whether you’re working on audience management, email marketing, or cross-channel orchestration, Simon Data simplifies the end-to-end lifecycle marketing process. With the visibility of a centralized platform, campaign metrics become easier to track and assess.

Automation

Automation is your best friend when it comes to executing campaigns at scale to cover all the stages of the customer lifecycle. Whether you’re trying to create more touchpoints on different marketing channels, gather key metrics, or create a welcome sequence, automation will make the process much simpler and quicker. Marketing automation improves the overall customer experience. It helps lower churn rates and maintain conversion rates—no more missing out on additional sales because you didn’t set up an automated trigger to remind your customers of their abandoned carts.

Lifecycle marketing metrics

How do you know if lifecycle marketing campaigns are effective? Let these metrics be your guiding star.

• Customer lifetime value (CLV): Often the backbone metric of lifecycle marketing, this measures the total revenue a customer generates over time, helping assess customer retention.
• Customer acquisition cost (CAC): CAC tracks the cost of acquiring new customers—compare it with CLV to ensure that you’re retaining high-value customers.
• Retention rate: Shows how well you’re keeping customers over time; higher retention means stronger loyalty.
• Churn rate: In the same bucket as retention rate, churn rate is the percentage of customers who stop buying from you—lower churn indicates higher engagement and satisfaction.
• Conversion rates: How many customers are moving from one stage to the next? Conversion rates identify funnel bottlenecks.
• Engagement metrics: This broad term includes website traffic, time on site, bounce rate, and interactions to gauge how well your content resonates.
• Net promoter score (NPS): NPS measures how likely customers are to recommend your brand, a key indicator of advocacy. Check the NPS pulse often through surveys!

How do you measure these metrics effectively? Using tools helps.

Tools for Lifecycle Marketing

A good lifecycle marketer and an effective toolkit work in tandem for better campaigns. Without one of the other, you can’t measure or optimize lifecycle marketing.

Data analytics

A lifecycle campaign is only as good as the visibility you have into it.

Analytics tools track marketing effectiveness, and the right user can interpret this information for optimizing strategies. As an example, Google Analytics provides in-depth website and app tracking, while Mixpanel specializes in customer behavior analysis to refine engagement strategies.

Customer engagement software

Lifecycle marketing thrives on reaching customers where they are. Tools like Klaviyo for email/SMS marketing and Braze for cross-channel messaging help you communicate across channels. Linking all these channel experiences so they play nice is the challenge of lifecycle marketing!

AI-powered tools

AI-driven tools help you personalize and execute campaigns with better opportunity to scale. For instance, Persado helps brands with AI-driven language analysis, and MoEngage uses to help you generate campaigns quicker..

Customer Data Platforms (CDPs)

Finally, CDPs are the glue that consolidate and activate customer data from multiple sources in real-time. This is a secret weapon for lifecycle marketers because it keeps a pulse on each customer and allows you to run targeted campaigns hands-free with the right setup.

You’re looking at one right now: Simon Data!

Lifecycle marketing campaign example: The Farmer’s Dog

Ecommerce brand the Farmer’s Dog is a prime example of a company making the most of each stage of lifecycle marketing. By centralizing their customer lifecycle into one comprehensive view, they ensure both new and existing customers receive the nurturing they need to make repeat purchases, become loyal customers, and share their enthusiasm with other potential customers. Let’s look at how the Farmer’s Dog works through each stage of lifecycle marketing and what best practices they have employed:

Awareness

At the awareness stage, the Farmer’s Dog has everything in place to become discoverable. They have a strong social media presence, they’ve created a meal plan survey to usher interested customers into their email list, and they even pay for acquisition via Facebook ads.

Best practices:

• Designing a targeted and professional website aimed at dog owners
• Including a survey button on the landing page to start prospects down the marketing funnel
• Appearing on large platforms like Today and CBS News

Consideration

During the consideration stage, the Farmer’s Dog nudges potential customers through email marketing. They also have a reviews page on their website highlighting some of the best praise they’ve received from important customers.

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Best practices:

• Using a survey to personalize email messaging as customers move through the awareness stage into the consideration stage
• Showcasing reviews from trusted sources like veterinarians

Decision

A good way to get warm prospects to make a purchasing decision is to give them a discount. The Farmer’s Dog offers a sign-up discount for a trial. The email comes from a person, not just the brand, and its visually simple format makes the message feel personal.

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Best practices:

• Asking for feedback in the form of a pop-up survey
• Collecting email addresses for every option
• Focusing messaging on helping the customer instead of pushing the brand

Loyalty and advocacy

The loyalty stage can be nurtured in more ways than one. The Farmer’s Dog uses an affiliate strategy to incentivize satisfied customers to spread the word about their pet food products.

Best practices:

• Implementing an affiliate program with clear benefits to the customer
• Making it easy to join with a “Join Now” CTA above the fold

Check out the Farmer’s Dog case study here to learn how they saved more than 80 hours of engineering work by executing a lifecycle strategy with Simon Data.

Make the complex simple

Without the right tools, it’s easy to get lost under a ton of data while you struggle to make it actionable. But with the right customer data platform at your fingertips, creating a unified and comprehensive lifecycle marketing strategy becomes easier.

Simon CDP helps you build a framework that will fuel your entire customer journey, making it easy to use complex data and enabling you to refine your marketing campaigns for better returns. Request a demo today.

When you think about Artificial Intelligence (AI), you might think of talking robots or the movie Her. 

In reality, most applications of AI are more commonplace and their use has exploded in every industry, including MarTech. At Simon, we’ve seen AI extremely useful in customer experience management, particularly when it comes to the personalization of content and customer data analytics.

To activate first-party real-time data and streamline marketing processes, marketing teams need to consider how to use AI within their martech stack.

AI, when combined with a cloud data warehouse and customer data platform (CDP), for example, can help marketers optimize their campaigns, predict customer behavior, and drive improved customer engagement.

Essentially, CDPs are a key piece in enabling AI to fully take advantage of all of your customer data from Snowflake and make each customer feel like your content is tailored just for them, increasing the chances of engagement and conversion.

How AI can improve customer loyalty through a CDP

Uncovering hidden audience and segmentation opportunities for your campaigns

Gone are the days you have to explicitly tell AI what to do. With a CDP’s AI agent, you can run goals-based campaigns with AI driving the audience segments.

It’s simple: Tell AI about the type of campaign you want to run, and it will suggest audience opportunities based on your customer data.

Simon has three AI agents proactively supporting campaigns: Strategy agents, data agents, and revenue agents. Strategy agents recommend untapped or ignored opportunities while data agents create customer attributes and segments based on campaign goals. Lastly, Revenue agents forecast your potential revenue, allowing you to invest in high-potential campaigns.

In practical application, you can use these AI agents for even better personalization and closing sales with timely messaging. A retailer could integrate a weather API with a CDP to segment customers based on their weather conditions. This way, customers with an upcoming heatwave get sunblock recommendations, and customers with a rainy week ahead get recommendations for umbrellas.

Enhancing personalization in customer marketing

How does AI come into play when using a CDP? Well, AI analyzes customer data to personalize recommendations, offers, and content in real time.

Let’s look at an example. Whenever you are grocery shopping, you’re likely following a shopping list to make your shopping experience efficient and successful. 

With AI, you could create the ideal shopping experience for your customers with personalized recommendations and offers so that your content leads them one step closer to a fulfilled shopping list.

In addition, by taking advantage of the unified customer view (also known as a customer 360) your CDP provides, you can enable AI to tailor your customer experiences across all touchpoints in perfect coordination. 

Crocs does this well by upselling Jibbitz charms that are based on previous buyers’ combinations. On each shoe’s page, there’s a widget recommending Jibbitz unique for that style of Croc:

Using predictive analytics in a CDP

What if you could predict each customer’s next purchase? With AI and a CDP, you could do exactly that. You can even predict when and how they are most likely to purchase. 

Armed with that data, you can create hyper-personalized content and real-time offers that will make both your customer and your bottom line happier.

When it comes to predictive analytics, it’s important to keep historical customer data in one spot, clean, and accurate (which is exactly why we recommend using a cloud data warehouse like Snowflake). 

Historical data is the foundation for accurate predictions. The more you have of it, the more accurate the predictions from AI become. With a CDP, your AI tool will have access to historical data from all of your marketing channels and data sources.

Simon Predict has a suite of capabilities to help you personalize messaging:

• Churn Prediction Model (calculates how close a customer is to churning)
• Purchase Prediction Model (calculates how close a customer is to their next purchase)
• Second Purchase Propensity Model (calculates how likely a customer is to make a second purchase)
• Product Recommendation Model (predicts what product a customer is most likely to purchase next)
• Lifetime Value Model and Future Revenue (predicts how much customers will spend on your brand)
• Engagement Index ( summary indicator of a customer’s engagement with your brand)

As an example, you can use second purchase prediction model to identify customers on the cusp of rebuying your product and give them an extra nudge with an email promo suggesting that they subscribe to save money, like K18 does here:

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Automated customer marketing for your marketing team

As a marketer, you probably wear many hats and wish you had more time to do everything possible to make your marketing as successful as possible. 

While AI can’t freeze time, it can save you time by automating marketing tasks like executing email campaigns, social media interactions, and chatbots. This saves you time but also provides your customers with a more personalized and accessible experience with your brand. Simon AI™ Social Moments applies this same automation to social and cultural signals, helping marketers act on emerging demand without adding manual work to their day.

The best part about using AI within your CDP is that while your AI tool handles the automation, your CDP provides it with an accurate log of past interactions (including conversations with chatbots!), ensuring consistent and personalized communication.

For instance, our chatbot is capable of guiding customers into the appropriate lanes to quickly get them talking to the right people at Simon:

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Automated customer segmentation

Analyzing customer data and creating effective customer segments is a time-consuming and arduous task. 

By using an AI tool that sits on top of a robust data infrastructure provided by your CDP, your customer data can undergo a more detailed and intensive segmentation process to create more opportunistic segments. 

With better segmentation, you can tailor content more effectively based on each segment’s behavior, demographics, preferred communication platforms, and data points.

