Chapter 5. Where Are You in Data-Driven Marketing Maturity?

Do you feel that your marketing efforts are disconnected? Perhaps you move from one email campaign to the next, not incorporating these results into a unified experience. Maybe you serve a targeted ad on social media and see decent conversion, but you fail to provide matching offers to that customer across different channels. These scenarios reflect early stages on the path to data-driven marketing maturity; here you will learn how to move from providing touchpoints, then to journeys, then finally to experiences.

This report is about helping your organization become more customer-centric in its marketing, primarily through the adoption of CDPs. CDPs give marketers a direct line of sight into their data so they can see how their customers’ behaviors are changing in real time and adjust their strategies to keep them engaged. When data is scattered between multiple systems without a central source of truth, it often leads to disjointed, repeated, or confusing brand interactions. Those brands that aren’t making the most of this data to target and engage different segments of their audience will miss the mark and frustrate potential customers.

Chapter 3 discussed the role of the CDP in collecting and deploying data to enable personalized customer experiences. In this chapter, an explanation is provided as to why customer-centric marketing is necessarily data-driven marketing.

Customer-Driven Means Data-Driven

The Preface reported that a Salesforce survey found that 66% of consumers now expect companies to understand their needs and expectations. Meeting this expectation can only be done by gathering customer data across touchpoints and activating it when and where customers demand. This delivery is difficult or impossible to do when marketing systems, teams, and channels are not sharing information. And customers notice: the same Salesforce survey found that while 76% of customers expect consistent interactions across departments, 54% feel that sales, service, and marketing teams don’t share information.

Not only are customers spending more time online, but they’re also engaging with more digital channels. Therefore, organizations are gathering more data on customer preferences and activities. Consumers expect brands to use this data in smart ways to provide frictionless experiences. The customer data platform is the key to unifying that data, personalizing the customer journey, and delivering on customer expectations.

To personalize each customer’s experience so that marketing is no longer campaign-driven but customer-driven means to unify and align customer data in delivering that experience. Like any fundamental organization shift, the adoption of customer-centric marketing takes a realignment of various elements in an organization over time. A data-driven marketing maturity model serves as a guide and benchmark in this journey.

Data-Driven Maturity Model

According to software development author and speaker Martin Fowler, a maturity model is a “tool that helps people assess the current effectiveness of a person or group and supports figuring out what capabilities they need to acquire next in order to improve their performance.” Table 5-1 provides three stages of an organization’s use of data in delivering a personalized customer experience.

Table 5-1. Three stages of marketing maturity assessed using people, processes, tools, and channels
Category Stage 1 Touchpoints Stage 2 Journeys Stage 3 Experiences


Technical experts needed to collect and use data

Marketing team has greater autonomy to work with data with relatively less technical expertise needed

Cross-functional teams across IT, marketing, and analytics with various expertise levels


Manual extracts to email, single-channel

Data can be circulated across channels using automation and pseudonymous customer details

Customer-specific data rapidly processed and combined with AI for micro-targeted and customer-specific offers


Database marketing, relational databases, data warehouse

Customer relationship management, data management platform

Customer data platform, APIs, data lake

Channel engagement




This maturity is assessed using four facets:


Who is able to use data in crafting the customer experience? What level of expertise is needed to do so?


What information technology systems are used to collect customer data?


How are experiences delivered to customers, and to what extent are they personalized?


By which channels are personalized experiences delivered to customers, and to what extent are they integrated?

A fuller profile of each maturity stage follows:

Stage 1: Touchpoints

At this stage of data-driven marketing maturity, the focus is largely on individual touchpoints. Campaigns are planned in discrete monthly or quarterly increments and executed one at a time.

Chapter 2 discussed some of the earlier technologies used to segment customers, such as writing SQL queries from relational databases. This is a time-consuming process that takes a fair amount of technical expertise. By writing a rules-based program using conditional logic or similar techniques, broad segments are created based on past behavior, demographics, and so forth.

Single-channel activation is the norm in this stage, often through email. While a customer may get a somewhat tailored offer in their inbox, it’s unlikely that store or call center representatives have that data to unify the customer experience. Due to this lack of integration, a customer may even purchase an item in the store one day, only to find an email with an offer for that product the next.

Stage 2: Journeys

In this stage, marketers have greater data-backed ability to determine where in the funnel or journey a customer is and then match with the right offer or message. For example, in-store and online data may be synchronized to an elementary degree so that a customer isn’t served with an email for a product they just purchased.

This is part of a wider marketing integration and automation: for example, a website may suggest customer add-on items that have traditionally sold with what’s in the customer’s shopping cart. Using these tools makes it quicker and easier to establish more targeted customer segments and provide them offers across various channels. With data from various sources stored in a data warehouse or a data lake and presented to end users in dashboards and business intelligence (BI) platforms, the barrier to delivering data-driven marketing is lowered.

Customer engagement at this channel may be multichannel while perhaps not omnichannel; this is especially so at the advertising and acquisition points of the customer journey. For example, the use of cookies and data management platforms, as reviewed in Chapter 2, let the same ad be shown to customers on both social media and email.

This is conducted, however, with possibly pseudonymous data. True customer-level data is not available to provide a more integrated customer journey. For example, customers may not be able to begin a purchase via one channel and complete it using another; buy-online and pickup-in-store are common applications here.

Stage 3: Experiences

At this stage, customer data has been unified into a single profile. Due to the complexity of reconciling customer data, the organization has invested heavily in technology (such as adopting a CDP) and has restructured to support the use of this data. For example, rather than work in discrete departments, cross-functional teams are constructed to optimize each touchpoint of the customer experience at this stage.

With this integrated profile that can handle the volume, variety, and velocity of customer data, organizations at this stage can deliver the right message or offer in real time, often using machine learning and artificial intelligence. Rather than predefined, rules-based segments, wholly personalized experiences can be offered to customers.

For example, an organization could offer a product that predicts when a customer is out of their product, and send recommendations to the customer on what to buy now and what will be needed soon. This takes a confluence of data sources such as customer order history, surveys, and perhaps even embedded device data, along with statistical methods or algorithms to predict inventory and deliver messages at the right time and place.

Products like this move the customer experience from multichannel to omnichannel. Regardless of the stage of the journey or channels engaged in, customers can expect a seamless, personalized experience. This experience is supported by an alignment of the organization’s people, processes, and tools.

Now it’s your turn to profile your organization’s maturity in using data to deliver personalized experiences. Take a look back at Table 5-1: which stage does your organization most resemble along each of the four facets? Wherever it is, keep in mind that Martin Fowler’s definition of a maturity model, introduced earlier in this chapter, requires that one help organizations figure out “what capabilities they need to acquire next in order to improve their performance.”

If your organization can deliver unique customer experiences using the people, processes, tools, and channels detailed in Stage 2 (or Stage 3, if it hasn’t already), it is best suited to adopt a CDP. Chapter 4 discusses that implementation in further detail.

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