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Introducing Simon Anonymous+, the first solution to resolve identity across every customer touchpoint

When it comes to brand websites, 90% of your customers are browsing anonymously or via unrecognizable devices — making it impossible to capture first-party data to create highly targeted, personalized marketing experiences.

But what if you could capture the thousands of unknown consumers or anonymous users that land on your site, create a personalized site experience, and sync them to paid advertising channels like Google Ads, TikTok, and Facebook across their entire journey? With Simon Data’s newest features, you can.

Amplify your reach with Simon Anonymous+

Meet Simon Anonymous+, the first solution to resolve identity across every customer touchpoint using the power of the Cloud Data Warehouse (CDW). It pairs the Simon CDP with enhanced ad integrations to help you target anonymous users, manage your paid media strategy across unknown and known audiences, analyze your campaign success, and improve your Return on Ad Spend (ROAS) — all directly from within Simon.

Simon Anonymous+ uses an Identity Graph to link anonymous visitors with various identifiers like HEMS and MAIDS. This connection empowers you to target these previously unreachable audiences by creating hyper-personalized campaigns, bridging the gap between unknown visitors and loyal customers, and amplifying match rates in downstream channels.

It can even personalize the site of a customer’s second visit based on their actions on their first visit, delivering an enhanced and more meaningful customer experience.

Beyond identification, Simon Anonymous+ unifies your paid and owned channels, ensuring a cohesive and engaging brand experience through our Journey builder and activation tools so marketers can take a customer-centric approach to enhance efficiency and impact across various advertising platforms.

Additionally, Simon Anonymous+ refines your marketing measurement by providing consistent identifiers across the entire customer lifecycle and pairing it with our experimentation suite. This, combined with direct integrations with CDWs and Marketing Measure platforms, allows for precise tracking and optimization of your marketing strategies, turning every interaction into an opportunity for customer growth and engagement.

Over the past few months, our customers have been excited to delve into Anonymous+. The results they’ve seen so far? An 80% increase in ROAS and a 98% bump in ad revenue, in addition to the typical 150% surge in social media revenue they usually experience using Simon Data’s CDP.

Simon Anonymous+ has also aided our customers in streamlining workflows and amplifying their reach by increasing the number of channels they can send anonymous audiences to, ultimately eliminating wasted dollars on underperforming audiences while maximizing their downstream audience.

Getting started with Simon Data

Choose Simon Anonymous+ as your all-in-one solution for managing and optimizing campaigns across the entire customer lifecycle to make every customer interaction and marketing investment count.

To get started, request a demo today.

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Last year, at Simon Data, we faced a decision: based on an accumulating molehill of market data, do we build our Customer Data Platform (CDP) to be deployed within a client’s data warehouse?

As background, we’ve always viewed the Cloud Data Warehouse (CDW) as the center of gravity around which an organization’s data strategy and applications should orbit.

About six years ago, we decided to build our platform in the Snowflake ecosystem and aligned early with their vision of bringing the applications to the data vs. data to the applications.

This paradigm (composability) is defined here, for the sake of simplicity, as deploying the software directly within a client’s data environment was a bit of a Rubicon decision. Tradeoffs around product roadmap and resources notwithstanding, this was an architectural decision from which we likely wouldn’t return.

On one hand, we continued to see market interest in this composability. We had quasi-competitors like Hightouch claiming the CDP category as then currently conceived was “dead.” I sat through many hundreds of hours of sales conversations in 2022 where a lack of “composability” was a perceived deal-breaker.

Fundamentally, and the biggest factor in the pro-composable camp, was that the composable vision aligned with how we view the data warehouse within the client’s data ecosystem.

On the other hand, we had just as many conversations with clients who had attempted to deploy a composable CDP and were unsuccessful. There is, practically speaking, a spectrum of whether the product is deployed within or on top of the data warehouse.

For example, when your product is designed to power personalized messages across channels, data does eventually need to leave a client’s CDW before the ad, email or SMS is seen by the customer.

If we were going to fully embrace composability, we had decisions to make around which components of our application would need to be composable, in what order, and by when.

Understanding what composability means in the MarTech space

Underlying this decision of this was a question of whether the market understood what it meant for the software to be composable and by what standard. Ultimately, we questioned whether this was a zero-interest-rate fever-dream like trading ape JPEGs or the Metaverse vs. a lasting trend.

Speaking with self-awareness of my confirmation bias, now that we have delivered a fully composable CDP to market, I can confidently say two things:

1. There is still an uneven understanding in the market of what it means to be composable
2. Composability is not a fad and will have huge implications for the CDP and broader MarTech space

I won’t try to solve #1 here. Braver people than I will continue to lead that charge. What’s more interesting to me (and one of the complicating factors in the decision set forth above) is that there has been relatively little discussion of topic #2.

Considering the implications of composability for the broader market, it’s important to reiterate just how challenging it was to distill market feedback around “composability.”From speaking to clients all day, the composable CDP narrative sounded more like an elementary school recorder lesson and less like an orchestra (i.e. high noise to resonance ratio). The market generally struck some consistent notes like data security.

Beyond that, as someone whose job it is to (1) understand and explain how the machine actually works and (2) translate it into the business value it delivers, I struggled to do both.

Composability often felt to me like a religion. It was something that people just believed to be good or essential without it having to be explained or proven.

A year later, the recorder lesson is starting to sound like improv a cappella. Everyone in the space has figured out on our own what harmony to sing, but without the accompaniment of a 9-figure sales and marketing budget to have Matthew McConaughey broadcast it.

Clients well understand the benefits, even if the terminology is not widely synchronized. Demand is substantial and concerns around data replication or non-composable approaches have formed a beautifully dissonant tone of their own.

Recently, I’ve realized that there’s a schism widening within the idea of composability and data warehouses, and it has huge implications for the CDP space and MarTech more broadly, and that’s what inspired me to write this article.It occurred to me in Mark Benioff’s response to a seemingly platitudinal comment by a research analyst about attracting data science talent during Salesforce’s earnings call in November.

Here’s Benioff’s response:

“Well, I think that, that is very much a primary focus of the company, which is that — when we started this Data Cloud, we thought we were just building a CDP. And a CDP looked like an exciting market opportunity. We’re #1 in enterprise marketing automation. That seems like a great opportunity.But the more we started working on this product, we realized, oh, every one of our clouds needs this Data Cloud. And so Sales Cloud needs a Data Cloud, Service Cloud needs a Data Cloud. Yes, Marketing Cloud needs a Data Cloud, called that CDP. And Slack needs a Data Cloud. Tableau also needs a Data Cloud. If you’ve seen any of my recent demonstrations and propose it with these great Tableau customers in Japan, they all need Data Cloud on the back end of Tableau. And this idea that the Data Cloud will become the heart and soul of the product, be the engine of all of Salesforce’s apps and say you can use our models, our AI models or you can bring your own models into the Data Cloud, which is a very cool feature.This idea that it also has this incredible level of capability. But the amount of data that it’s already managing and the amount of data that it’s already ingested, that is what is shocking to us. And I think that you’re going to see as we get deeper and deeper into this so you can really see the level of data that we’re handling, the trillions and trillions of transactions. This is going to be the key to the AI working for enterprises.”

Overall, on that earnings call, Data Cloud was mentioned 42 times. Marketing Cloud was mentioned 3 times. From his comments, two things are explicitly clear:

1. Salesforce views the Data Cloud as the foundation underpinning the rest of their applications (i.e., similarly to how composable CDPs view the data warehouse and recognize that other applications, like analytics, should do the same)
2. Salesforce is making a long-term bet that their clients will move all of their data into the Data Cloud

If you believe those two things, you must also believe that now Salesforce and Snowflake are competitors.

