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How to choose the right customer data solution

Use this guide to review solution pros and cons and key buying considerations. Plus, download our free worksheet to help you make the right purchase for your team.

Chris Sell

Chris Sell

Data-driven marketing: It’s a buzzword you’ve heard plenty of times. In theory, it’s the ideal state that all data and marketing teams want to reach. But in reality? It’s a challenging state to achieve. 

Truly effective data-driven marketing relies on a couple of key elements: Having a complete and reliable view of the customer and targeting the channels that matter most to your audience. That means having a single source of truth for customer data and tapping into a range of best-of-breed marketing tools.

So, how can marketing and data teams achieve this ideal state? A customer data solution may be the answer.

While there are countless solutions in the martech ecosystem, they’re not all equal. This customer data platform guide will lay the groundwork for your team to conduct meaningful research and select the right option for your organization. 

In this article we’ll provide pros and cons, key buying considerations, and a free downloadable worksheet to help you make a purchase that will unlock a data-rich future that amplifies marketing ROI.

Today’s customer data solutions

Nearly two-thirds of marketers say that decisions made with data are superior to those based on gut instinct. Yet, data is often unreliable or inaccessible.

This is when teams turn to a customer data solution. A customer data solution can help unlock and activate data for marketing campaigns. There are four primary solution categories:

  1. Marketing cloud
  2. Packaged customer data platform (CDP)
  3. In-house solution
  4. Composable CDP 

But before diving into the details of each solution, this customer data platform guide will cover 10 critical buyer considerations that you should review before starting this search.

Key buyer considerations for customer data solutions

Time to value

As you begin exploring solutions, evaluate the resources available to your team and the solution’s implementation process. 

Key questions to ask:

  • How much engineering and data team support does the solution need? 
  • How much time investment will the solution require from other teams? 
  • How long is the implementation and onboarding process? 
  • How long will it take for our team to see value from the solution? 

Marketing execution speed

When leveraging your company’s first party data, you’ll likely encounter challenges navigating the intersection between data and marketing.

Key questions to ask:

  • Will marketing need ongoing engineering and data team support once the solution is set up? 
  • Will marketing be able to activate data through self-serve tools? 

Cross-channel campaign functionality

Assessing and driving value across several campaigns simultaneously is paramount to marketing success. Take note of each solution’s ability to power campaigns for audiences across several channels at once.

Key questions to ask:

  • Can I add new marketing channels to the tool after setup? 
  • Can the solution support the marketing channels my team uses today? 
  • How easily can we launch cross-channel marketing campaigns? 

Data trust and reliability

When data is separated from the primary source, it can become unmanageable and unreliable.

Creating a secondary or separate “source of truth” (i.e., another location for your data to be stored) and your organization immediately loses the value of having a centralized and organized data pool. These additional silos mean that different functions within your organization are operating off of entirely different sets of data. This can lead to duplicate or similar campaigns and, ultimately, a loss of trust with your audience.

Key questions to ask:

  • How well does the solution work with our company’s existing single source of truth (like a data warehouse)?
  • Will the solution require copying and storing our company’s data in another platform?

Artificial intelligence

Marketers are already tapping into the power of generative AI, and its role in campaigns will only grow. Your organization may also want to consider potential generative marketing capabilities, which apply generative AI to marketing workflows. The solution you choose should keep up with this growth and be able to incorporate new and developing AI models.

Key questions to ask:

  • How well does the solution incorporate AI models, both existing and potential new models?
  • Will this solution help future-proof our marketing?

Security and compliance 

Data security and compliance should be a priority for every organization. Teams that don’t consider data privacy measures could face complex and costly legal challenges. 

Key questions to ask:

  • Does the solution leverage our company’s existing data security policies? 
  • Does the solution use our company’s existing data or copy it to a new location?
  • How important is information security to our organization?
  • Will our CIO or CTO allow us to copy sensitive data into a third party solution?

Standardized measurement 

Measuring campaign performance is critical for marketers — it helps prioritize resources and, most importantly, informs future campaigns so they can perform better. Ensuring your solution can simplify this process is an important part of vetting customer data solutions. 

Key questions to ask:

  • Will the solution standardize marketing performance measurement? 
  • Does the solution easily integrate into our organization’s marketing analytics dashboard?

Cost 

Budget is a big part of any vendor selection. When vetting composable CDPs, CDPs, marketing clouds, or in-house solutions, it’s essential to look out for potential ballooning costs. If the solution requires you to host a copy of your data in its system, there are likely “hosting fees” buried in the contract. Over time, as the number of data sources grows and becomes more complex, this cost will continue to increase.

Key questions to ask:

  • What does the solution cost?
  • Does the solution fit within our organization’s budget?
  • Are there any long-term costs we should consider? 
  • What is the scalability and long-term ROI of this solution? 

Real-time capabilities

For marketing teams that want to activate campaigns and content in real time, the system needs to receive real-time data. Many solutions receive partial real-time data, and rely on data transfers from the data warehouse for the rest of it. These transfers happen at a predefined frequency (daily, weekly). 

But these predefined data transfers create a problem for marketers focused on real-time capabilities. 

Key questions to ask:

  • Can this solution support near real-time use cases, such as web personalization?
  • Which sources or channels can the solution receive and process real-time data?

Modern data stack support

Many marketers today are moving away from storing customer data in third-party platforms. Instead, they store this data in a centralized data warehouse like BigQuery or Snowflake. This philosophy is key to the modern customer data stack, which uses platforms that leverage centralized data in the data warehouse. 

