Product Personalization: Good or Bad?

(cross-posted from the CQuotient blog)

Personalizing products and offers to suit customers’ unique tastes is a core element of CQuotient’s product focus. So I perked up when I started seeing negative articles on personalization over the past few weeks, triggered by a book called The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser.

In an interview with the New York Times, Mr. Pariser says:

Personalization on the Web is becoming so pervasive that we may not even know what we’re missing: the views and voices that challenge our own thinking.

People love the idea of having their feelings affirmed. If you can provide that warm, comfortable sense without tipping your hand that your algorithm is pandering to people, then all the better.

Personalization channels people into feedback loops, or “filter bubbles,” of their own predilections.

The gist of his argument is that personalization technologies censor what we see. Comfort wins, diversity suffers, and you are worse off as a result.

While some of Mr. Pariser’s comments make sense to me, I do think he is painting with too broad a brush. If thenews we receive is heavily censored by personalization technologies (or anything else for that matter), it is a dangerous thing and worth being vigilant about. But I don’t see how this applies to personalizing productrecommendations.

Personalized product recommendations help you discover things you end up liking that you would have never thought of looking for. It is a great answer to the problem of finding good needles in an almost infinite haystack. Now, as my colleague Graeme Grant points out, the reality is that most forms of personalization out there start and end with addressing the customer by their first name. But personalization stalwarts like Amazon and Netflix (that’s how I stumbled on 24 :-)) have shown what’s possible.

Greg Linden, who was part of the team that created Amazon’s personalization engine, says personalization is all about serendipity

Eli has a fundamental misunderstanding of what personalization is, leading him to the wrong conclusion. The goal of personalization and recommendations is discovery. Recommendations help people find things they would have difficulty finding on their own.

If you know about something already, you use search to find it. If you don’t know something exists, you can’t search for it. And that is where recommendations and personalization come in. Recommendations and personalization enhance serendipity by surfacing useful things you might not know about.

That is the goal of Amazon’s product recommendations, to help you discover things you did not know about in Amazon’s store. It is like a knowledgeable clerk who walks you through the store, highlighting things you didn’t know about, helping you find new things you might enjoy. Recommendations enhance discovery and provide serendipity.

Greg goes on to write that news personalization also promotes serendipity and pulls people out of their comfort zone. I am not sure I agree with him on this point – I am more in agreement with Eli on the potential negative effects of personalizing news.

But when it comes to product personalization, we don’t get a filter bubble. We get a serendipity amplifier. And we can all use more serendipity in our lives.

 

Impact of “Big Data” on Retail: The McKinsey View (Part 1 of 2)

The McKinsey Global Institute (MGI) , the research arm of consulting firm McKinsey & Company (disclosure: I am an alum) released a report on Big Data and analytics last week. I have summarized key insights from the retail section of the report at the CQuotient blog. Please head over there if you are interested. Thanks.

 

Measuring Promotional Effectiveness is Getting Harder

Last week, I read about the results of a promotion run by location service Foursquare and retailer RadioShack.

RadioShack is giving Foursquare users who “check in” to its 5,000-plus locations special discounts for doing so. Those checking in for the first time receive 20% off qualifying purchases, as do “mayors” (designated users who frequently check into a location). All other users who check in receive a 10% discount.

How did the promotion do? Apparently, very well.

RadioShack customers who use the location-based mobile application Foursquare generally spend three and a half times more than non-Foursquare users, said Lee Applbaum, CMO of RadioShack, while speaking at the Ad Age Digital Conference. The retailer noted that Foursquare users spend more because they tend to purchase higher-priced items like wireless devices.

My first reaction was, “Sure, these users spent more but how do we know it is incremental? Was there a control group?” That got me thinking about how we would design an experiment to measure the incremental impact of such a promotion.

