Beyond the Magic Quadrant: Discussing Gartner's CDP assessment and Simon's vision for the category

Recently, Gartner released a Customer Data Platform Critical Capabilities assessment Magic Quadrant report. This report is less a milestone and more of a juncture in analyst coverage of what has been a confusing space to navigate for the better part of the past decade.

The CDP acronym has been used to describe hundreds of vendors, many of which serve different purposes and solve different problems. Categorizing all CDPs against the same set of dimensions is therefore not only an unenviable task but also arguably not useful for this particular category. 

Unlike other categories that Gartner and other analysts evaluate, CDPs have been used as a bit of a catch-all term to describe various software applications used to consolidate, transform, and activate customer data. The variety of tools used to deliver these capabilities not so roughly corresponds to the different strategies businesses use around data management.

In other words, categorizing CDPs against the same set of dimensions is a bit like categorizing all athletes, regardless of the sport they play, against the same set of dimensions.

Comparing Salesforce, Twilio, and Amperity against the same set of dimensions is like comparing Lebron James, Tom Brady, and Tiger Woods and rating them on their speed, vertical, and long jump distance. That comparison is probably being generous to Twilio and Amperity, and certainly Salesforce (Gartner’s above-and-beyond prize horse that won’t run on a track except its own and charges you rent for the stable it occupies). 

At Simon, we have a different vision for where the category is headed. The goal of this article is to both respond to Gartner’s coverage of CDPs from our perspective (after all, it’s not an easy category to tackle) and express our views on how we see the CDP category — and Simon — continuing to evolve.

Our view of the CDP category

Different types of CDPs solve different problems, which is partially why lumping them together and placing them on a grid is complicated. 

We view the CDP as a CRM application that should connect to enterprises' customer and non-customer data assets as seamlessly as possible, enabling marketing teams access to all data and the tools to power their campaigns across any channels at the latency demanded by the use case.  

“Seamlessly,” in our case, means an application connected to a client’s cloud data warehouse, with the ability to ingest data that lives outside the data warehouse as well. 

Some CDPs function as a marketing data warehouse. Others perform data quality functions and route data back to the data warehouse. Few CDPs on Gartner’s Magic Quadrant are primarily focused on data activation for marketing use cases. None of the CDPs listed in the “leaders” quadrant offer both a marketing UI and real-time data activation, for example. 

Understanding Gartner’s dimensions

There are 13 dimensions to Gartner’s analysis, ranging from data collection and profile unification to core CDP workflows like segmentation to non-category-specific considerations like data privacy and system integration relationships. 

Reiterating the above point around intra-category direct comparison, some of these capabilities are much more important than others if you envision your CDP as a data routing solution versus a marketing automation hub. 

Gartner previously categorized CDPs into four buckets: marketing cloud CDPs, CDP engines and toolkits, marketing data integration CDPs, and CDP smart hubs. We view this sub-categorization as essential and only comparison within the sub-category as useful. However, Gartner has since abandoned that sub-categorization in favor of direct comparison across the category. 

One perplexing decision on their part is to combine B2B and B2C functionality in the same assessment. Other analysts, more logically, separate B2B and B2C-focused vendors into different categories. 

The biggest hurdle to CDP adoption is data integration and upstream data quality challenges and maintenance. Gartner, as a services company, seems to approach this as a services problem and places weight on system integration relationships. This makes sense in a legacy enterprise context, but this comes at the expense of undervaluing vendors for whom this problem is mitigated with a more modern deployment strategy. 

Embracing composability

Composability is a key trend shaping the CDP space. We believe tremendously in the value of being a composable CDP and have invested significant R&D effort in enabling our CDP to be deployed within a client’s data warehouse. 

We believe Gartner undervalues this capability in two ways: 

1. The business value and capabilities that a composable deployment enables, beyond simply the architectural principles it follows
2. How this divergence between (1) CDPs that interoperate with cloud data warehouses and (2) CDPs that want to become your data cloud, and what this means for the category going forward

The business outcomes enabled by composability

Composable deployments significantly reduce the time, effort, and cost required to implement a CDP. 

Having a CDP oriented to a source of truth for customer data that supports other types of software applications also means that the CDP can focus on what the CDP should do best — create an interface for customer marketers to interact with all of the data they need to power their customer experiences, and to do so without friction between marketing and the data teams that support them. 

As it pertains to capabilities like data science and experimentation, a composable architecture enables teams to train models that can interpret the entire customer profile and other relational data relevant to making predictions about the customer lifecycle. 

