Monthly Archives: March 2007

Optimizing Mail Drops for Consumables

DataBase Marketing, Driller Q & A, Newsletters

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Please note: The business discussed below is a “continuity business”, where customers consume the product and need to either reorder from the company every few months or seek alternatives sources for the product. In this scenario, the behavior of customers is generally governed by the Latency Metric.

Q: Currently we mail our current customers direct mail every 6.5 weeks.  We have a new VP and he is asking if that is the optimal spacing of mailings.  I’m wondering if there are any best practices for setting up frequency tests?  If you can shed any light on how to set up such a test I would greatly appreciate it.

A: Well, do you know how you got to the 6.5 weeks number in the first place?  Somebody must have thought it was a good idea based on some kind of data (I hope)!

Obviously, there is some significant financial risk in simply “moving the drop around” and testing results that way.  You can do it, often by slivering off parts of the drop and dropping then at different times, but there could be a substantial financial penalty for approaching the problem this way – both on the cost and sales sides.  This is especially true when you have a current schedule that seems to be working.

The first thing I would do, if possible, before taking on the risk of messing with the mailing is to see if you can find any segmentation /  frequency that makes more sense from the customer data itself.  Since you also have a web site, there probably is evidence of “natural purchase cycles” the customer engages in that operate outside the mail drop – customers ordering “when and how they want to”.

Can you find evidence that the average purchase cycle is more like 5 weeks or 7 weeks?  How does this differ by product line, or packaging of the product?  Both segmentation by actual customer behavior and segmentation by product line will generally provide increased profits, provided the cost of dropping different mail streams does not overpower the increased sales.

For example, if someone can buy a “90-day supply”, well, 6.5 weeks is a bit  early for the mailing, I’d think.  If they can only buy a 30-day supply, well, it seems to me that 6.5 weeks could be a bit late.  Look to actual  purchase cycles by product line / supply length and see if you can find any patterns in the purchase behavior.

The key to this kind of analysis is to line up all the customers so that the purchase cycles match.  In other words, you need to enforce the same start date.  One way to do this, for example, is look at all new customers who started in January 2007; of the ones that bought again, when did they purchase – 5 weeks, 6 weeks, 7 weeks out?  What percentage of new starts in January (or any other month) purchased in each of the subsequent weeks?  Be aware choosing a single month may create results that have a seasonal bias, but I’m not sure that is relevant in a product line like yours.

A more complex but possibly more accurate way to do this is to “normalize” the start date of all new customers in 2006 and then look at the subsequent purchase patterns – given the same start date, what percent bought again 5 weeks out, 6 weeks out, 7 weeks out? You can achieve virtually the same thing by taking each month of current year and running it through the same drill as the one described above for following year, though it won’t be as accurate.

Once you have nailed the cycle for new customers, you can move on to see if  there is any change in optimal cycle date as customers age.  My guess is the cycle probably gradually lengthens until the customer defects.  If this is  true, it might be worth it to do two mailings with different cycles – one cycle for customers who became new customers in the past (say) 6 months and all other customers.  It’s likely in this business there could be an important behavioral difference between new and current customers that would allow you to deliver a more optimized mailing cycle.

Failing access to any analytical means to drill down into the data first, because either you lack the resources or simply don’t have the time, set up your next drop with flagged segments based on “weeks since last purchase” and look at profit per customer.  You could also back into this if you have good promotional history on your customers.

In other words, if you are going to drop “everybody” at the same time, there must be a segment where for this single drop, the time since last purchase based on arrival of the mail is 5 weeks ago, 6 weeks ago, 7 weeks ago, and so forth.  If you flag these segments before the drop in the database, you should be able to go back and determine sales per customer mailed for each segment.  This will tell you if your timing should be adjusted.  Further, you might divide these time-based segments, if there are enough members in the  segment, along various product lines.

Then, once you have a handle on the general cyclicality of different segments, you can get to profit per segment by using control groups to measure the lift and profit by segment.

A careful analysis of the next drop (or as I said, a previous drop if you have good history) should tell you which drop cycle for each product line is optimal.  From there, you have to look at economies of scale and decide if  you can afford that kind of segmentation.  You may find that due to the economies of scale in the mailing, you simply cannot drop 50% of your mail one week and the other 50% the next, for example.  But you might find enough support in your analysis to either justify the current 6.5 week drop as the most efficient, or to move it up or back somewhat.

