Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement by Martin Kihn & Christopher B. O'Hara

Author:Martin Kihn & Christopher B. O'Hara [Kihn, Martin & Christopher B. O'Hara]
Language: eng
Format: epub
Tags: Business & Economics, Customer Relations, marketing, General, Sales & Selling, Management
ISBN: 9781119790129
Google: 6WsHEAAAQBAJ
Publisher: John Wiley & Sons
Published: 2020-11-05T00:04:03.117522+00:00

Use Cases

This may seem beyond obvious, but data unification for its own sake doesn't produce value. Yes, a “single view” or “single source of truth” around customer data is highly valuable, but it's akin to writing an encyclopedia that no one has read. Broad and aspirational CDP use cases are needed to start the project (“we want to transform our entire company around data”) but companies need to pay the bills too. What does the marketing department want from data transformation? The analytics team? Defining the initial use cases is crucial to getting the company aligned around creating near-term success, and showing the executive suite that the juice is worth the squeeze. Every use case needs refinement that drives highly specific outcomes.

As an example, if a business goal for the company is “reduce the cost of new customer acquisition,” then that should be aligned to a specific data transformation use case such as “refine customer segmentation to increase conversion rates on targeted campaigns.” Initial data-driven use cases must have a compelling and measurable outcome. “Know more about my in-store customer” is an important, but squishy, goal. “Combine point-of-sale data with digital display campaign data to better understand video media investment in key segments to increase conversion rates by product,” in contrast, is a measurable, achievable goal.

You cannot track the effectiveness of your data transformation without a clear performance framework, and having KPIs to align with is paramount. Many companies start with simple KPIs that show platform adoption. For example, a company with fairly rudimentary segmentation around gender, age, and income might want to add some behavioral characteristics (“sports lovers,” etc.) to start to make creative executions more personal. So the initial goal might be “go from 20 to 200 segments” but the advanced goal, tied to ROI, might be “use advanced segmentation to increase conversion rates on online purchase by 5%.” This helps align data and business goals together, such that executive alignment can be achieved more quickly.

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