Transforming Customer Data into Insights

Chapter 4. CDP Implementation: Aligning People, Processes, and Tools

Have you ever heard the expression “Everyone is now a marketer”? There’s some truth to this saying, at least as it relates to CDP implementation: to be successful means aligning values and incentives across teams, from the C-suite to frontline staff. CDPs also rely on cutting-edge technologies and business processes, which often deviate from traditional practices.

In Chapter 3, you learned about the new demands for creating customer-centric marketing and the opportunity for brands to do so with a CDP. But no technology implementation is just about technology: it takes an alignment of people, processes, and tools. Organizations overlook this distinction at their peril: a survey of over 1,700 C-suite executives collected by consultancy McKinsey found that integration of advanced technologies stands a 45% chance of delivering less profit than expected, while the likelihood of surpassing profit expectations stands at just 10%. In this chapter, you’ll see how to ensure a successful CDP adoption and how to measure that success.

Aligning the C-Suite

Traditionally, the adoption of a new marketing technology rested with IT and, of course, marketing. A CDP adoption and subsequent shift of marketing to a customer-centric approach, however, needs organization-wide recognition, which even touches the C-suite. If a company has decided it wants to be customer-first, a CDP implementation works best if it’s a top-down directive where all stakeholders involved with customer data agree it’s important.

Even leaders of areas who ultimately won’t use the CDP, such as human resources or finance, should be informed early that a CDP will be implemented and how it will be evaluated. As you’ll see later in this chapter, some traditional key performance indicators (KPIs) for marketing are no longer the right fit for a CDP-equipped organization, so transparency is required to make the change.

The C-suite may also not just need to be aligned but augmented to successfully adopt a CDP. For example, a leader such as a chief experience officer or chief customer officer may be needed to oversee and coordinate not just the CDP but customer-centric marketing as a whole. In fact, Gartner has reported that 90% of organizations now have some C-level role equivalent to a chief experience officer or chief customer officer.

Establishing Ownership: Marketing, IT, and Analytics

Previous marketing technologies took advanced technical abilities such as manually writing database queries to create and extract segments. This was often performed with the support of IT by marketing researchers. By contrast, a CDP is built to lower the barrier to entry for powering data-driven marketing and personalization. So what role does IT play in the CDP as opposed to marketing?

Typically, a marketing department holds the budget for a CDP as its primary user. This is not to say that IT, as an organization’s typical standard-bearer for the collection and processing of data, does not play a part in its implementation. IT must be informed and buy into the benefits of a CDP for the same reasons as the C-suite: customer-first marketing is an organization-wide shift.

Logistically, a CDP’s features such as integration of PII and activation across channels mean less technical intervention by IT. These tasks might have been extremely time-consuming or even impossible for the savviest engineers to conduct. Perhaps, paradoxically, it is with this burden of tedious technical work lifted that IT may be needed more than before to partner closely with marketing: to deliver a customer-first experience across the brand.

Many customer-centric organizations adopt cross-functional teams structured around the customer journey, combining resources and talent across the organization to optimize each part of that journey. These teams include not just professionals from marketing or IT, but also those from analytics and data science to help measure and monitor the team’s efforts. Organizing cross-functional teams ensures that objectives are shared across functions and that all efforts are ultimately for the sake of the customer.

CDP Project Roles

While broad buy-in is necessary, some roles will play a more active part in a CDP’s implementation. The CMO should provide overall leadership and project prioritization and define what success looks like. The CIO also provides leadership for integrating systems and lending support where needed.

Because the CDP is a product of the digital world, marketing leaders in ecommerce, content marketing, or email are likely to be more active in the rollout than others. Finally, a CDP implementation takes heavy involvement from engineers to integrate and map the manifold data sources. This may also affect the architecture of digital offerings like websites and mobile applications, so those product leaders should expect to be involved.

Aligning Frontline Staff

Delivering a customer-first experience requires alignment across the organization, from the C-suite to frontline staff. Advisory firm Gallup has found that nearly 85% of employees worldwide are not engaged at work. This disengagement is especially frequent among frontline staff, who often hear about organization initiatives last or not at all; frontline software firm Yoobic found that 34% of frontline workers felt disconnected from HQ.

