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What is a CDP?

A customer data platform (CDP) is martech software that creates a unified view of your customer data from your various systems in a single place.  This unification makes data accessible throughout the entire organization. The beauty of a CDP is that it operates behind the scenes, providing three key functions: customer data management, unification, and activation. As a result, marketing teams end up with customer profiles that are both accurate and comprehensive. Which in turn, attracts customers and inspires them to buy. But, how does a CDP differ from a DMP?

What is a DMP?

A data management platform (DMP) is a software platform used for collecting and managing data. They allow businesses to identify audience segments, which can be used to target specific users and contexts in online advertising campaigns. A DMP should organize your data, give audience insights, and help with overall ad budgeting. However, there are problems with DMPs around data privacy that by and large have yet to be fixed.

What are the Key Differences Between a CDP and DMP?

A key difference between Data Management Platforms and Customer Data Platforms is the type of data they focus on. The reason DMPs have issues around data privacy is that this platform mainly works with third-party data for managing paid digital advertising and marketing platforms. However, a CDP unifies all your data with a special interest in first-party and zero-party data that can use personally identifiable information (PII) for marketing functions.

The Role of CDPs and DMPs in your Marketing Strategy

Data Management Platforms

What a DMP does well is help marketers create better ad targeting by understanding their audience on a deeper level. Over time, the targeting becomes more honed as more data is gained. The role of a DMP is better ad targeting and improving media spending over time.

Customer Data Platforms

In contrast, a CDP shines in its ability to unify and activate all your data. With a CDP, anyone with permission in the organization can view and harness the datasets. CDPs can also integrate with various systems to make the most out of your data.

How CDPs and DMPs Work Together

CDP vs. DMP Side by Side

CDPs can help DMPs get across the last mile.  By itself, a DMP cannot store PII, but a CDP can push audiences with customer PII (name, email, phone, etc.) to DMPs to pass to demand-side partners (advertisers). This process allows a DMP to use PII without having to store it. Similarly, if a customer clicks on downstream advertisements, a CDP will ingest the PII data for further segmentation and analysis.

Together, a CDP helps utilize and capture that extra bit of data a DMP cannot use on its own. The results are even more accurate ad targeting and cost-efficiency.

Why Use a CDP Over a DMP

While there are some advantages to having a DMP we predict they will become obsolete over the next few years. Here’s why.

First-Party Data

Data Management Platforms were built around the concept of sharing audiences. For this reason, they cannot store first-party data in their system. DMP’s reliance on third-party data and cookies will be its downfall. As third-party data restrictions become greater and greater, owning and collecting data becomes more vital. Unlike a DMP, a CDP can utilize that first-party data across your various systems including advertising platforms.

Unified Data Source

Because there are restrictions on the types of data a DMP can store, it is impossible for a DMP to be a single source of all your customer data. A CDP however, is compliant with security regulations that allow it to store all types of data including PII. CDPs ingest customer data from all systems across the organization and unify the information into a single customer view. This view is a 360 look at everything you know about your customer in one place.

Powering Your Customer Journey Orchestration

Another benefit of a Customer Data Platform is its ability to integrate your single customer view data back into various systems. For example, a CDP gathers real-time and historical data from your CRM, website, reviews, etc into one place.  This customer data can now be used to create segmentation. These segments can then be used to orchestrate customer journeys across all channels regardless of the system. To that point above, there are endless possibilities of where you can use your customer data with a CDP. On our site, we list several integration options.

If you need help choosing the right CDP for your business goals, check out our CDP Buyer’s Guide.

In the newest installment of the Data Unlocked Podcast, Jason Davis, Simon Data’s co-founder and CEO links up with two thought leaders:  Tom Tunguz, Managing Director at Redpoint Ventures and Bill Stratton, Head of Media, Entertainment and Advertising at Snowflake.

The Big Question

As always, Jason asks Tom and Bill how marketers could unlock opportunities with better data capabilities.

Tom’s Reply Summarized – The Data Supply Chain

So Redpoint, we’ve been calling 2020 the decade of data. There’s been lots of innovation in data for an awfully long time. But the cloud data warehouses and the modern data stack have really changed the way that people use data. It’s brought the benefits of insights to almost every single employee. Data is created in some place, and then it needs to be processed and it needs to go where it’s going to create value. And over the last couple of years, we’ve seen meaningful innovation in parts of that data supply chain.

So the first is moving the data from where it’s being produced. And so you have ETL vendors there, Extract, Transformation and Load, who are doing quite well. They’re piping that to a data warehouse where it lives. And you’ve got, obviously, Snowflake, which has become just a monster business doing that. You have modeling layers and that’s a place where data engineers create single definitions for metrics.

And then the last part is really making use of the data once it’s inside of the data warehouse. There are machine learning applications and ways of building predictive models.

Then there are pieces around the data supply chain. There’s data security, making sure that people are using data in a compliant way and only the people who actually have access to it are accessing it. And then you have data observability. It’s really understanding through my data supply chain, is everything working well? And if it’s not, why? And enabling people to go fix it.

One that strikes me is this notion of Reverse ETL (read more about Reverse ETL here or here). Reverse ETL comes in, sits on top of the data warehouse and the SaaS application and then allows you to analyze the data very simply. Now those are, at a high level, sort of the biggest categories that we see today.

