How Data and Decisioning Technology Help Marketers Deliver What Consumers Really Want


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You’re on a journey right now, whether you realize it or not. Every one of us, as a consumer, is engaged in a meaningful relationship with multiple brands, a series of customer journeys. Some may be just beginning, others coming to an end. They may be changing because of a change in our own lives. And while you may not be thinking about what the next step in that journey will be, it’s a certainty that the marketers behind those brands are thinking about it to ensure that the next best customer experience is just around the corner.

To help them create those experiences, marketers rely on technology and data. Lots of data. In fact, you can think of data as the foundation of the marketing technology pyramid, with decisioning in the middle and great customer experiences at the top. It’s a simple enough illustration, but with one very big problem: the gap between data-driven insights and customer-driven experiences can be cavernous and dangerous, in effect putting the customer journey in peril.

The brutal truth about marketing

Over the years, segmentation has become synonymous with personalization. Companies scrub their customer data, analyze it, and then organize it into neat little segments that can be aligned with different offers. The challenge with this manual approach to personalization is that it can lead to a mechanical approach to marketing. It treats customers as groups of individuals rather than individuals and creates common experiences rather than unique experiences. This kind of brute-force decisioning is an improvement and a good first step. But remember, it requires us to place customers into large groups that may not be the right or good fit. It’s also extremely difficult to scale – figuring out every segment and permutation of matching content.

Of course, marketers realize that segmentation isn’t the ideal decisioning system, but it’s better than the alternative: indecision. Deciding which offer or piece of content is right for each customer is impossible when you have millions of customers. Or is it? Just ask Amazon. They deliver meaningful, contextual, highly individualized experiences to millions of customers every day. How do they do this effectively at scale? By incorporating machine learning into the decision-making process.

In a traditional campaign, marketing waits for IT to cleanse and compile the customer data, for data scientists to analyze it and for third parties to enrich that data so they can make educated decisions about who gets which offer. This process can take weeks or even months, by which time customers may have moved along to a different place on their journey.

Machine learning is at the core of new technologies such as customer data platforms (CDPs). CDPs are designed to do what human beings can’t do: remove the traditional barriers of data-driven decisioning (incomplete data, poor decisioning at scale) and allow for real-time, truly one-to-one marketing.

Now let’s look at how CDPs handle campaigns. Even before the campaign begins, the CDP surfaces insights about which content or offers are best suited to the campaign. If it’s a customer acquisition campaign or win-back campaign, the CDP will take these considerations into account as it recommends the right offers based on the latest customer data. Notice that this process takes place without the intervention of IT or third-party data sources. CDPs can create and orchestrate experiences right from the customer data you have today, and even identify new content affinities in your data that yield additional and previously unknown insights. It then aligns those offers with each customer and delivers them across the optimal channel, in effect allowing marketers to launch highly personalized campaigns right from their customer database.

Uncovering hidden interests

For example, Haymarket Media, a company that provides content to the healthcare sector discovered several things about its readers that it didn’t previously know. They learned that nurse practitioners often displayed an affinity for specific types of medical content, even when self-identified as primary care providers, and that doctors weren’t relegated just to reading physician-specific publications but many had crossed over to read the content on nursing publications such as Oncology Nurse Advisor as well.

These insights allowed Haymarket to uncover a whole new audience for topical content that had previously been unknown and untapped. Another unexpected benefit was the creation of “lookalike” lists among its readers. By matching content affinity patterns from customers across its various digital destinations, Haymarket was able to expand its pool of targeted candidates across different types of content, allowing them to deliver more relevant content and ultimately get more value from original content.

Rich in data, poor in decisioning

The current marketing campaign system is broken or, more specifically, fractured. Customer data is divided across different physical silos, analytics are backlogged due to a shortage of data scientists, insights are distributed to different business units without context and marketing campaigns often lack omnichannel awareness. Even when customer information is captured and communicated, it’s rarely actionable. Knowing that a customer recently opened an online support request and may be at risk of defection is much different than being told that customer x should receive a winback offer right now via Facebook with a 20 percent coupon off their next purchase.

The reality is that most marketing departments are rich in data but poor in decisioning. They can find out what’s happening, but they don’t know what to do about it. That gap between knowledge and action, between data and experience, creates bumps in the customer journey. Customer data platforms and machine learning enable marketers to create unique, positive customer journeys by understanding, anticipating, and orchestrating what the next great experience will look like. And, ultimately, consumers aren’t looking to go on this journey alone. They expect brands to meet them where they are on the road, understand their needs in the now and empathize with the things they care about.

Decisioning is where the rubber meets the road

Delivering great customer experiences can put your brand on a better path to profitability. As McKinsey discovered in their groundbreaking research several years ago, optimizing customer experiences can reduce operating costs by as much as 25 percent and increase revenues by as much as 10 percent. Fortunately, most businesses are already on their way to improving their customer journeys. They’ve built a solid foundation of customer data and, in some cases, have automated their decisioning capabilities through CDPs and other technologies.

For brands in every industry, the customer journey is already underway. Its importance has been decided for us by a generation of consumers who not only expect, but demand, convenience, personalization, and privacy at every touchpoint. The biggest decision left to make is how you plan to meet them there.

James McDermott
James McDermott is co-founder and CEO of Lytics. James was previously CEO of Storycode, a mobile software company acquired by Postano and vice president, business development at Webtrends, an analytics and optimization company.


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