For example, say you have a segment of customers who have purchased from your site in the last two days. You could send them a generic post-purchase message, or you could utilize an AI-tool and send them a personalized post-purchase message that asks for a review, prompting the user back to your site with a discount:

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Customer journey optimization

Keeping track of when, where, and what communications were sent to your customers is a daunting task to begin with. Not to mention that analyzing customer journeys for each contact and coming up with a next step is nearly impossible. 

With a CDP, you can map the customer journey for all of your contacts, which can then provide AI with the historical data needed to identify opportunities to optimize customer touch points along their journey.

Improving customer engagement with AI

AI is supposed to help cover your blind spots. Since its rise in popularity in 2023, it’s now more proactive than ever. AI tools can predict where your customers live based on the campaigns you run, show customers products they want before they know it themselves, and recommend tweaks to campaigns to optimize for better LTV.

Using AI within a CDP, marketers can automate loyalty programs that reward customer engagement and incentivize repeat purchases. For example, a loyalty program for a restaurant reservation system could offer a discount on their 10th reservation with automated messaging via email, text, and in-app notifications.

Additionally, AI-powered chatbots that provide 24/7 customer support and answer questions efficiently can help improve customer engagement and loyalty.

Have you ever had nights where the shipping status of your latest purchase has kept you up all night? You can probably guess that their offices are closed and no one is available to answer emails or calls. 

But, if the retailer has an AI-powered chatbot set up on their website, you could get the answers to all of your shipping-related questions 24/7.

Challenges and considerations for implementing AI in a CDP

In theory, AI might seem like a silver bullet to many workflow challenges, but there are many considerations you need to work through before implementing AI tools in your CDP.

Data quality and integration issues

Your AI tool is only as good as the data you feed it — a literal case of “you are what you eat.” To ensure the output from your AI is accurate and useful, the customer data you are importing should be clean, correct, and successfully integrated with your CDP. 

Remember: you also need a larger amount of historical data for your AI tool to start learning your customer base.

Bias and fairness concerns in AI algorithms

Even though AI algorithms are not human, they were created by humans. Unfortunately, this means AI, like humans, will always have a degree of bias. If you notice any sort of bias in the AI tool’s output, investigate its data sources and the algorithm to correct it.

The need for transparency and explainability in AI decisions

While AI is currently all the rage, the use of its output is not without accountability. As a marketer, you should be able to understand how the AI tool is making decisions and have the knowledge to be transparent about how it is being used. You, your company, and your customers will appreciate the transparency!

Balancing automation with human interaction

We’ve all had a negative experience with some form of automation — whether that be chatbots, automated phone calls, or other commonplace applications of AI automation. 

AI is far from perfect in its current state, which is exactly why there should be a balance between automation and human interaction to minimize customer dissatisfaction or errors.

Ethical considerations of using AI in customer experience

There are also ethical considerations when it comes to using AI in customer experience. 

As mentioned, there are risks like biased decisions that reinforce stereotypes or negative behaviors, as well as data privacy concerns. People complain that data is used without their consent to train AI — and you want to ensure your AI meets the demands of data privacy laws.

There’s also the environmental cost of AI, with large electricity and water demand.

Copyright and ownership

Since AI’s inception, federal courts have ruled against its favor in one major way: you can’t claim AI work is copyrightable. This means companies shouldn’t use it in content that’s central to the brand.

Best practices for using AI in a CDP

When it comes to using AI in a CDP, having a strategy in place is key. Below are some best practices that can help ensure the successful and ethical use of AI in marketing:

• Define clear goals and objectives for AI-driven loyalty initiatives
• Invest in high-quality data and ensure its accuracy and completeness
• Choose AI algorithms that are transparent and explainable
• Monitor and evaluate the performance of AI models regularly
• Continuously learn and adapt AI strategies based on data insights
• Ensure ethical and responsible use of AI in all aspects of customer experience

Conclusion

When AI is implemented correctly and strategically with a CDP, marketers can drive customer loyalty through the roof by unlocking the potential of your customer data. 

You can use AI to predict future purchases or churns, create highly personalized and automated content that reaches your customers at the right time, and increase the efficiency of your day-to-day as a marketer. 

Data quality and transparency are essential to maintaining the ethical use of AI. Without the right data, the output from your AI will be flawed and biased. 

But with the right considerations and planning, AI can help you implement extensive and personalized customer loyalty programs. 

Make the most of your customer data by exploring and bringing AI solutions into your marketing strategies. 

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NEW YORK, March 24, 2025 - Simon AI, a leading customer data platform for enterprise marketing teams, today announced the launch of Goals-Based Marketing, a new approach to data-driven marketing that translates business objectives into revenue-generating customer experiences at scale.

The first release in this new approach is an AI-enabled segmentation workflow that empowers marketers to build better audiences without technical expertise or guesswork.

At the core of the new offering are three specialized AI agentic teams — Strategy Agents, Data Execution Agents, and Revenue Impact Agents — that work together to analyze customer data, create high-value attributes, and forecast campaign performance.

The solution introduces Smart Fields and Smart Segments, which uncover hidden patterns in customer data and create targeted audiences tailored to specific business goals.

"When I founded Simon AI 10 years ago, our vision was to enable 100x the data for personalized customer experiences. The recent explosion in cloud resources and large language models has opened a new world of applications, allowing us to finally bring this vision to full fruition."^{Jason Davis, CEO, Simon AI}

The challenge for most marketers today isn't a lack of data but knowing how to effectively use their data to drive business outcomes. Simon AI's Goals-Based Marketing approach inverts the traditional workflow. Instead of starting with audience selection, marketers begin by defining their business objectives.

The AI-powered platform then identifies the right data points and audience segments to achieve that goal, providing predicted performance metrics to inform campaign decisions.

"Every marketing technology tool built over the past decade starts with segments or workflow orchestration," said Dylan Flye, SVP of Sales & Partnerships at Simon AI. “But that's not how marketing actually works. Marketers have goals and KPIs to hit, then develop strategies to meet them. Our tool addresses the most fundamental question: who exactly should you target to achieve your business goals?"

The new workflow follows a simple three-step process:

• Define your goal: Marketers input their business objective (acquisition, retention, upsell)
• Discover hidden insights: AI creates Smart Fields and Smart Segments from existing data
• Select winning audiences: Marketers review AI-recommended segments with predicted conversion rates and revenue impact

Beta customers are already seeing significant results. 

"AI opens doors where machines, not humans, tell us the best recommendations," said Steve Mastrocola of SeatGeek. ""It removes manual decision-making from the equation, letting us focus on optimal timing and customer communication."

Unlike general-purpose AI tools that focus on content generation, Simon AI's solution delivers tangible marketing outcomes that impact key performance metrics. The platform works with existing customer data, requires no lengthy setup, and provides immediate actionable insights without relying on data science teams.

"AI opens doors where machines, not humans, tell us the best recommendations. It removes manual decision-making from the equation, letting us focus on optimal timing and customer communication."
^{Steve Mastrocola, SeatGeek}

Read the SeatGeek case study here.

Simon AI is already developing additional AI-enabled workflows to expand its Goals-Based Marketing approach. The company is actively testing specialized agents, such as a Weather Agent that can adjust audiences based on predicted weather conditions, and industry-specific expert agents to further enhance the platform's capabilities and deliver even more targeted marketing solutions.

Goals-Based Marketing with AI-Enabled Segmentation is now available to customers in Private Beta as part of Simon AI's customer data platform.

For more information, visit simon.ai.

About Simon AI

Simon AI is more than a CDP — it's a complete solution that empowers marketers with the data, technology, and expert resources they need to stop guessing and start delivering what customers want. Leading brands like ASOS, Mattress Firm, and 1-800-Flowers partner with Simon to build sustainable, revenue-driving customer marketing programs. From data readiness to campaign execution and beyond, Simon supports brands at every stage, ensuring they deliver personalized, 1:1 experiences across every customer interaction. Learn more at simon.ai.

By now, we’ve woken up to the reality of AI: It’s not scooping up jobs wholesale. Instead, the marketers using it are saving time, money, and headspace.

Still, there’s always the feeling you’re missing the next big thing with AI. 

Many of us are already stuck in our ways. We use AI that gives us shoddy approximations of complete work, and we’re left cobbling subpar results together. 

Successful AI implementations incorporate AI from the offset. They require intentional tool experimentation and selection, team onboarding, and routine auditing. What’s more, they require the right top-of-the-line AI agents for the right job.

Let’s teach you when, where, and how to deploy AI in your marketing strategy with the right fit for your team and industry.

First off: What’s the difference between basic AI and AI agents?

Current definitions for AI tools are like snowflakes: each definition is unique. Because it’s the Wild West of AI, nobody totally agrees yet on who is who in the AI world. 

But for now, let’s roll with Dharmesh Shah’s definition. AI agents have these key elements:

• They're powered by Large Language Models (LLMs)
• They have access to tools and APIs (their own little utility belt)
• They can remember things (at least during their running time)

If you want to learn more about AI agents in general, we have an article for that.

For now, let’s learn how to use these robots to their best effect.

When to use AI agents (and when not to!)

AI agents are key — got it. So, where are marketers deploying AI agents? This is your guide to deploying AI that’s tasteful and painless. 

Let’s start with some universal green lights and red flags to look for. Successful marketers choose their AI battles.

🟢 Here are the green lights for AI agents:

• High-volume, repetitive decisions: AI makes quick decisions, faster than humans. You can set AI to approve transactions or bucket support tickets without lifting a finger
• Real-time optimization needs: AI can make lightning-quick decisions in real time. This makes it ideal for something like optimizing ad bids, a task that takes a human more time to react to rapid changes
• Complex, multi-channel orchestration: Connecting the dots across multiple channels can take hours, days, or weeks of human work. But for AI, managing marketing campaigns across several channels is simple when it integrates with them
• Data-rich environments: Give AI some structured data, and it can extract and transform it in moments. After all, AI is trained on large datasets; it eats data for breakfast!

🚩 These are the red flags for AI agents:

1. Brand-critical creative decisions: AI can’t write your positioning doc for you. Because this requires a creative perspective, it shouldn’t be left to an agent trained in other companies’ branding
2. One-off campaigns: AI learns patterns from repeated actions, and we better learn how to use AI when we create processes for it. If you want to try something off-the-cuff once, best to do it yourself
3. Low data volume scenarios: AI trained off of a handful of data points won’t make sound decisions. Remember: AI eats data!
4. Highly regulated communications: Does your industry have strict compliance requirements? AI-generated content and decisions aren’t always 100% in compliance, so if you plan on using AI, tread with caution

You’re probably already seeing the cracks in some marketing suggestions with these red flags. Some marketers have been using AI to create ad copy for their cryptocurrency companies out of compliance and create new brand charters.