Bill’s Reply Summarized – An Industry View on the Data Supply Chain

My role is within the media, entertainment and advertising verticals. And so I certainly agree with Tom and Redpoint that the decade of data is certainly upon us. I think this data wave is probably going to extend longer than just the decade. It’s a great frame of reference because the next seven, eight years are going to determine a lot of new things.

And the stack and the supply chain that Tom just talked about is very much how we look at it at Snowflake. Not only is the data supply chain evolving, and certainly Simon is part of that, as well as Snowflake. The industries that we’re serving, their supply chains are also changing and evolving. And so when you have the dynamic of evolving data supply chains and evolving industry supply chains, it creates this shuffling effect that creates a lot of opportunity for the marketplace and the investment side and in companies like Snowflake.

An Example from Bill

So let me just give one example. In my 25 years of media, most of the big brands like Disney or NBC or ESPN or CNN or HBO, were what we call wholesalers. And the end customer’s relationship was with the cable company or the satellite company. So the supply chain existed to have data that came from the end customer point. But the wholesalers didn’t have a lot of data because they didn’t own a customer relationship. Now that supply chain is switching and many companies are establishing a direct-to-consumer, in this case, streaming relationship. And that is creating a new set of supply chain data customers that need to adhere to what Tom has described as the data supply chain.

What we start to see is that the data warehouse is starting to blur the line between the upstream and downstream data supply chain. So the point that I’m trying to make is that I’m not even sure convenient definitions, like data warehouse, are really even appropriate anymore because at least from a Snowflake perspective, we’re seeing data collaboration and data sharing and applications come to the data stack. And that sort of starts to change definition.

So maybe the one prediction I’ll make, is that I think what we call the data supply chain and even the names that we refer to them as are going to change and also blur so that new partners, new companies, new situations are going to emerge, and frankly, the companies, that take advantage of this. What we’re doing at Snowflake, positioning ourselves as a data cloud, not a data warehouse, will drive this evolution.

Tune In

This conversation and more on operational analytics continue on the Data Unlocked podcast. Tune in here:

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You’ve been carefully collecting and curating your customer data. Yet, you’re left with the nagging question – now what? The good news is you are not alone. Many businesses struggle to figure out the best way to use their data for optimal results.

We’ll discuss how to use your customer data for marketing with certainty and maximize the use of your customer data.

Using Customer Data with Greater Certainty

In our recent 2022 State of Customer Data Report, we looked at emerging trends in the world of data, one of which was marketers’ lack of confidence in data usage. This lack of conviction stems from the overwhelming amount of data available. As a result, many marketers struggle with a lack of data integration between technologies or data sources. This gap translates into difficulty turning actions into insights and ultimately proving ROI.

The graph below is from a report commissioned for Simon Data by Forrester consulting. It shows how orgs feel about using customer data for marketing. Most respondents didn’t feel highly confident in their abilities. Additionally, the response tally was under 100%, meaning a proportion of marketers felt such a lack of confidence that they declined to respond.

Maximizing The Customer Data You Have

We reached out to some thought leaders in the customer data space to get their opinions on our trends. Tim Duncan of Bottle Rocket gave us his take on using customer data for marketing confidently. Bottle Rocket is an experience consultancy that provides services that drive business results and exceed customer expectations.

“Data is the fuel that powers a martech stack and its quality and richness has everything to do with how confident your marketing teams will be in the campaigns they deliver. Moving into 2022, being able to make sense of and properly action on this data will start to become table stakes for most serious digital marketing organizations. There is a compounding effect that eats away at long-term growth for marketing teams that seemingly never reach a place where they have a robust data profile that they efficiently action on across all their digital channels. Using a composable CDP like Simon Data that give both marketers the flexibility to collect data but also make sense and action on it is an easy way to set teams up for success in this area.”

-Tim Duncan, Product Growth Lead at Bottle Rocket (Ogilvy Experience)

Provided that, his quote directly relates to maximizing the value of your data by creating a complete picture of your customer. And, the best way to build these robust data profiles is by using a CDP.

Making the Most of Customer Data with a CDP

One major problem with the amount of data is that it lives siloed through various systems. For example, an eCommerce company will have many martech systems- loyalty programs, ESPs, reviews, mobile apps all collecting different data. Each system offers a slice of insight into your customer. However, without bringing all the data together in one place, you don’t have the whole pie.

A CDP collects and gathers data from those disparate systems. Then, it cleans and consolidates each bit of an individual’s customer data into a single customer view. This view is a robust profile of all information you have on a user.

Segmenting Your Customer Data

This is precisely the problem we saw when working with Tripadvisor. They collected a large amount of data from various sources. Yet, they had no aggregated view of all of their customer data. Not being able to see or access all your data can create a sense of uncertainty about what you’re doing.

While we prefer to operate with perfect information on our customer preferences, that isn’t always possible. However, creating a single customer view where all the data you have collected is available is the best way to understand your customer’s behavior. That’s what Simon + Snowflake enabled for Tripadvisor’s marketing team- a complete picture of all of their data and behavior.

Through this partnership, Tripadvisor personalized experiences driven by advanced segmentation, automation based on behavior, and dynamic campaigns. For example, suppose you were going on a vacation to Dove Mountain and looking at activities in the area. In that case, Tripadvisor can now see that information in conjunction with nearby hotel pricing. When a hotel in that area drops in price, you receive a valuable message alerting you of the drop. This campaign triggers a meaningful experience that sends deals directly to members that previously browsed hotels in that area.