This is possible in a traditional CDP deployment, but the models will be limited to learning the data that is made available to them. 

As it pertains to data privacy, a composable deployment means that no data leaves a customer’s data environment. It’s not possible to imagine a better approach to data privacy than not actually storing or handling customer data. 

This approach has become a gold standard for enterprise data privacy and security teams and we believe it should be incorporated into any assessment of privacy. 

Lastly, because composable deployments can be implemented in days or weeks and not months or years, while there is meaningful change management and data modeling work that can accompany a composable deployment, gone are the days of massive consulting and/or system integrator contracts surrounding a composable CDP deployment. This approach saves clients six figures and is a multiplicative force on time to value and ROI.  

Overall, Gartner seems to look at composable CDPs the way that a 20th century mechanic might look at an electric vehicle, noting the absence of pistons or valves but also not capturing that the vehicle doesn’t require gasoline. I would largely attribute this to a difference in the ideal customer profile, but this is becoming less so with enterprise adoption of the modern data stack. 

Simon’s vision and short-term roadmap

Building upon the benefits of a composable approach, and, more broadly, delivering a CDP vision that supports organizations who have adopted a modern approach to their data stack, there are three key areas that Simon Data is uniquely positioned to impact. 

Customer lifecycle optimization 

There are many siloes within an organization, from tooling to data and teams. Customer marketing and advertising teams are no exception. 

Often, given these siloes, advertisers aren’t effectively coordinating their paid media strategies — and budgets — with downstream customer marketing efforts. This is especially true for organizations using enterprise marketing cloud tooling without a modern CDP in place to coordinate across data silos. This results in suboptimal efficiency (i.e., ROAS, CAC, etc.) due to targeting suboptimal customers or prospects. 

First-party data activation tooling, historically within the hands of CRM and retention marketers, is increasingly attractive to paid media teams as third-party cookies are deprecated. 

Those most prepared are looking at new first-party data strategies and how a CDP can complement their cloud data warehouse approach to drive media efficiency outcomes. 

At Simon, we offer a product called Anonymous+, a tool built entirely on top of Snowflake and does not rely on third-party cookies. It offers a workflow that brings marketing and advertising teams together, on a fully-governed, unified Customer 360, resulting in better media performance. 

Anonymous+ resolves customer identity across known and anonymous touchpoints. This enables brands to unify and leverage customer data across the entire customer journey — from first touch to first purchase and throughout the customer lifecycle. 

This is the first and only of its kind solution in the CDP space. Several Simon customers have seen mid-double-digit improvements in paid media efficiency and seven-figure outcomes leveraging this product. 

High ROI use cases require more non-customer data than customer data 

Great personalization is more than “Hi [first_name]”. Marketers also need context about customer intent, discount sensitivity, product affinity, etc. 

For a customer of ours like ASOS, for instance, there’s a ton of basic customer intent data that can be used to create the right marketing and engagement experience: the number of products they viewed, how many times the customer has visited the site or made a purchase, and what acquisition channel(s) the customer engaged with. 

But there’s also a deeper layer of context and product-related data that can make personalization even more impactful. For example, there’s the price within the category of products, inventory, weather in the customers’ location, and what the SKU(s) viewed imply about other purchasing preferences.

Marketers need easy access to this level of customer intent and context to design the next-best interaction. Our connected architecture enables our application to access this type of data in a way that’s significantly broader than other CDPs and to also do so in a way that doesn’t result in data overload or storing data for the sake of it (after all, in a composable deployment, we don’t store or charge for it). 

Access to this non-customer data in Simon, including things like inventory, pricing, and cost data, is available across the entire customer lifecycle, and leveraging non-customer and product-related data helps marketers and advertisers optimize everything from channels to media costs and even product selection. 

Our goal at Simon is to help D2C customer marketers leverage all types of customer and non-customer data by building products that are easy to use, easy to integrate with, and that minimize the complexity of marketers’ interactions with the data. ‍

The CDP is an application owned by marketing and should optimize marketing outcomes 

Marketing teams should demand measurable results from their CDP, especially in the current sustained macroeconomic environment. Winning customer marketing teams will have to do more with less. 

Not a hot take, but at Simon, we believe GenAI will have a lasting change on marketing workflows, and that advances in GenAI will be best applied to a true Customer 360 from the CDW. 

We see AI as key to achieving a future where Simon autonomously delivers value to customers by leveraging available data and adapting to customer behavior and market trends.