Another way to approach the “timing problem” relative to economies of scale would be to try “reminder to re-order postcards” instead of mail or catalogs to some members of the group that require special timing considerations.  For example, new customers might not really need a catalog on their first drop, a postcard driving them to the phone or web site to reorder might be enough.

No silver bullets, I’m afraid. Just good ‘ol fashioned sloggin’ through the data ought to get you to where you want to go!

Jim

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CRM, Chief Customer Officers, and XXM of the Month

Analytical Culture, DataBase Marketing, Marketing / Tech Interface

In response to my comments on the potential for Marketing to lose a seat at the strategic table, Curtis Bingham comments on the difference between a Chief Marketing Officer and a Chief Customer Officer.  It’s not that I am opposed to the idea of a CCO, I’m just wondering, why are they needed?  I asked the same question about CRM when it came on the scene.  I mean, to me, CRM is Marketing; what would Marketing do if CRM was in charge of the customer relationship?  So then Curtis puts forth this gem:

“In some companies I’ve worked with, the CMO is so myopically focused on outward – bound marketing and “pushing” information on the customers that it takes a CCO to bridge the gap between what marketing hopes customers want and the customer reality.”

And then it hits me.  That’s really what is happening from a macro organizational perspective; it answers the question of “why” people are Deconstructing Marketing. Current CMO’s can’t do the job I used to know as “Marketing”.

As someone who came from the database marketing side, all my experience has been in industries rich with customer data, and in these industries, the CMO is the CCO, performing all those functions, because that is simply the nature of the business, it is all about the customer and always has been.  I think what we are seeing is as more companies get access to their customer data and want to act on it, the skill sets of the CMO’s in those companies are lacking relative to the financial opportunity presented by having the data?  This conflict results in functions like “CRM” and “CCO” being stripped out of what I know as Marketing and created as new functions to address the new opportunity that “outward focused” Marketers don’t have the skills to address.  Unless, of course, the CMO steps up to the challenge of a data-driven organization and grabs hold of it.  Otherwise, the CEO simply fills the gap with another position. 

And that squares with the idea database marketing folks would make great Chief Customer Officers – they have both the Marketing skills and the Customer-centric empathy, plus a knowledge of process optimization all in one package.

Another issue of course is one of scale.  Not that HSN was a huge company at 2 billion in sales or so, where I managed to handle all the “Customer Centric” functionality as well as the Marketing.  But compared to Sun Micro or Cisco, I suppose at some size a single function like Marketing simply cannot pay enough attention to everything that is going on so you have to break it up – or do you?  I suppose that depends on the kind of talent you have access to.

Either way, at some level, as companies become more data-driven and so customer-centric, the traditionally trained “outbound CMO’s” are going to have to get with the customer-side program or will lose a lot of their power.  They will have to, because the financial leverage in customer marketing / analytics / accountability is so huge it’s bound to dwarf anything an “outbound CMO” can come up with.

Plus, the pressure to improve process optimization / accountability is only going to get more powerful as our friends over in IT keep rolling out their favorite XXM (Xxxxx Xxxxx Management) flavor of the month.

This all begs a larger question for me: If the above is true, then is there a market for training “outwardly-focused” CMO’s in the art of customer-centricity?  Or are they simply going to “let go” and cede control to the CCO’s because Customer Marketing is just too hard?

A pithy question we can perhaps discuss at the Don, Ron?

“About the Blog” as a Post

Analytical Culture, Analytics Education, Marketing / Tech Interface

I had a request to publish my “About the Blog” page as a post so people could comment on it.  Here ya go Jacques.

From the Drilling Down newsletter, 12/2004:

What is the number one characteristic shared by companies who are successful in turning customer data into profits?  The company fosters and supports an analytical culture.

Web analytics and Pay-per-Click Marketing in particular have served to teach many people the basics of applying the scientific method to customer data and marketing – creating actionable reporting, tracking source to outcome, KPI’s, iterative testing, etc.  The web has allowed companies to dip a toe into the acting-on-marketing-data waters at relatively low cost and risk when compared with offline projects.  And many have seen incredible ROI.

I think web analytics could be poised in the future to serve a greater role – teaching people / companies the optimal culture for success using analytics, also at relatively low cost and risk.  It’s going to be much harder to drive this concept but more rewarding if as users we can make this happen, because today’s web analysts (and maybe analytical apps) could potentially be among tomorrow’s leaders in a data-based, analytics-driven business world.