This dynamic personalization is the type of experience a CDP can offer. To see more on the value, Simon enabled for Tripadvisor read the full case study!

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In the newest installment of the Data Unlocked Podcast, Jason Davis, Simon Data’s co-founder and CEO links up with Josh Curl. Josh is the Co-Founder and CTO of Hightouch, the leading Reverse ETL Platform (learn more about what reverse ETL is). Hightouch is a data platform that helps marketers sync customer data from a data warehouse to their CRM, marketing, and support tools.

How Reverse ETL Makes the Most of Data Warehouses

As always, Jason asks Josh how marketers could unlock opportunities with better data capabilities.

Josh and his team know that data warehouses are evolving. They are now much more prolific across a lot of organizations.

Data warehouses contain immense amounts of data which is typically vital to drive analytics reports. But there are unique data points and insights that are locked inside of a warehouse. The goal of Hightouch really is to use those insights and data points for more operational use cases. This includes driving day-to-day value and driving action rather than analytics. The big change with Reverse ETL is in operations is not using the data warehouse as a place to just dump data and perform analytics. It is moving that warehouse to the center of a pipeline allowing marketers to take data from the warehouse and send actions and data into downstream SaaS tools.

Using Data to Drive More Effective Marketing Campaigns

And so it’s really about taking your new source of truth and better aiding that with some new SaaS tools. If you look at the sales teams and B2B and so on, it’s crucial to arm your go-to-market team with more information about how your customers are engaging with your brand. Obviously, always having more rich data available is really beneficial to a sales organization. As a result, the marketing teams have better access to attributes about customers. This enables marketers to execute fine-grained personalization, which means conducting more effective ad campaigns, personalization, email campaigns, etc.

This conversation and more on operational analytics continue on the Data Unlocked podcast. Tune in here:

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The amount of data that exists is exponentially increasing every year. Yet, companies (and marketers) aren’t always sure of the best way to use their collected data. Creating effective segments and personalizing messages can feel like an uphill battle. A bright spot in recent years has been the ability to use predictive modeling to create ‘smart segments‘. However, creating the foundation for these segments can still be challenging. This post will examine how Simon tackles predictive modeling and some predictive modeling use cases.

What is the Predictive Modeling Process?

You can’t know what will happen in the future, but you can try to predict it. Machine learning (ML) has amplified marketing technology to create, process, and validate new models to forecast outcomes. Still, these models are built on top of your unique customer data to ensure that the predicted results are specific to your business.

Step 1: Gather Your Data

Companies have a lot of data. Maybe even too much data. Most of this data lives across various siloed systems throughout an organization. CDPs like Simon Data help alleviate this challenge by collecting and consolidating all your customer data into one place. Then, Simon bridges these disparate pieces of data into a single customer view that marketers can access. Once your data is neatly in one place, you can begin orchestrating more effective cross-channel journeys that drive better engagement and ROI.

Step 2: Analyze Your Data

Data alone does not drive action. Marketers still need to figure out who, how, and when to engage with users across their customer lifecycle. Yet, with so much data, determining how to message different users effectively is still challenging. The process costs also marketers time and results in missed opportunities relevant to the customer’s journey.

Simon Predict speeds up the analysis of your data by giving you the crucial information needed to drive effective actions in a matter of minutes. This process is possible in the bespoke design of Predict’s three distinctive ML models- churn propensity, likelihood to purchase, and product recommendations. Additionally, each makes it easier for marketers to increase the value of their customers by improving their personalization. The goal is to create meaningful moments that strengthens your customer relationship. Below, we share some of the many predictive modeling use cases to show how each works.

The Churn Prediction Model

Simon Predict’s churn propensity model scores each customer from 1 to 100, rating them on their likelihood to disengage. Knowing which customers you are about to lose is invaluable information that helps marketers de-risk this likelihood. Imagine targeting customers that are likely to churn in retention campaigns, where they receive a unique promotion. Or, imagine omitting these high-risk users from certain communications, like regular emails that contribute to negative customer experiences.

Let’s pretend that you are an e-commerce pet supply business that runs on a subscription model for this use case. Your goal is to help increase the number of lifetime value (LTV) users by running a retention campaign. With Simon Predict’s churn propensity model, you see that several users are at risk for churning. Therefore, this is the right time to create a positive experience and convert them into satisfied, LTV users. To kickstart a more promising customer experience, you choose to run a segmented campaign. Then, this campaign sends users at risk for churning down a unique customer journey that gives them a discount. Yet, knowing what specific deals to offer varying levels of churn risk users is still hard to nail down.

By quickly running experiments within Simon’s journey management tool, you can easily test what types of promotions maximize engagement and ROI. Using Simon Predict and Simon Journeys together, you see that providing a 35% discount on auto-ship pet food to 40% churn risk users and a 20% discount on auto-ship pet food to 30% churn risk users gives the greatest return. Additionally, by encouraging users to enroll in auto-ship programs, you helped turn these users from being disengaged customers into regular buyers that guarantee purchases.