We've identified four main challenges our customers face in realizing that today:

1. Difficulty in using data effectively for campaign creation
2. Generating ideas that may not always align with business goals
3. Learning from past actions
4. Lack of time, energy, or skill to create campaigns and their components

Our first GenAI-powered content creation tool went GA in Q3 2023. This tool helps non-technical marketers create complex content personalization code from simple natural language prompts. 

Now anyone can create complex personalization logic by writing a few sentences describing their use case (which used to require complex code) without the need to ask for technical support.

Normally, my process for content creation was to go to Simon documentation, look for the section on how to format, and then go through trial & error to get the results I needed. With Simon AI for Content, I asked the AI…and it created what I needed in maybe 30 seconds.” — Valerie Decker, Lifecycle Marketing Manager at Zillow

Our vision for GenAI goes well beyond content creation. Generative marketing can expand to the audience and channel optimizations as well. 

Our long-term vision is to use AI to decrease time to value and increase ROI. We believe we can do this by using AI to develop a “semantic understanding” of our customers' data, and then use that understanding to automatically create “complex data aggregates” and marketing campaigns that drive customer value.

In summary, these three investment areas, alongside our connected architecture, will enable our customers to automate audience, channel, and content decisions across the entire customer lifecycle and personalize interactions with any data point, all while optimizing for revenue and non-customer data that matter most to the business (e.g., margin, LTV, inventory, etc.). 

This is a broad vision relative to the current capabilities of most CDPs that don’t operate as part of a broader marketing cloud ecosystem. 

Contrasting customer profiles

With the above note on the completeness of product capabilities, Simon Data is not the right fit for every type of customer. Simon is purpose-built for customer marketing teams at customer-focused businesses to deliver the next generation of customer experience. 

Relative to the overall array of customers that Gartner advises, Simon is naturally not going to cover the breadth of capabilities Gartner assesses. 

We believe this is a strength of our product vision and execution. CDPs that are focused on solving a massive array of customer problems and those who rank highest in Gartner’s completeness of vision axis are often the CDPs our customers are least satisfied with when they switch to working with us. 

Our customers value a platform that can deliver measurable ROI as a customer marketing and personalization solution that enables them to access all of their data — that can be implemented in days or weeks and not months or years. 

Many enterprises, including the largest, are adopting a data-warehouse-centric approach to data management and CDP deployment. We believe this trend will benefit the broader industry, and our clients, and this has already proven to be a significant market opportunity for Simon. 

Our predictions for the future of the category

As more large enterprises centralize their data in a cloud data warehouse and expect that their applications come to the data (as opposed to bringing their data to all of their applications), such enterprises will realize huge efficiencies and will reap the tremendous benefits that AI can create for organizations with centralized and well-structured data. 

Many CDPs included in Gartner’s report that do not currently orient around data centralization in this way will either adapt or struggle. These include all of the Customer Data Infrastructure CDPs (e.g., Twilio and Tealium) and all of the C360-focused CDPs (e.g., Treasure Data, Amperity). 

CDPs that fit as part of a broader marketing cloud strategy (e.g., Salesforce, Adobe) are naturally going to score well in a measurement of surface area. These products are also capable of depth if you’re operating with infinite resources and budget. These platforms also naturally lock customers into their ecosystems. 

Going forward, we expect teams to value efficiency and measurable ROI. From the conversations we have with customers, the most important considerations around data architecture are: (1) limiting data replication throughout the enterprise, (2) cost management, (3) avoiding vendor lock-in and (4) creating best-in-class data privacy management. 

For this reason, we see the “CDP as a data cloud” as an unsuccessful strategy for our customers.

A few appendix notes:

1. It’s probably a bit of a quandary as to how Gartner treats the big marketing cloud players because while their overall product surface areas can achieve anything (with the above caveat around budget and resourcing), it’s hard to know where they draw the boundaries around their CDP products. They also all have huge relationships with Gartner that create an obvious conflict of interest. 
2. Gartner omitted a lot of the more technology-persona-focused solutions that teams often use to achieve their CDP use cases. While this isn’t surprising, given the scope of Gartner’s evaluation, it’s worth noting that this report includes almost entirely packaged software that marketing teams buy (as is Gartner’s definition of a CDP) but without as much consideration around how data teams solve for CDP use cases. 
3. It’s unclear how the MQ scoring relates to anything resembling the way we or the businesses we speak to see the market or the vendors our prospects and customers ultimately select in a competitive process. This likely has to do with many of the factors discussed here. 

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