How, When, & Where Customers Want Personalized Recommendations


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As technologies evolve, so do customers’ needs and desires. In the 30+ years that we’ve been chronicling customer-critical technologies, we’ve witnessed an amazing shift in the degree to which marketers, merchandisers, and advertisers can target offers and deliver truly personalized recommendations.

Personalized Recommendations Often Delight! Today, there are amazing (and affordable) technology services that e-marketers can use to personalize the recommendations that we see on their web pages, in the emails they send us, in mobile apps we use, in the SMS messages we receive, and at the point of sale. When well-tuned, these personalized recommendation engines provide amazingly relevant suggestions, offers, and promotions.

When Do You Want Personalized Recommendations. In general, we’ve found that customers react positively to highly relevant and personalized ads. But hate it when they receive irrelevant pop ups at inopportune times. The “when” part of the equation is relatively easy. When we’re looking, shopping, and searching, we’re open to personalized, relevant recommendations. But when we’re engaged in another task, we hate being bothered with offers and recommendations, no matter how personalized they are. But there are times when it’s OK to recommend things to us—e.g., when we’re relaxing, browsing, hanging out, at leisure.

Many B2C marketers make use of advertising networks, which rely on the cookies that many of us leave on sites we visit often—cookies that allow the ad networks to ID us when we turn up somewhere else and provide offers/make recommendations that essentially “follow us” from place to place. For example, Ronni Marshak discovered that when she goes to her favorite WebSoduku site to unwind at the end of the day, she’ll see ads from her favorite e-tailer for the clothes she has been considering but hasn’t yet bought. Since she’s in leisure mode, she’s actually quite receptive to these highly personalized recommendations. I’ve noticed a similarly eerie pattern when I’ve been researching flights to a particular location on an aggregation site, like Orbitz. I’ll often receive an email offer for a flight to that destination from Delta Airlines or American—a site I didn’t actually visit. Since it’s just an email I can ignore when I’m busy doing other things, I don’t mind seeing the option. I may go back to click on it when I’m back in travel-planning mode.

Where Do You Want Recommendations? Location-aware ads and recommendations have been around for a long time. When you log onto a website from a different country than your home base, you notice that the website is now tailored to that region, and the merchandise and offers are country-specific. Now, with our mobile phone-based browsers and apps, many offers are increasingly location-based.

Consumers have told us conflicting things about location-based advertising on their mobile phones. They say: “I don’t want any ads on my mobile phone.” But, “when I’m in a store, I want to know what the special deals are that I’m eligible for.” They’ll say, “I don’t want my phone to track my location,” and, “if I check into Foursquare, I’d like to see relevant offers at each location,” and, “if I need to find something, call a cab, tow truck, find an ATM, I want to use my current location as the starting point.”

High Say, Low Do?? Or High Do, Low Say? In working with consumers and business customers to understand what they want, what they fear, and what works for both customers and sellers, we’re beginning to see some patterns emerge. As always, there’s a variance between what customers say they want and what they do. That’s why it’s important to correlate observation, analytics, ethnography with customer co-design and interviews. In the case of mobile location tracking, I suspect that most customers prefer the seamlessness of having location-aware recommendations available for the asking, without having to wait. They say they don’t want offers pushed to them without asking. Yet, when they receive a relevant location-aware notification or text message, they often act on it.

Here’s a back-of-the envelope sketch I discovered recently that accurately depicts the three perspectives we use to better understand customer behavior:

Patricia-Seybold-Group-Approach-to-Customer-Requirements-GatheringPatricia Seybold Group’s Approach to Customer Requirements Gathering

(Documented by one of our clients)

What’s Being Overlooked? The Customers’ Perspective

As marketers and merchandisers, we often fall prey to tracking customer behavior because we can, and to making offers we think that customer (or group of customers) will be interested in. This works well when Amazon recommends a new book by an author whose books I’ve been buying and reading.

What’s Missing: Problem Solving Recommendations. Personalized recommendations don’t seem to work as well when I’m in problem solving mode.

Here’s an example of a missed opportunity for good, targeted recommendations:

Recently, I discovered a problem on my lawn. Some critter was digging it up in a particular way: just peeling back an inch of turf and leaving the grass turned up and the dirt/roots exposed. After conferring with local friends and neighbors, we posited that the culprit was one or more skunks going after grubs (Japanese Beatles abound in my gardens). So, on one neighbor’s recommendation, I bought a Havahart trap at the local hardware store.

So far, all of my research/decision-making was off the trackable net. But then, I began searching for information: How to bait a trap for skunks? How to trap and transport a skunk without getting sprayed? What kind of grubs do skunks eat? When is the best time of year to eradicate the grubs in my area? What’s the best way to do that? With a few well-phrased searches, I was able to get good, actionable answers to all of my questions. And the recommendations that appeared in the margin of the searches became more and more relevant to my situation. But, I ignored the paid for ad placements and just followed the organic search results to locate and purchase the appropriate beneficial nematodes (tiny worms) to spray on my lawn that will destroy the grubs organically. I felt good about being able to come up with a game plan to solve my problem in a few minutes of searching. But it made me appreciate the power of knowledgeable searching, the importance of tuning and tagging your searchable content, and the value of volunteer contributions in problem-solving forums. Today, the technology assist that is best suited to provide recommendations to solve a problem is the kind of knowledge management that’s applied to customer support systems; not the kind that’s used by marketers for e-merchandising.

I can imagine that in another couple of years, a smarter problem-solving type recommendation engine will emerge that could have anticipated my questions, offered answers and relevant solutions, personalized to my situation/area.

What Else Is Missing? Fortuitous Recommendations

As Ronni Marshak thought through the question of the KINDS of recommendations that customers want and value in different circumstances, she added a lot of nuance to a seemingly simple idea. It turns out that there are many different types of recommendations customers want and value in different circumstances. One particular class of recommendation that’s under-used, in Ronni’s opinion, is what she calls “fortuitous” recommendations.

Republished with author's permission from original post.

Patricia Seybold
With 30 years of experience consulting to customer-centric executives in technology-aggressive businesses across many industries, Patricia Seybold is a visionary thought leader with the unique ability to spot the impact that technology enablement and customer behavior will have on business trends very early. Seybold provides customer-centric executives within Fortune 1 companies with strategic insights, technology guidance, and best practices.


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