For example, do you think analyzing / understanding new interactive data streams where the interface is not a browser will be any different, in terms of the culture required to turn interactive customer data into profitable business actions?  I don’t.

Look, a “request” is a request, whether a click, IP phone call connect, cable TV remote button push, verbal command, card swipe, RFID scan, etc.  You’re still asking a computer to do something.  The request has a source, is part of a sequence (path), and has an outcome. 

Analysis of these requests will face challenges and provide potential benefits similar to those provided right now in web site analytics.  This is the beginning of analyzing the interaction of computers, people, and process.  

Without a doubt, no matter what form these requests take, there will be a “log” of some kind to be analyzed.  Usability?  Conversion?  ROI?  These issues are not going to go away, and companies need to develop a culture that properly embraces analyzing and addressing them.  Companies not developing this culture will find themselves continuing to bump along the “drowning in data” road and will never optimize their interactive customer marketing.

As I see it, here’s the “culture” issue in a nutshell: as a company, you have to want to dig into data and really understand your business.  This pre-supposes that you (as a company) believe that understanding the guts of your business through analytics will drive actions that increase profits.  If the company doesn’t generally support this idea, there is no incentive for anyone to pursue it and the company just happily bumps down the road.

Of course most people don’t really relate to the “company”, but their own division or functional silo.  So you might have manufacturing / engineering groups who live and die through analytics but marketing is not held to the same standards and thought processes.  This is where the idea of Six Sigma Marketing comes in, it’s a “bridge” of sorts that tries to say (perhaps to the CEO and CFO), “Hey folks, if the engineers can engage in continuous improvement through ongoing analytics, so can the Customer Service silo and the Marketing silo and perhaps others.”

At a higher conceptual level, analytical culture takes root when management makes it known they are not afraid of failure, and want employees not to be afraid of it either.  

Another way to say this is experimentation and testing are encouraged throughout the company.  Failure is a regular occurrence, and is even celebrated because through failure, learning takes place.  Show me a company with no failures or that hides failure and I’ll show you a company that is asleep at the switch, afraid of its shadow, a company soon to be irrelevant to the market it serves.

Hand in hand with accepting failure must be continuous improvement.  Even though failure is embraced as a learning tool, the lesson of the failure both prevents it from happening again and results in new ideas with a higher potential for success.  These twin ideas of embracing failure / continuous improvement are at the heart of every business successful in using analytics to improve profitability.

“Evidence” of a company with the right bones to grow an analytical culture is this: you see the various levels of employees working in cross-functional teams with a common problem-solving mission.  Instead of people in a silo groaning about members from other silos being present at a problem-solving meeting, people are instead asking, “Where is finance, where is customer service?”

The most common place “analytics” live in a company is in Finance with the “Financial Analysts”, who are mostly tasked with analysis related to financial controls and producing financial reports.  If marketing or customer service was willing to expose themselves to the rigor of these analysts, they would undoubtedly be able to improve their business areas.  But that exposure takes substantial guts and confidence in your abilities, and a “culture” that supports a scientific process.

And you can’t engage in this process without analytics; success and failure need to be defined and measured.  The easiest way to encourage this culture to take root is to team a department head with a Financial Analyst familiar with the area.  

Often, you find this finance person already has insightful questions that could lead to improvement, but “never asked” because “it’s not my job”.  And often, to make changes in a business today, you need IT support of some kind.  That’s the basic cross-functional unit – Finance rep, IT rep, and a department head.  

I would also argue that if Marketing has a seat at the table in the strategic, “Voice of the Customer” sense (as opposed to being relegated to Advertising, PR, and Creative), then marketing is part of the core unit.  Then you add other disciplines as needed based on the particular problem you are trying to solve.

If the culture is flexible enough, this can turn into “Business SWAT” where the best and brightest cross-functional teams roam through the company as “consultants”, tackling the hardest business problems, which (surprise) are usually cross-functional in nature.  And “blame” is never on the agenda, it’s about “how can we help you make it better?”  You need a culture that is clear about this idea in order for people to expose themselves to the analytics-based scientific process.  Success and failure are defined by the analytics.

