2014: The Year of Customer Intelligence
This is the year it happens. This is the year that all the technologies retailers have been talking about over the last five years merge into a singular solution. Looking back over the last half decade of "what's hot this year" articles, behavioral marketing was an initial hot topic, followed by big data, predictive analytics and omnichannel marketing. While each of these prognostications were important in their time, it was like reading the Cliff Notes version of Hamlet; there's a lot more to the story, Horatio.
This is the year that begins to redefine brands, driving profits to new levels and allowing marketers to finally begin realizing the true definition of relevance that's oft overlooked by vendors and under-represented in the industry. This is the year of customer intelligence.
Nothing new, but now understood
Starting in the late '90s, everywhere you turned vendors were talking about the ability to deliver the "right message, with the right offer, to the right person, at the right time, through the right channel." It was universally accepted as a talking point, so every vendor did just that — talk. As marketers, we let vendors own that concept, despite the fact that no one was truly delivering on that promise.
Why? Let's take the easiest example: the right message/offer. To truly deliver the best message and offer, retailers would need to know who the customer is, what's their historic value (inclusive of purchase returns), hopefully know their predicted value, what channel(s) they preferred, if the brand-level relationship was strong or potentially at risk, and if it was at risk, why so. I know, it's a lot of information, but it's necessary.
Now let's say I have a moderate customer database (inclusive of visitors), so I only have to get that information, well, say around 1.3 million times, updated in real time. That's in the neighborhood of 100 million classifications. Is anyone actually doing that? No (fine, as always, with the exception of Amazon.com). Why? It just wasn't achievable for most mere mortal marketers … until 2014. So what's suddenly changed? Nothing, the change has been a gradual one.
Big data gave marketers the ability to centralize data. For many marketers (though rare with vendors), most deployments weren't of the Tupperware container-based "push anything you want to us and we'll store it" variety. While that can be used for segmentation, the truly innovative piece to this puzzle is contextualizing every piece of data, be it behavioral, purchases, returns, demographics or applied learning. What that means is that each piece of info stored in your "big data" is "known and expected." Why is that important? Read on.
Large-scale machine learning
If your big data platform understands what your data is, it can ask questions about your data and store the answers. I know, it sounds very Terminator-esque, but it's not that crazy. One of the most basic questions is "how engaged is a specific customer currently compared to the past?" When the system arrives at an answer and stores it, it's learned something.
That said, there are vendors whose primary focus is building machine learning. Some of these companies are building vast libraries of machine learning and enabling more complex questions to be asked and answered, such as "who out of all my customers is most likely to leave my brand, and are they important to save?" While the machine learning itself is rife with potential, it means nothing without the ability to act in place.
2013 was the year everyone started talking about omnichannel marketing. From email marketing vendors to product recommendation providers, everyone was clamoring to attach themselves to this concept. As a quick reminder of what omnichannel marketing really means (since the true definition is often misunderstood), omnichannel marketing means every marketing channel, including display ads, email, on-site messaging, print and social, are all delivering the same message, offer or product to consumers. That said, most of these vendors are dealing with incredibly small data sets and almost no customer-centric machine learning.
There's a definition that's a little less general. For those of us who are focused on bringing the vision to life, omnichannel marketing also involves automated channel identification and message/offer tuning. Both of these practices are based on additional machine learning created to understand when/if it makes sense to escalate the message and/or offer while also identifying the marketing channel that will have the highest probability of getting the desired response at the most effective cost.
Some assembly required
This is why 2014 is going to be the year that changes our world. The technologies have been established, they've matured and now they're being blended together to breathe life into the practice of customer intelligence. Some marketers will elect to build in-house, while others will look to their large-scale CRM platforms to attempt to solve the challenge. Some will attempt to piece together the solution that will meet their needs from a sea of vendors, while others will work with a new breed of vendors specifically created as customer intelligence platforms.
Regardless of your path, a new world of marketing opportunity is unfolding. Relevance that's customer facing, but also supports your business objectives around profit maximization, lifecycle optimization and marketing spend tuning.
Angel Morales is the co-founder and chief innovation officer of Smarter Remarketer, a customer-centric marketing intelligence platform.