Four Steps to add personalisation to your CDP (Customer Data Platform)

Recently we took part in Big Data London event. What we felt that there is a challenge that has continued to plague marketers, despite the promise of solutions such as customer-relationship management (CRM), master-data management (MDM), and marketing-resource management (MRM). These solutions can help companies consolidate and streamline data, manage segmentation, organize work flow, and improve customer relationships and potentially improve brand engagement.

Four steps to effectively activate your data

Incorporating a CDP into your organisation, whether integrating on an existing master- data-management or customer-relationship-management system or starting from scratch, requires mastery of four areas:

1. Data foundation: Building a clear view of the customer

Many companies have the elements of a relatively complete view of the customer already. But they reside in discrete pockets across the company. It is only when data is connected that it becomes ready to use. The CDP takes the data a company already has, combines it to create a meaningful customer profile, and makes it accessible across the organisation.

“Creating models that cluster customer profiles that behave and create value in similar ways requires advanced analytics to process the data and machine learning to refine it. Over time, as the system “learns,” this approach generates ever-more-granular customer subsegments. Signals that the consumer leaves behind (e.g., a site visit, a purchase on an app, interest expressed on social media) can then expand the data set, enabling the company to respond in real time and think of new ways to engage yet again. Furthermore, the insights gleaned extend beyond a customer’s response to a specific campaign, for example by driving more targeted product development.

2. Decisioning: Mine the data to act on the signals

The decisioning function enables marketers to decide what is the best content to send to a given customer for a given time and channel. Customers are scored based on their potential value. A set of small business rules and machine learning regression models then matches specific messages, offers, and experiences to those customer scores, and prioritises what gets delivered and when. This allows small companies to make major improvements in how they engage with their customers by developing more relevant, personalised engagement, within a single channel or across channels, based on a customer’s behavioural cues.

More sophisticated companies build up a decisioning model that works across all distribution channels. That requires advanced modelling and analytics techniques to identify the impact of one channel on another as a customer proceeds along his/her decision journey. A travel company took this approach recently and saw coordinating messages across channels drive a 10 to 20 percent incremental boost in conversion rates and customer lifetime value.

Effective decisioning is based on repeated testing that validates and refines hypotheses and outcomes. Over time, these can become increasingly sophisticated as models and algorithms build on each other.

3. Design: Crafting the right offers, messages, and experiences at speed

Understanding your customers and how to engage them counts for little without the content to deliver to them with personalisation. Designing great offers at each channel test and engage consumers exclusively within their own channel. Real benefits then be integrated bringing together people from relevant functions (marketing, digital, legal, merchandising, and IT/DevOps) who focus on specific consumer segments or journeys.

These teams have clear ownership of consumer priorities and responsibility for delivering on them. The cross-functional team continually develops new ideas, designs hypotheses for how to engage customers, devises experiments, and creates offers and assets. Analytics help size opportunities, test impact, and derive insights from tests. That content is then tagged so that it can be associated with a trigger and be ready to go when needed.

4. Distribution: Delivering experiences across platforms

Distribution systems are simple “pipes” that send the ad or message that fed into them. Often, they can be quite manual and just blast out communications to wide segments of people with little tailoring. But connect the CDP engine, with its predetermined triggers and tagged content, to that distribution system and a formerly blunt marketing instrument becomes a far more directed one sending specific messages to distinct customer subsegments across all addressable channels.

That distribution system is often a platform itself that lives in the cloud. Other “point” solutions (marketing technology solutions for a specific task) can be connected into the CDP as well.  The best distribution platforms create a feedback loop that sends customer response, engagement, and conversion data back into the CDP.

Implementing the data-activation framework

Unlike a wholesale IT transformation, deploying a CDP isn’t a replacement of current customer data systems, but rather an operational solution that can optimise on an existing systems. In our experience, many marketers already have a large part of the marketing-technology equation in-house; they’re just not using it properly.  The promise of data-activated, one-to-one marketing is not only possible but is now increasingly expected by today’s customers. It is now the key to transforming simple customer transactions into enduring relationships.

Kai Vollhardt is a Partner at McKinsey’s European Marketing & Sales Practice, and coleads the global customer experience and personalization @ scale work. In this capacity, he serves clients primarily in Europe and North America on strategy, commercial transformations, and customer journey optimization.

*The author would like to thank Julien Boudet, Brian Gregg, Jason Heller and Caroline Tufft from McKinsey & Company for their contributions to this article.

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