If you think about it, web site management ruled by analytics is a microcosm of this Business SWAT set-up.  You have marketing, finance (ROI component), and technology all working together based on the data.  That’s why I think there is a higher mission for the web analytics area / people; they are building the prototype that can teach companies how to go about measuring, managing, and maximizing a data-driven business.

At the highest level of this culture, managers “demand” these SWAT teams because the success rate and business impact is so high.  As the various departments or functional silos produce wins and losses, capital (budget) flows to where the successes are and away from the failures.  When managers see this happening, they jump on board, because they want the budget flowing their way.  This creates a natural economic supply and demand scheme with a reward system for participation built into the process.

One caution: when the culture gets to this level, the analytics group must be sanitized from the reporting hierarchy.  It can’t report to finance, or marketing, or IT anymore.  It has to be completely independent, which usually means reporting directly to the CEO.  There has to be confidence in the integrity of the results of all testing based on standards.  All the little “pools” of analytical work throughout the company must be gathered into one.

What kind of companies do you see really engaging in this kind of culture right now? Those that for legacy reasons have always had access to their operational and customer data and have been using analytics for years.  For these legacy players, web analytics is a “duh” effort – they get it right out of the box, because it’s more of the same to them.  But many types of businesses have not had this access to data before and web analytics is the first taste they are getting of the power and leverage in the scientific method.  I think this “accountability” disease we’ve created in web analytics and search marketing will continue to spread and infect every business unit.

The longer-term question is, can we flip this model over, can the successful culture of cross-functional approach and continuous improvement used in web analytics be used to create a “duh” moment for other areas of the company?  Will “best practices” and success stories create an environment where people say to the (web?) analytics team, “Hey, can I get some of that over here?”  In other words, will the analytical culture develop?

Methinks there is more going on with web analytics than meets the eye; it’s potentially a platform for the creation of a new business culture, a culture based on the scientific method – Six Sigma Everything.  Sure, it’s awkward and maybe the web is not meaningful enough yet to many companies.  But as we thrash all this out, there is something greater being learned here.

Right now, many CRM projects can’t show ROI because nobody knows what to do with the data, how to turn it into action that improves the business.  Sounds very much like web analytics 5 years ago…and look what we talk about now.  KPI’s, turning data into action.  The analytical culture playing out.

What does this mean for the people currently involved in web analytics?  If I was a young web analytics jockey, I would be preparing for the spread of the analytical culture, and seriously thinking about learning some of the tools traditionally used in offline analytics – the query stuff like Crystal Reports, the higher end stuff like SAS, SPSS, and so on.  Search the web for “CHAID” and “CART” and see if you like what you read about these analytical models.  If this kind of stuff interests you, you are much closer to being a business analyst than you think.  And guess what?  Analysts who can both develop the business case and create the metrics and methods for analysis – like you have to do for a web site – are rare.

It takes a particular mind set, and that mind set is not common.  Most of the people with the right mind set go into the hard sciences, but demand on the soft side of business (marketing, customer service, etc.) is just beginning in our data-driven world.  

On the hard side, (with all apologies to the real engineers out there for the exaggeration) the drug works or it doesn’t, the part fits or it doesn’t.  The development of softer-side marketing and service analytical techniques is always going to be populated with a lot more gray area than there is on the hard side, and it takes a special skill to conceive of and develop the metrics required.  But we should be trying to bring the same analytical rigor to the soft side of business that the hard side has always had to deal with.  The trick is to apply that rigor without damaging the mission.

For example, the whole “fire your unprofitable customers” thing from some factions in CRM.  That’s ridiculous.  What you want to do is identify them and then act appropriately, whether that means controlling their behavior, not spending additional resources on them, or not doing the things that create them in the first place.  That’s the gray showing.  You don’t just hit the “reject button” on a customer.

Customer data is customer data.  It’s all going to end up in one place eventually as the analytical culture spreads, and those with the skills to apply the scientific method across every customer data set are going to be rare and in very high demand.  Don’t spend all your spare time watching the Forensic Files on Court TV.  You’re a business analyst.  Get out there and learn the rest of your craft!

And, please consider doing whatever you can, whenever you can, to spread the analytical culture within your company.  If most of what your analytics involve is “online marketing”, reach out to “offline service” or another silo and ask if you can help them with anything.  What’s the call they would like to take less of, can you use the web site to make that happen – and prove that it worked?  Can you use the web site to generate offline ROI?  

Web analysts, you are the cross-functional prototype.  Please teach others how to optimize the entire business.