Snowflake vs. Salesforce in the battle for customer data

It would be at best insufficient and at worst incorrect to say that this now means organizations will have to choose where they want their data to live. As those of us in the MarTech space know, it’s never that simple. Organizations that choose to centralize all of their data in Snowflake may still choose to bring their data into the Salesforce Data Cloud (unfortunately, both now use this term).

Given how Salesforce markets, sells, and then builds (or more often repackages) software, we’re years away from a customer having all of their enterprise data in Salesforce, if that’s ever even possible.

Beyond that, I started thinking about practical applications of Benioff’s statement in our client’s terms.

What if a client uses something other than Tableau for analytics and something in the Adobe ecosystem like Test & Target, as is often the case?

What if a client has invested heavily in centralizing their data in the Snowflake ecosystem (as is also often the case) and is concerned about maintaining parity between systems?

Will the Salesforce Data Cloud be extensible to support applications beyond its scope like data sharing, clean rooms, enrichment and identity resolution, or will it revitalize the corpse of Krux and try to make it compatible?

It struck me how the bet Salesforce is making obviously runs counter to the composable CDP vision and less obviously represents a complete divergence from the underlying trends that led to its emergence.

The reason I compare this to a schism is that it’s narratively driven and is not dichotomous in terms of the practicality of the deployments or even the belief sets around them.

My actual bet? In a multi-polar power dynamic, there tends to be a detente on practical dimensions even while there’s sword rattling on matters of strategy and vision.

They both have the scale, although Salesforce is about 10x the revenue and Snowflake is about 4x the growth.I have tremendous faith in the Snowflake product, team, and vision. Regarding Salesforce and other MarTech that orient to being your data platform, I think Benioff’s own words describe the challenges of their approach better than I ever could.

If Salesforce succeeds, I could certainly see some of my original hesitations — that composability represents collective industry navel gazing — coming to fruition.

I think it’s far more likely that Salesforce won’t be the ones to upend this trend, and just as they changed their views on the CDP space a half dozen or so times, they will ultimately walk this back (along with being the world’s largest AI company).

As the dust settles on Black Friday and Cyber Monday 2023, the outcomes of the retail extravaganza have left the industry buzzing. This year’s BFCM kept economic uncertainty, consumer spending expectations, and the ever-evolving landscape of B2C marketing top of mind, especially for marketing teams.

This begs the question: was Black Friday and Cyber Monday a triumph for marketers and retailers in 2023, and how did it stack up against the benchmarks set in the past?

Recently, Shopify unveiled staggering statistics around this year’s event, revealing that merchants under its umbrella collectively soared to $9.3 billion in sales over the weekend — a notable 24% surge compared to last year.

At Simon, we've delved into the data within our Customer Data Platform (CDP) to analyze the results of the BFCM rush. And, beyond the numbers, we spoke with our customers to glean firsthand insights into the nuances of this year's holiday fervor.

Here’s a look into how our customers prepared for BFCM, the metrics we saw on our platform, and some trends we noticed ‌from this year’s holiday craze.

Preparing for a busy Black Friday and Cyber Monday weekend

With 20-50% of our client’s annual sales, projected revenues of ~$20.5B, and an increase of 380.2M events within Simon’s CDP occurring on BFCM last year, Simon’s engineering team prioritized providing real-time data for personalization, scalability, and optimal performance for our customers by:

• Rebalancing our client infrastructure
• Refreshing segments as quickly as they changed in the data warehouse so clients could respond immediately to changing customer preferences and market dynamics
• Enhancing our AI capabilities for improved actionable insights
• Providing security and support around the clock

Our team spent time with customers to better understand their send volumes, campaign strategies, and execution deliverables. We also worked closely with partners Attentive and Dynamic Yield to provide smooth integration and comprehensive support during BFCM.

“Huge shout-out to the Simon Data team for ensuring we were set up for success during the busiest time of the year. This was one of the smoothest Black Fridays we’ve ever had, and that is in part to you and all of your support!” - Josephine Tretheway, Senior Acquisition Associate, The Farmer’s Dog

BFCM 2023 was a record-breaking year for Simon Data customers

Overall, our customers had an incredibly successful Black Friday and Cyber Monday season, seeing strong year-over-year growth (nearly 45% in some cases) and conversion rates across all of their brands, even the smaller ones.

Despite sales beginning in early October, several companies saw a decline in purchases from mid-October through mid-November, signaling that shoppers were waiting longer (until “Cyber Week”) to make larger purchases compared to previous years.

However, the delay in purchases didn’t stop customers from responding at a record rate to promotional and event-triggered notifications, such as abandoned cart emails and product recommendations, which were particularly effective for our customers.

Nearly all of our customers will also continue to offer heavy-hitting discounts throughout the entire month of December — well beyond the traditional weekend-long BFCM event. It's likely this trend will continue into next year.

Analyzing the metrics of BFCM in Simon’s CDP

Our on-call product engineering and customer service teams had a relatively smooth BFCM, but the numbers from our CDP show just how busy the weekend was. From midnight Black Friday through 11:59 p.m. Cyber Monday, there were:

• 209,329,235 emails sent, a 144% increase from the average daily amount of emails sent
• 7,148,676 SMS sent, a 72.1% increase from the average daily amount
• 2,542,272 push notifications triggered, a significant decrease from the daily average, indicating that marketers prioritized email sends over push notifications during the weekend
• 2,789,130,173 total contacts synced, a 208% increase above the daily average

Many of our customers happily noted faster sync times in the Simon CDP and were incredibly appreciative of all the prep work our engineering team did to set accounts up for success.

When it comes to email deliverability for our customers, we couldn’t have asked for a smoother BFCM event. We delivered over 130 million messages at a delivery rate of 98.99% and a platform-wide open rate of 49.5%.

Simon Mail gives our clients unique access to their historic customer data, allowing them to target recipients more aggressively during this time, but in a way that minimizes their risk of deliverability damage.

One customer reported a record-breaking year, with their first sent email promotion generating the most revenue they’ve earned from a single email. Because of its success, the marketing team was able to decrease its spending on paid media and further optimize and extend its holiday campaign for the full week past Black Friday.

The continued success of BFCM and the impact of CDPs for marketing teams

Black Friday and Cyber Monday continue to be a successful yet challenging time of year for marketers, who must continue to deliver even more personalized customer experiences to stand out in a competitive market.

Thankfully, consumers have been responding positively to brand promotions and offers, and, with an extended sales season in the forecast, marketers will have the opportunity to end the year on a high note.

When we asked what our customer marketing teams relied on the most to deliver 1:1 personalized experiences during the busiest time of year, many cited the benefits of having access to real-time data to build 360 customer profiles and the ability to easily orchestrate campaigns all within one platform — emphasizing the power of using a CDP built on a data warehouse like Snowflake.

f you’d like to learn more about how Simon is leading the charge in enabling CRM marketers to access and deploy their data for personalization, request a demo today.

Both Gmail and Yahoo have recently announced that starting in February 2024, they will begin requiring more stringent email authentication protections for senders sending more than 5,000 messages daily. For many senders, these changes are nothing new, as this is email best practice.

