Unhelpful Personalization Is Not a Winning Strategy

BrandPost By Bryan Kirschner
Oct 20, 2020
Technology Industry

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By Bryan Kirschner, Vice President, Strategy at DataStax

Throughout my career, I have both taken inspiration from and found practical guidance in a principle of servant leadership: “Make sure that other people’s highest priority needs are being served.”

Discovering the next bingeable series and having a well-tailored “Sweater Weather” playlist at my fingertips when I wake up to a foggy October day in Seattle aren’t necessarily my highest priority needs.  But the excellence of Netflix and Spotify, respectively, at using data to help me discover new and delightful content unambiguously makes my life better.

And we can see glimpses of how transformative data-driven personalization at their level of excellence could be for businesses and their consumers.

A Boston Consulting Group survey found that consumers who experienced the highest level of a personalization experience were more likely to buy something other than what they’d originally planned, spend a different amount of money, and also report a higher NPS score for the retailer.

That does not surprise me. If a company can help us discover something new, different, and delightful, we’ll change our purchasing plans (perhaps even spend more)—and be happy about it.

For example, Cengage described “Cengage Unlimited” in a recent interview as “kind of like a Netflix for education where you’re paying on a per-semester or per-year basis.” Excellence at helping people discover new learning opportunities is laudable in itself.

But it also gives me a visceral sense of how helpful transformative data-driven recommendations would have been for me during my college experience. Sending me to college was one of my parents’ proudest accomplishments—it was also the biggest expense in their lives to that point. During my first two years, I struggled so much to find courses I enjoyed, a major that felt right, and confidence in a career path that I felt both personally miserable and guilty that I was wasting my parents’ money.

A data-driven leg up on insight into what might be right for college students who happen to find themselves in a situation like I did sounds like addressing high-priority needs.

Likewise, this is also true for the healthcare industry. When a doctor describing digital patient experiences says “we’re not just providing care in new ways; we’re hyper-personalizing medicine on behalf of our patients,” he’s literally talking about saving lives.

Patient noncompliance” has widespread and serious negative health impacts, often for reasons as readily solvable through telehealth as forgetting instructions or skipping a follow-up test because they are unable to take the time off from work or can’t find transportation.

Helping people overcome barriers to care seems like a high-value objective for a data strategy in medicine—but there’s reason to embrace “help” as a North Star in any industry.

Gartner research shows that “help” personalization outperforms  “recognition” personalization.

Appreciation for helping save time, reducing anxiety about making the wrong decisions, or getting a better deal are associated with better purchase or repurchase results. In some cases, demonstrating recognition without delivering help is associated with a negative impact on purchase or repurchase.

Does your data strategy take a stand on “help” beating “recognize?” Does it aspire to better understand customers’ highest priority needs? If not, no matter how much you invest in personalization, you may find that you’re ceding the customer relationship in the long run to competitors whose strategies do.

Read about the data imperative in planning for recovery here.

About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.