Let’s be the bigger marketer and learn how to apply AI agents for our industries without risking our reputation.

Watch AI agents turn goals into hyper-personalized campaigns

Industry-specific use cases: AI agents at work

These are the best use cases for a handful of big industries. By this point in AI’s lifespan, we better know what works and what doesn’t – see what you can apply to your job!

Retail & e-commerce

Best for: Inventory-aware promotions, cross-sell recommendations

What is an e-commerce storefront if not a cache of data ripe for extracting? Unsurprisingly, AI pairs well with retail because it’s more adept at sifting through dozens (or hundreds or thousands) of products and their details than human counterparts.

Retail has been using AI agents for years, ever since Amazon paved the way.

Every “An item on your wishlist is on sale!” notification is powered by AI. No, there isn’t some gnome who lives in each shopper’s computer that manages their wishlist. It was AI the whole time!

Similarly, there’s no gnome individually handpicking related products for each e-commerce item. Unless you consider AI a gnome:

Avoid: Brand voice, creative design

See also: Our red flag list of AI no-nos. AI agents aren’t so great at thinking outside the box; they’re good at thinking within the confines of their training data. Don’t use them to differentiate your brand!

Example: Dynamic pricing optimization

Dynamic pricing is another e-commerce example that’s blowing up. Amazon is notorious for this strategy — their product prices change every 10 minutes.

TikTok is also a rising star in dynamic pricing, cutting user deals with exclusive coupons if they revisit product pages or interact with ads. You can check the “My Offers” section of the TikTok shop for products on sale related to what you’ve bought or wishlisted.

Financial services

Best for: Risk-based engagement, life event marketing

Financial services can tailor their offer to users who may not qualify for a particular credit card or service due to their credit score, offering alternatives for their needs with AI-powered recommendations.

They can also target shoppers who’ve started a new business, are searching for a home, or are paying off student debt. While these referrals used to be manual (for instance, a bookkeeper recommending a legal writer to create a contract for your small business), intent-based data gives AI agents what they need to serve your products to the right people. Many digital advertising platforms employ AI for this purpose.

Avoid: Compliance-sensitive communications

Because many AI agents use the data you give them for further training, you shouldn’t give them sensitive customer data. There are also notorious stories of chatbots giving users sensitive company information because they’re jailbroken. Be sure your customer-facing AI agents are secure!

Example: Next-best-product recommendations

A next-best-action model can be applied to financial services — to help them sell the next-best product to a user. Since financial services normally require an extended research stage for customers, companies in the financial sector want to be proactive in guiding customers to the next step.

For instance, you can identify customers who frequent a particular store and offer them a co-branded credit card. Or, you can offer payment deferment options for large purchases. Many banks give you the “Pay Over Time” offer next to purchases over a certain threshold:

Travel & hospitality

Best for: Ancillary revenue optimization, loyalty programs

AI agents can be used to upsell ancillary products. For instance, a hotel website uses an AI agent to recommend spa services or restaurant reservations from their in-house stores.

You can also send shoppers emails about their loyalty programs. Use this for notifying them about points close to expiring, what they can afford with their points, and specific bonus points offers tailored to them.

Hilton Honors does this right with tailor-made offers for your rewards tier:

Avoid: Crisis communications

Nothing reads “apathetic” quite like an AI-generated response to a crisis. It’s better to send nothing at all.

Steer clear of genAI and formulaic responses to disasters, and leave that to a human communications specialist!

Example: Dynamic package bundling

If a traveler is buying tickets for Aruba, maybe they also want a hotel in Aruba? Tours in Aruba? 

Travel+Leisure gives an example of personalized package recommendations based on the travelers’ purchase behavior. This data model recommends vacations to members based on where they are in their planning process by combining four key datasets: transaction history, inventory, demographic information, and web activity. More data, better recommendations.

Media & entertainment

Best for: Content recommendations, engagement timing

The OG AI recommendation engine: “People also read…”

News and entertainment media have been using this for years. That’s what kept 2010s teens trapped on BuzzFeed for hours.

You can also use AI for SMS push alerts tailored to your users’ interests. Apple TV notifies you when an episode of a show you watch releases:

Avoid: Original content creation

BuzzFeed flew too close to the sun. Recommending content was one thing, but then they started writing content with AI.

The AI generated content hasn’t paid off, and though BuzzFeed implemented it in response to setbacks, ad revenue continued to fall.

Many media companies are still using AI…covertly. But if you’re caught, the repercussions are significant. Nobody wants to consume AI content branded as creative expression yet, so you’ll potentially damage your brand permanently.

Example: Churn prediction and prevention

Entertainment companies can use AI to stop churn before it starts. 

For instance, bundles reduce streamer churn. In response to this information, streaming services are recommending service bundles to users who use one service under an umbrella (think Disney+, Hulu, and ESPN+).

Implementation essentials

AI agents are widely applicable. You just have to choose the right one for your use case!

Here’s your AI agent implementation starter pack.

• Quality data: AI agents depend on data. If it’s high quality, the more the better. Establish centralized repositories to store and manage data, like a data warehouse or a CDP
• Consolidated data: AI agents need consolidated and cleaned data, not inaccurate or outdated or inundated with duplicate content. This makes integration with existing CRMs, analytics tools, and the like simpler if those data sources are cleaned (and AI automation can help you with that, too!)
• Clear metrics for success: To steer the ship, choose your guiding star. What you should measure depends on the AI agent you’ll deploy, but some useful stats to check for are the accuracy of responses, task completion rate, average response time, cost per interaction, user engagement, and error rate
• Careful consideration: A lot of teams patch holes in their workflows with low-cost AI plugins. What would save them more time is selecting one AI agent that would significantly improve their workflow. Don’t make the mistake of a plugin for everything.

Future-proofing your strategy

AI can be future-proof; Amazon’s product recommendation engine has been around for over 10 years, and it’s still kicking. So even though some AI agents are a fad now, the technology has been around for decades and will carry on.

Futureproofing your strategy just requires you to build on the right foundations. AI isn’t your “get rich quick” scheme; it’s your index fund. Choose carefully, and it will pay you back. The quickest wins are usually the smallest, and that applies to your AI agents’ foundations, too.

Your AI agent should be scalable, taking on greater demands without the risk of losing performance. This is because, ostensibly, your team will grow and your customer base will grow. Select AI agents with that in mind!

Like all good processes, you need documentation to cement an AI agent as a permanent fixture at your company. The best implementations of AI over the past couple of years have been heavily documented so everybody on the team can use them. Otherwise, you’re all individually reinventing the wheel when you work it. Disseminate your information!

Keep these in mind, and your AI strategy will outlast the hype cycle.

Conclusion

Despite all the cautionary tales we’ve shared, it’s really not that hard to get started with AI agents. 

Your first step is to find a problem that you can automate with data and machine learning. Maybe one of the examples above piques your interest, or you can read another one of our guides on AI for personalization or AI for customer loyalty.

Then, use your favorite research tools to spot an AI agent that solves your problem. We’re marketers. We love research!

The teams that will bake AI into their processes for the long term find agents that are tailored to their specific problems, not glamorous new products that everybody is using for the hype. Use your marketer’s eye to spot the difference, and you’ll be on your way to using AI like it’s second nature.

Remember when adding a customer's first name to an email was considered "personalization"? Those days are firmly behind us, with the retail AI market racing toward $31.1 billion by 2028. Today's retail marketers leverage AI agents to create genuinely individualized shopping experiences that actually work.

But forget the flashy headlines. What's actually happening in the trenches? Here’s how AI agents are transforming marketing in ways that matter.

1. Chatbots that finally don’t suck

Traditional chatbots were glorified FAQ systems — useful for basic questions but ultimately frustrating for customers with complex needs. Today's AI shopping assistants are entirely different animals.

These conversational agents handle everything from "Do these shoes come in blue?" to "Help me design a home office for a small space with a boho aesthetic." They remember what customers like, learn from every interaction, and connect with inventory systems to recommend products that are actually in stock.

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The results speak for themselves. Retailers using these AI assistants see conversion rates jump 15-30% and fewer returns, with many also experiencing decreased return rates as customers make more informed purchasing decisions. 

Why? People make better decisions with personalized guidance instead of scrolling through endless generic product pages.

2. Inventory management with actual (artificial) intelligence

Most retailers have approached inventory with a shrug. AI agents (and CDPs) can change that equation by connecting dots humans simply can't see.

They analyze everything from seasonal patterns and social media trends to weather forecasts, predicting demand before it happens. They don't just track stock; they optimize where products should be and when. They go beyond tracking what’s in stock by proactively optimizing where products should be and when.

The real magic happens when AI spots non-obvious patterns — like how certain kitchen gadgets sell better after appearing on cooking shows, or how specific products sell together in particular seasons. Simon AI™ Social Moments applies this intelligence to live social demand, helping retailers anticipate which products are about to spike and align inventory before customers feel the shortage. When connected to your CDP, these systems align inventory with your highest-value customer segments, ensuring you never disappoint your best customers.

3. Dynamic pricing that reads the room

Static pricing is all about compromise. AI agents handle dynamic pricing that maximizes both sales and profits by understanding when discounts actually drive conversions versus when they're giving away margin.

AI pricing systems can analyze competitor pricing strategies well, but they also spot patterns humans miss, like how loyal customers are less price-sensitive for new arrivals but more discount-driven for basics. By connecting pricing strategy to customer data in your CDP, you can protect margins where possible while offering targeted discounts only where they'll drive desired behaviors.‍

4. Product recommendations that aren’t basic

Those "customers also bought" widgets have set the bar pretty low for retail personalization. AI agents create genuinely individualized recommendation systems that consider a customer's entire relationship with your brand.

These AI agents go beyond traditional demographics like age and income to analyze behavioral patterns, such as how customers browse, what content they engage with, which products they view together, and even how long they spend on different pages. This behavioral understanding reveals the "why" behind purchases, not just the "who."