However, it is important to make sure that you haven’t overlooked anything. Starting in February, senders who want to deliver mail successfully to Gmail and Yahoo will be required to do the following:

1. Authenticate email messages with SPF and DKIM
2. Have a Domain-based Message Authentication, Reporting, and Conformance (DMARC) policy in place
3. Include a one-click unsubscribe in their messages
4. Messages must pass DMARC alignment
5. Keep spam complaint rates at a reasonable level

For all Simon Mail senders, points 1 and 4 are taken care of. All Simon Mail senders use sending infrastructures that accomplish these functions and ensure compliance. For those Simon Mail senders that utilize Simon to manage email suppressions, compliance with point 3, the 1-click unsubscribe requirement, will be in place before the deadline of June 1, 2024. If you are a Simon Mail sender managing your own suppression lists outside Simon Mail, you should check to make sure you have a proper 1-click unsubscribe mechanism in place.

What Simon Mail can't take care of for senders is the implementation of a DMARC policy and spam complaint control. Both of these elements are firmly in your control.

DMARC is an authentication protocol that helps receivers of your emails, such as Gmail and Yahoo, more accurately fight phishing attacks. All that is initially required for a DMARC implementation is to add a simple TXT record to your domain’s DNS. While there is more to DMARC that we encourage you to explore below, it is strongly recommended to make sure you have at least a bare-bones DMARC policy published via a TXT record in your domain’s DNS.

To be compliant with a bare minimum DMARC policy, you will need to replace {YOUR DOMAIN} with:

DNS Record Type: TXTHost/Name: _DMARC.{YOUR DOMAIN}.comValue: v=DMARC1; p=none; fo=1

Once that record has been added, you are in compliance with the DMARC requirement. However, your DMARC policy is not yet functional and is providing minimal value to your brand.

Why implement a DMARC policy?

You can dig deeper into DMARC, but in this post, I want to highlight the benefits you will see by using this tool to protect your brand’s reputation and outline roughly what this protocol does.

It’s important to know that the bare minimum DMARC policy referenced above is not a functioning DMARC policy. Rather, it is simply ‌the first step toward implementing DMARC fully, as it lacks any way for the brand to receive reports of DMARC failures and does nothing about suspicious messages. A fully implemented DMARC policy should have at least an email address indicated by the rua tag that receives those reports.

When DMARC is fully implemented, it offers many benefits. Let's discuss them below.

Boost your recipient's confidence in your legitimacy

First, it allows domain owners to instruct receivers, such as Gmail and Yahoo, on what to do with messages that do not pass these authentication checks. Brands can use this to tell receivers to quarantine or outright block messages that look suspicious. Domains can even request a report about the failure and let the mailbox provider decide where the message should go — more on that in point 2.

Although DMARC does not guarantee your messages will reach the inbox, it does boost your recipient’s confidence in the legitimacy of your messages while reducing the instances where phishers can successfully impersonate your brand.

Better identify potential authentication issues and phishing attacks

DMARC allows domain owners to receive both aggregate and forensic reports back from receivers, helping them identify potential authentication issues as well as potential phishing attacks using their brand.

Having this insight into whether messages that fail authentication are legitimate messages that you need to address or if they are phishing attacks gives you the confidence you need to move your DMARC policy from reporting mode (p=none) to enforcement mode (p=quarantine or p=reject) without disrupting any legitimate mail delivery. Only when DMARC is in enforcement mode do you receive the true benefits of the protocol.

Access the tools you need to control your brand representation

Finally, a DMARC policy allows senders to participate in or get easier access to other tools and benefits such as BIMI that allows brands to control better how their brand is represented in recipients’ inboxes. Not to mention, it puts brands ahead of the game when it comes time for Gmail and Yahoo (or someone else) to continue increasing their requirements.

As time passes, fully implemented DMARC is surely going to become more and more of a requirement for marketers to deliver mail to recipients’ inboxes. We suggest starting to explore DMARC now if you haven’t already so that you can experience its benefits sooner rather than later and avoid any future fire drills. Let your account manager or customer success team know if you want to learn more about DMARC and its benefits.

As the platform product manager here at Simon Data, I understand how significant the holiday season is for our clients and the critical role Simon plays in providing a seamless customer experience. With the peak holiday season just around the corner, we’re excited to help our clients prepare for this holiday season. Here’s how.

Analyzing campaigns to understand the holiday rush

The holiday season is undeniably the most lucrative time for many businesses. Our clients will see 25 to 50 percent of their annual sales occur during this time, with projected revenues of around $11.3B on Cyber Monday and $9.2B on Black Friday.

It’s a time when customer interactions and transactions peak, making it essential for businesses to be well-prepared. From hyper-personalized marketing campaigns to efficient data management, Simon is at the heart of making it all happen seamlessly.

Our account managers are busy analyzing how and what campaigns customers are using to better understand send volumes, campaign plans, and execution deliverables to make sure everything runs as smoothly as possible.

Providing scalability and performance

In preparing Simon for the holiday season, one of our top priorities is providing the scalability and optimal performance of our customer data platform. During major marketing holidays, our incoming event traffic will double in volume, and we need to be prepared to handle the increased load efficiently.

Last year, we saw an increase of 380.2M events for Black Friday and Cyber Monday traffic on top of our usual WoW traffic. Using this data, we ‌proactively rebalanced our client infrastructure this year to ensure our system usage was saturated with room to grow throughout the season.

Delivering real-time data processing and enhanced personalization

The holiday season demands real-time data to power a personalized experience. Customer behavior can change rapidly during this period, and businesses need up-to-the-minute insights to make informed decisions.

Simon’s connected customer data platform guarantees that segments are refreshed as quickly as they change in your data warehouse, enabling our clients to respond quickly to changing customer preferences and market dynamics.

We’ve also enhanced our AI capabilities to provide businesses with actionable insights that enable them to create highly targeted and personalized marketing campaigns. Our brand-new GenAI product is a perfect example of how we’re incorporating real-time data to power enhanced personalization.

With GenAI, our customers can write a prompt in plain text and insert the recommended Jinja into their templates. The result is a custom, personalized message to our customers’ clients. From product recommendations to personalized offers, Simon ensures each customer feels valued and engaged.

Monitoring data security and compliance

Here at Simon, we’ve always had data security and compliance at the forefront of our platform, both in peak seasons and slower times. Our platform was built to allow our customers to take action on all of their customer data without the overhead.

Our approach to security follows the same principles. We recognize the importance of the first-party data our customers share with us and have built our security program around protecting the data you entrust us with.

Simon Data is SOC 2 certified, and we continuously monitor all associated controls to ensure we remain compliant at all times. We are also compliant with GDPR and CCPA privacy standards. Our security and compliance materials can be requested from our Trust Center.

Ensuring integration and customer support

We know that Simon can be just one piece of the puzzle. To truly empower our customers during the holiday season, it’s essential that our platform seamlessly integrates with other tools and systems.

We’ve worked closely with our partners, most notably Attentive and Dynamic Yield, to provide smooth integration and comprehensive support to address any issues that may arise. We’ve established shared slack channels, multiple lines of communication, alignment at the front lines all the way up to executive leadership, and a shared, deep commitment to our shared clients, which occurs 24/7/365 with a strong emphasis during this peak season.

Simon Data is ready to take on the holiday season

Simon is fully prepared to make this holiday season a resounding success for our customers. We’ve focused on scalability, personalization, security, and integration so that our platform meets the demands of this peak period and more.

I’m proud of the strides we’ve made to empower our clients and provide them with the tools they need to thrive during the holiday rush. With Simon by their side, our customers can look forward to thriving during the busiest time of the year!