The Purchase Protection Model

Likelihood to Purchase

Like the previous use case example, Simon Predict’s likelihood to purchase model scores each customer on how close they are to making their next purchase. Simon helps marketers improve their odds of converting users along their customer journeys by providing marketers with this intel. Now, marketers access unique insights that influence more effective and personalized campaigns.

Let’s follow the same pet store in the example of Simon Predict’s churn propensity model. Now that the pet store has increased their number of LTV users, they want to increase their total revenue by improving their number of purchases. To help start the initiative, the marketers want to run a “dog days of summer” sale campaign that will target brand awareness and purchase propensity. First, users are segmented based on their purchase likelihood score. Then, we send users down different customer journey branches based on those scores. An example of these branches is scaling discounts. The more likely they are to buy a dog bone or a bag of catnip, the lower the discount. Additionally, this purchase prediction grouping allows businesses to drastically improve conversion rates while influencing positive customer experiences.

Product Recommendations

Understanding your customers’ likelihood to churn and purchase is vital information drastically influencing your marketing tactics. But, Simon Predict doesn’t stop there. Simon Predict offers a product recommendation model that helps marketers tailor their content with information unique to each customer experience to take personalization a step further.

Let’s round out our pet store’s “dog days of summer” sale campaign by maximizing the moment each user receives their scaling promotional email. In addition to offering unique discounts designed to increase their likelihood to purchase, you can populate campaigns with tailored product recommendations. For example, a customer is browsing a tug-of-war rope for their dog or abandons new cat food in their cart. These customers get a deal that corresponds to their likelihood to purchase. They also get a personalized message that shows products you know they’re interested in, further increasing your chances of turning interest into conversion.

Customizing your predictive data modeling

These predictive model use cases are only a few of the many ways we have seen clients successfully use Simon Predict. The three bespoke models provide vast opportunities for countless use cases based on your campaign goals. Still, in situations where the out-of-the-box capabilities don’t match your business needs, Simon Predict’s custom models option exists. This fourth option is built on your data and tailored to your unique use cases. Yet, when building out your models, the key things to remember are to be specific about your goals and set test groups to compare results. Simon Predict will help you do the rest. If you keep those things in mind, you’ll be seeing better engagement and ROI in no time!

To see a personalized demo on Simon Predict, request a demo today!

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For the fourth consecutive quarter, Simon ranks as a “Leader” in the G2 Winter Rankings for Customer Data Platforms (CDP).

G2’s quarterly CDP Grid® ranks products based on customer satisfaction and market presence. This cycle, we held our spots as overall Leader, and Leader for the Momentum Grid®, Enterprise Grid®, Mid-Market Grid® and Small-Business Grid®.

“Whether you’re a digital start-up or an established enterprise brand, the key to driving results quickly and easily begins with data, and we’re proud that our customers are seeing the connection between a powerful CDP and business outcomes,” said Jason Davis, CEO and co-founder of Simon Data.

“We’re especially gratified to see Simon Data’s recognition in the Mid-Market Implementation and Results Index reports for CDPs because it demonstrates we’re delivering on our promise of enabling data-driven customer experiences, quickly and effectively.”

–Jason Davis, CEO, and co-founder of Simon Data

G2 rankings depend on how verified users evaluate customer satisfaction, as well as data from online sources and social networks. Below are a few key testimonials from our winter reviews. Highlights include Simon’s exceptional onboarding process, ease of use, and countless integrations.

Simon Data reviews sourced by G2

In short, these are only a few of the ratings that helped us qualify for these recognitions this winter. Check out the rest of our G2 reviews submitted by customers here:

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Happy Data Privacy Day 2022! If this is the first time you’ve heard of this celebratory day, here’s a little history. Data Privacy Day is an international holiday, initially celebrated in 2007 by European countries. Two years later, the holiday was adopted by the United States, and today, Data Privacy Day is celebrated by 51 countries! It aims to bring awareness to businesses and users on the importance of protecting their personal data and privacy online.

How Alternative Value Exchange Can Keep You In Control of Your Data

In our recent 2022 State of Customer Data Report, we touched on five trends we see around customer data. One of which was the greater privacy concerns.

Greater Privacy Concerns

Bringing awareness to data protection and privacy online meant changes to customer data regulations. This shift has slowly been happening over the past decade. Still, the most significant change came when Google announced it would block third-party cookie tracking. Dubbed the cookiepocolypse, this shift has changed the way businesses need to think about how they collect and use their customer data.

Customers want to have more control over what data companies can gather. A reported 88% of customers wish to consent over their data usage. We suspect this number will only increase, so we predict similar restrictions to second-party data usage.

Only 7% of consumers know how companies use their information once they consent. As marketers, it’s our job to inform users of intended data usage. Customers are more likely to opt into data collection if they see the enhanced value gained from using their data.

Why You Should Own Data Collection

This quote examines how Peter Rice, Director of Marketing Systems Strategy at Kepler Group, thinks about developing trust in a privacy-centric world. Kepler Group is a global agency focusing on helping its clients harness the power of data and technology.

