Last updated: Customer data activation strategy: Why you need one, ASAP

Customer data activation strategy: Why you need one, ASAP


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In the National Hockey League, a goalie’s save percentage is a key indicator of success. Typically, a good season means he kept his percentage above .915. Yet every hundredth of a percentage point matters. As proof, Toronto Maple Leaf goalie Jacques Plante holds the record with a .944 save percentage in the 1970-71 season.

What does this have to do with a brand’s customer data activation strategy? A lot, as it turns out.

Customers today are just as dynamic and unpredictable as a pro hockey team on offense. Engagements – the shots on goal in this analogy – can come from anywhere: in-store, online, at checkout, or during a service call.

Like goalies, today’s brands need to make decisive, real-time responses if they want to succeed. Any delay could cause the brand to lose in the form of missed revenue opportunities, broken customer trust, and damaged brand reputation.

This real-time pressure highlights the need for an enterprise to forge an effective, efficient customer data activation strategy.

What is a customer data activation strategy?

A customer data activation strategy is the process of unlocking value from that customer data through the development of insights that inform specific actions.

In the past, brands viewed customer data activation mainly as a vehicle for audience segmentation. Owned by the marketing and sales teams, this ability to use data to target specific audiences and tailor engagements boosts conversion rates and strengthens customer relationships.

Yet now, two sweeping trends are expanding its importance:

  1. Brands must know the purpose of their customer data. Consumer privacy and data protection regulations detail the legal bases for brands to process customer data. These purposes act as the gatekeepers for data activation. For example, if a customer fills out a warranty but declines the “I want to receive marketing messages” option, the brand’s data activation strategy needs to honor her wishes by keeping her data out of the company’s marketing automation solution.
  2. Activation must inform engagements at every touch point. Today, insights gained from customer data analysis can impact every engagement: during online browsing, in-store with a sales rep, at an e-commerce checkout, and even in a customer support call.

Real-time, predictive: spot-on customer engagement 

NHL goalies have remarkable reflexes. When they sense a shot, their brains process the incoming data, gain an understanding of the situation, and send billions of neurons to the appropriate place to activate a response – a stick save, a pad block, or a snag with the mitt. All this happens in real time.

In theory, the modern customer data activation strategy should operate similarly. After receiving customer inputs, that data should flow in near real time to the brand’s martech stack. This “brain” can then interpret the data, understand the situation’s context, decide on an action, and inform the right engagement systems.

As a result, every experience can be relevant, timely, and personalized on the customer’s terms.

Yet goalies don’t rely solely on athletic ability. They study the opposing team’s strategy and the tendencies of offensive players so they can predict where shots will come from.

In the same vein, an activation strategy shouldn’t solely focus on reactive engagements. Artificial intelligence (AI) and machine learning (ML) technologies can find hidden trends and insights.

Through these tools, brands can evolve away from the concept of pre-defined journey stages and instead predict where, when, and how customers prefer to engage.

The customer data activation challenge

If you’ve seen a hockey game, you know the goalie has a tough job. Opponents block their vision. The puck is in constant movement. And shots hardly ever stay on a fixed trajectory.

Similarly, customer data activation is a major challenge for enterprises today. Multiple data silos make for a fragmented view of the customer and inhibit exceptional cross-channel experiences. Moreover, data privacy regulations affect different customers and different regions, which make it difficult to understand what data can be activated and what data should stay on the bench.

Customer relationship management (CRM) and data management platforms (DMPs) can help companies consolidate and streamline data, organize workflows, and improve customer relationships.

But they don’t take full advantage of digital signals customers provide across touchpoints. Instead, they rely on antiquated list pulls, basic segmentation, and campaigns.

Brands also struggle to manage all of their customer data on one hand and deliver real-time customer experiences with the other. While data warehouse and data lake solutions may handle the sheer amount of customer data ingested by an enterprise, they cannot make the data available at the speed customers demand or help with real-time decisioning.

Customer data platforms: bringing order to the chaos

The customer data platform (CDP) market category has existed for several years, but widespread adoption has been slow. According to IDC, “CDPs deliver extraordinary data inventories and analytical power, but without broadening the way people think about data, innovation will be limited.”

These prebuilt systems centralize customer data from all sources and then make it available to other customer engagement systems. Many of the early CDP solutions focused on marketing and sales use cases, which largely explains IDC’s critique. They also didn’t fully address the scale and availability requirements of global enterprises.

Now, however, adoption is on the rise as CDP solutions make major improvements in areas such as data privacy and cross-channel impact. With the ability to collect customer data from all sources, an enterprise can bridge silos and gain a full view of its customers.

Through integrations, a CDP can orchestrate the right customer data to the right engagement systems in real time. It also can feed AI/ML solutions with clean, reliable customer data, which can elevate automated decision making, adaptive modeling, and nimble data utilization to scale hyper-personalized engagements.

So, as companies pursue a customer data activation approach that can deliver game-winning personalization, CDP solutions are an emerging force. Will they be a martech all-star on par with Jacques Plante’s achievements in the NFL? Only time will tell.

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