Measuring campaign impact is a top priority for businesses looking to optimize their marketing output. One way to understand the value and impact of campaigns within lifecycle and CRM marketing is by measuring incrementality.

This method allows marketing teams to distinguish the actual impact of their efforts, independent of external factors.

This blog post explains how to measure incrementality within CRM marketing campaigns, as well as outlines how a Customer Data Platform (CDP) can help you uncover actionable insights.

What is incrementality?

At its core, measuring incrementality seeks to answer a fundamental question: "What would have occurred if the marketing campaign had not been executed?" This test aims to differentiate between outcomes influenced by CRM campaigns and outcomes that would have unfolded naturally. By doing so, marketers can precisely quantify the genuine impact of their campaigns, excluding any effects that would have manifested organically.

But before understanding the process of measuring incrementality, you must establish a foundation for providing accurate and meaningful results. This includes defining the specific goals for each of your CRM marketing campaigns. Whether boosting revenue, increasing customer lifetime value (LTV), or driving engagement, having well-defined objectives will inform your measurement strategy for your incrementality test.

You also need to segment your audience. Dividing your target audience into distinct segments or cohorts enables a more nuanced analysis of the incremental impact within each group.

Within these cohorts, you should create a control group that doesn't receive the marketing intervention and a test group that does. This provides a baseline against which you can assess the results of the test group. You should also ensure that individuals are randomly assigned to the control and test groups to minimize bias and create a fair comparison.

The different approaches and techniques for measuring incrementality

There are several approaches and techniques to measure incrementality in CRM marketing campaigns.

• A/B Testing: A/B testing involves splitting your audience into two groups: one exposed to the marketing campaign (test group) and the other not exposed (control group). By comparing the outcomes of both groups, you can estimate the incremental impact of the campaign.
• Holdout Groups: A holdout group is a subset of your targeted audience excluded from a marketing campaign. To measure a campaign's absolute effectiveness, you can compare the behavior and outcomes of your holdout group against the segment that received the content.
• Matched Market Testing: If creating a control group is difficult, you can use matched market testing. This involves comparing the performance of your target market with a similar market that didn't receive the marketing treatment. The differences in outcomes indicate the incremental effect of the campaign.
• Time Series Analysis: Analyze historical data to identify patterns and trends before and after the marketing campaign. By analyzing deviations from these patterns, you can estimate the incremental impact of the campaign in question.

Interpreting the results of an incrementality measurement strategy

Once you've executed your incrementality measurement strategy, the next step is to interpret the results and extract actionable insights by:

• Ensuring that your results are statistically significant, which indicates that observed differences are unlikely to occur due to chance.
• Comparing the outcomes of the test and control groups to quantify the incremental impact. Be sure to express the results as a percentage increase or use metrics aligned with your campaign goals.
• Analyzing the results across different audience cohorts or segments. This can reveal whether the campaign's impact varies among different demographics, allowing you to tailor future strategies accordingly.
• Separating the effects of the marketing campaign from other external factors that might have influenced outcomes. This enhances the accuracy of your measurements.

How a Customer Data Platform (CDP) helps

A Customer Data Platform plays a vital role in enhancing the accuracy and effectiveness of incrementality measurement. By combining customer data from various sources, a CDP offers a comprehensive view of customer interactions and behaviors in real time and improves the omnichannel customer experience.

This, in turn, aids in creating robust control and test groups, improving the reliability of your incrementality measurements. The insights from incrementality measurement provide a valuable roadmap for refining and optimizing your CRM marketing campaigns.

Conclusion

Measuring incrementality within lifecycle and CRM marketing campaigns is crucial to success. You can use approaches like A/B testing, holdout groups, Matched Market Testing, and Time Series Analysis to develop actionable insights to optimize (and drive value from!) your future campaigns.

A CDP can help improve the accuracy and effectiveness of incrementality measurement by providing a comprehensive view of customer interactions and behaviors from various sources and help deploy your campaigns.

By leveraging the power of a CDP and implementing effective measurement strategies, businesses can make data-driven decisions, improve campaign performance, and drive better results in their CRM marketing efforts.

There are plenty of reasons to invest in a first party data strategy that don't include the all-important reason of staying on the right side of the ever-evolving regulatory landscape. One of those reasons is that it's not always your legal issues you need to worry about but that of your data suppliers or publishing partners or one of their vendors. You don't want to be caught leaning too hard on any given dependency (like, say, cookies).

The best way to ensure a data driven marketing program that's built to last is to make sure you own your most strategically essential data. A first party data strategy doesn't require a 2- to 5-year roadmap or massive digital transformation. The first step is understanding what data you need to collect on your most strategic activities.

The camera analogy

I like to use a camera analogy to illustrate what it means to think about the data you need to collect and own. The camera needs to move to achieve the desired perspective. If the goal is to gather strategic data rather than exhaustive documentation, you don't need every camera angle. Just as the photographer chooses the angles necessary to provide a complete enough story in the fewest shots, including only the required visual information, your job is to figure out what first party data points answer the questions you have about customers.

In this analogy, each camera angle is analogous to a metric - formed and influenced by a combination of events, customer attributes, and contexts. Exhaustive documentation is slow, costly, unnecessary, and likely misleading. During the planning phase of building a first party data strategy, you need to pick the best tools for the job. You don’t want to be the blind men arguing about the elephant they’re all describing (Is it a snake (trunk)? Is it a wall? (torso)? Is it a tree? (leg)). But you also don’t want to obsess over trivial data points as if all metrics and measurements are equally important in every given scenario. For an acquisition campaign, you'll likely want to investigate LTV:CAC. For a reactivation campaign, you'll likely highlight conversion rate and maybe AOV or average cart size.Among others, the big things you're trying to understand include:

• How your business works
• How your customers think and behave
• How your customers receive and use your products

Different businesses will need to measure things differently. Not every angle will be interesting, useful, or even necessarily relevant in every context. First and foremost, consider customer experience. Think about the moment someone becomes aware of your brand, their first purchase, their first unboxing, the entire process, A to Z. Within that, you ask: Are there any feasible measurements here? Can any of them help to move the needle toward your ultimate goal?

Understanding the entire customer journey

The customer journey is not a report spun up by a black-box analytical console measuring user touch points. The customer journey is a real-world experience that your customers go through every single day.

Some experiences are more digital than others. Some are completely offline. Even for digital-only customers, you don't know much about their day-to-day product experience - even the best propensity models would benefit from a dose of offline behavioral data.At the same time, it's critical to understand where the touch points are and - either through deep analysis and customer research or common sense -  develop hypotheses on what parts of the journey most affect the customer experience.

Digging for nuance

On the flip side of being picky about what first party data gets incorporated is expanding your capacity to measure different things across the customer experience. This may require more in-depth qualitative and quantitative research to understand precisely how someone might be interacting through a mobile app - such as surveys or social monitoring. The point is to determine the strategically valuable first party data points and attributes that you're not yet capturing and to find a way to achieve that while offering your customers value in exchange for the data you need.

Laying out product & business complexities

From a high-level perspective, you want to be sure you have visibility across the entire customer journey. This requires pulling from multiple data sources; for some companies, it could be dozens of sources. Below are a few to consider.