“Developing customer trust is paramount to building loyalty and presenting a brand image that is authentic and inviting. Customers’ expectations for choice and control over the data they generate has grown massively over the past few years, and at the same time, technical and legislative initiatives have limited the granularity and speed at which brands can collect and enhance user information broadly. For this reason, it is critically important that brands have control over their own data. Without this control, it is significantly more difficult to collect, maintain and respect consent signals, and it will become increasingly difficult to still provide authentic, personalized and relevant experiences to customers. Brands that are able to gain this control and maintain transparency will be better positioned to make great use of consented data.“

-Peter Rice, Director Marketing Systems Strategy at Kepler Group

Alternative Value Exchange

Brands are in a bit of a bind. Customers want extraordinary personalized experiences, yet less of their information is readily available. The solution focuses on zero-party and first-party data by controlling the data collection process. We call this process the alternative value exchange. This statement means that in exchange for customers answering questions about the type of experience they expect, brands agree to deliver. Once again, companies can gain the valuable information they need. In return, users can feel confident that their data directly impacts their overall experience.

The Future of Data Privacy

As the adage goes, the best defense is a good offense. Before third-party and second-party data are no longer feasible, your data strategy should already have shifted away from them. Collecting your own data allows you to control the information you receive and put it towards delivering an exceptional customer experience. It also helps build trust between you and your customer. When a customer sees the data you collected used, as you stated, it strengthens your relationship with them. Strong relationships lead to better brand loyalty and retention.

To read more on maximizing your marketing efforts in a privacy-centric world, check out this blog!

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In the newest installment of the Data Unlocked Podcast, Jason Davis, Simon Data’s co-founder and CEO links up with Sara Tresch, SVP, Digital Transformation & User Experience at Charles Schwab.

Prioritizing Data in Your Business Strategy

The main topic that this duo discussed is recognizing and optimizing where data fits within a company’s business plan. By now, most companies track customer data to some extent. Sara mentions that considering how consumers’ actions level up to business goals is crucial to proper data management.

You can take those fact-based decisions into your product or your marketing plan and then rise those up and start to really show them why for what you’re doing and get support for it. – Sara Tresch

Jason mentions that understanding what brands want to measure first is critical instead of collecting data broadly and then creating goals. Above all, data works best when adequately embedded into outcomes, goals, and strategies. In the same vein, this topic was recently discussed by Simon in a recent collaboration with Sageflo. It’s crucial for marketers to consider how we use data and who we provide access to analyze it.

Creating a Feedback Loop

Once a plan is in place, Sara mentions how important it is to improve it continuously. As a result, a constant feedback loop helps marketers make informed decisions that ultimately lead to customer retention. She notes that this is becoming an increasingly automatic system. Sara challenges marketers who think they don’t have time to experiment or consider a feedback loop to start small. Consider the benefits of a feedback loop and why it is crucial to success: hitting targets, more easily meeting client needs, and better data quality.

This conversation about weaving data into business strategy and more on feedback loops continues throughout this episode. You can tune in by clicking the button below.

LISTEN TO THE DATA UNLOCKED PODCAST

A Category Overview of the Different Types of Customer Data Platforms on the Market

You’re thinking about investing in a Customer Data Platform (CDP) but don’t know where to start? With 153 different CDPs on the market (24 of which were new in 2021!), figuring out the starting point is daunting. CDPI, Forrester, and Gartner have tried to simplify this task by breaking CDPs into different categories. Still, buyers are left little clarity on which type of customer data platform is best for them without clarity on the various types.

Rather than naming each category of CDP, we will focus on what each type of CDP does for your business.

What is a CDP?

There are many definitions of a CDP out there. At its very core, a CDP is martech software that creates a unified view of your customer data across various systems in a single place.  This process increases data availability throughout the entire organization. To be a true CDP, the software must perform four key functions: customer ingestion, unification, activation, and integration. The end goal is for marketing teams to have customer profiles that are both accurate and comprehensive.

To be a true CDP, you must:

• Ingest customer data from multiple sources
• Unify customer profiles into a single view via identity resolution
• Activate “Real-time” customer segments
• Integrate customer data to other systems, out-of-the-box

This transformation helps get the right data into the hands of the right people to make more informed decisions.

The Three Types of Customer Data Platforms

Now that we have seen the outcomes any CDP should enable, we can dive into the results you can expect from the different types of CDPs.

Data Streaming CDP

Data Streaming (also called data or data integration by other accounts) is the first and most basic category of a CDP. This type of CDP focuses on the fundamentals of what every CDP should do- gathering, ingesting, and centralizing an organization’s customer data. This collected data is then linked to customer identities and made available for other systems.

Best For You If…

The pros of this type of CDP are its strength around tag management, streaming data collection, and aggregation. These platforms can create audience segments, predictive models, and identity resolution. This type of CDP is best for businesses with large events volumes, many data sources, and destinations, and a significant amount of technical resources to implement and maintain the thing.

Not the Best Fit If…

The downside to this type of platform is its lack of greater capabilities. Marketers will still need to rely heavily on data teams for greater campaign automation. Data Streaming CDPs will need other supporting technologies to be most effective. This reliance is taxing on resources such as tech resources, downstream orchestration needs, and cost at scale.

Orchestration CDP

Orchestration (sometimes called Campaign CDPs or Smart Hubs) specializes in building customer profiles and segments. Later, these segments create more personalized messaging. This platform aims to increase audience engagement via better targeted and tailored messages and recommendations. In large, orchestration CDPs are focused on empowering cross-channel marketing to enable marketing outcomes.

Best For You If…

The benefit of this type is its ease of use for marketers. Generally speaking, Orchestration CDPs have an intuitive interface that allows non-technical users to create segments quickly. This design helps marketers tailor and strengthen customer experiences independently of other teams.

