Customer Accounting: How to Speak Finance

Analytical Culture

Let’s say you have decided to build a relationship with the CFO or a peer in Finance.  How do you get started?  Here are two report concepts and charts that will give you much more to talk about than you can squeeze into one lunch.  By taking Finance’s own numbers (Periodic Accounting) and recasting them into the numbers that matter for Marketing (Customer Accounting) you create a very solid bridge and basis for building out a plan.  Note to yourself: And the plan is?  Make sure you think about that first…how can you help Finance / the Company achieve their Cash Flow and other Financial goals?

Report 1: Sales by Customer Volume

Core Concept: The idea here is to decompose a CFO’s financial quarter (or any financial period) into the good, better, best customer volume components that make up the financial period.  It’s a “contribution by customer value segment” idea.  Benefit: Graphically demonstrates to the CFO the “risk” component of customer value in the customer portfolio and supports the idea Marketing could mitigate financial risk by “not treating all customers in the same way”.

Take any periodic statement time frame – a month, a quarter, a year.  Gather all the customer revenue transactions for this period, and recast them into the total sales by customer for the period.  Decide on some total sales ranges appropriate to your business, and produce a chart on the percentage of customers with sales in each range, including non-buying customers, for the chosen periodic accounting time frame.  For example:

 By Volume

Run this report each period, and compare with prior periods.  In general, you want to see the percentage of customers contributing high sales per period to grow over time, and the percentage of lower revenue customers to shrink.  This means you are increasing the value of customers overall.  If the numbers are moving the other way, this is the type of customer value problem you would expect CRM or a smart retention program to correct, and if you are successful, you should see the shift in customer value through this report.

Report 2:  Sales by Customer Longevity

Core Concept: This report is a “Flashcard”, if you will, that demonstrates the Customer LifeCycle.  If you have trouble communicating complex LifeCycle / LifeTime Value concepts to Financial people, this Flashcard takes their own numbers and decomposes them into a vivid picture of why the LifeCycle matters.  Benefit: Opens the door for your budgets to be determined by different metrics than are currently used; what good is a “quarterly budget” when the underlying customer issue can be much more dynamic?  Wouldn’t the CFO like you to “do what it takes” in the Current Period to preserve profits in Future Periods?

Take any periodic statement time frame – a month, a quarter, a year.  Gather all the customer revenue transactions for this period, and recast them relative to the start date of the customer.  In other words, when looking at the revenue generated for the period, how much of it was generated by customers who were also newly started customers in the same time period?  How much was generated by customers who became new customers in the prior period?  How about two, three, and four periods ago?  More than 4 periods ago?  Depending on the length of the period you use, you may end up with a chart looking something like this:

LifeCycle

You can run this analysis at the end of each period and track the movement of customer value in your customer base.  Generally, you want to see increasing contribution to revenue from customers in older periods, meaning you are retaining customers for longer periods of time and growing their value.

If this kind of idea interests you, the full background on explaining the LifeCycle / LTV to Finance is here.

Reporting versus Analysis: The “Actionable” Debate

Analytical Culture, Marketing / Tech Interface, Web Analytics

Gary Angel and Eric Peterson have been having a great exchange surrounding the definition of KPI’s, and more specifically, the requirement that they be actionable.   Gary started out with the position the “criteria of actionability is unsound in almost every way” but I think both he and Eric have resolved in the middle somewhere – it’s really about context.  Gary is right, to take any metric “naked” at face value without surrounding context is simply not good analytical practice.  But I would argue (and I think Eric agrees) that to build a KPI in the first place, you must already have the required context, or you don’t have a KPI.  So that leaves us with “how you define a KPI” as (I think) the final resting point, and there really isn’t anywhere to go after that.  Your comments on my analysis welcome.

However, I think the ideas Gary has exposed run deeper than just the KPI discussion.  The situation Gary is addressing – making sure people really understand that every metric requires business context to be functional – requires attention because web analytics is a very fast growing field with a lot of brand new people in it who may have not been exposed to proper analytical training. Or, not challenged to do any real analysis by weak managers.

These new people frequently don’t understand the difference between Reporting and Analysis.  A “Reporting” mentality (provide data) leads to the improper use of analytical ideas like KPI.  Analysis (provide insight) would automatically take into account a lot of other factors, as Gary has suggested.  Knowing all those factors (because you are doing real analysis), you can certainly take movements in a KPI as actionable.  As Eric says, that “action” is often a more focused analysis of some kind.  KPI’s are really just “tripwires” that alert you to a problem or opportunity that requires further analysis.