Customers

‍As we dig deeper into the concept of first party data, we can think about the benefits of independence concerning not being strategically reliant on too many external data sources. We can also think about data independence as each department within the organization having independent access to an easy-to-use interface for interacting with privacy-compliant customer data. With an efficient segmentation process housed inside the marketing department, markteres can optimize and automate experiences on a more nuanced level. The goal is to get your marketing program to a point of sophistication so they're less concerned with broad channel metrics and more about optimizing performance on customer segments across the entire lifecycle.

‍Website & in-store

‍Customers interact with your content and products in various ways. You need to be able to understand the impact of each touchpoint and the import of each channel. With a strategy for capturing a weighted multitouch attribution, you can use insights gathered to move spend to the most efficient channels as you make informed decisions about what to downgrade. Email may be an essential touch point for one business, where another's would be in-store, on-site, in-app, SMS, or social. When you can combine attribution with segmentation, you can ramp up your decision-making sophistication. For instance, you may pin an egregiously low-ROI channel or product for the chopping block next quarter, but first, you pause and dig a bit deeper. You discover that the channel or product that seems so unpopular is actually viral but only among higher-LTV segments. Though still technically "low ROI" with no means of accurate attribution, you can easily make a case for the product or channel's business value.

‍Customer service

‍For many brands, customer service is the only live-human touchpoint a given customer may ever encounter. That makes the humble call center the hub of inspiring loyalty or crushing any hopes thereof. Quite often, interaction with customer support is the most critical in a data arsenal. Customer Support is where things can go very wrong, even for savvy businesses. For many brands, customer support is also a revenue-driving function that can turn suboptimal experiences into new opportunities.

Next steps

Take a step back to get a sense of the essential areas for measurement. Is there good coverage? Do you need more or fewer metrics? Who needs access to what data, and to what degree? What is the use case of operationalizing this or that dataset? And if you want to learn more about first party data, click here to watch our on-demand webinar, It's Time to Own Your First-Party Data Strategy.

We’re living in an era where data reigns supreme and marketing has evolved from art to a precise science. For CRM marketers, this means the ability to harness the power of first-party data to unlock insights, build personalized experiences, and ultimately drive revenue growth are all paramount. Snowflake’s 2023 Modern Marketing Data Stack report offers a compelling glimpse into the state of modern marketing. This report, based on a comprehensive analysis of 8,100 Snowflake customers, sheds light on the technologies that marketers are turning to to unify, analyze, and activate data effectively.Within the report, Snowflake identifies ten critical technology categories that serve as the foundation for modern marketing data stacks, which include:

• Analytics & Data Capture
• Data Enrichment
• Customer Data Activation
• Advertising Platforms
• Measurement & Activation
• Integration & Modeling
• Business Intelligence
• AI & Machine Learning
• Privacy Enhancing Technologies

These categories represent the bedrock upon which modern marketing stands. However, the success of a marketing data stack lies not only in identifying these categories but also in selecting the right technologies and solutions within them.

A solid set of customer data is only the beginning.

You can’t personalize experiences, or create audiences using rich contextual data without the right technology in place. You need the right tools to be able to collect and make use of that data in order to bring your CRM personalization strategy to life. In order to deliver on your personalization strategy, your marketing data stack should allow you to accomplish these goals:Connect your customer data across systems and sources: Your data stack must be able to connect to the data within your cloud data warehouse and make it easily accessible to your marketing team. By doing this, you’ll be able to more easily recognize patterns in behavior, create custom segments, and deliver personalized messages without having to work with outside data or IT teams. Manage customer profiles: Unified customer profiles enable you to see a detailed view of user activity, preferences, and interests, so you can engage your customers with highly targeted and personalized touch points. The right tools will offer the ability to resolve customer identities, and track user progress through your customer lifecycle. This will help you optimize your campaigns and adapt your messaging as needed to deliver the best customer experiences. Orchestrate and deliver personalized experiences in real-time: By combining user data, audiences, predictive models, and low-latency integration, marketers are able to create experiences tailored to different customer needs, delivering the right message at the right time in the right channel. Access real-time reporting for quick analysis and segmentation: One of the greatest competitive advantages in any market is being able to react to real-time information as it comes. By responding quickly to changes in customer behavior or interests, you stay at the top of customers’ minds and continue to create valuable brand-to-customer touchpoints that lead them down the lifecycle journey

Technology can be a powerful tool in creating efficient, personalized customer experiences—but only if it’s used correctly.

It’s the features that make or break any tool’s ability to carry your personalization strategy to the finish line and deliver on the parameters outlined in Snowflake’s Modern Marketing Data Stack report.Additionally, it’s always good to think ahead: when building your modern marketing data stack, make sure you invest in tools that can handle digital, email, call center data, or any other channels you may want to build into your CRM personalization strategy, now and in the future.But achieving this level of data fluidity is challenging. Data captured in one channel may be formatted in one way, but may need to look completely different when being sent to another channel. Further complicating things, connecting these channels requires a significant amount of coordination across business groups.Your organization will need to define parameters for data integration, outlining how you plan to handle identification and unification across channels, and which unique identifiers will be used to stitch the data back together.Additionally, you’ll need to consider how granular your data needs to be in order to take action. For example, point-of-sale data may enable you to access every single transaction a user has made, but do you really need that level of granularity for sending targeted email content? Or, do you simply need aggregate information of a user’s typical types of purchases?

The best way to do CRM personalization at scale is by combining a cloud data warehouse (CDW) and a marketer-friendly customer data platform like Simon Data.

Cloud data warehouses (CDWs) are cloud-based databases that store valuable data from nearly every part of the business, including first-party customer data. CDWs were primarily constructed as a business intelligence tool, and access to the data is oftentimes limited to IT, engineering or other technical gatekeepers. Think of your CDW as your dad’s garage. It’s completely chock full of valuable stuff, and a total mess - rendering it completely impossible for anyone who’s not him to find anything. So while CDWs provide a centralized source of customer data, supporting activities like personalization and segmentation requires brands to invest in technologies that integrate directly with a CDW, like Simon Data, to enable marketers to access and use the data within. Connected customer data platforms, like Simon Data, are designed to integrate data from your CDW and other sources to create unified customer profiles that are easily accessible and usable for marketers. As Simon unifies data across digital touchpoints, it provides a more comprehensive view of your customers, making it a great addition to any tech stack. CDPs provide marketers with a centralized source of customer data that can be used to build segments, create targeted campaigns and deliver personalized messages to customers.There’s no one size fits all approach to personalization. Successfully building a modern marketing data stack for personalization depends on your data infrastructure, available technology, as well as your team’s organization and governance, among other things. If you’d like to learn more about how Simon is leading the charge in enabling CRM marketers to access and deploy their data for personalization - request a demo + free trial today.

Today, we’re excited to share that Simon Data has been recognized as a Leader in Snowflake’s Modern Marketing Data Stack 2023: How Data-Forward Marketers Are Redefining Strategies to Unify, Analyze, and Activate Data to Boost Revenue executed and launched by Snowflake, the Data Cloud company.

Snowflake’s report identifies the best of breed solutions used by Snowflake customers to show how marketers can leverage the Snowflake Data Cloud with accompanying partner solutions to best identify, serve, and convert valuable prospects into loyal customers.

By analyzing usage patterns from a pool of over 8000 customers, Snowflake identified ten technology categories that organizations consider when building their marketing data stacks.

The ten categories include:

• Analytics & Data Capture
• Enrichment
• Identity & Activation
• Identity & Onboarders
• Customer Data Activation
• Advertising Platforms
• Measurement & Activation
• Integration & Modeling
• Business Intelligence
• AI & Machine Learning
• Privacy Enhancing Technologies

Simon Data was recognized  in Snowflake’s report as a Leader in the Customer Data Activation category.

