Personalized Ads Don’t Always Work?

(cross-posted from the CQuotient blog)

According to a recent MIT study reported in MediaPostpersonalized advertising doesn’t always work.

Contrary to popular practice, personalized ads not only don’t drive conversions, but are likely to be ignored, according to the study by MIT Sloan School of Management Prof. Catherine Tucker and London Business School Prof. Anja Lambrecht.

This was very provocative indeed! The key finding of the research was:

When online shoppers were simply looking at a product category, ads that matched their prior Web browsing interests were ineffective. However, after consumers had visited a review site to seek out information about product details — and were closer to a purchase — then personalized ads became more effective than generic ads intended for a mass audience.

I found this to be simultaneously obvious and confusing.

Obvious in that what you show a shopper should (of course!) be tailored to where they are in the purchase process. Confusing because the study effectively assumes that personalization is only about creating product-specific content.

In my opinion, this is a very narrow definition of personalization. The way we think about this at CQuotient, what you say to the customer at a point in time should be tailored to their current state and past history.

For example, if the customer’s current state suggests that they are near the beginning of the purchase process for back-to-school shopping for their 9 year old boy, showing them an ad or presenting them an offer for SKU #823272 (“boys short-sleeve baseball tee”) is suboptimal.

However, an ad along the lines of “Get your child ready for back-to-school. Great selection of boys uniforms and sports-themed casual clothing  in the stores right now”, with images of smart-looking 9-10 year old boys wearing the mentioned merchandise, and a 25%-off-all-purchases-this-weekend coupon, may be just right for that customer.

It may sway her to shop with us rather than the competition, spend the majority if not all of her back-to-school clothing budget with us, and do so this very weekend. A win-win outcome.

Knowing the customer’s current state helped us determine the right level of personalized messaging, and knowing her past purchase history (loves sports-themed casuals, preppy looks, and responsive to coupons) told us what to emphasize in the message and design the right promotional offer.

From this perspective, the finding that customers just beginning the purchase process don’t respond to product-specific ads is neither surprising nor a blow to personalization. But it does underscore the importance of thinking about personalization in a holistic way.

 

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

A few weeks ago, I blogged about a recent McKinsey & Company report on the emergence and impact of “Big Data”. I highlighted the retail areas where significant gains may be achievable by harnessing analytics and big data. In this post on the CQuotient blog, I complete my summary of the report. Please head over there if you are interested. Thanks!

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)