The simplest way to set this up would be to randomly divide the population of Foursquare users into a Test group and a Control group. The Test group customers would get a pop-up message on their smartphone/tablet when they were near a RadioShack alerting them to the promotion. The Control group won’t get this message. You wait for a month and calculate the difference in spend-per-Test-customer and the spend-per-Control-customer to get at the incremental spend per customer (this isn’t quiet correct since we are ignoring time-shifting effects like purchase acceleration but that’s a topic for another post).

But this simple-minded scheme won’t survive contact with reality.

  • We have sites (example) that are on the lookout for Foursquare promotions and publicize them to their visitors. If a Control group customer visits these sites, they  are “exposed” to the promotion and should no longer be in the control group.  Unfortunately, we can’t adjust the numbers to account for this move since we have no way of knowing if any particular Control customer was exposed or not.
  • Last week, I blogged about the issues posed by social-media-driven coupon sharing. Obviously, that applies here as well. I tell my friends and family about this cool RadioShack promotion and – boom! – the Control group takes another hit. At least, in this scenario, if we have access to the social graph of the sharing user, we can (theoretically) check if the sharer and their immediate connections are in the control group and exclude them from the analysis. Easier said than done, since it is not clear how we would get our hands on the data. But the data exists.
  • It is in the interest of both Foursquare and RadioShack to get the word out as much as possible, since that increases the amount of total sales from the promotion. The persnickety concern that incremental sales may be zero (or worse) may not get much airtime with the “bias to action” crowd 🙂

In general, the uncontrolled spread of promotions through indirect sharing (via websites) and direct sharing (through Facebook/Twitter etc.) taints control groups and makes incremental measurement tricky. We need to find a way to around this problem.

Any ideas?

(cross-posted from the CQuotient blog)

The Effect of Social Media on Coupon Strategy

In conversations with retail executives about couponing and other customer-specific pricing strategies, a new concern has appeared.

For the longest time, coupons have been one of the primary mechanisms for executing what economists call price discrimination. By identifying price-sensitive customers and sending them coupons, you win/keep their business without giving away discounts to customers who would have bought at the pre-coupon price anyway.

A fundamental assumption behind this approach is that coupon receivers will not share the coupon (or  even just the fact that they got a coupon) to the non-receivers. Clearly, you stand to lose heavily if loyal (but price-insensitive) customers come to know that others are getting discounts but they are not. Regardless of how price-insensitive you are, you are likely to feel that it is unfair when another customer gets a discount and you don’t.

This “no sharing” assumption has been steadily losing its validity with the increasing use of digital coupons since you can effortlessly forward an email coupon to a friend or family member. Furthermore, a number of coupon aggregation sites (example) have emerged to make it even easier to find coupons for products/retailers you are interested in.

In both these cases, however, there is a bit of natural “friction” that somewhat diminishes the negative consequences of coupon sharing. While forwarding email coupons is easy, you are more likely to forward to immediate family members or close friends – you probably won’t do a mass forward to your entire address book. To take advantage of coupon aggregation sites, the customer has to take the trouble of finding them, checking them on a regular basis, or signing up for daily emails. By definition, price-insensitive customers are less likely to take the trouble to do so and hence are less likely to be “exposed” to the problem.

Well, things have gotten a whole lot worse recently, thanks to social media. Apparently, people are increasingly posting coupons they receive to their Facebook page (sharing? bragging? who knows) and as a result, the juicier coupons are spreading virally and the coupon-receiver’s social graph is becoming aware of it very fast.

What does this mean for the retailer?

  • Irate emails and calls to the toll-free number from customers demanding why they didn’t get the coupon.
  • Discounts to customers who don’t “need” the discount

Note that coupons with unique customer IDs don’t help. If a customer shows up at the store with a coupon that was sent specifically to her sister-in-law, you can’t really tell her that she can’t get the discount since her ID doesn’t match the ID on the coupon. You will have a big customer-service problem in your stores.

So what can the retailer do to address this problem?

One strategy is to segment your price-sensitive customers based on certain parameters and design coupons in line with these parameters so that customers “self select”.  Common examples of this strategy: student/senior-citizen discounts for public transportation and museums etc, airlines requiring a Saturday night stay to segment leisure vs business travelers and so on.