The research reflects how customers are adopting solutions from a rapidly changing ecosystem and highlights the convergence of adtech and martech, the increased importance of privacy enhancing technologies, and the heightened focus marketers have on measurement to maximize campaign ROI.

“Empowering modern marketers to harness the true potential of their data is a game-changer, and the Snowflake Data Cloud has been instrumental in driving this transformation,” said Denise Persson, Chief Marketing Officer at Snowflake. “By harnessing the power of Simon’s Connected Customer Data Platform and tapping directly into Snowflake, enterprises are crafting tailored experiences that truly resonate with their customers. Simon Data’s leader status in the Customer Data Activation category of Snowflake’s Modern Marketing Data Stack Report is a testament to their commitment to delivering differentiated experiences to our joint-customers. Together, we’re shaping the future of marketing.”

“We’re honored by Simon Data’s recognition as a leader in Snowflake’s 2023 Modern Marketing Data Stack report,” said Jason Davis, Simon Data’s CEO and Co-Founder. “Enabling marketers, regardless of technical acumen, to tap into the wealth of data within Snowflake’s Data Cloud is our core focus. The synergy between Simon Data and Snowflake has unlocked remarkable possibilities for our clients – marketing and data leaders at B2C brands like Tripadvisor, 1-800-Flowers, JetBlue, WeWork, Resy, and Vimeo are revolutionizing personalized marketing at scale. Simon’s Connected Customer Data platform integrates seamlessly with Snowflake, bridging the gap between transforming data into impactful, personalized marketing.”

If you’re interested in learning more, click here to read The Modern Marketing Data Stack 2023: How Data-Forward Marketers Are Redefining Strategies to Unify, Analyze, and Activate Data to Boost Revenue.

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The CDP (customer data platform) is a critical component of any MarTech stack. A CDP’s main function is to aggregate, unify, and centralize customer data from various sources into a single, comprehensive view. But traditional packaged CDPs have challenges with connecting data from all platforms, and many organizations are making the move to composable CDPs. What should you know about preparing your CDP for composability?

Understanding the difference between packaged vs composable CDPs

Traditional CDPs were created to help marketers collate the myriads of ways that they interact with customers. These packaged CDPs were built specifically for Martech applications, making them rigid with regards to what information can be accessed.

Unfortunately, this tends to lead to siloed customer data that can’t be integrated into the organization’s overall enterprise data strategy. This includes customer data that is outside the organization’s privacy protections.

On the other hand, composable CDPs have a modular and flexible architecture. This allows you to aggregate and unify data from multiple sources, not just predefined marketing applications. Composable CDPs pave the way to create a customized data ecosystem that meets the specific needs of an organization, provide a single unified view of every customer, and perform real-time data processing and analysis while building their tools around the data warehouse, keeping the data warehouse as the central source of truth.

Ask Simon Data: What’s a connected CDP?

One of the biggest problems created by packaged CDPs is that customer data is replicated multiple times as it moves across marketing platforms. This movement often falls outside the governance and security standards set by the CDW, and also creates inefficiencies for both tech and marketing teams.

Simon Data’s Connected CDP is a hybrid approach that offers all the benefits of Composable CDPs within a packaged interface. With Simon Data, marketers can access customer data directly within the cloud data warehouse, build complex customer segments and easily activate them in end channels – all while the resulting data is transmitted back on a continuous loop between marketing channels and the data warehouse.

We call this a “Connected CDP” because companies can capitalize on the benefits of composability – placing the CDW at the heart of their marketing and data infrastructure and maintaining data governance and security standards. Marketers benefit because they can use workflows and features that make it easy to access and deploy this data in marketing experiences.

Before you get started on the switch to a composable or connected CDP, it’s important to perform a readiness check on your organization’s data maturity level. There is some important pre-work that needs to be done before you can take advantage of the benefits of these deployments, you need to perform a readiness check on your organization’s composability maturity level.

Readiness check! Data maturity level evaluation

Step 1: Before you start – Ensure data accuracy

Your data will only be as good as the time you’ve spent cleaning it. So it’s important to plan what data will be used, define data quality metrics, and choose the right tools for the job.

Identify high-quality source datasets. Choose a dataset to serve as a reference point, a “golden record”. For example, when considering customer data you want to choose the dataset with customer information that has been maintained and updated over time. This may be Shopify customer data or CRM data.

Understand your data. You can’t expect to implement checks and balances for data accuracy if you don’t understand your existing data sets. Going through this exercise will help you identify areas of concern about your data quality and accuracy.

Here are 5 questions to ask about your current datasets:

1. What is the source system? (e.g., Shopify, Salesforce, etc.)
2. How is the data collected? (e.g., web events, user-provided, employee-entered, etc.)
3. How often is the data collected?
4. How is the data updated? For example, does new data overwrite existing data? Does new data create a new row, and old data is flagged as non-valid?
5. How is this data currently leveraged?

Define quality metrics. It is important to get the entire organization on the same page about what data quality means. This will be enforced by creating clear metrics. Metrics to consider are completeness, consistency, accuracy, validity, timeliness, and relevance.

Choose the right tools for the job. There are tools available to help you with the process of data validation, cleansing, profiling, and auditing. For example:

• Ingestion tools: Fivetran, Meltano
• Storage and querying: Snowflake
• Transformation: dbt, coalesce

Step 2: Maintain data accuracy

A successful composable & connected CDP deployment requires that the data stored in the data warehouse is accurate. But how can you ensure data accuracy once data is in the CDP?  

Identify which are the highest quality datasets. Identifying a reference table with good data quality, for example the “Highest Quality” Customer Dataset could be CRM Data or Customer data in an eCommerce platform.

Data cleansing. Once you have determined which dataset will be the golden record, use it as a reference. You can leverage tools like dbt and coalesce to define your data model. In this step, you’ll perform data standardization, data normalization, and data deduplication

Enrich the golden record data set. Once the golden record is identified, enrich its data set with other data sets like registration data and sales transactional data. The data team will expose this data set to the marketing team, for QA & UAT to ensure that the golden record of customer data is sound.

Be sure that the data team works with the marketing team to be sure deduping was done properly, householding logic is sound, and ensure that it will be possible to perform granular segmentation with the data.

Step 3: Maintain compliance

Once the highest quality dataset has been identified and enabled, there will probably be data governance issues that need to be resolved

PII data handling. You will need to identify PII standards for the data set. You’ll need to answer questions such as:

• How is PII data collected?
• What data can be collected and stored?
• Who can access PII data?

Let’s look at an example. Users provide PII data such as name and email address via a webform. This data is stored in the CRM system, and then is loaded into the CDP. Data analysts with access to the CDP should not have access to PII data due to security and regulatory compliance reasons. This means that the PII data needs to be filtered or masked in some way.

Data access. It is important to know who has access to PII data, and expose data based on roles to keep in PII compliance.

To continue the above example, the data analysts would all be assigned a role within the CDP. This role governs their access to PII. This means that access is not an attribute that can be assigned to individual users. This makes it easier to control access to sensitive data.

If a user is required to access PII data, they would be assigned a different role that enables them to see names and email addresses.

CCPRA, GDPR compliance.   As data privacy legislation continues to grow, it’s important to make sure your data warehouse is set up properly for compliance, this includes an easy way to download customer data and remove customer data if a customer requests it.

