Forget search. Invest in personalization.
That’s the message we are increasingly hearing from technology vendors who are marketing the decision-engine tools that promise to deliver personalization in a meaningful and impactful way. According to Forrester Research, orchestrating customer interactions in real-time across channels is the biggest challenge facing customer experience professionals.
Does that mean that search is going away? No, but it's interesting to note that eMarketer’s most recent forecast shows search losing its historical lead in digital spend. Brands may finally be tired of doling out when almost half of all web traffic is driven by bots, not humans.
But I digress. The real story isn't the decline of search so much as the rise of contextually relevant content delivery. Driving this trend are advances in insights, data and, of course, personalization technology. If a brand knows you, or at least can make an educated guess of who you are and what you are interested in at that moment, then the need to search out relevant content is at the very least diminished, if not absolved.
While in Amsterdam recently, we saw a targeted, in-store promotion solution. It’s a digital display in which a camera captures people's profiles as they walk past a store window. Then, based on recognition of physical features, the display then matches content to a profile of that target segment. Instead of the customer searching, the display determines the product and promotional materials displayed in real time.
CEO Paul Broersen of Adnovate, the company behind the solution, told us that stores using the technology have seen a 25 percent lift in sales directly associated with this kind of push-based content. It’s not Minority Report, but it's getting closer — and all without the creep factor.
In the financial services world, we’ve seen a major insurance company begin to use custom research to determine the “money mindstate” of a site visitor. Based on matching the person with the mindstate type, the customer can be presented content, images, text and offers that are most likely to get them to engage with the brand.
Again, instead of waiting for someone to search terms like life insurance, the brand is proactively connecting with people by understanding the context for their site visit and presenting content that is relevant to their interests. That increased relevance directly translates into increased conversion rates.
Segmentation and Differentiation
In both cases, the key to success is being able to deliver a contextually relevant experience based on what you know or don’t know about the individual before you, how well you can match them to a profile that is more behavioral than demographic, and your ability to do so instantaneously.
This requires segmentation. If you know a lot about an individual’s likes, needs and behaviors, and you are able to collect and track that data, you are halfway to the holy grail of one-to-one marketing.
The other half is actually being able to deliver differentiated experiences based on that understanding. We call this individualized digital experience. From an intellectual perspective, it's not that difficult. If you recognize who someone is, understand what they want and are able to deliver the right content at the right time, on the right device, you will more effectively engage with the individual.
One Less Middleman
The real trick though isn't understanding the people you do know, but understanding the vast majority whom you don’t. This is where a new approach to segmentation comes into play. On the a relative continuum of customer relationships — from unknown to known — there are various ways to capture data points with ever-increasing accuracy to determine what profile an “anonymous” customer most likely belongs to.
For example, browser information can help determine location, time, device and to some degree even hint at household income (we know, for instance, that Apple users tend to have higher HHI than PC or Android users). If the visitor has been cookied during a previous session, their history can be leveraged to indicate their interests. In the case of the in-store customer profiling technology mentioned above, the physical appearance of an individual gives real insight into what sort of profile they fit.
Armed with this input, a marketer can use past experience of what content works with a given profile and proactively deliver a more compelling and relevant experience — all without relying on search. Which brings me back to my original point.
Are we entering a world where push takes over from pull? I’m not sure yet, but I am sure that the confluence of new data tools, profiling algorithms and multichannel content marketing reduces a marketer’s dependence of search, taking out one more middleman between consumer and brand.