Category Archives: DataBase Marketing

Lab Store: Background & ROMI Formula

DataBase Marketing, Lab Store

Many of you in the blog audience might not know I have been writing about both offline and online Marketing Productivity since late 2000 on my web site through articles, an e-mail newsletter with about 7,500 double opt-in subscribers, and a blog-like database marketing article review section. Anyway, back in 2000 it was pretty hard to get people to listen to Marketing Productivity ideas because there was just too much money sloshing around. Who?cares about Productivity when you have unlimited budgets?

So to prove out some of the concepts I was talking about, I started my own online store for under $1,000, all costs in. Those of you who have seen me speak at Search Engine Strategies or the eMetrics Summit on customer retention know this web site as the”Lab Store“, which has turned into a large enough business on that same $1,000 infrastructure my wife now runs it as her full time job. The Lab Store is a great resource for online marketing research and testing because I control everything (unlike most client situations) and perhaps more importantly, I don’t have to ask permission to release results. So from time to time, I pull analysis out of the store operation I think will be interesting to other people and provide it for your review.

That said, I’m not linking to the Lab Store site because I’m not fond of the idea that our competitors might use this blog to improve their business. Lab Store Analysis Examples will link back to this post to provide context for new subscribers.

ROMI – Return on Marketing Investment

When we execute different tactics designed to increase customer value in the Lab Store, we measure the results using an incremental flow-through model I call ROMI (to differentiate from the extremely lightweight ROAS). We define ROMI in online retail this way:

Sales – Cost of Product = Gross Margin
Gross Margin – Variable Overhead Cost = Gross Profit
(Gross Profit – Marketing Cost)/ Marketing Cost = ROMI

where Variable Overhead Cost is basically the cost to process, pick, pack, ship, and service the incremental orders generated by the marketing, service, or operational initiatives. ROMI answers the question, “For every $1 I spend on a marketing, service, or operational effort, how much cash flows through to cover fixed costs? What do I get back, after all variable costs, including the cost of the effort?

Examples of how this works with efforts other than Advertising:

New Customer Kits
Managing Customer Experience

Overview of Lab Store stats and metrics can be found here. Pics here.

I’m not clear on the Chief Customer Officer concept…

DataBase Marketing, Marketing / Tech Interface

I don’t have any problem with the direction Jeanne Bliss provides regarding how to become the “customer champion” in your company, especially the idea of aligning with the CFO and CIO.

What I’m trying to figure out is why this is not Marketing’s job; seems to me the CMO should be the Chief Customer Officer, complete with the ultimate responsibility for Customer Service.  Otherwise, it seems like this CCO position is just an excuse for people keeping their heads in their own silos and letting somebody else worry about cross-functional processes, customer experience, and defects.

In other words, do we really need a unique exec to be able to create / enforce / enable cross-silo functionality on the “soft” side (marketing, service, some fulfillment) of the business?  After all, the CIO and CFO operate across all the silos, why can’t the CMO?  As Ron said in his excellent piece What Marketers Should Learn From IT, the CMO now needs to get involved in the whole business and act cross-functionally to be successful, as the folks in IT have learned.

I guess the answer is probably that “Marketing” has been redefined over the years and has somehow lost the strategic seat at the table, morphing downward into “MarCom”.  This has not happened at all companies – I can tell you at most truly data-driven companies, the CMO is the Chief Customer Officer, because these folks / the company understand how the totality of the customer experience affects Marketing Productivity.

Perhaps the answer to Kevin’s question on what happens to the Marketing folks in the catalog business as the web takes over is this: they become Chief Customer Officers or consultants to them like Jeanne Bliss, formerly of Lands’ End.  After all, they already know how to do the Chief Customer Officer job.

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.