In the retail context, segmenting by age or other customer characteristics (e.g., demographics) is on questionable ethical and legal ground. It may be better to segment based on buying behavior. For example, you can group your price-sensitive customers based on when they shop.  You may find (like many retailers do) that a sizable number of customers tend to shop at lunch-time on weekdays, possibly because their workplace is close to the store.

Now, create a coupon for 30% off purchases made between 11.30am and 1.30pm today and tomorrow and send it to everyone. For most customers, it will be impractical to take advantage of this coupon if they don’t work near a store or if they can’t take off at lunch-time to shop.  And amongst those who do, price-sensitive customers are much more likely to take the trouble to get out of their workplace and make the trek to the store.

Results (hopefully!) : Everyone gets the coupon so social sharing won’t create a customer-service nightmare. Price-insensitive customers are unlikely to take advantage of the coupon since it is too much trouble. Price-sensitive customers are likely to take advantage of the coupon since it matches the way they shop anyway.

Of course, the downside of these segmenting strategies is that since each coupon is targeted to just one segment of price-sensitives rather than all price-sensitives, the impact of each coupon run on the business is smaller. We can’t have everything, I guess.

I am sure there are many other ways to address this problem. Ideas?

 

 

 

Analytics and Free Will

I like to think that I have free will.  So it was devastating to read in yesterday’s Wall Street Journal that, thanks in part to analytics, the thing that puts bread on my family’s table, free will is getting busted!

Jonah Lehrer writes:

In recent decades, scientists studying the human brain have steadily eroded traditional notions of free will and autonomy. It turns out that our choices are often circumscribed by mental circuits beyond our control and outside of conscious awareness.

But now, thanks to new forms of data, such as cellphone information, and powerful analytical tools, scientists can see the forces that shape our lives from the outside. They can discover striking correlations and document all of the different ways that the world around us—from our social networks to the neighborhoods in which we live—influences everything we do.

The author goes on to give examples (mostly drawn from research studies on social networks) of how our friends and family affect our behavior.

  • If our friends are obese, we are more likely to be obese
  • If our friends are happy, we are likely to be happy
  • If we have a diverse circle of friends, we are more likely to be creative in our entrepreneurial pursuits

I couldn’t understand how this implied the absence of free will so I kept reading.

Such studies are a reminder that John Donne was right: No man is an island. Although we can’t help but believe in our autonomy—free will is a fiction we need—this latest research suggests we’re not nearly as free as we typically assume.

That’s why, for instance, researchers can make accurate predictions about our eating habits, academic interests and political beliefs based on the trail of data secreted by our smartphones. It’s also why companies such as Amazon and Netflix can develop shopping algorithms that know exactly what we want, even though they know nothing about us. The data generated by the group can be used to decipher the individual.

The author makes two arguments:

  1. If the people around you influence your actions, you are showing less free will.
  2. If I can predict what you will do next, you are showing less free will.

The people around us constrain our actions, for sure, but they don’t usually narrow them so heavily that there’s only one pre-ordained choice for us to make in every situation.  There’s still plenty of decision-making wiggle room.

Regarding his second point, I don’t see why he needs to bring in “the trail of data secreted by our smartphones” or “powerful analytical tools” to make his case. Here’s an example that is several thousand years old:  I offer you product X (substitute X = goat, yacht, iPad, whatever) either for free or for a thousand Y (Y = ears of corn, shekels, dollars, whatever). Can I predict which option you will choose? I can. Does this mean that you don’t have free will?

Want examples of  the “striking correlations” the author refers to? As price rises, demand tends to go down. On rainy days, you will see more umbrellas. If your kids come home with the flu, you are more likely to catch it.  Yawn.

Perhaps the author means “individuality” when he says “free will”. From that perspective, the article makes much more sense. But it won’t get blogged about as much 🙂

Bottom line: Free will’s existence may well be under attack but there’s no evidence that analytics or smartphone data is to blame. No need to change my comfortable (and possibly wrong) beliefs.