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Using a CDP as a CRM Misses the Point – and the Potential

6 minute read
Sherene Hilal avatar
CDPs are both one of the most disappointing investments brands made last year and the number one investment they want to make. Why the disparity?

Customer data platforms (CDP) are both one of the most disappointing investments brands made last year ... and the number one investment they want to make, according to Gartner and CommerceNext.

Why? According to Gartner’s recent report, “A Marketer’s Guide to What Is — and Isn’t — a Customer Data Platform,” (registration required) half of enterprise marketers say they’ve deployed a CDP as their CRM system.

That explains a lot.

CDPs continue to be the shiny object brands use to give structure to the customer data that’s scattered across increasingly complex technology stacks, stacks that can consist of up to ... wait for it ... 80 different point solutions. But with many brands admitting they’re using CDPs as glorified CRMs, it’s clear there are issues on both ends: Practitioners are using high-potential solutions for low-value tasks. And many CDPs aren't designed to address a focused set of business or industry use cases necessary to be able to deliver on their promise, thus putting themselves in a position to be underutilized and, ultimately, undervalued.

Yes, CDPs can serve as a more sophisticated CRM (though, what’s the point?), but their real, and often unrealized, value is serving as the central intelligence that guides personalized marketing efforts across the stack to drive improved business outcomes.

Viewed through the lens of consumer-facing or retail marketers, here are three areas to consider when implementing this technology for use beyond a CRM:

1. Understand Your Customers Through the Lens of Your Business Context  

Instead of collecting static customer data, create a living understanding of each individual’s intersection with your business.

CRM systems are designed to offer businesses basic and persistent data points about things like demographics and transaction history. This information has very little value if it’s not intimately connected to a brand’s unique product set or offering, and viewed directly through the lens of business context.

But why should you look at your customer and product data together?

Like customer behavior, products are not static either. They’re constantly being merchandised; added and removed, going in and out of stock, changing in price, and so on. When you have the ability to marry these types of product changes with the known behaviors of your customers, you can unearth insights that lead to opportunity for both the brand and the customer.

For example, when you combine what you know about a customer (s/he bought those pants and looked at that shirt) with product data (the shirt dropped in price), you reveal an opportunity to drive a valuable repeat purchase (resurface the shirt to the customer and see if you can capture the sale now that the price is lower). A CRM clearly doesn’t do any of this. A CDP will if it’s set up with this type of usage in mind.

Related Article: Sidestep Common CDP Traps With the Right Focus and Priorities

2. Instead of Just Having Insights, Automatically Act on Them

The idea of being able to automatically act on insights is the holy grail of marketing, and something that’s haunted marketers for at least the past decade. It’s also the promise of many advanced marketing technologies, though their ability to do it often still exists near the future-end of their product roadmaps. Despite the demand by marketers and the intention by technology companies, automatically acting on insights is still yet to be widely achieved.

Learning Opportunities

CDPs within built-in AI and machine learning have the potential to bridge this gap by acting as the intelligence layer that sits central to technology stacks, and distributes “orders” to other technologies. In other words, they can treat the insights that it issues as instructions, rather than having to do the heavy lifting of learning and acting.

In the previous example, the CDP does all the heavy lifting by automating what would have otherwise been a series of manual tasks, all just to combine two data sets and arrive at insights and opportunities at the individual customer level. Manually, this does not scale.

A CDP, however, has the potential to surface these granular opportunities across thousands or millions of individuals, simultaneously. If it’s then empowered in its role as the central intelligence guiding the rest of the marketing stack, it can next inform the technology that will act on the insight or opportunity it’s surfaced to achieve that coveted holy grail through campaigns.

When the data, resulting insights and action all exist in one place, the system can take in feedback (clicks, opens, etc.) from those decisions to improve the campaigns and/or individual communications (e.g., swap out recommendations, change content type, surface an ad instead of an email). In this way it can continually optimize to drive better results.

Related Article: Decisioning – The Only Way to Accelerate Analytics to Value

3. Instead of Pushing Audiences to Different Channels, Set Goals the CDP Can Manage

Perhaps some of your marketing goals include lowering your overall cost-of-acquisition or increasing repeat purchases from your email sends. Maybe it’s simply to drive more revenue from every digital marketing initiative.

No matter what the goal, as the central intelligence layer, the CDP should dictate which campaigns and through which channels you can achieve that end. For example, if your goal is to preserve margins, the system might recommend a series of campaigns that layer in a tiered discount strategy for individuals to ensure the brand is not surfacing discounted offers to customers who don’t need a discount to convert and different discount levels for others, depending on the level at which they’re known to convert. The system may also suggest sending an email to some individuals while surfacing an ad to others based on their likelihood to engage with each medium.

By honing in on top-line business goals, marketers can leverage the insights revealed by their CDP to drive better performance from their digital channels and ultimately provide a more welcoming and personalized customer experience. As the marketing function continues to shift from a cost-center to a profit-center, the ability to match the output of technology to the goals of the business becomes an imperative that reinforces the value of an investment.

By organizing around what makes a CDP truly successful — beginning with differentiating it from a CRM — marketers can not only streamline workflows but also, and more importantly, keep their customers at the heart of each initiative. In the long run, the brands that invest in and properly leverage technology that can drive critical retail objectives will reap success.

About the author

Sherene Hilal

Sherene Hilal is the SVP of Marketing and Operations at Bluecore. In this role, Sherene leads the commercialization of products and business operations, with a focus on enterprise readiness.

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