This Is How You Discern the Person in All That Customer Personalization Data
Bring attribute data, behavioral data and deep engagement data together, all in one place.
Recently, while I was watching one of my kids perform in a musical rendition of Horton Hears a Who, I recognized that Dr. Seuss's life lessons about personhood are applicable in business, too. A person is a person, not an audience or a segment. A person is at the heart of your business.
To succeed in business, we need to understand our customers as people. And we need to do that holistically, creating a portrait of each person's interests, wants and needs.
Personalization starts with a deep understanding of the person. The problem is, when we look at data about a person, our understanding is often too broad and not nearly as in-depth as should be. We need to bring attribute data, behavioral data and deep engagement data together all in one place.
1. Attribute data
A lot of basic attribute data about prospects and customers tends to be scattered across your organization, doing you absolutely no good.
Attributes describe any characteristic of a person. An individual's geolocation, industry or company, the source that brought him to the site right now, whether or not he's from a customer account or a target prospect account, whether he's a member of the loyalty program or not -- all of these are considered attributes.
Each of the attributes I've listed are typically captured and stored in different locations. It may exist in a CRM system, an order fulfillment system, a data warehouse, a marketing automation system, a personalization platform, or even jotted on a notepad on the desk of a salesperson.
Keeping attributes stored in different places typically means they can't be leveraged for personalization. Instead of letting this attribute data languish in silos, bring it all together into a meaningful, actionable, unified customer profile.
2. Behavioral data
Of course, the characteristics of a person don't give you the whole picture of who that person is and what she is interested in. You need to include that person's behaviors as well.
Whenever people interact with you, they're telling you something by using the language of clicks. Everything that happens in digital channels counts as behavioral data -- what they're clicking on and what they're not clicking on. Think of each click as a potential signal of interest.
Retail companies need to know which products a person clicks on, which products she adds to her cart, and which emails have captured his interest enough to make him click through. People provide important clues about themselves and their interests by the actions they take.
Likewise, B2B companies should take note of whether a person is consuming a particular white paper, blog post, web page or webinar. This kind of valuable interaction data can tell you what's on your customers' minds.
3. Engagement data
On top of basic behavioral data, engagement data goes further to allow you to uncover a deeper understanding of an individual. By collecting engagement data, you can discern between a website visitor who is just clicking around and one who is demonstrating real interest. Behavioral data on the surface can show you, for example, that a person has clicked on a red shirt and a blue shirt. But that's not the whole story, and it doesn't give you much insight into his interests.
If you go beyond the clicks and begin to analyze the time he spent engaging with each article or product page, you can clearly hear the message that person is sending. Let's say he clicked on the product page for the red shirt and left after five seconds. Then he clicked on the product page for the blue shirt and spent a minute scrolling, hovering over the picture and swiveling it around. He read reviews. He checked store availability. This engagement data makes it obvious that he has no interest in the red shirt but is seriously considering the blue one.
Just like a store clerk watching a visitor interact with products in a store, you'll be able to use deep engagement data to make a meaningful recommendation to that person.
Personalizing the data, and taking action.
Data is the fuel for personalization. How you leverage individual customer data is the difference between successful personalization and missed opportunities.
Once you realize a person's a person at the heart of your business, and once you bring together all of the attribute, behavioral and engagement data you have on each person into a unified profile, you'll be ready to start personalizing -- all the way down to the individual level. This is the power of unified customer data as part of a personalization platform.
For example, attribute data tells you that one of your B2B prospects is the head of IT in the financial services industry. Behavioral data tells you that she has clicked on several blog posts in multiple categories, but has not viewed any videos. Engagement data tells you that he spent the most time interacting with blog posts about the benefits of FinTech for attracting global customers. Combining all the data, it would make sense to send a personalized email with information on global customer acquisition that includes a video that's relevant for IT buyers in the financial services industry -- a much more effective email than you would have sent otherwise. Then, via a connection to your CRM, this insight about the prospect can be shared with the salesperson, who can have a well-informed and timely conversation with this prospect.
You can't deliver a relevant experience like this if you can't pull all the pieces together. The key components for effective personalization are the unification of data at the person level and, simultaneously, your ability to use it.
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