“Remember that a person’s name is to that person the sweetest and most important sound in any language.”
– Dale Carnegie
Dale Carnegie penned the quote referenced above decades ago, and it served as a foundational strategy captured in his famous book, “How to Win Friends and Influence People
.” More than an author writing about interpersonal relationships, Mr. Carnegie was a forerunner of the value of personalization.
Personalized relationships have always been an important dynamic in business, yet they’ve gotten harder to achieve in the age of autonomous and semi-autonomous customer interactions. Now we have fewer face-to-face meetings but collect more data – which presumably increases the expectation around how well we think a company should know us.
A major challenge with this expectation is that there is no discernable and agreed upon view of the proper level of personalization or what it means to personalize an interaction. Ask anyone to define personalization and you may get one of the following responses:
- A company knows the preferred suffix by which to reference me
- An online retailer recommends things for me to purchase after I put something into my cart
- A company confirms my next appointment without my asking
- The organization knows that I do not want to be called at home
- The brand will reach out to me because I am about to cancel my service
Each of these examples illustrate an element of personalization, but an interaction with a brand can be complex and require multiple components to personalize a single communication. So, if we identify that there are multiple elements required to deliver a customized interaction, it only makes sense that we leverage a multi-dimensional approach to drive personalization.
What is Multi-Dimensional Personalization?
Personalization is a hot topic in the area of analytics and business automation, but there are a lot of conflicting messages in the market as to what it entails. One common viewpoint has been to simply equate personalization with offer and product recommendations, as well as the analytical techniques used to drive these capabilities. Although there are impressive examples of this in the market, optimized recommendations are only one part of a personalization strategy. An effective personalization approach takes on many different inputs (i.e. dimensions) to be effective – just like the way we use various cues (consciously and unconsciously) to drive our own face-to-face interactions. These interactions can be defined as multi-dimensional personalization (MDP), which I define as:
“The capability to enable a channel agnostic interaction where any aspect of the communication can be defined based upon some innate knowledge specific to the recipient.”
Given the definition above, we can think of using a variety of methods to drive interactions – whether the personal interaction is face-to-face or initiated by an autonomous process. In the Automating Intelligence whitepaper, MDP is defined as having five distinct, yet interrelated, dimensions:
WHO – How do I talk to you in a way that demonstrates an innate knowledge of what makes you unique?
WHAT – How I do anticipate your individual needs and convey something to you that is neither obvious nor trivial?
WHEN – Is it clear why we are having this conversation currently?
WHERE – Am I prepared to talk to you in a consistent way based on how you want to interact?
WHY – Why are we talking and why should you be interested?
It’s critical to approach any customer interaction with these dimensions in mind, as well as leverage available and customized data, and various analytical techniques. This will ensure you are driving a personalized interaction that will be recognized by your customer.
With this flexibility, however, comes some complexity. I believe that trying to do everything through a single algorithm is very difficult for companies of any significant business complexity and scale. Personalization only comes with having many iterations and learnings under your belt. You need to tackle this feat with a multi-dimensional strategy that uses an array of analytical tools. As a result, it will take additional work and effort to implement a true MDP strategy.
In the next installment of this blog series, I will explain why multi-dimensional personalization is worth the investment and describe the techniques required to develop a robust MDP solution.
Tom Casey is Executive Account Director for Teradata. He has nearly 25 years of experience working with, designing solutions around, and helping global customers make analytics actionable. As a data analyst, Tom has successfully implemented the use of statistics to better segment and target customers in support of major corporate programs. He’s a featured speaker at conferences, author of several papers, and has a solid track record delivering enterprise-scale analytical solutions.
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