AI might identify distinct shopping personalities like "research-intensive browsers" who read every review before buying, or "impulse trend followers" who purchase quickly after viewing trending items. These behavioral segments tell you far more than age or zip code ever could.

This helps solve the "discovery problem" in modern retail. With thousands of products, customers can't browse everything. AI agents act like a personal shopper who actually understands individual preferences and can surface products customers would never have found on their own.

When your recommendation engine connects to your CDP, it becomes even smarter. If a customer buys gifts in specific categories before certain dates each year, AI can proactively suggest items before they even start searching — moving from reactive suggestions to proactive discovery.

5. Customer loyalty programs that actually drive loyalty

Traditional loyalty programs follow the same tired formula: spend money, earn points, get discounts. The problem with this formula is that it’s transactional, not emotional, and it treats everyone like they want the same rewards.

AI agents fix this by creating personalized reward systems after analyzing customer data to see what motivates different customers. Some customers want early access to new products. Others want personalized discounts. Some value recognition or special experiences.

Connected to your CDP, AI agents continuously refine their understanding of what drives loyalty for each customer. The system adapts when it notices someone ignores point redemption emails but always clicks early-access offers. This builds genuine emotional connections rather than just temporary transactional relationships.

6. Tailored and streamlined marketing messaging that resonates

For many retail marketers, personalized content creation is a never-ending treadmill — and often a shot in the dark. From thousands of product descriptions, daily social posts, campaign copy, and email subject lines — the demands never end.

AI agents can generate retail-specific content at scale while maintaining your brand voice. For a product launch, an AI can create descriptions for different channels, social posts for various segments, email variants for testing, and blog content highlighting key features — simultaneously.

These agents connect content directly to commercial outcomes, analyzing which product descriptions convert better, which subject lines drive opens for specific segments, and which social content generates more engagement.

"Traditional marketing has always been reactive: plan campaign, launch, analyze results, adjust, and repeat. Marketers often respond to the past rather than anticipating future needs."

When tied to your CDP, AI content systems understand different segments' language preferences and shopping motivations, creating truly personalized content at scale. For example, a fashion retailer might automatically generate different product descriptions emphasizing sustainability, performance features, or style credentials depending on the customer segment.

The really cool part? This content creation is getting predictive. AI can guess what will resonate with specific customers based on their patterns. Imagine creating thousands of unique weekly emails without burning out your creative team, with the AI figuring out which products to feature, what offers make sense, and even when to hit send for each customer.

7. Customer lifetime value that informs your marketing strategy

Traditional CLTV has always been a backward-looking metric — useful for analysis but not great for planning ahead. AI agents are transforming it into a forward-looking tool that shapes strategy and spending decisions.

"The most sophisticated approaches connect predictive CLTV directly to acquisition spending, with AI in your CDP automatically adjusting channel investments based on which sources bring in the most valuable customers long-term—not just the cheapest to acquire today."

Instead of just calculating what customers have spent historically, AI predicts future value based on early purchase patterns, engagement signals, and comparisons to similar customer groups. This lets retailers identify high-potential customers early and invest in them accordingly.

Here's where it gets interesting: AI might discover that customers who buy certain accessory categories as their second purchase end up having 3-4x higher lifetime value than average. Armed with this insight, you could strategically promote these categories to first-time buyers, essentially fast-tracking value creation.

The most sophisticated approaches connect predictive CLTV directly to acquisition spending, with AI in your CDP automatically adjusting channel investments based on which sources bring in the most valuable customers long-term—not just the cheapest to acquire today.

This creates a virtuous cycle where your marketing budget naturally flows toward finding customers with the highest potential. And since these AI agents live right in your CDP, they can instantly turn insights into action across all your marketing tools, ensuring you're not wasting resources on customers who will never deliver sufficient returns.

From reactive marketing to proactive marketing

Traditional marketing has always been reactive: plan campaign, launch, analyze results, adjust, and repeat. Marketers often respond to the past rather than anticipating future needs.

AI agents in a CDP break this cycle by enabling:

• Predictive personalization: AI predicts which content, products, and offers will resonate with specific individuals before you launch by analyzing patterns across browsing behavior and purchase history
• Real-time optimization: No more waiting until campaigns end to improve them. AI continuously monitors performance and adjusts subject lines, content blocks, and offers on the fly
• Behavior-triggered marketing: AI identifies behaviors that signal purchase readiness or churn risk, automatically deploying the right message at the perfect moment instead of following a predetermined calendar

Getting started without getting overwhelmed

If all this sounds impressive but intimidating, you're not alone. The good news: you don't need to transform everything overnight. Here’s how to get started.

Clean up your customer data

Before implementing any AI agents, ensure your CDP has clean, accessible data from your cloud data platform. Identify your most reliable data sources and focus on initial AI applications there.

Pick high-impact, low-complexity use cases

Start with:

• Smarter abandoned cart recovery
• Better product recommendations
• Re-engaging lapsed customers
• Automated audience segmentation

Set clear success metrics

Define concrete KPIs upfront: conversion increases, engagement metrics, or efficiency improvements.‍

Build feedback loops

‍Create processes to regularly check how your AI agents are performing:

• Weekly performance reviews: Analyze what’s working and what’s not
• A/B test against your current approaches: Compare AI-driven marketing to your standard methods
• Ask your customers: Gather direct feedback about personalized experiences

Build internal capabilities alongside vendor partnerships

While working with vendors to implement AI agents, develop internal expertise:

1. Create cross-functional teams: Include marketing, data, and IT teams in your AI implementation
2. Identify your translators: Find team members who bridge the gap between marketing objectives and technical capabilities
3. Document everything: Create guidelines for how AI agents integrate with your existing marketing stack

The goal isn't to become AI experts overnight but to develop enough understanding to effectively leverage these tools and with your CDP.

What's coming next

As AI agents evolve, we're seeing several trends emerge:

• Multi-agent collaboration: Different specialized AI agents work together as an ecosystem rather than a single system trying to handle everything
• Cross-brand intelligence: AI agents facilitating collaborative marketing between complementary brands based on shared customer interests
• Predictive experience design: Moving beyond reactive personalization to anticipate customer needs before they're explicitly expressed

Retail marketers who want to stay ahead should watch out for the following.

Smarter martech integration

1. CDP-powered personalization: The combination of rich customer data with AI creates unprecedented personalization. Audit your data collection and identify personalization priorities now
2. Cross-channel consistency: AI will increasingly coordinate messaging across channels. Map your customer touchpoints and identify inconsistencies that need addressing
3. Privacy-first approaches: As third-party cookies disappear, AI working with first-party data in your CDP becomes invaluable. Develop ethical data collection strategies that offer real value exchanges.

Future-proofing your marketing team

The retail marketers who will thrive with AI will:

• Double down on strategy and creativity. As AI handles the tactical execution, focus your human talent on strategic thinking and creative development
• Get good at guiding AI. Train your team to effectively evaluate and refine AI outputs rather than just creating from scratch
• Establish clear boundaries. Build principles for how your brand uses AI in customer interactions, prioritizing transparency and genuine customer benefit

AI shouldn’t replace marketers — human creativity still drives retail marketing. We can use AI as an amplifier that handles the tedious analysis and execution so we can focus on strategy and creativity. 

And isn't that what we all want? Better marketing that doesn't require sacrificing our personal lives on the altar of optimization.

As marketers, we're constantly pushed to innovate our customer engagement strategies and find new ways to maximize customer lifetime value and optimize revenue. Upselling, when done right, is a powerful strategy that can significantly boost our bottom line and customer satisfaction.

The challenge? Executing upselling in a way that feels personalized and valuable to each customer. This is where Customer Data Platforms (CDPs) are helpful. By unifying customer data, CDPs allow us to transform our upselling approach from broad generalizations to precise, value-driven interactions.

There are many ways marketing teams can leverage a CDP to create upselling strategies that resonate with customers and drive business results. In this guide, we'll dig into the nuts and bolts of effective upselling, examine how CDPs enhance these efforts, and provide practical strategies you can implement in your organization.

But before we dive into the role of CDPs for upselling, let's reframe our understanding of upselling in the current retail environment.

What does strategic upselling look like today?

Upselling goes beyond simply encouraging customers to buy a more expensive product. Strategic upselling is about identifying opportunities to provide additional value to customers in ways that align with their needs and preferences — and this strategy requires more personalization than ever.

In practice, this means:

1. Basing decisions on comprehensive customer insights, not assumptions

2. Focusing on enhancing the customer's experience, not just increasing transaction value

3. Tailoring offers to the customer's current situation, purchase history, and lifecycle stage

4. Coordinating efforts across all customer touchpoints for a seamless experience

Why bother with strategic upselling?

The benefits of implementing a robust upselling strategy are multifaceted:

1. Revenue growth: A well-executed upselling strategy can significantly increase average order value (AOV). For example, Simon AI customer BARK used strategic upselling and increased its revenue by 20%

2. Enhanced Customer Lifetime Value (CLV): Successfully upsold customers often demonstrate higher lifetime value 

3. Improved customer satisfaction: Upselling can lead to customers finding products that better meet their needs

4. Operational efficiency: Focusing on upselling to existing customers is generally more cost-effective than acquiring new ones

5. Competitive edge: Sophisticated upselling tactics can set a brand apart in a crowded marketplace

By using CDPs to inform and execute upselling strategies, we can amplify these benefits and create a cycle of value creation for businesses and customers.

Implementing CDP-driven upselling strategies

We work closely with our customers to help them implement a CDP-driven upsell strategy. Here’s our playbook.

Step 1: Data integration and unification

The foundation of any CDP-driven strategy is comprehensive data collection and consolidation. By bringing together data from multiple sources, you create a complete view of your customer's journey. 

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First, ensure your CDP is ingesting data from all relevant sources:

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1. E-commerce platform (purchase history, browsing behavior)

2. CRM system (customer demographics, support interactions)

3. Email marketing platform (email engagement metrics)

4. Mobile app (in-app behavior)

5. In-store POS systems (if applicable)

Your CDP will create a unified customer profile with all these data points, likely stored in your Cloud Data Platform. This might involve setting up API connections or data pipelines with your IT or data team.

Step 2: Segmentation setup

Segmentation is essential, and the more granular you get, the better you can create more relevant and compelling offers by grouping customers based on their behavior. 