Another pro of these CDPs is their data assembly, analytics, and enhanced segmentation. These enhancements allow you to specialize segmentation on an individual level- allowing for greater degrees of personalized messaging in outbound campaigns, real-time interactions, and recommendations. From there, this tailoring can be used across multiple channels. Which is to say, if you use multiple channels, or plan to in the future, this CDP is the best option. It eliminates channel silos and gives you  360 marketing view.

Also, if you’re a marketer and personalization and speed matter to you, this CDP is right for you. Orchestration CDPs allow you to work independently of your data/tech teams while still creating campaigns that go beyond segmentation and have a personal feel.

Not the Best Fit If…

Not all marketing teams need a high level of campaign orchestration, and that’s okay. If you are currently using one channel to communicate with your customers and are looking for insights without activation across multiple channels, this type of CDP isn’t right for you.

Automation CDP

This type of CDP (also called delivery CDPs) focuses on the swift execution of marketing campaigns. These platforms provide data assembly, analytics, and enhanced segmentation, all serving message delivery via email, website, mobile apps, and more.

Best For You If…

The main pro of this type of CDP is the level of automation involved in campaign development. It provides a native execution on messaging. If you are looking for a hands-off CDP that will work behind the scenes, this is the CDP for you.

Not the Best Fit If…

Many automation CDPs started as a messaging system that added CDP functionality as an afterthought. These types of CDP are good at automatically launching preset campaigns but often struggle using real-time data to adjust messaging. If you’re looking for a messaging platform with a lightweight CDP, these are great tools. However, if you need more robust data capabilities, there are better CDP options.

The Marketing Cloud

I have seen a few categories list separate cloud CDPs, but I chose not to for a few reasons. As stated above, one of the requirements of being a true CDP is out-of-the-box integrations with other systems, which is a pro of cloud platforms. Also, not all cloud CDPs function the same way. Yes, they are all built by large companies. Still, they offer various solutions, which I feel fit in the CDP categories above.

How to Buy the Right CDP for You

Finding the right CDP is all about determining the functionality your business needs. Before buying a CDP, you should assess the problems a CDP will solve for you. If you’re looking for solid data unification with low marketing segmentation or campaign orchestrated functionality, a data streaming CDP is a good fit for you. If you’re looking for high degrees of personalized communications across all channels, an orchestration CDP is best. Finally, suppose you are looking for a CDP that automatically sends out messages with a lesser degree of personalization. In that case, an automation CDP is what you want.  

Regardless of which type of CDP you end up buying, make sure it can ingest, unify, and integrate your data, enabling you to create audience segments.  

For more help on choosing the right CDP check out our CDP Buyer’s Guide.

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Customers are concerned about the security and misuse of their personal data, yet they continue to expect high-level personalized experiences. With the enforcement of governing regulations and laws like GDPR and the newly implemented California Privacy Rights Act (CPRA), which adds GDPR-like consumer rights to the existing California Consumer Privacy Act (CCPA), collecting customer data in 2022 has become much more difficult. Because of these changes, the collection of zero-party data has become increasingly important to marketers who want to continue providing quality customer experiences. In contrast to first-party, second-party, and third-party data, which are indirectly (and often covertly) collected from customers, zero-party data is data that a customer voluntarily shares with your company. In this article, we’ll discuss zero-party data and why it’s important in 2022.

What is zero-party data?

Zero-party data is data that comes directly from customers. The term was first used by Forrester Research in 2018, who defined it as, “data that a customer intentionally and proactively shares with a brand, which can include preference center data, purchase intentions, personal context, and how the individual wants the brand to recognize [them].”Marketers can use quizzes, surveys, etc. to collect zero-party data and offer rewards and incentives to encourage customers to volunteer their information. Zero-party data gives you specialized, unique information that you can use to build more personalized product suggestions and interactive customer experiences.

Why is zero-party data important?

A zero-party data strategy is the best response to an increasingly privacy-centric world, where data privacy regulations like the GDPR and CPRA define how consumer data can be collected, managed, and used. Sites are now required to inform visitors of their use of third-party cookies, and customers often opt out. When given the choice, many people don’t want to hand over their data to a third party. Big tech companies like Apple have also made privacy changes that prevent app creators for iPads and iPhones from collecting data without explicit user permission via pop-ups or user settings. As of September 2021, only 21% of mobile users worldwide using iOS 14.5 had consented to app tracking. Google is moving in the same direction, committing to phasing out third-party tracking cookies from its browser by 2023. With these changes, marketers are beginning to recognize the value of zero-party data. It gives customers control over whom they share their information with and how much of it they share, and it creates transparency about why a brand is collecting that data and what they intend to do with it.This strengthens customer relationships, making people feel valued and increasing the likelihood that they’ll share insightful information with marketers who can use it to improve customer experiences and drive more sales.

Difference between first-party and zero-party data

The major difference between first-party and zero-party data is that first-party data is data collected passively from customers using cookies and other tracking technologies, while zero-party data is data customers give you directly.First-party data includes data about customer interests and behaviors such as personal information, purchase history, time spent on a page, and frequency of clicks, and it is owned by the company. Zero-party data is requested and collected by the company and provided directly by customers. The customer owns their own zero-party data and it cannot be sold without their consent.

