Category Archives: DataBase Marketing

LifeCycle Marketing

Analytical Culture, DataBase Marketing

Lisa Bradner from Forrester Research called to discuss LifeCycle Marketing, which is kind of a coincidence given the Sense and Respond post below. Apparently folks are having some difficulty with implementing the concept…

The Customer LifeCycle is really just a process that you can map, just like any other business process. At each stage of the LifeCycle you have an expected result based on the behavior of other customers as a whole or in the customer segment. You measure the behavior of individual customers against the process benchmarks, and when the customer is behaving as expected, you do nothing, taking no action. If the customer behavior is “out of bounds” with the expected result, you take action. This method generally allocates marketing spend to the highest and best use. If this sounds a bit like Six Sigma for Marketing, well, you’re right, it does. You have a problem with that?

Another way to look at it is this: there is a “tolerance” band for behavior and the ability of marketing to affect behavior depends on where the customer is within that band. If the customer moves too far outside the band, it becomes impossible for marketing to really do anything at all to affect behavior. So as long as the customer remains in that band, it conserves marketing resources to take no action. As the customer begins moving towards the band, significant marketing action is triggered and needs to be taken before the customer moves too far outside the band. So you have a reallocation of marketing resources towards highest and best use, always pushing marketing spend to where it will be most effective. It’s a marketing resource allocation model of sorts.

Let’s take a simple retail example of how this works. Let’s say you look at new customer purchase behavior, and you see for new customers who make a second purchase, they usually make the 2nd within 45 days of the first purchase. So, you can look at new customers and divide them into 2 groups; those that are doing the “expected” and those that are not, based on the 45 day rule. Applying the LifeCycle concept, any new customer that makes a second purchase within 45 days of the first, marketing does nothing (inside the band). This conserves marketing resources and margin dollars that would have been lost to discounting. That money is then reallocated and spent on the customers crossing over the 45 day tripwire without a second purchase (outside the band), and since you have more to spend (courtesy of the reallocation), the programs can be more effective.

Further, let’s say that you analyze this 45-day idea looking at the marketing campaign that generated the new customer. You have only 2 campaigns and the days between 1st and second purchase is 60 days for one and 30 days for the other (average 45 days). So, the first thing you ask yourself is why is the behavior different – media, copy, offer? The second thing you ask is should we reallocate spend from the campaign with a 60 day window to the campaign with the 30 day window, which would generally increase cash flow? And the last thing you do is adjust the original 45 day trip wire to 2 distinct tripwires, one for the 30 day campaign and one for the 60 day campaign (if you keep the 60 day campaign). You are optimizing the marketing system based on the unique LifeCycle profiles of these new customers, generally lowering costs and increasing margins as you optimize.

The thing is, this is really fundamentally the same as optimizing a web site. It’s the same idea, only with different variables and more detailed data. I think that’s why many of the web analytics folks seem to “get it” and are now working on systems to automate it. I saw a shopping cart demo last week with this kind of LifeCycle profiling built right into it. You could run the profiles and execute the LifeCycle-targeted e-mails right within the same interface.

Behavior predicts behavior. If you use behavioral metrics like Latency and Recency, you can discover these LifeCycle patterns and use them to your advantage. Every marketing system, B2C or B2B, has LifeCycle processes in it. By understanding these processes you can focus resources and increase the overall profitability of all your marketing efforts.

Why are companies having troubles implementing such a relatively simple concept? I dunno, guess we will have to see what Lisa has to say in her report…but why do companies have trouble implementing just about every data-driven Marketing or Service effort? More often than not, the root cause is lack of a proper analytical culture to support the effort.

Sense And Respond Marketing

Analytics Education, DataBase Marketing, Marketing / Tech Interface

Ron Shevlin of database marketing powerhouse Epsilon thinks a new core competency requirement for marketers is the “ability to move customers through the buying cycle with a sense-and-respond capability”. This is something I often talk to people about, it’s really a subset of the “I have the data, now what do I do?” problem. Marketers are more familiar with creating campaigns based on nameless, faceless GRP’s than the behavior of real people. And that’s the problem.

I think part of the problem is in segmentation, they simply don’t understand how powerful behavioral segmentation is, how different it is than using demographics – and they lack the ability to ask for / get this information in a format that drives action-oriented thinking. The granularity of “people” as opposed to GRP’s throws them off. With Sense And Respond Marketing, or what I would call Relationship Marketing, you use the Customer LifeCycle to influence messaging which is meaningful to people based on behavior, not demographics. The behavior is the message, not the age, income, make of car, or whatever. Using behavior makes so much more sense when you see an 80 year old on a Harley.

Here’s an example. One thing that happens with interactivity is people tend to “gorge” themselves on something, get tired of it, and move on to the next experience (video games, Friendster). So you have to work very hard to hold on to them. At HSN, we used to listen very carefully to what customers said on the air and reviewed comment trends in customer service every single day. One thing we started hearing was “I’ve only got 10 fingers” which is the customer saying “you are selling too much jewelry”. At the same time, we were looking at the LifeCycle of best customers and found that most of them were fashion buyers who started buying in jewelry – regardless of how old they were or what their incomes were.