Once you’ve integrated your data, create customer segments based on upsell potential. A common way to segment customers for upsell is by grouping them into buckets, such as:

	High-value potential	Customers who frequently buy, but stick to lower-priced items
	Brand loyalists	Customers who repeatedly buy from the same product lines
	Feature enthusiasts	Customers who always opt for products with advanced features
	Periodic upgraders	Customers who upgrade their purchases every set number of months

Set up these segments as dynamic lists that automatically update as customer behavior changes.

Step 3: Trigger event configuration

In marketing, timing is everything. Identifying and configuring the right trigger events ensures your offers reach customers at moments when they're most likely to be receptive. 

Determine key events that trigger upsell opportunities, such as:

• Cart addition: When a customer adds an item to their cart
• Abandoned cart: When a customer adds an item to their cart but does not purchase
• Post-purchase: Immediately after a customer completes a purchase
• Product view: When a customer views a product multiple times without purchasing
• Customer service interaction: After a positive resolution of a customer issue

Depending on how detailed your customer data and advanced your upsell strategy are, consider using even more specific triggers, such as:

• Behavioral triggers: Multiple product comparisons in higher tier categories, increased browsing time, wishlist additions, and price threshold patterns
• Lifecycle triggers: Customer tenure milestones, upgrade history patterns, category exploration beyond usual purchases, shift in browsing patterns, re-engagement
•  Engagement triggers: Newsletter interactions with premium product content, social media engagement, product review submissions, customer support inquiries about advanced features, and educational content consumption about premium products
• Context triggers: Location-based store visits, product upgrades, professional changes in status, life event indicators (moving, relationships, etc.), and usage of current products

Now, configure your CDP to flag these events in real time. Integrate your CDP with your MarTech stack to capture these triggers so you can send the proper communication through the right channel — at the right time.

Step 4: Upsell offer design

While we always stress the importance of using real-time customer data in marketing, a successful upsell campaign is both an art and a science. Your offers should balance business objectives with genuine customer value.

For each segment and trigger event, design appropriate upsell offers. Here are some that our customers frequently use:

	High-value potential + cart addition	Offer a premium version of the product in cart
	Brand loyalists + post-purchase	Suggest a complementary product from the same brand
	Feature enthusiasts + product view	Highlight advanced features of a higher-tier product
	Periodic upgraders + customer service interaction	Offer an early upgrade with special pricing

Then, create appropriate segments with triggers and corresponding offers.

Step 5: Channel integration

A seamless multichannel experience requires careful integration between your CDP and various customer touchpoints. The proper setup ensures consistent messaging across all channels. Set up your CDP to push these upsell offers to the proper channels:

	Website	Use your CDP to dynamically update product recommendation widgets
	Email	Integrate with your email marketing platform for triggered upsell emails
	Mobile app	Push personalized in-app notifications with upsell offers
	Social media	Leverage social media to deliver targeted ads and personalized content to identified upsell segments

Note: Feed the same personalized upsell data to all channels to ensure multi-channel consistency.

Step 6: A/B testing framework

Testing your marketing efforts is crucial for optimizing your upsell strategy and maximizing ROI. You can identify which combinations work best for each segment by systematically testing different approaches.

To implement an A/B testing framework:

1. Set up control and test groups for each segment

2. Create variations in upsell messaging, timing, and offers

3. Configure your CDP to assign customers to these test groups randomly

4. Establish clear success metrics (e.g., conversion rate, AOV increase)

Run these tests for at least two weeks before analyzing results and implementing changes.

Step 7: Feedback loop and iteration

To continually improve your marketing upsell campaigns, you must monitor and refine them. Regularly analyze the performance of your campaigns to evolve your strategy based on actual results. Here’s how to establish a process for continual improvement:

First, set up automated reports in your CDP or analytics tools to track key upsell metrics. Then, schedule regular reviews (bi-weekly is often adequate) to analyze performance. You should use your CDP's machine learning and AI capabilities to help analyze these metrics and identify new segments or triggers. Finally, regularly update your upsell matrix based on performance data.

This ongoing optimization can lead to steady improvements in upsell effectiveness over time.

Step 8: Training and change management

The success of your upsell strategy depends on getting your entire organization aligned, so don’t forget about the human element of running a marketing campaign.

Be sure to train your marketing team on how to use the CDP for upsell strategy management, educate customer service reps on new upsell prompts and when to use them, and keep your merchandising team informed so they can align product strategies and inventory

Measuring success: KPIs for CDP-driven upselling

You can’t manage what you don’t measure. Ensure you’re tracking the right KPIs while implementing your upsell strategy. Our customers often look at:

Consider creating or using a dashboard in your CDP that tracks these KPIs in real-time, allowing you to quickly identify and capitalize on successful strategies while course-correcting less effective ones.

Elevate your upsell game with data-driven strategies

A CDP-driven upsell strategy should focus on fundamentally transforming how you engage with your customers. By leveraging unified customer data, you can create more relevant, timely, and valuable customer experiences while driving significant ROI.

If you keep the data and experience of your customers at heart, they’ll appreciate recommendations that are genuinely helpful and relevant to their needs.

Remember when "agent" meant someone in a suit trying to save the world from Dr. No? Well, times have changed. Today's agents are more silicon than suited, and they're changing how we approach marketing and customer data. And, unlike James Bond, they don’t need a license to analyze — just some well-structured data and clear objectives.

The great AI agent debate (spoiler: It's not that complicated)

HubSpot's Dharmesh Shah recently shared some thoughts about AI agents on LinkedIn. While some folks are caught up in endless debates about what constitutes an "agent" (can’t we just decide if a Hot Dog is a sandwich instead), Dharmesh offers a refreshingly practical take: it's all about the spectrum.

The three flavors of AI agents

1. The chatty types (conversational agents)

These are the extroverts of the AI world. They're the ones handling your questions in chat interfaces. They're like that super-knowledgeable helper who never needs a coffee break and has instant access to your entire knowledge base. They can handle everything from “What’s your return policy?” to, “I need help building a complex marketing automation workflow.” And, unlike human agents, they don’t get cranky handling their 1000th password reset request of the day.

2. The task masters (workflow agents)

These are the Marie Kondos of the AI world — they love organizing information. They handle everything from data analysis to image generation, following either predefined steps or taking direction from an AI "manager" (aka orchestrator). They can be triggered manually, run on a schedule, or spring into action when specific events occur, like when a new customer enters your CDP.

These are the agents that make marketers' lives easier by handling the grunt work, such as:

• Automatically segmenting your customer base based on behavior patterns
• Generating and A/B testing email subject lines
• Creating first drafts of social media posts based on your brand voice
• Monitoring campaign performance and make real-time adjustments

Simon AI™ Social Moments is a real-world example of workflow agents in action, automatically detecting emerging social signals and preparing activation-ready audiences when demand starts to rise.

3. The hybrid heroes (app-like agents)

The Swiss Army knife, but for AI. These agents combine chat interfaces with traditional UI elements like buttons and forms. They're flexible enough to run autonomously, pause for human input, and send you notifications when they're done. It's like having a personal assistant who's equally comfortable with conversation and clicking buttons.

These are particularly powerful for marketing tasks that require both analysis and creativity. For example, they might:

• Help you design and execute multi-channel campaigns
• Create and optimize landing pages
• Manage your content calendar while suggesting trending topics
• Handle influencer outreach and relationship management

Watch AI agents turn marketing goals into hyper-personalized campaigns

What makes these AI agents tick?

According to Dharmesh, all these agents share some common traits:

• They're powered by Large Language Models (LLMs)
• They have access to tools and APIs (their own little utility belt)
• They can remember things (at least during their running time)

But what makes them truly special is their ability to combine these elements in ways that actually make sense for your business. It's not just about having AI – it's about having AI that understands your specific marketing context and goals.

The future of marketing is collaborative

In the future, these agents might start working together, like a high-tech version of the Avengers. Imagine your customer service agent automatically collaborating with your data analysis agent to provide deeper, more personalized responses to customer queries.

Picture this scenario: A customer reaches out about a product recommendation. Your conversational agent handles the initial interaction, while a workflow agent analyzes their purchase history and browsing behavior. Meanwhile, a hybrid agent pulls relevant content and offers, creating a personalized response that feels both informed and human.

The power of AI agents + customer data

To understand just how transformative these AI agents can be when combined with robust customer data, let me share a perspective from our CEO, Jason Davis. He recently drew an interesting parallel between customer data and security footage outside a retail store.

Think about it: a well-placed security camera could tell you exactly what cars your customers drive, when they typically arrive, which nearby stores they visit, and who they shop with. Now imagine having that same level of insight into your digital customer interactions.

The challenge? Most brands today are swimming in data but drowning in complexity. Their customer insights are locked away behind complicated SQL queries and cross-departmental red tape. That's where AI agents make the difference.

These agents can turn mountains of unstructured data – from customer service calls to online reviews to browsing patterns – into actionable insights in real time. No more waiting days for reports. No more getting lost in technical overhead. Instead, you have a clear, contextual understanding of your customers when you need it.

What this means for marketers

For those of us in the data-driven marketing world, this has huge implications. AI agents aren't just about automating tasks – they're about creating smarter, more responsive marketing systems that can:

• Process and analyze customer data in real-time
• Provide personalized customer experiences at scale
• Execute complex marketing workflows without constant human intervention
• Adapt to changing customer behaviors and preferences

The real difference here is the shift from reactive to proactive marketing. Instead of waiting for problems or opportunities to become obvious, these agents can spot patterns and trends early, allowing you to adjust your strategy before your competition even notices what's happening.

The bottom line

As Dharmesh puts it, the goal isn't to get caught up in definitions but to focus on usefulness. Whether you're working with chatbots, workflow automation, or hybrid solutions, the key is finding ways these agents can help your team work better and create more value for your customers.

Think of it this way: Nobody cares if your car is technically a sedan or a crossover as long as it gets you where you need to go. The same applies to AI agents – their classification matters far less than their ability to solve real problems.

Looking ahead at AI agents in marketing

While we're excited about all the possibilities, we're even more excited about how these technologies will integrate with customer data platforms like Simon. The combination of AI agents and robust customer data management will create marketing experiences that feel less like automation and more like a genuine, data-informed experience. Allowing marketers to stop guessing and deliver personalized experiences yet still make it home for dinner. 