If your organization leverages a data warehouse and the modern customer data stack, you’ll need to confirm your company is storing customer data in one of these databases. If not, you may need to invest in data transformation.

Key questions to ask:

  • Does our company already have a modern customer data stack?
  • Does this solution require investment in the modern customer data stack?
  • Is our company willing to invest in the modern customer data stack?

When surveyed, 32% of marketers identified marketing analytics and competitive insights as the most important factors in supporting their marketing strategies over the last 18 months. This ranked higher than any other category. 

Approximately 20% of the average US marketing budget is spent on data. (CMO by Adobe)

The solutions

With these considerations in mind, let’s dive into the four most common customer platform solutions.

Marketing clouds

Marketing clouds were an early player in the customer platform space. Composed of a “suite” of tools, your marketing team can use a marketing cloud to manage campaigns and nurture customer relationships.

These systems typically offer tools for email marketing, social media sharing, advertising, and more. Two of the more popular marketing clouds are Adobe Experience Cloud and Salesforce Marketing Cloud. 

These platforms operate on a set of data brought in from an original source — the data warehouse and, in some cases, other inputs like web data. From there, your marketing team can act on the data to create targeted campaigns in the channels within their marketing cloud.

Key buying considerations for marketing clouds

Packaged customer data platforms (CDPs)

Packaged CDPs (also called traditional CDPs) compile customer interaction data from various sources to rebuild a partial customer profile. Your marketing team can access this profile to activate campaigns across various end destinations, including ads, emails, SMS, and more.

CDPs were introduced to address the challenge of new and evolving marketing channels (channel proliferation). Marketing clouds couldn’t keep up with the growing number of destinations marketing teams needed to reach. 

The list of CDPs on the market is extensive. The CDP institute reported in July 2022 that more than 160 defined vendors were in the space. Some of the more well-known CDPs are Salesforce Customer Data Platform, Segment, Hightouch, Census, Tealium, and mParticle. 

Like marketing clouds, CDPs require a data copy or transfer. Only once the data is in the CDP system can your marketing team leverage it. Some data is sent directly from the source to the CDP. 

As data sources and volume grow, much of it will go to the data warehouse managed by your data and engineering teams. This means most relevant data must be copied or transferred from the data warehouse to the CDP.

Key buyer considerations for packaged CDPs

“Regarding CDPs, we want to control our own destiny, we want a platform that will scale with us. A CDP can be a useful tool but is not a substitution for an owned first party modern data stack on Google Cloud. I think people are making a mistake putting all their eggs in that basket. How do you ever migrate in the future if you want to move away from a third party vendor? It’s like starting over to extract yourself. By owning the architecture, you avoid vendor lock-in. That’s what we love about the architecture we’ve built with GrowthLoop.” -Brian Shield, SVP | CTO, Boston Red Sox

In-house solution

The rise and sophistication of the data warehouse has created a true single source of truth for most companies. All customer data from all sources can be housed in a single and secure location. With the data housed in one place, data and engineering teams can focus their efforts, ensuring the company has well-organized, clean data.

Your marketing team may see the value in tapping into that data directly. One way to do this is to build an internal system or process. This approach requires the following steps: 

  • Your organization hires a team of analysts that can write SQL queries
  • Marketers define their campaign audience to the analysts
  • Analysts write manual SQL queries to pull that audience out of the data warehouse 
  • Analysts share CSV files of the audience data
  • Teams offer feedback on the data
  • Analysts and marketers iterate on the queries for audience data until it is finalized
  • Marketers launch the campaign 

Eventually, your company may realize this method is costly, inefficient, and prone to security issues. 

Some organizations pursue building an interface that lets marketers pull data directly, aiming to remove the need for a team of analysts. The organization tasks the engineering team with building a tool that lets the marketer run their own SQL queries. 

While building an in-house solution may solve an inefficiency problem, it can be costly and come with security risks. There is also a real challenge when it comes to ongoing support. As new marketing channels create more data sources, the marketing team will need access to new data tables in the data warehouse or will need to add new data pipelines. 

When thinking about build versus buy options, consider how your marketing team’s internal product will receive ongoing support if you decide to build.

Key buyer considerations for an in-house solution

Composable CDPs

Modern marketing teams are now turning to composable CDPs.

These solutions unlock the ability to execute data-based marketing and sales projects directly from your company’s data warehouse. Notably, composable CDPs do this without requiring any data to be transferred or copied.

Your marketing team can use a self-serve (no-code) interface to create and segment audiences inside your company’s data warehouse. They can then activate those audiences to channel destinations, such as CRMs, email marketing tools, and ad platforms.

With a flexible model built on your data cloud warehouse, your marketing team can swap channels and tools based on their needs. Meanwhile, the source of data remains the same. 

This closed-loop solution ensures your company maintains the integrity of its single source of truth for customer data. It does this while empowering marketers to leverage all your company's data efficiently and effectively.

Key buyer considerations for composable CDPs

Summary: Customer data platform guide key buyer considerations

Summary of all the key buyer considerations across all the customer data solutions.

Determining the right customer data solution for your team

There’s no question that leveraging customer data is critical for marketers today. How to leverage that data — and finding the right platform for the job — may not be as clear.

Ultimately, you’ll want to choose a solution based on your business needs and use case. Consider requirements for marketing channels, data, AI, analytics, and other capabilities requirements. 

Most importantly, reflect on your organization's future needs. Will your customer data solution grow with you? Will it support future AI systems and martech solutions? Predicting the future is impossible, but preparing for the future is the next best option.

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