Frontline staff must be actively trained and encouraged to provide personalized customer experiences; their incentives must be aligned with this mission. For example, instead of seeing a customer complete a transaction online rather than in-store as a loss to their store’s earnings or commissions, frontline staff must be encouraged and rewarded for sharing this capability to interested customers. Ultimately, this approach should be designed to benefit both the customer and the organization at large, not just a siloed individual or team staff.

Use of the same data at the frontline as in the rest of the organization means a consistent delivery of experiences, a primary aim of omnichannel marketing. Moosejaw, featured in Chapter 1, provides an excellent example of empowering frontline staff to provide a personalized, omnichannel experience to customers.

What Powers a CDP and Why Does It Matter?

In Chapter 2, you learned about the relational database and the data warehouse as foundational information architectures for database marketing and other early digital direct marketing efforts. The CDP also relies on some core technologies, such as data lakes and APIs. MLOps and continuous AI integration are also used to coordinate the real-time collection of data and delivery of digital experiences to customers.

Data lakes

Data warehouses collect and process data in predefined and prestructured form at regular intervals. By contrast, a data lake takes data as is, from any possible source, in either a batch or streaming process. This structure accommodates that variety and velocity of modern customer data as detailed in Chapter 2. But a data lake in itself is not a CDP, as it is primarily an infrastructure for collecting and storing data. It does not perform the four tasks defined by Gartner as constituting a CDP: data collection, profile unification, segmentation, and activation. The data lake is best seen as a complementary or supporting technology to a CDP.

APIs

Creating an omnichannel experience requires quick and flexible transfer of data across channels and devices: in-store data must be available for email campaigns, social media, call centers, and so forth. An API can be used to standardize and facilitate this sending and receiving of data across platforms and devices. APIs provide a standard set of protocols for end users to request data. This request is taken by the API to backend data sources, and a response is then delivered. CDPs typically use this technology to orchestrate the many data sources and endpoints involved in its production. Figure 4-1 shows how an API works.

Figure 4-1. How an API works

MLOps

In traditional database marketing, developers wrote static database queries to define segments, then manually implemented them into campaigns. With a CDP, data is used across channels to create personalized experiences based on recent or even real-time data. Rather than draw segments from predefined rules, a CDP can use machine learning to deploy algorithms that improve segmentation and personalization over time without being explicitly programmed to do so. This is a less discrete product life cycle for campaign and personalization management than static database queries. The field of ModelOps or MLOps has taken shape to monitor and deploy these algorithms.

Measuring CDP Success

In Chapter 2, among other approaches, you learned about mass marketing and DMPs. While the latter is a direct marketing tactic, it shares with mass marketing an emphasis on filling the top of the traditional sales funnel with a large quantity of potential customers. With this goal, reach and impressions have historically been a primary marketing metric, as converting a large number of new leads was a primary objective. Marketers would spend exorbitant amounts on digital advertising to acquire new customers, and maybe that customer would come and convert and buy once and you’d never see them again. The marketers would then have to spend more money on digital advertising to acquire a new customer. You’re always feeding the top of that funnel.

By contrast, a goal of the CDP is to predict and deliver on what customers are looking for in a brand over their lifetimes, not just at the awareness stage. That means different KPIs are needed to measure a CDP’s success. Organizations have long known that retaining, upselling, and cross-selling current customers is the quickest way to increase the top line, but they may not have had effective ways to deliver on this strategy in a digital context. The CDP gives marketers the tool to do this in a way that other marketing systems have not. That makes customer lifetime value an important metric to track for the CDP-enabled organization. Related revenue-based metrics include average order value and return on ad spend: again, the organization is focused less on attracting the largest base of potential customers and more on helping existing ones. The real value of the CDP is not just acquisition but loyalty and retention.

An organization’s operational efficiencies should also improve under a CDP, as marketing, IT, and analytics professionals spend less time writing time-consuming database queries and more monitoring AI-powered customer experiences in real time. The time to launch campaigns and related initiatives, then, should fall dramatically under a CDP. Marketers and other customer analysts are able to spend more time analyzing and acting on the data than on processing and preparing it. When reports are accessible across your organization, or even sent automatically, you can do a lot of things much more quickly.

Moreover, because the CDP has integrated customer data collection, processing, and implementation in one infrastructure, total IT spending may even fall as fewer niche marketing tools are needed.

In this chapter, you learned how to align people, processes, and tools to ensure a CDP is implemented successfully—given the organization is at a sufficient maturity level, which will be defined in Chapter 5. Chapter 6 offers some final takeaways and recommendations for providing a unified customer experience.