And isn't that the real dream? Not just better marketing, but better marketing that doesn't require sacrificing your personal life on the altar of optimization. These agents aren't just tools – they're partners in creating marketing that works better for everyone: businesses, customers, and yes, even us marketers who occasionally like to see our families.

Human creativity remains at the heart of marketing, with AI serving as our amplifier and enabler. These tools give us the support to push creative boundaries, think more strategically, and deliver better results.

‍80% of your future revenue comes from your existing customers. After someone makes a purchase, you have the opportunity to transform them into a life-long customer. While acquiring new customers is important, nurturing and growing relationships with existing ones often proves more cost-effective and valuable for sustainable business growth. This article explores the data-driven strategies and actionable tactics that can help marketers achieve these outcomes, turning post-purchase moments into lasting customer relationships.

Understanding post-purchase customer data

To personalize the post-purchase experience, you need to understand what your customers are actually doing. This means collecting and organizing the right customer data in one place.

Many companies use a customer data platform (CDP) to bring together all their customer information — what people buy, how they browse your site, how they engage with emails, and what feedback they give. When you have all this data in one place, you can spot patterns and take action.

Having this complete view of each customer helps marketing teams create more relevant experiences and find new opportunities to generate revenue, such as post-purchase opportunities. It also helps your marketing, sales, and support teams work better together because everyone can access the same information.

By using this information and tracking specific behaviors and engagement patterns, teams can better predict customer needs and proactively address retention risks. Here are the key data points that drive effective post-purchase personalization:

• Purchase history: Frequency, value, and product categories show buying patterns and highlight the potential for upselling or cross-selling
• Browse behavior and abandoned carts: Browsing activity and incomplete purchases reveal interests and opportunities for re-engagement
• Engagement metrics: Email open rates, click-throughs, and loyalty program participation offer clues about how connected customers feel to your brand
• Support interactions and feedback: Service inquiries and reviews provide insight into customer satisfaction and areas for improvement

Using predictive analytics tools, you can spot early warning signs (like customers engaging less often) and opportunities (like suggesting complementary products based on what someone just bought).

High-powered post-purchase tactics

Let’s look at specific ways to use customer data to keep customers returning. 

Use timely, relevant communication

Every interaction after a purchase should reflect the customer's individual journey. The key is to send the right message at the right time using specific and relevant messaging. Here’s what that looks like in practice:

• Order confirmations: Don’t just send the tracking number. Include personalized details about their purchase to build trust and excitement
• Thank-you messages: Show appreciation for the customer’s choice, teach them how to maximize their purchase, and include product recommendations or loyalty rewards to add value. For example, if they bought a coffee maker, send them brewing tips
• Delivery updates: Real-time tracking helps reduce post-purchase anxiety and keeps customers informed about their purchase

Amazon uses real-time updates to manage expectations for shipping like the example below.

Sephora engages with personalized recommendations based on past purchases right at the bottom of the order confirmation email.

Make the unboxing experience count

The moment someone opens their package is your chance to make a lasting impression. But you don’t need elaborate packaging or expensive extras. You can:

• Choose branded packaging that aligns with your brand identity
• Add small surprises, such as samples, thank-you cards, or discount codes
• Incorporate eco-friendly packaging options to meet sustainability expectations

Apple sets the standard here. Their packaging is simple but makes their products feel premium.

 Thankfully, you don’t need Apple’s budget to create a memorable unboxing experience. Even a well-designed thank you note or clear setup instructions can make a difference.

Include product usage and care instructions

The sooner customers can successfully use what they purchased, the more likely they are to buy again. Here’s how to help:

• Share care tips or maintenance guides specific to the product
• Offer tutorials or FAQs to make setup and use simpler
• Communicate updates about product improvements or compatible accessories

Dyson is a great example of how to provide detailed video guides for each product, which in turn helps lower support inquiries and increase customer satisfaction.

Launch behavior-triggered retention campaigns

Behavioral data allows brands to engage customers at the right moment with tailored campaigns. Instead of sending the same automated emails to everyone, use what you know about your customers to send messages that matter to them:

• Notice someone hasn’t bought their usual moisturizer in a while? Send them a gentle restock reminder
• Did they buy a phone? Follow up about compatible devices when it makes sense
• See they’re browsing similar items to their last purchase? Share relevant recommendations

‍

Here’s how Fullscript handles this:

Use customer data to retain customers

Let’s move beyond basic marketing messaging and look at how to use your customer data even more strategically.

Build dynamic customer segments based on purchase patterns

Start by grouping customers based on how they shop with you. Look at:

• How often they buy
• What they typically spend
• Which products they prefer
• How they engage with your brand between purchases

This helps you spot patterns and segment audiences based on customer behaviors, preferences, and lifetime value, allowing you to create focused and impactful campaigns.

Create a personalized loyalty program

Loyalty programs work best when they reflect how your customers actually want to interact with your brand. Consider:

• Rewarding more than just purchases, such as writing reviews or referring friends
• Offering early access to new products or exclusive discounts
• Personalizing rewards based on what customers value

Take Starbucks Rewards, for example. Starbucks noticed customers use their app to order ahead, so it made the rewards program work seamlessly with mobile ordering. The result? Customers use it more because it naturally fits into their coffee routine.

Automate workflows without losing personal touch

Automation helps scale your retention efforts, but it shouldn’t feel robotic. Your workflows should respond to customer behavior, address churn risks, and nurture loyalty while freeing up resources for strategic priorities. Smart automation means:

• Sending relevant care instructions right after a purchase
• Following up at logical times based on the product lifecycle
• Reaching out when customer behavior suggests they might need help

For example, if someone's engagement starts dropping off — maybe they're opening fewer emails or their usual purchase time has passed — that's your cue to check in with something relevant to them.

Use predictive modeling to pinpoint retention opportunities

AI-powered predictive models analyze customer behavior to anticipate customer needs. Notice someone is returning items more often than usual? They may need help finding the right fit. If a customer is browsing more expensive versions of what they own, they may be ready for an upgrade. If a loyal customer goes quiet, reach out and offer them a personalized reward.

Gather valuable customer feedback

To get helpful feedback, you have to ask the right questions at the right time. Here are some key moments to gather insight.

• Ask about a delivery experience while it’s fresh
• Check in about product satisfaction after they’ve had time to use it
• Incentivize participation with loyalty points or discounts
• Keep surveys focused and brief — respect their time

Most importantly, show customers you’re listening. If someone leaves specific feedback, follow up with how you’re addressing their concerns. Advocacy also plays a key role in retention. 

And remember, referral programs and incentivized reviews amplify loyalty, while shareable experiences like branded packaging and exclusive benefits encourage user-generated content (UGC).

For example, Peloton inspires advocacy with community challenges and social engagement, while Zappos builds trust with simple return policies and responsive customer service.

Re-engage dormant customers

Sometimes good customers drift away. Maybe they forgot about you, found another option, or didn’t have a reason to return. Before writing them off, try re-engaging them to rekindle their interest.

• Review their purchase history and suggest products that are similar
• Offer them something special to come back, but be smart about it. Instead of offering a generic discount, try, “We saved your size and style preferences!”
• Show them what’s new since their last visit, especially if you’ve made improvements to products they’ve used or complemented their past purchases

These campaigns bring dormant customers back and reinforce their connection to your brand.

Measure success and optimization

When it comes to tracking the success of your post-purchase personalization efforts, focus on metrics that tell you something useful.

• Customer lifetime value (CLV): How much does the average customer spend with you? Do they spend more or less over time? How long do they typically stay active?
• Churn rate and repeat purchase rate: Track how many customers disengage and how often they return to make additional purchases.
• Net promoter score (NPS) and customer satisfaction score (CSAT): Gauge customer sentiment and identify how your strategies affect loyalty and advocacy

Behavioral campaign metrics to watch:

• Response rates: Measure how frequently customers engage with campaigns like replenishment reminders or upsell offers
• Conversion rates: Track the percentage of customers who take a desired action, such as completing a repurchase or redeeming an offer
• Incremental revenue: Assess the revenue generated by specific campaigns to understand their impact on overall growth

A/B test frameworks

Start simple:

• Try different email subject lines to see what gets opened
• Test various offers to find what motivates purchases
• Compare different timing for follow-up messages

The goal isn't to test everything — just focus on changes that could meaningfully impact your business.

Post-purchase implementation best practices

You don’t need to implement everything at once. Start with one area where you can make an immediate impact. 

• Improve your order confirmation emails
• Create better product guides
• Set up customer segments

Once you see what works, expand from there.

Overcome post-purchase personalization challenges

• Data silos: Can’t see all of your customer data in one place? Start with connecting your most important data sources first
• Cross-functional misalignment: Information scattered across teams? Set up regular meetings to share insights and align priorities
• Scalability: Too many customers to personalize manually? Use automation tools to handle the basics. If you have limited resources, focus on your most valuable customer segments first

How to plan your approach

• Start small with a specific customer group
• Measure results carefully
• Expand what works
• Drop what doesn’t

Build stronger customer relationships through data-driven retention

Good retention tactics comes down to a simple idea: paying attention to your customers and responding in helpful, relevant ways. Use your customer data to understand what customers need, then deliver value consistently. 

Simon AI can help. Our CDP consolidates customer data and helps marketers deliver personalized campaigns that turn challenges into growth opportunities. Schedule your demo today to see the results Simon AI can deliver.

Connecting with customers meaningfully is the cornerstone of any successful marketing strategy. But with so many touchpoints and channels to navigate, it can feel overwhelming to know how to build personalized experiences that keep customers engaged, increase lifetime value, and reduce churn. 

This guide covers a range of actionable customer engagement tactics, from Abandoned Cart Recovery to Zero-Party Data Activation, powered by unified data and real-time insights. 

Whether you're working on retargeting campaigns or boosting customer retention, consider these 26 essential tactics as a practical framework for building stronger relationships and driving real results at every stage of the customer journey.

Let’s dive in.

A: Abandoned Cart Recovery

Re-engage customers who add items to their cart but don't complete the purchase, using targeted messaging and incentives based on cart value, products selected, and browse behavior.

Example: A luxury fashion retailer sends triggered emails featuring abandoned items with social proof messaging and a 10% discount that expires in 24 hours, achieving a 25% recovery rate.