Use the best platform for composable CDP

With a little preparation, it is possible to take advantage of the flexibility and modularity of a composable CDP. Connected segmentation from Simon Data transforms Snowflake into a customer segmentation engine for your marketing team, all without data leaving your cloud warehouse.

By natively connecting to Snowflake, your Data and Engineering teams enjoy the security, governance, and data-sharing benefits of their cloud data warehouse. And, with a no-code, intuitive UI, marketers also get access to Snowflake data without writing a single line of code

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This spring, we started the discussion around all the ways first party data is going to play a critical role in paid media strategies for B2C brands. We focused on the impact of Google’s cookie deprecation, signal loss, Apple’s privacy updates, and budget constraints seen all across our market of customers, prospects and peers.

Change is hard, but it isn’t all so dark and ominous. After collecting the experiences from our work with hundreds of partners – at brands, agencies, and vendors – we confirmed that there is still a (BIG) opportunity to drive revenue growth with paid media. Our playbook, published earlier this year, summarized all the ways you can win with by bringing your CDP into your Paid Media strategy.

But why should you trust us? At Simon Data, we understand the weight of a strategic decision to double down on data and have no plans to stop supporting our partners even after all of this reflection. And to ensure that businesses aren’t taking our advice only to fend for themselves, we called on our friends at Power Digital, a tech-enabled growth marketing firm, to share their expertise.

Why you should lean into TikTok

Consumer-facing businesses have already started adopting the method – establishing first party and zero party data as the drivers for audience creation and segmentation. But marketers still need to identify the most effective and efficient channel mix to deploy these audiences so business teams tracking metrics like ROAS, LTV, AOV, CAC can agree that money is well spent.

Consumer behavior and channel engagement are crystal clear, TikTok and short form video is leading the way across audience growth and user engagement, providing an exciting opportunity for brands on and off the platform.

On average, TikTok’s CPCs and CPMs are 25-40% lower compared to Facebook or Instagram. More brands are looking to TikTok to drive lower traffic costs to their site during a time when traffic costs soar across all marketing platforms.

Rob Jewell, Chief Growth Officer at Power Digital

TikTok’s impact on brand growth today

• TikTok’s userbase has grown at an incredible pace, with ~1b active monthly (DemandSage, TikTok)
• TikTok has surpassed Google as the most popular domain, establishing itself as a top search destination for Gen Z (Epsilon)
• Digiday reports that brands are increasing their spend on TikTok compared to other channels and seeing dollar go further (Digiday)
• Statista found that average users have aged up with 58% being 25 and older (Statista)

These data points, plus conversations with our own customers and prospects, confirm that TikTok is where the consumer is and that the consumer is responding to video. Simon has long held a philosophy that brands and advertisers must reach the consumer where they are, and have built our product to enable just that.

Simon Data + Power Digital

Over the course of the last several months, Simon Data referenced this research as we defined our product roadmap. With an understanding that our customers and prospects are already engaged with the walled gardens we are all so familiar with, we sought to achieve complete channel coverage and prioritized a native integration to TikTok that would complement our existing actions with Facebook, Twitter, Pinterest, Google, etc.

As a result of this months-long effort, we’re excited to help brands push audiences for targeting, retargeting and suppression to TikTok to be reached with the most effective ads, and do so with expert support. For a more technically inclined audience, documentation that walks through the functionality of Simon’s native integration with TikTok is published and publicly available on our docs site.

Lucky for us and our mutual customers, Power Digital is a TikTok partner with over 60+ clients on the platform and a dedicated team of TikTok experts and creators solely focused on client performance. Their TikTok division offers everything a brand could need to perform on TikTok including Paid Advertising, Performance Creative, Organic, Testing, and Influencer marketing. Their track record helping brands like SmartSweets, Sambazon and NZXT drive incremental revenue growth made them an ideal partner for client success.

Outside of the existence of integrations with the most relevant channels, Simon Data is the connective tissue between the business’s first-party data strategy and custom audience creation and management. Simon Data provides access to all of the business’s first-party data with easy segmentation capabilities to automate and manage audiences across a variety of end channels within a single platform. No longer will paid experts need to manage multiple CSV uploads with outdated data within each paid channel!

A business that decides to integrate the power of a CDP with a thoughtful paid media strategy will see very real outcomes from a side of the business that cannot afford reckless spending. For B2C/DTC, digitally native retail brands, this partnership is excited to offer the above services from digital marketing experts and data activation veterans in the name of data-driven and growth-focused marketing.

Interested in optimizing your TikTok strategy or starting new? Check in with Power Digital for a complimentary nova appraisal with a custom marketing audit and revenue forecast, or reach out to me directly.

Additional resources:

• Simon: Learn about how Simon Data can help support your paid media strategy
• Simon: Connect and Authorize Simon’s Native TikTok Channel Action Today
• Power Digital: Learn more about Power Digital’s offerings around Paid, Creative, Organic, Influencer
• Power Digital: Unlock Your TikTok Playbook

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The promise of personalization is alluring: imagine a complete one-to-one experience for every customer, completely optimized and driven by every data point collected about that person.

This vision has long been a collective pipe dream among marketers, and yet achieving it has been impossible for many. There are many reasons for this: personalization as an aspect of marketing strategy has largely not been well-defined – thus creating misaligned expectations for the practice. Furthermore, accessing the data required to do personalization and scale is still something that many organizations struggle with.

In this two-part blog series, we’ll walk you through the end-to-end process for developing a personalization strategy, including determining how to slice and dice your audience, an overview of the tools you need to bring your strategy to life, and how you can overcome some of the biggest personalization challenges that B2C marketers face today.

Let’s first reframe our understanding of personalization

At Simon Data, we define personalization as any experience that is delivered to a person based on known data about them. By that definition, personalization can exist on a spectrum: it can be one-to-many, one-to-few, or one-to-one.

The companies that are getting the most ROI from personalization know two secrets: First, successful personalization relies on your ability to use data to answer questions about your customers. And second, in order to see value from personalization, the strategy has to be inherently customer-centric, rather than business centric.

Here’s what we mean by that: Your business goal may be something like “increase the number of second purchases within new customers,” but a customer-centric approach requires you to work backwards from your customers’ perspective. You need to understand the different segments that exist within that new customer audience, and the reasons they are or aren’t making another purchase.

Building a customer-centric personalization strategy begins with obtaining a deep understanding who is interacting with your brand, and identifying how and why they differ from one another.

There are reasons why some people take one action, and others don’t. Your job is to uncover why those differences exist.

By doing so, you can use those insights to create relevant, personalized experiences that provide value to those cohorts and drive the action you want them to take.

If you’re feeling overwhelmed by this task, we get it. After all, there are tons campaigns to personalize across channels and a myriad of ways to divide up your audience.

But instead of personalizing all the things, or focusing on only what’s easiest, (i.e. %First Name!), you should turn to your customer data to identify the largest or most valuable customer segments, and the problems that matter most to these.

Focus on solving those customer problems first. As personalization is used to solve one problem, then the next, and so on – you’ll eventually end up with a net-different experience that’s personalized for most of your audience.

This leads to all the metrics that CRM marketers love to see: increased revenue, higher LTV and AOV, and ultimately increased engagement for longer periods of time, and across all marketing channels.

Step 1: Determine your key customer segments

The first step is to uncover which of your audiences and experiences are a good fit for personalization.

Most marketing platforms offer some form of pre-built segments – including us. These can be created using predictive models, or your available data via combining customer attributes and behavioral data to produce segments such as: high LTV, cart abandoners, shoppers based in a specific location, category specific shoppers, discount shoppers. The list goes on.

