Loyalty Lab Blog

by Joshua Tretakoff | VP, Account Management

A telling piece in today’s DIRECT that may shed a little light on why so many businesses have been unable to shore up their customers in the wake of the economic downturn.According to the CMO Council, “just under 47% of marketers around the world have ‘good insights into retention rates, customer profitability and lifetime value,’ “And, “more than three-quarters of the respondents believe they aren’t realizing the full revenue potential of their existing customers.”

Wow.  Talk about untapped potential. We live in a world of massive computer horsepower and a slew of talented analytics folks. Just choose the right data collection mechanism (say…oh, I don’t know…a loyalty program? ), and a set of embedded analytics tools, and you’re seeing some pretty significant profitability swings. It may not be as sexy as paying for a Superbowl commerical, but I bet it yields a lot greater ROI.

by Joshua Tretakoff | VP, Account Management

A guy I used to work with often likened certain business practices as “driving down a dark country road, at night, at 80 MPH, with headlights that only see 5 feet ahead.” Not only was it fraught with peril, but you couldn’t even see what you had just hit, once you hit it. This thought came to mind today when I read the results from a new study from Retail TouchPoints and The Aberdeen Group, titled Responsive Customer Loyalty: Creating Customer Commitment in Retail.

Specifically, as RetailWire points out, “…the report makes it all too obvious that many retailers are still checking ‘don’t know’ and ‘don’t measure’ when it comes to key metrics around loyalty programs like churn, retention, and customer satisfaction.” Worse, it seems that 35 percent of retailers seem not to be measuring basics such as year-over-year same-store performance, basket size, and customer retention rates.

The good news is that the report seems to point to loyalty programs as a hope by many of the surveyed to start capturing this data. Now, let’s hope they start to make decisions from it as a result!

by Michael Greenberg | President

While perusing a short but interesting bit of research on multi-channel customer behavior by Opinion Research Corporation on Marketing Charts, I noted some interesting data that points out the difference between Loyalty and Value.

The chart shows the dollars spent with the subject company and with its most important competitor. Note how spending with the company increases as the number of buying channels increases:
1 channel – $44
2 channels – $51
3 channels – $62
4+ channels – $82
This is consistent with what most companies see.

But now if you calculate the share of wallet using both companies, the picture changes:
1 channel – 36% share
2 channels – 60% share
3 channels – 52% share
4+ channels – 48% share

Granted this is a fairly small sample size for this type of analysis, but some interesting questions arise. Which segment of customers should marketers target? Those with the most available wallet share (1 channel)? Those with the largest available to spend (4+ channel)? Or should you focus on migrating to multiple channels?

The answer depends on a variety of other factors, which I’ll address in other posts.

But by examining this basic example, you can begin to see how indicators of loyalty (share of wallet, net promoter score, year over year retention) aren’t always the same as customer value, and that the best options for high return on investment are found by looking at several factors, not just customer value.

by Joshua Tretakoff | VP, Account Management

Always nice to get public validation on basic functions: nice article in Internet Retailer about the success of targeted email from Bath & Body Works.. Targeted Email is often undervalued, since email is so cheap to blast out. However, targeted emails produce dramatically higher results, and usually can be the difference between someone reading your email, or hitting the old delete button.

Loyalty’s not always about points; its about relevance and connection. How you speak to your loyal customer can be as important as how you reward them.

By Mickey Neuberger | Senior Director, Loyalty Strategy
Just a quick plug to a recent white paper I wrote providing a high-level framework to help marketers design loyalty programs based on their businesses. Click here to read.

by David Rosen | Senior Vice President

In my last blog posting I commented on many marketers’ skeptical response to the vastly superior performance of loyalty members versus non-members.  While few would argue that a gap in frequency and order value between members and non-members should exist, the doubters contend that the performance is merely a manifestation of inherently better customers opting-in to a program that rewards their existing loyalty.