B: Behavioral Segmentation

To deliver more relevant experiences, group audiences based on their interactions across channels, including website behavior, email engagement, purchase patterns, and app usage. 

Example: A streaming service segments viewers based on content preferences, viewing times, and device usage to promote relevant new shows and personalized upgrade offers.

C: Conversion Rate Optimization

‍To improve the percentage of visitors who take desired actions, test different customer journey elements, from landing pages to checkout flows, using both A/B and multivariate testing. 

Example: A subscription meal kit service tests different combinations of social proof, pricing display, and trial offers on their landing page, increasing sign-up conversion by 32%.

D: Data Unification

‍Connect and consolidate customer data from multiple sources, including CRM, e-commerce, email, mobile apps, and point-of-sale systems, into a single customer view for improved targeting and personalization. 

Example: A retail brand combines in-store purchase history, online browsing patterns, email engagement, and loyalty program data to create comprehensive customer profiles for targeted holiday campaigns.

E: Email automation

‍Deploy behavior-triggered email campaigns that respond to customer actions and lifecycle stages with personalized content, optimal send times, and dynamic recommendations. 

Example: A beauty retailer automatically triggers a replenishment series 60 days after purchase, featuring product education, user-generated content, and a timely reorder discount.

F: First Purchase Programs

‍Convert first-time buyers into repeat customers through tailored communications, educational content, and targeted offers based on their initial purchase behavior. 

Example: A D2C brand enrolls new customers in a 45-day program that includes product tips, complementary item recommendations, and a special discount on their second purchase. This increases the repeat purchase rate by 40%.

G: Geographic Targeting

‍Tailor content, offers, and experiences based on a customer's geographic location, considering local weather, events, and regional preferences. 

Example: A marketplace automatically adjusts featured services, pricing, and availability based on zip code while incorporating local weather data to promote seasonal services.

H: Holiday Campaign Planning

‍Coordinate peak season marketing messaging, offers, and experiences across channels, with specialized segmentation and timing based on historical customer behavior. 

Example: A department store creates targeted Black Friday campaigns with different timing and offers for in-store vs online shoppers, early birds vs last-minute buyers, based on past holiday shopping patterns.

I: Inactive Customer Campaigns

‍Target customers who haven't purchased or engaged within a specified timeframe, using personalized messaging and incentives based on their historical preferences and behavior. 

Example: A meal delivery service identifies customers who haven't ordered in 60 days and sends a personalized win-back series featuring their favorite cuisines and progressively stronger offers.

J: Journey Mapping

‍Visualize and optimize customer touchpoints across the entire lifecycle, from acquisition through loyalty, identifying key moments and opportunities for personalization and engagement. 

Example: A financial services company maps distinct journeys for different customer segments, from early-career investors to retirees, with tailored content and product recommendations at each stage.

K: KPI Tracking

‍Measure key performance indicators across marketing campaigns and customer interactions, including conversion rates, customer lifetime value, and return on ad spend. 

Example: A subscription box service tracks acquisition costs, retention rates, and customer lifetime value by acquisition channel and cohort to optimize marketing spend and product offerings.

L: Loyalty Programs

‍Reward repeat purchases and brand advocacy through points, perks, and exclusive experiences while gathering valuable customer data for personalization. 

Example: An airline manages a tiered loyalty program offering escalating benefits from priority boarding to suite upgrades, driving engagement through personalized milestone notifications and challenge campaigns.

M: Marketing Automation

Automate marketing processes and customer communications based on behavior triggers, segmentation rules, and personalization logic across channels. 

Example: A banking app automatically triggers personalized notifications and offers based on account activity patterns, like suggesting a high-yield savings account when checking balance exceeds certain thresholds.

N: Net Promoter Score (NPS) Analysis

‍Track and analyze customer satisfaction and loyalty metrics through systematic feedback collection and response analysis, using insights to improve customer experience and identify advocacy opportunities. 

Example: A subscription box service segments customers based on NPS responses, triggering targeted retention campaigns for detractors and referral programs for promoters. This results in a 20% increase in customer advocacy.

O: Order Confirmation Emails

‍These emails transform standard purchase confirmations into personalized marketing opportunities with relevant recommendations and engagement prompts. 

Example: A food delivery service enhances order confirmations with personalized restaurant recommendations, time-based reorder prompts, and loyalty program progress updates.

P: Predictive Analytics

‍Leverage historical customer data and machine learning to forecast future behaviors, identify likely churn risks, and anticipate customer needs for proactive marketing. 

Example: An e-commerce company uses purchase history and browsing patterns to predict the next likely purchase timing, automatically triggering personalized campaigns when customers are most likely to buy.

Q: Quick Wins Analysis

Identify and implement high-impact, low-effort optimization opportunities across the customer journey using data analysis and testing methodologies. 

Example: A marketplace identifies and fixes top conversion drop-off points through lightweight A/B tests, improving checkout completion rates by 15% within one month.

R: RFM Segmentation

Segment customers based on recency of last purchase, frequency of purchases, and monetary value spent to identify and target different customer value tiers. 

Example: A retailer identifies and rewards VIP customers based on RFM scores, targeting their top tier with exclusive holiday preview events and early access to sales.

S: Seasonal Campaigns

‍Plan and execute marketing programs with coordinated messaging, offers, and experiences across channels around peak business periods. 

Example: A travel site launches targeted summer vacation campaigns with personalized destinations and pricing based on past booking history and browse behavior.

T: Transactional Emails

‍Optimize required notifications with marketing opportunities while maintaining high deliverability and engagement rates. 

Example: A fintech app includes personalized financial tips and relevant product recommendations in payment confirmation emails, driving additional product adoption.

U: Upsell Campaigns

Promote premium products to existing customers based on their purchase history, usage patterns, and behavioral signals. 

Example: A streaming service encourages individual subscribers to upgrade to family plans by highlighting shared viewing patterns and family-friendly content preferences.

V: VIP Programs

‍Deliver exclusive experiences to high-value customers through targeted perks, early access, and personalized service. 

Example: A luxury retailer offers top-tier customers private shopping events, personal stylist services, and early access to new collections based on their spending patterns.

W: Welcome Series

Create automated email journeys for new subscribers that build engagement through personalized content and offers. 

Example: A meal kit service introduces new customers to their service through a series of personalized recipe recommendations, cooking tips, and targeted promotions.

X: X-Sell Campaigns

Promote related products based on purchase history, browse behavior, and product affinity analysis.

Example: A fashion retailer promotes coordinating accessories within 24 hours of a dress purchase, featuring personalized shoe and jewelry recommendations based on the customer's style preferences and past purchases.

Y: Yield Management

‍Optimize revenue through dynamic pricing and inventory management based on customer demand, historical patterns, and real-time market conditions.

Example: An e-commerce marketplace automatically adjusts product pricing and promotion levels based on inventory levels, competitor pricing, and demand forecasts to maximize revenue per customer.

Z: Zero-Party Data

Activate directly collected customer preferences and intentions through quizzes, surveys, and preference centers. 

Example: A beauty brand personalizes product recommendations and content based on skin concerns and beauty goals collected through an interactive quiz.

Ready to personalize the marketing experience?

The best customer marketing strategies start with great data, but they succeed when you can activate that data across all your channels — quickly, easily, and precisely.

Simon AI makes it easier to execute every one of these strategies. Our platform helps you unify customer data, automate personalized campaigns, and optimize marketing performance across channels without needing a team of engineers.

Simon AI is more than a CDP. We give marketers the data, technology, and expert resources they need to stop guessing and start delivering what customers want.

Ready to see how Simon AI can power your customer marketing strategy? Take a virtual tour.

For marketers today, having powerful customer data platforms isn't enough — marketing success requires the right blend of strategy, technology, and expertise. At Simon AI, our Services team plays a crucial role in helping clients unlock the full potential of their customer data and drive meaningful marketing outcomes. 

We sat down with Paige Rotar, who leads Simon AI's Services team, to discuss how they're helping clients navigate the evolving CDP landscape while driving impressive results through innovative approaches to campaign strategy, identity management, and AI-powered optimization.

Let's jump right in with the challenges marketers face today. What are the common roadblocks you see when brands try to turn their customer data into effective campaigns?

Marketing has never been more complex than it is today. The biggest challenges we see often come down to three main areas: connecting data and marketing strategies to actual outcomes, fully adopting and leveraging the technology they already have, and uncovering hidden insights in their data. Additionally, if the hurdle is simply having enough resources to execute effectively, we can help fill that gap as well.

That’s interesting — how have these challenges impacted a brand’s ability to actually execute successful campaigns?

What we’re seeing is that many brands struggle to unlock the full potential of their customer data, which leads to missed opportunities and campaigns that don’t perform as well as they could. That’s why we designed our Lifecycle Services program: to bridge this gap. It combines human expertise with data-driven insights and AI-powered recommendations to help brands maximize their marketing ROI.

You mentioned that Simon’s services bridge these gaps. Can you walk us through your approach to helping customers?

Sure! First, we start with our core CDP capabilities. You’ve probably heard the term “customer 360” thrown around a lot — that just means our CDP sits on top of your customer data and creates a unified customer profile with that data. We first ensure a stable identifier is in place, and from there, Simon helps optimize targeting, segmentation, and predictive insights. 

Our Lifecycle Services takes this foundation to the next level. We use the data and segmentation strategies you have in place but combine them with expert marketing strategies, actionable insights, and AI-driven recommendations to help transform campaigns into high-performing, revenue-generating engines.

So, whether a client needs help refining their marketing strategies, connecting their tech to their strategy, executing campaigns, or continuously optimizing performance using our strategists and latest AI innovations, we’re there to support them every step of the way.

Let’s dig into that. What does strategy engagement actually look like for your customers?

Every engagement starts with understanding what success looks like for that specific client. What’s their focus? Success criteria? What baseline metrics do they want to measure or optimize against?

We conduct an initial audit of their customer experience, then design or iterate on their personalized omnichannel strategies to improve engagement and conversions. Depending on their needs, we might focus on campaign strategy design, content and personalization recommendations, and segmentation strategies.

Our new AI features are exciting and have been useful for the entire support process. It helps our customers make smarter decisions about who to target, what content to deliver, and how to continuously improve their campaign outcomes.

We often hear about execution being a bottleneck. Can you share an example of how your team helps clients speed up their campaign execution while maintaining its effectiveness?