Benefits of zero-party data

Regulatory compliance

A zero-party data strategy allows you to collect customer data without going against data privacy laws and regulations. It respects your customers’ right to data privacy while allowing marketers to continue providing true personalization amidst the increase in government data use restrictions and browser privacy features.

Greater customer trust

Zero-party data allows you to be more transparent about the data you’re collecting and the value that customers receive in exchange for their data. Privacy concerns are reduced, making customers feel more at ease and creating trust between you that leads to better, longer-lasting relationships.

Higher-quality data

While second- and third-party data are available to you and your competitors alike, zero-party data is unique to your business. Because this is data you have specifically asked your customers for, it is more precise than the data you might receive from other sources. A zero-party data strategy allows you to collect data that directly relates to your business and products. By allowing your customers to share unique information with your business exclusively, you gain a significant advantage over your competitors.

Lower acquisition costs

Focusing on zero-party data eliminates the cost of purchasing information from second- and third-party data sellers. In addition, it doesn’t require the same level of mining and analysis to gain insights, since you are directly requesting the information you need. This saves both time and money.

Zero-party data examples

Quizzes and questionnaires

Quizzes are a simple and fun way to collect zero-party data and help you determine what customers are looking for or what they prefer. With the help of a quiz, customers can figure out what products best fit their needs to make better purchasing decisions, while giving you valuable data to refine your marketing strategies. Beauty and skincare companies like Fenty Beauty and The Ordinary use questionnaires to collect zero-party data. Fenty Beauty’s Shade Finder allows customers to find the perfect foundation shade for their skin tone. And with The Ordinary’s Regimen Builder, customers can find products to create a skincare routine that addresses their specific concerns. These tools lead to more purchases and satisfied customers, while providing the companies with data to better market to their customers’ specific needs.

Polls

Polls are a fast and effective way to get feedback from customers, prospects, fans, and followers. By asking customers questions such as, “What are you looking for today?” at strategic points of the customer’s journey on your website, you can learn more about their interests and use their answers to direct them to specific product pages on your site.Social media polls are also a great way to gather customer thoughts and opinions on specific products. A simple poll asking customers to choose between two options, e.g., “Which do you prefer?” allows them to give you direct feedback that can support product recommendations.

Pre- and post-purchase surveys

Pre- and post-purchase surveys can help your company learn more about customer expectations before and after a purchase. Pre-purchase surveys can help you discover which products visitors are looking for, while post-purchase surveys help you better understand their purchase experience. Subscription pet brand and Simon Data client, The Farmer’s Dog, offers 20% off to first-time visitors, who are presented with a pre-purchase survey that asks questions about their dog’s weight, age, activity level, medications, and the foods they eat. At the end of the survey, The Farmer’s Dog makes suggestions for two weeks worth of meals that the visitor can add to their cart with the promised 20% discount.

Subscription, loyalty, and membership programs

Customer loyalty, membership, and subscription programs allow customers to share information about themselves in exchange for benefits like loyalty points or exclusive discounts. You can ask customers for information such as their birthday, skin type, style, or other preferences when they sign up. Starbucks uses its loyalty program to reward members with a free drink on their birthday, while craft retailer Michaels offers exclusive discounts through their rewards program.

Preference centers

Preference centers allow customers to choose how they are marketed to. Brands such as Apple and Spotify use this method to allow users to share their interests. First-time users choose their preferred artists and types of music so the apps can make personalized recommendations.

Limitations of zero-party data

The major challenge with zero-party data lies in its collection. Even with greater protections in place, some customers are still concerned about data privacy and the misuse of their data. Though zero-party data collection is more transparent, people may still be hesitant to share information—or at least too much of it. If you ask customers for too much information, they may become overwhelmed and stop responding. Marketers must ensure that data is collected ethically, and customers must clearly understand that their information is being collected and could be used for marketing campaigns. Maintaining transparency with your customers is the best way to build trust so you can continue to offer them personalized experiences.

Optimize your zero-party data with a CDP

Companies using a zero-party data strategy will not only learn more about their customers, leading to an improved customer experience, but they will also be seen as more trustworthy with customer data. And that trust will ultimately translate to more sales. Zero-party data can be collected across a wide variety of channels, and it’s crucial that you can easily access and activate this data in one location to build better pictures of your customers. Simon Data’s customer data platform (CDP) gives you one tool that you can use to combine and consolidate your zero-party data, manage your customer data profiles, and create unique, personalized experiences based on zero-party data attributes. Discover how Simon Data can help your organization maximize the value of zero-party data by requesting a customized demo today.

Just because you can call someone, text them, or drive by their house doesn’t mean you should. Am I right? The same is true of cross-channel marketing. Everyone’s doing cross-channel – but how do you know if you’re really nailing it?

You’ve likely heard of the five P’s of marketing. Check out the five C’s of Cross-Channel Marketing –  brought to you by Ragnarok and Simon Data.

1. Cues from Data

More information than ever is available to marketers: take advantage of that bounty! No high-growth company can wait weeks for reports to be delivered to make decisions. Enable your teams to act on results quickly and confidently by leveraging tools like Simon that not only synthesize your results but can also push highly segmented cross-channel messages directly to your customers.