While these out-of-the box segments offer a ton of value for resource-strapped teams, if you have the bandwidth, you may find more benefit from starting from scratch.

After all, your brand and your customers are unique, and the way you define your most valuable customers may differ from others.

If you need a place to start, check out the above list of 30+ questions you should ask about your customers to inform your segmentation strategy. The answers to these questions should inform the way you build your segments, and illuminate the experiences for which you want to personalize.

The list of questions fall under three main sections: who are your users, what are their needs, and what’s their experience. Here’s a peek below at some of the questions to consider:

Who are your users?

• Who are the general sets of users engaging with your brand?
• What makes your users different?
• What data describes or highlights their differences?

What are their needs?

• How are your customers’ needs different?
• What are they trying to achieve?
• What problems are they trying to solve?
• Do they have different goals from other users?

What’s their experience?

• How is a single, static experience not relevant to them?
• What parts of their experience are misaligned?
• At which points in the customer experience do the largest number of users fracture?

Step 2: Design personalized experiences for each segment

So you’ve answered a bunch of questions – now what? You need to turn the answers to those questions into an actual strategy. The problem is that many of our clients find that organizing their thoughts in response to so much data can feel overwhelming.

To aid your thought process, we built a template to help our clients organize their thoughts around personalization campaign ideas.

Initiative: Brief description of the high-level objective

Goal: Sentence outlining the specific or measurable goal or task at hand.

Segment or cohort

Differentiators

Motivations

Data Points

Data Sources

Current State

Solution

Name of the segment or cohort you want to target

and/or statement to define the segment

What differentiates them from other customer segments?What’s motivating them to act?Which data points describe or highlight their attributes?Where does that data live?Can be within your cloud data warehouse or other sourcesHow is the current static experience not relevant to them?

List out solutions or ideas for personalizing the experience.

One important caveat: this is a simplified approach.

In reality, each statement should be aligned with quantitative and qualitative data. For instance, if you’re using data to define your segments, then the key differentiators should reflect the factors that define each segment or include additional metrics to further profile each group. Here it is in practice using our previous example of increasing second purchases among new customers within the first 30 days after their first purchase.

Initiative: Increase Customer Lifetime Value

Goal: Increase second purchases among new customers, within 30 days after first purchase.

Segment or Cohort

Differentiators

Motivations

Data points

Data Sources

Current State

Personalization Solution(s)

New Product Finders

First purchase date is within 30 days ago AND purchased product SKUs were added within the last 90 days

User is primarily interested in new productsUser wants to impress family and friends with the latest style or gadgetNew user flag

Referring channels

Search keyword

Conversion source

Purchase history

Category affinity

Price point affinity

Browsing behavior

Last product or category viewed

Promo code redemption rate

Email and/or SMS engagement

UTM tags

Ecommerce / POS data

Website activity

Email marketing

SMS marketing

Ad channels

Majority of new customer experience focuses on promotions rather than new products

Email and SMS messages focused on new products

Dynamic homepage content promoting new products

Retargeting ads promoting new products

Single Minded Shoppers

First purchase date is within 30 days ago AND user has at least 2 sessions browsing [product category]  AND activity date is within 30 days

User is shopping for one type of product or product categoryUsers want to save time by quickly finding and purchasing a known product

OR

User is looking to make a high-consideration purchase and is doing research

Ad, email, SMS and homepage messaging are not tailored to users
product affinity

Dynamic email and/or SMS comms based on product affinity

Dynamic homepage  promotion and retargeting ads promoting add-ons or related products based on previous purchase

Product or category specific-content to aid research for high-consideration purchases

Discount Shoppers

First purchase date is within 30 days AND user redeemed promo code OR engaged with discount emails

User only converts when products are on saleBudget conscious users that want to buy premium products while getting a great deal

Not every user reacts to the same promotions the same way

Dynamic discounts ranging from 10-35% sent to top priority customers based on engagement, AOV, and LTV

Dynamic homepage content promoting latest discount

So if you’ve made it this far – you’ve established your most important segments, and have come up with viable personalization strategies. Now the only remaining task is to determine where to start. Prioritize your campaigns by comparing the perceived level of effort required to launch the desired experience for each group against the estimated return on investment (ROI).

Step 3: Identify the data you need to launch your campaign.

Grappling with personalization often forces you to come to terms with some very hard truths pertaining to the data you have readily available.

So much of personalized marketing depends on sending the right message to the right person at the right time – all of that can be impossible if your data is a mess, out of date, requires you to submit a ticket and wait for your data team to respond – or, just plain inaccessible.

Because you’re building a personalization strategy that’s based on the idea that different customers need different experiences, the data you collect should help illuminate the differences that exist between customers.

Identifying the exact data points or attributes you need to drive these differentiated experiences depends greatly on your business, your products, and your customers. But generally, it falls into a few categories:

• Engagement Data – Where and how did they engage? e.g. Mobile/iOS, Email, In-App
• Identity and Location Data – Who is the customer and where are they located? e.g. Customer Name, Email Address, IP Location, MAID
• Event Data– Did an action occur or not occur? e.g. Click, Abandoned cart, Abandoned check out, Purchase
• Event Context – What information describes the event? e.g. SKUs, Campaigns or Promotions

Raw data alone isn’t always the most useful. Using a customer data platform (CDP) helps transform the raw data from your data warehouse into unified customer profiles, which enables your data to be more useful and descriptive of the characteristics of your customers.

The importance of contextual data for personalization

Contextual data is information that provides a broader understanding of an event, person, or item.

Segments are often built around high-aggregation data attributes—such as whether someone is a new or returning user—but the more contextual data you gather about your customers, the more effectively you can target and time your personalization campaigns.

Contextual data can influence which segment a customer falls into, the timing or frequency of your messages, the channels you use to deliver the message, and the context of the message itself. This type of data can include things like weather, traffic location, seasonality, past purchases, preferred channels, and more.

Think of your segment as the main ingredients of a recipe. Contextual data acts as the spices and flavors that elevate the dish to a new level. Just as the right combination of spices can transform a dish from ordinary to extraordinary, gathering comprehensive contextual data about your customers enhances your ability to craft targeted and well-timed personalization campaigns.

Examples of contextual data

• Preferences: What are their shopping, communication, and channel preferences?
• Content/Product Affinity: What content or product types have they engaged with or purchased?
• Attribute Affinity: What descriptive attributes of the content or products do they gravitate towards?
• Price Point Affinity: What relative price points have they bought or browsed?
• Time Indicators: What is the time of day, day of the week, and/or month of the year?
• Geographics: Where are they? Are they always in the same location or different?

By no means is this a definitive list of all the contextual data you should collect about your customers, but hopefully, it helps you start thinking about where you have gaps in your current state. The data you collect should help quantify all of these differences about customers. By using the strategy planning template as a guide, you can determine if you have the necessary data to segment your customers in order to support your goals.

But of course, a solid set of customer data is only the beginning and having raw data alone is not very useful – the true value comes when you transform this data to be more descriptive of characteristics of your customer, and establish a strong technical foundation to activate this data and bring your personalization strategy to life.

Your technical foundation for personalization depends greatly on your company’s data infrastructure, available technology and configuration, budget, revenue goals, the way your internal teams are organized, and data governance. In our next post, we’ll dive into how navigate all of those things when making investments into personalization technology, such as CDPs, email service providers and more.