Absolutely. One of our key approaches is to first identify optimizations that can be applied across multiple campaigns. We first test our hypothesis on an underperforming campaign and then extend our learnings to other campaigns that can benefit from the same improvement to scale ROI and ensure they get the most out of Simon’s capabilities.

Email deliverability is still a massive concern for marketers. How does your team help ensure messages land in the right inboxes?

Our Email Deliverability Services combine CDP-powered insights with expert guidance to optimize send time, enhance segmentation and personalization, and ultimately, ROI. We’ve developed a three-tiered service model that lets clients start with essential monitoring and scale up to comprehensive program management as needed. 

By leveraging first-party data for personalization and advanced segmentation, we help ensure messages not only reach the right audience but also arrive at the right time across all channels. We’re focused on protecting and maximizing this revenue stream.

Let’s talk about identity management. How does Simon’s services help brands build that essential 360-customer view you mentioned earlier?

Identity management is incredibly important to any business that needs accurate, stable customer records — which, let’s be honest, is pretty much everyone these days.

We start with a thorough audit of existing data sources, then conduct an identity workshop to determine the best practices that make sense for their specific business. From there, we implement those practices into a single, consolidated customer table. 

In some cases, we might focus on establishing a single customer ID. For others, it might be as comprehensive as running deduplication and data cleansing processes. The end result for all these cases is to have a unified and accurate customer table that becomes the foundation of effective marketing.

You’ve spent your entire career in MarTech. How has that experience shaped how you help clients succeed?

Having consulted in MarTech across various brands and industries, I’ve seen firsthand how technology adoption can make or break marketing success. Although knowing marketing best practices is important, understanding the tech behind the curtain — and how to optimize it — is equally important. 

It’s not the technology itself, and sometimes not even a team’s marketing expertise, holding marketers back. It’s the process of adopting and fully utilizing new tech like AI. Thankfully, my experience helps me quickly spot where clients might be struggling and work with them to drive the results they’re looking for.

The CDP market is evolving rapidly. What excites you most about leading the Services team during this transformative period?

What really excites me connects back to our “Anti-CDP” approach. We know that having a CDP alone isn’t enough to drive positive marketing outcomes. The magic happens when you blend the right strategy, people, and technology together. 

That’s where our services team comes in. We’re the people who help make that blend work. And now, with AI in the mix, we can make even more sophisticated recommendations based on exponentially more data.

Looking ahead, how do you see marketing evolving, and what opportunities do these changes create for marketers?

We’re seeing huge momentum in personalization at scale, AI and machine learning, predictive analytics, omnichannel marketing, and first-party data strategies. What’s exciting is that our product team stays ahead of these trends, which allows our services team to leverage cutting-edge capabilities, like our AI beta, to support use case recommendations and strategy development.

One final question: What should marketers know about working with Simon AI’s Lifecycle Services team?

At the end of the day, we’re focused on three things: making your life easier, your processes more efficient, and your data more streamlined. Beyond that, everything we do ties back to driving incremental return on investment and customer lifetime value for your org.

Cross-selling is often overcomplicated, leading many marketers to delay implementing a qualitycross-sell marketing strategy. But you can launch an effective program in just four weeks by focusing on the essentials and building incrementally. This quick-start guide will walk you through the process, starting with choosing the right strategy for your business.

Step 1: Choose your cross-sell strategy

Your cross-selling approach should align with your business goals, product portfolio, and customer journey. Our most successful customers typically start with one of three core strategies, each serving a distinct business objective:

The completion strategy

Use the completion strategy to help customers build complete solutions by suggesting natural product pairings. The works particularly well for retailers who offer complementary products that enhance a customer’s initial purchase. My, wouldn’t these throw pillows look awfully nice on your brand-new couch?

The evolution strategy

Guide customers to more advanced products as their needs grow and skills develop. This approach is ideal for brands with tiered product lines or serve customers through different life stages. Looks like someone's ready to graduate from smartphone snaps to "Oh my god, who's your photographer?”

The ecosystem strategy

With the ecosystem cross-sell strategy, you can show how multiple product lines work together to create better experiences across categories. This might be your cross-sell strategy if your company offers diverse but interconnected product lines. Because one smart device is lonely, but a smart home is a party!

To determine which cross-sell strategy to start, consider your current product mix and customer behavior. 

• Are you selling items that complete a look/solution? Start with the completion strategy
• Do your customers’ needs typically evolve over time? Embrace the evolution strategy
• Are you focused on promoting multiple product lines that work together? The ecosystem or connected strategy could be your answer

Step 2: Set up your customer data foundation

‍Comprehensive customer data is invaluable — but don’t let it be the enemy of good cross-sell marketing. Start with the data points you already have and build from there — even two or three can get you started.

Purchase history

• What to track: Products bought, purchase frequency, category preferences
• Why it matters: Shows natural product combinations and buying patterns
• Quick win: Focus on your top 25% of customers to spot common purchase pairs

Browse behavior

• What to track: Product views, category interests, time spent
• Why it matters: Reveals what customers are actively considering
• Quick win: Look for customers viewing similar items multiple times

Engagement metrics

• What to track: Email opens, site visits, app usage
• Why it matters: Indicates interest and readiness to buy
• Quick win: Note which product categories drive the most engagement

Step 3: Set up your cross-sell triggers by strategy

‍Your cross-sell timing is just as important as the products you recommend. Here's when and how to reach out based on your chosen strategy.

Completion strategy: Strike while the iron’s hot

‍Post-purchase (48 hours): The excitement of a new purchase creates the perfect moment to suggest complementary items.

Send a personalized email showcasing how additional products enhance their recent buy. "Love your new coffee maker! Here's how our premium filters can make every cup taste even better."

‍Browse pattern (3+ views): When customers return to the same product multiple times, they show serious interest. Use website notifications to highlight related items that complete the experience.

‍"Noticed you've been eyeing that stand mixer? These attachments turn it into a pasta-making powerhouse!"‍

‍Cart abandonment (4 hours): Sometimes, customers need a gentle nudge with alternatives. Follow up with similar products at different price points or with slightly different features.

‍"Still deciding? Shoppers who viewed this also loved..."

‍Evolution strategy: Grow together

‍Seasonal transitions: Help customers prepare for upcoming changes with timely recommendations. Show your fall/winter collection to summer clothing buyers when winter approaches.

‍"Your summer wardrobe was on point — here's what's trending for fall."

‍Lifecycle moments: Match product recommendations to customer milestones. As children grow, suggest age-appropriate toys or recommend pro-level gear as photographers advance.

‍"Ready to take your photography from hobby to side hustle? These upgrades can help."

‍Category exploration: When customers browse more advanced products, provide educational content alongside recommendations.

‍"Since you're exploring our DSLR cameras, here's how they can elevate your photography game."

‍Connected strategy: Build the perfect ecosystem

‍Multi-category browsing: When customers explore different product categories, show how they work together.

‍"Your smart speaker is just the beginning — see how it orchestrates your entire home."

‍System completion signals: Identify gaps in their product ecosystem and suggest missing pieces that enhance functionality.

‍"Make your smart home work harder: adding a hub lets all your devices talk to each other."

‍Replenishment timing: Use purchase history to predict when customers might need replacements or upgrades, then suggest system expansions alongside.

‍"Time to restock your coffee pods? Our new milk frother turns every coffee into a café-worthy creation."

‍The success of your cross-sell strategy hinges on when and how you present it. Each trigger should feel like a helpful suggestion for your customers, not a sales pitch. You should monitor customer response rates to fine-tune your timing and messaging for each trigger type.

‍Step 4. Choose the right channel

‍Every channel has its sweet spot. Here's how to match your message to the moment and build your program week by week.

Email

Email marketing shines when you want to provide in-depth information or paint the full picture. Use it for:

• Detailed product education that helps customers understand value — think detailed buying guides and usage tutorials
• Visuals showing how products work together
• In-depth solution showcases with lifestyle imagery and customer testimonials

Mobile & SMS

When timing is everything, mobile is your go-to channel. Use it for:

• Time-sensitive offers that create FOMO. “4 hours left to complete your kitchen set!”
• Quick-action prompts for impulse-friendly add-ons.”Your order ships in 2 hours — add these matching items now?”
• Location-based suggestions when customers are near your store. “In the area? Pick up those items you viewed.”

Website

Your website is your digital storefront, so make sure product discovery feels natural and intuitive.

• Interactive product explorers that encourage browsing. "Drag to see how these pieces work together"
• Smart recommendations that adapt to browsing behavior. "Others completed this look with..."
• Real-time personalization based on cart contents. "These accessories were designed for your selection"

Step 4: Launch your cross-sell marketing plan

Week 1: Choose your focus

• Pick one product category that has clear cross-sell opportunities
• Select one trigger type that you can execute well
• Identify one customer segment who is most engaged

Week 2: Set up tracking

• Configure basic data collection and metrics for each data point
• Set up your chosen trigger
• Create message templates that focus on customer benefit

Week 3: Launch your cross-sell pilot program

• Start with a small customer group, think 10-20%
• Test one channel and monitor early results
• Gather qualitative feedback from customer service

Week 4: Measure and adjust

• Look for any early signals: track clicks and purchases
• Note which products pair best together
• Watch for unsubscribes or negative feedback (which is also valuable data!)
• Refine your approach

Success metrics to watch

• Recommendation acceptance rate (aim for 5-15% to start)
• Time to second purchase (shorter is better)
• Customer feedback/satisfaction post cross-sell
• Return rates on suggested items (should match or beat your average)

Common roadblocks we hear all the time — and the solutions for them

1. “We don’t have enough customer data.”

Start with the data you do have, like basic purchase history. Focus on your top 25% of customers and look for common product combinations.

2. “Our tech stack isn’t ready.”

Begin with manual triggers in only one channel, then automate as you grow. A thoughtfully crafted email sequence can work wonders while you build automation. Sometimes the "artisanal" approach yields better results anyway.

3. “We’re short on resources.”

Pick your battles. One well-executed cross-sell program for your best customers beats a scattered approach every time. Success will help you make the case for more resources.

You don't need a perfect setup to start making smart product recommendations. Begin small, focus on one clear opportunity, and build from there. Your first cross-sell program doesn't have to be complex — it just needs to be relevant and helpful to your customers.