My response:  Of course we expect to see a higher proportion of best customers enrolling and participating.  But, a deeper dive routinely shows that responsiveness, frequency and order value improve even within this self-selecting segment.

So, how can we measure the impact?

The single key is the ability to isolate similar groups of customers for comparison in order to remove the “bias.”  For companies that already have strong customer capture (e.g. on-line/catalog companies and other retailers/travel/hospitality companies that have strong customer identification), pull a relevant subset of non-members that exhibit nearly the same pre-enrollment behavior of the loyalty member group.  For instance, if the average frequency of the new member group prior to enrollment was 1.7 purchases and $125 average spend annually, select a group of non-members with the same metrics.  With this rigor, comparison of behavior post enrollment (or alternatively non-enrollment) will clearly illustrate the lift most-directly correlated with this single change in customer relationship.

For businesses where the vast majority of customers shop anonymously, we recommend holding out a market for comparison – essentially piloting the program in order to see the overall impact on purchase metrics.  In the online world, a number of platforms allow the ability to serve loyalty membership information to selected members only – in effect creating a virtual pilot group.

A final note on new customers.  New customers by their nature do not have prior purchase history from which to compare.  Again, we’d expect customers who are more likely to be best customers to enroll upon their first purchase.  Rather than discount this as self-selection, be sure to use this insight as a means of reaching out to this group with a great new member/customer marketing program.  Clearly this is the group who will be most receptive and will yield the highest return on your marketing investment.

By Joshua Tretakoff | VP, Account Management

When most of our clients think of loyalty programs, they think of traditional points programs. While there’s no doubt that having a hard benefit program like that is an excellent, easy to understand value proposition to both the client and the consumer, David and his team work with our clients to go beyond just the basics to focusing on the true ROI.

Case in point: this week, many consumer electronics retailers are experiencing a phenomenon that increases in frequency every year. Traditionally, big screen TV purchases spike just before the Superbowl, but what is not often known is how many returns those same purchasers make. While some frustrated retailers go so far as to call this fraud, most “big-box” folks accept this as the cost of doing business. In fact, retailers like Costco go so far as to make this part of their value proposition.

Now, imagine those same customers are part of a loyalty program. Using tools like Loyalty Lab’s, the client could then identify a segment of these customers who repeatedly do this, and isolate only that segment from receiving future discounted promotions, thus averaging the margin impact. Since promotions authored through the Loyalty Lab system are tied to the specific customer, not a code or coupon, this ensures the promotions have no “seepage.” Since this is done in the background (invisible to the customer), this subtle technique would allow the customer to continue to patronize the client, loyal to their business policies, while minimizing the impact from their undesirable behavior to the retailer as a whole.

The bottom line is getting a customer is half of the battle; keeping them requires as innovative strategies and intelligent financial savvy to ensure you address each customer the way they should be addressed. After all, you are already competing against other companies; do you really want to compete against your customers, too?

By David Rosen | Senior Vice President

We invest a tremendous amount of effort estimating program ROI, defining key tracking metrics and looking back at actual program performance. More often than not, what we observe after six months far exceeds the estimates we bake into our “proof of concept” financial estimates.

But can a loyalty program really take all the credit for the large differentials between member and non-member behavior? To what extent are we merely seeing “self selection” of consumers who are more engaged playing itself out — albeit now in a world where we are far more adept at recognizing and reporting on loyal behavior?

The broad answer is yes and yes. Self selection does contribute to core differences in average order size and transactional frequency metrics. More engaged consumers have better purchase metrics and are more likely to notice and take the time to join a loyalty program. In fact, successful loyalty programs count on this effect as it is most important to build a deeper dialog and relationship with this segment.

But, our emperical data proves that even among “best customers” profit-driving metrics are measurably boosted beyond the baseline because members are further engaged by the program.

Proving this empirically requires a level of pre-program customer data capture that not all marketers possess; but when a brand can clearly define control groups of both new and existing customers, the results are eye-opening and typically dramatic.

Next post: “Methodologies for Rigorous Testing of Program Lift”