So we have customers telling us we sell too much jewelry, and we end up losing a lot of them because they get bored. But at the same time, best customers are created when someone starts buying jewelry and moves into fashion. We have a natural transition from new customer / jewelry to best customer / fashion that some customers found their way to and others did not. Knowing this behavior exists and that it’s very profitable for HSN, can we influence it? Can we get more people to make the jewelry to fashion transition with a marketing campaign of some kind?

Well, the first thing is timing. When to drop the campaign? You can’t drop it on a “date” to all customers, you have new customers coming on each day and they are going through a LifeCycle. However, the data said if the customer did not start buying fashion by the 120th day of their LifeCycle, they would probably never buy fashion. So somewhere in that 90 – 120th day after becoming a new customer, we need to hit them with a “buy fashion” message.

OK, so what is the message?  Well, we know from customer comments (and remote selling in general) that people are reluctant to buy fashion remotely because they are worried about fit. So what would be the easiest fashion item to sell a remote customer? How about something like a running suit, you know, Small-Med-Large-XLarge?

So we put together these special fashion shows geared to “no brainer fit” fashions and had them run at very specific times on the network that we could promote to the customer in advance. We dropped a very simple piece that said, “We’d really like you to try our fashions, here is $10 off, here is when to watch” kind of thing. And we dropped it somewhere in the 90 – 120 day window after the customer’s first purchase. Understand, these pieces went out every week but they went to very specific people with specific behavior who were entering “the zone” of 90 – 120 days after first purchase of jewelry.

And we literally printed money from that point on with this program. For every $1 in cost, we generated $25 in incremental (versus control) profit in the first year of the customer life, every day, day in and day out, as a higher percentage of new customers converted into long-term, highly profitable fashion buyers.

Was that a hard program to design? Not to me, seems completely logical. You have behavior, you know the customer, you have timing points, copy is simple and direct. I think Ron probably had something a little more sophisticated in mind when he wrote Sense And Respond Marketing, but the basic concept is the same (and after all, we were dealing with mainframes and snail mail at HSN in 1994, so cut me some slack!).

So why is it again that people have this “I have the data, now what do I do” problem? I suspect it’s because they may have the data, but it’s not in any kind of actionable report format that generates ideas. GRP Marketers simply don’t know how to ask for the data / can’t get the data in a format that lends itself to creating effective campaigns. And that’s a shame, because it’s pretty simple to have someone do it for you or you can do it yourself.

Flat Rate Shipping?

DataBase Marketing

In Pricing and Ecommerce – Some Thoughts, Adelino points to some of the most common pricing models for shipping, including the traditional catalog “by order total range” and the Amazon “threshold” model and provides some spot-on analysis.

While one could argue that in the “range” model, the cost of shipping as a percent of order falls, I don’t think consumers really look at it that way. I think they wonder why they are “punished” with higher shipping costs for placing larger sized orders. Perhaps they don’t. But they sure like flat rate pricing for shipping when they see it.

We played extensively with S + H at Home Shopping Network in the early days, and what we found to be most successful was a flat shipping charge. I continue to use this approach with clients on the web today, where possible. It drives average order size like nobody’s business as people pile more stuff into the cart because “it doesn’t cost any more”. If you understand the financial model of the mail order business, you understand that boosting average order size drives profits like (almost) nothing else you can do. Also, being able to promote “Shipping is $6 per order, no matter how many items you order” in your advertising and all over the site eliminates any questions people have about shipping, which reduces cart abandonment.

This approach, of course, takes an intimate knowledge of the business, a study of UPS (if that is your carrier) rate structures, and understanding the general geography of your customer base. You have to know what your real average shipping costs are and peg your flat rate accordingly. For heavy products, you should build some shipping cost into price in order to lower the flat rate, which will not work in a commodity product environment.  And good merchandising that encourages customers to upsell themselves helps a lot to drive the average order size. We even have some people who put flat rate at under cost and charge the difference to marketing, which is an accounting view of this approach. It certainly could be argued that the flat rate is a retention marketing technique, if not an acquisition technique as well.

Amazon is perhaps a unique case in that both the margins and average prices are low relative to product weight; they’d probably get killed on flat rate and a threshold approach serves them best.

If you had a shipping bill of $120K last year and you shipped 20,000 orders, your average cost to ship an order is $6. Why not charge a $6 flat rate per order and see what happened to your average order size? Sure, your shipping costs will go up with average order size but unless your margins are thin you will drop a lot more money to the bottom line.

Clearly, the success of this approach depends on the breadth of your lines and their sizes / weights. But if you generally ship in a 12 x 12 x 12 box or less and you’re not shipping rocks, it should work out for you. Take a look at what each extra pound above 1 lb and 2 lbs really cost you versus a 20% increase in average order size. You’ll see.

As Adelino says, the trick is in pouring over the financial details of the business.