My concern (and in the end, I think Gary’s) is that often the Reporting mentality is Robotic and that the reaction taken to change in a KPI might be equally Robotic if you don’t have the proper context.  What often happens in Pay-per-Click testing is a great example of this, and a lot of the multivariate stuff people are now addicted to is an extreme example. 

You can look at conversion rates, make changes to landing pages, and try to optimize the “Scenario”.  This is Reporting, not Analysis.  Can you provide insight into why the changes you made worked?  For example, can you explain the improvement in terms of Psychology or Consumer Behavior?  Usability?  If so, that would be Analysis, and the answers would be applicable to a wide range of other challenges on the site.  Without knowing why the changes worked, you are left with simple Reporting that applies to only a single specific Scenario.  Nothing was really learned here.

Take this same idea to the extreme, and you get what often happens in multivariate testing.  You can certainly run a multivariate test on 5 variables at the same time, and find a “winning combination”, but this is Reporting, not Analysis – in fact, it’s black-box reporting in the extreme.  For example, how do you know that you chose the 5 most important variables to optimize?  How do you know the options you chose for each variable are the most powerful?  Isn’t it just as likely that the final optimization you achieved is suboptimal, a local maximum, as it is the solution is truly optimal? 

In other words, isn’t it possible that what you have created with the robot is better than you had, but is not even close to being the best it can be?

Dear Reader, you’re asking, why should I care about this Reporting versus Analysis issue?  Because here is what will happen without real Analysis: you are going to “hit the wall”.  One day, there will simply be nothing left you can do to improve on what you have done.  Reporting is only going to take you so far.  Frustrated, you will probably Analyze the situation and realize you have “optimized” yourself into a corner by taking something that was fundamentally broken in the first place and making it better than it was.  You can’t make it any better unless you wipe it out and start again.  That’s a huge waste of resources, right?

See CRM if you need an example of what can happen when you automate worst practices.  And they’re going to fix it 8 years later by bolting on Business Intelligence?  Um, shouldn’t the Analysis have come first?

Lab Store: The Next Inspector

Analytical Culture, Lab Store, Marketing thru Operations

This is a great B2B example of a Marketing / Customer Service program operating in Fulfillment from a vendor of ours.  It drives profitability on the vendor side as well as increased satisfaction on the customer side.  Simple as a rock, effective on a number of levels, and measurable.

When we open a carton (usually 6 or 12 items in a carton) from this vendor, the first thing we see printed on the inside lid of the box is this message:

Remember
Our Customer is the
Next Inspector

Think about that message.  If you are packing the vendor boxes, you see this message every time you start to seal the box.  Every time.  How much “training” would it take to have the same effect?  In addition to the more direct message it sends to a packer about quality control at the carton level, it also sends a broader message to employees concerning customer experience and care.  After all, the employees know customers see the same message.

Simple, direct, impactful.

As a customer, when we opened these cartons for the first time, we thought, “Wow, that is pretty neat.  These guys really give a crap about what they do.”  Whether they really do care or not, of course, is up for speculation, but that is not the point, is it?  We think they care.  In fact, my wife’s response to this message was to cut off the carton flap with the message on it and put it over the packing station.  Not sure they planned for something like that, but a nice “halo effect” :).

And, unlike most of the vendors we deal with, we have never received a mis-packed box from these folks in 6 years.

I talked with the vendor about this and he filled me in.  The idea came out of Marketing as a potential solution to a packing error problem that was causing nasty-gram traffic in Customer Service and the hard loss of customers.  An Operational defect that had a direct and trackable negative effect on both Customer Service and Marketing – as these process problems almost always do.  He doesn’t know what the ROI is because it’s silly to even calculate it – the incremental profit generated by decreased packing errors (cost reduction in “make good” shipments and returns processing) since program implementation is so large relative to the cost of printing the message on the box that the ROMI would have 8 or 9 figures to the left of the decimal point.

That’s not including any customer metrics like slowing of customer defection rate and halo effects, because those are obvious to them.  Customers simply stopped defecting due to mispacked packages.

Period.  Do you need to run a lot of math on a result like that to figure out if it’s profitable?