2. Consider What You Really Need

Now, let’s think about what data you actually need to power the experiences you’re trying to enable. Steven, one of the Ragnarok Co-CEOs, says, “many teams have built a model where they’re collecting everything but then find after an audit that nobody was using most of it.” Use case gathering is an essential part of data set up to ensure you’re capturing what you need and forming actionable dashboards. While a deluge of data is at our fingertips, it’s important not to get lost in the flood and focus on what’s essential. This episode of Data Unlocked Podcast can be a big help in fine-tuning your data and turning learnings into actions.

3. Choose the Correct KPI

Do not obsess over the wrong metrics. If your business needs to drive more repeat purchases, open rates aren’t where you should be focused. Yes, opens are one part of the funnel, but it’s not the whole story. We recommend ensuring your channel owners have a view of the entire funnel and where drop-offs occur. Ultimately, all efforts should be traced back to a mission-critical KPI they’re supporting. To ensure you’re learning along the way, create structured learning agendas to focus on how and where you are proving your biggest growth hypotheses.

4. Cross-Team Communication

Don’t silo your squads- Foster collaboration by building cross-functional pods responsible for achieving shared goals. Create an environment where sharing ideas and acting on them is encouraged by hosting weekly focused brainstorming sessions. Be sure to celebrate wins and even share losses in team forums where everyone can learn together. Simon Data has put together a great post about cross-functional collaboration.

5. Customer-Focused Mindset

Bolster your data-driven insights and intuition with the voice of the customer! How can you do this quickly? Here are a few tips:

1. Have a representative from your Customer Success team help steer all big company initiatives. They should be involved early on – not just as a last look before initiatives go live!
2. Use surveys to contextualize the data and dig deeper into the drivers of behaviors when you understand the what but not the why.
3. Leverage your social channels for quick gut checks! You’ll find out in a matter of seconds what they really think.

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Byline by Aaron Smith, CEO and Co-founder at Sageflo.

In the last decade, many companies have transformed their digital presence and evolved from a multichannel strategy to an omnichannel strategy. One of the most important differences between the two is that multichannel focuses on engaging customers, while omnichannel focuses on improving customer experience. With multichannel marketing, the goal is to cast the net as wide as possible to make sure more people are aware of a business. With omnichannel marketing, the goal is to create a consistent customer experience for people who are already aware of and engaging with a business. At the core, data is what drives both strategies. The key to shifting from simply engaging customers to truly enhancing the customer experience lies in how that data is leveraged across each channel.  

“When a company makes customers feel appreciated, 76% indicate they’ll keep their business with the brand, 80% say they will spend more with the brand, and 87% will recommend the brand to friends and family members.” (Forrester)

During the pandemic, companies that were digitally ready prevailed. In a report from Forrester, e‑commerce in the U.S. grew 30% in 2020, its fastest growth rate since 2002. But the research firm also projects that 72% of retail will still take place offline in 2024. Bearing that in mind, as companies move through the many phases of digital transformation, it’s imperative for them to consider not only what data they’re using across channels, but how the data is being used and who is provided with access.

Marketers have primarily used data across online marketing channels like web, email, social, and mobile to engage customers wherever they’re engaging with a brand. However, when it comes to offline marketing channels, like interactions with customer care or in-store service, these associates are rarely, if ever, empowered with customer data that can truly impact their conversations with customers.

With customer data being collected at every interaction, customers know and expect companies to be aware of their likes, status, engagement, and purchase history. In fact, 74% of consumers think knowledgeable in-store staff is important to their brand experience. (Oracle)

Brands are evolving and seeing the impact of having fewer data silos and more access to customer data. How that data is then shared across teams and made readily accessible is what makes the difference between an average and highly personalized, dynamic customer experience.

Consider these two examples:

1) The Farmer’s Dog utilized Simon Data’s customer data platform to organize and activate data, saving the marketing team 80 hours a month and increasing the rate of email experimentation by 10x. Faster path to personalization by allowing the marketing team to execute quicky and efficiently without IT slowing things down

2) Vivino utilized Simon Data to deliver highly personalized recommendations while simultaneously optimizing message cadence, channel mix and lifecycle specific content for each customer. Not only did this lead to a 3x increase in revenue per email, but overall engagement increased 2.4x.

Taking Customer Experience to the Next Level

Having a holistic view of all customer activity, purchases, and messaging is imperative and starts with enabling brands to listen, think, and speak to customers from one, centralized platform. Simon Data + Sageflo Archiver, captures all that data along with a visual timeline of every customer’s messaging journey and engagement, so that customer service and in-store teams can begin to understand customer interests and intent, and engage with customers in more individualized and meaningful ways that drive customer experience.

Brands that are focused on CX holistically are leveraging data in ways that drive higher customer engagement not only via digital touchpoints but across all channels where customers interact with that brand, including in-store and call center support.

Sageflo Radiate and Simon Data partner to give brands even more detailed insight into customer messaging and engagement, so that brands can utilize this data in real-time with customers and uncover an additional layer of interest and intent.

Looking ahead to 2022, customers will continue to expect hyper-personalization and demand a more holistic customer experience. As brands continue to compete for customer attention and share of wallet, focusing not only on having the right data, but using that data more effectively will need to be top of mind. Companies that can offer customers the most seamless experience between both the digital and physical shopping worlds will be the ones that grow their business and expand market share.

Learn more about Sageflo Archiver here
Learn more about Simon Data here

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