ETIQUETTE : THINGS YOU JUST DON’T DO

Ok, prepare for a rant or two.

First, REPLY ALL.  If I ever find out who designed / enabled the easy to find and use REPLY ALL button I will go to their house and run over them with my truck.  Then back up and run over them again.

It should not be an option, probably ever.  It should not be an easy to use and easy to find option, probably ever.  face it, do you really really ever need to reply all?  Sure, maybe, once in a while.  But if that button was hard to find you would find that you really don;t need to fill up everyone’s mail boxes with all kinds of stuff, relevant or not.

I despise when our team sends back and forth to each other, little jokes, comments, funny pictures and videos.   I mean, if a couple of folks are having a ha ha ha conversation (“Oh yeah?”  “Sez you!”  “Yo mama!”) and sending it out to the entire 50-member group, with sizzling comments and cute pictures, that easily adds up to hundreds of emails.  99% of which are immediately deleted and 89% not even read.  (I have the statistics.)  Come on, people!

This week I got over 575 emails, over 300 were reply-all conversations back and forth.  Only 5 or 6 were relevant to me, those I needed to actually read.  I have a rule that puts in junk box now any picture or video attachments.

I’m the guy that now deletes any email not sent directly to me.  If you send it to a group you did not send it to me.  You do not need for me to specifically read it, you sent it to many people.  Any of them can read it and if I need to know something, I will find out.  That is, if you want me to read it, and if you want me to get the info, send it to me.  A little extreme?  Perhaps, but things are getting out of hand.

Remember that 1960s musical, “How to Succeed in Business Without Really Trying”?  Here’s an early conversation between two corporate types.

Guy X “Did you get my memo?”

Guy Y “What memo?”

Guy X “What memo?  My Memo about memos.  We’re sending out too many memos and it’s gotta stop!”

Guy Y “Okay.  I’ll send out a memo.”

Funny, yeah.  But not so funny.  So, stop the madness.  Be that guy in your group that says STOP reply all.

Second, elevator etiquette.  Look, everyone knows it, when the doors start to close you folks outside the elevator stand back and do not attempt to get inside.  That’s a universal rule.  That will stop the doors and for your safety the doors will slowly open.  Then let you in and after that will start closing again.  And probably stop because some other jack ass sticks his hand in to get on.  Again.  Groan.

Yesterday I got on the elevator by myself and the doors started to close.  When they had just about touched a slender hand slunk in and opened them up.  A Barbie wanna-be smiled at me and said ,”Sorry” and shrugged as the doors started to close.  Just before they closed I stuck my hand out and stopped them.  I walked just outside the doors and looked at her and as the doors began to close again I stuck my arm inside and stopped the doors and got back inside.  “Sorry,” I smiled.  She did not smile at me as the doors closed again.

So yes, there are jackasses out there like me.  But you don’t know which side of the elevators doors we might be on.  So therefore everyone please remember the rule:  when the doors start to close let them close.  Leave them alone.  Wait for the next elevator.  What are you in such a rush for anyway?  To read that funny email and reply all?  Jeez!

Third , come to meetings on time.  It’s not that hard.  It’s okay to even arrive a minute or two early.  I know what you’re trying to demonstrate: that you are so busy and so important that you can only run from one meeting to another and only get there after it starts.  Again and again.  Day after day.  So we who are already there have to set around and chit chat (Watch the game last night, what is Paris Hilton doing now, see the youtube video, etc.)  Or if we go ahead and start without you we will have to back up and start again when you arrive.  You have wasted everyone’s time.  If there are 9 people in the meeting and you are 5 minutes late that is 45 man-minutes that are spent because of you.  Again and again.  Day after day.

Now I know sometimes there is just no other way.  You really do have back-to-back and you cannot get to the next one early or on time.  But if it happens every day, several times a day (you know who you are) it is really just discourteous and disrespectful and no one buys that crap about you being so busy and so important.  A 10 am meeting means it starts at 10 am because people have begun to arrive a couple of minutes before 10 am.  It’s not that 10 am is the time people start to arrive and 10:05 or 10:10 it actually starts.  Because that likely will make it go over the 11 am ending time.

It’s like the speed limit sign.  When the sign says 55 mph speed limit, that is not a lower limit, but an upper limit.  That is not a minimum but a maximum.  Read the fine print.  When you are invited to a 10 am meeting it’s okay to be there one minute before, be prepared, and contribute and all will be well.  We will be much more productive.

Okay, the rants are over.  For now.

 

 

LIFETIME VALUE: HOW PREDICTIVE ANALYSIS IS SUPERIOR TO DESCRIPTIVE ANALYSIS

DESCRIPTIVE ANALYSIS

Life-Time Value is typically done as just a calculation, using past (historical) data.  That is, it’s only descriptive.

While there are many versions of LTV (depending on data, industry, interest, etc.) the following is conceptually applied to all.  LTV, via descriptive analysis:

1)Uses historical data to sum up each customer’s total revenue.

2)This sum then has subtracted from it some costs: typically cost to serve, cost to market, maybe cost of goods sold, etc.

3)This net revenue is then converted into an annual average amount and depicted as a cash flow.

4)These cash flows are assumed to continue into the future and diminish over time (depending on durability, sales cycle, etc.) often decreasing arbitrarily by say 10% each year until they are effectively zero.

5)These (future, diminished) cash flows are then summed up and discounted (usually by Weighted Average Cost of Capital) to get their net present value.

6)This NPV is called LTV.  This calculation is applied to each customer.

Thus each customer has a value associated with it.  The typical use is for marketers to find the “high valued” customers (based on past purchases).  These high valued customers get most of the communications, promotions / discounts, marketing efforts, etc.  Descriptive analysis is merely about targeting those already engaged (much like RFM).

This seems to be a good starting point but, as is usual with descriptive analysis, contributes nothing about WHY.  Why is one customer more valuable, will they continue to be?  Is it possible to extract additional value, but at what cost?  Is it possible to garner more revenue from a lower valued customer because they are more loyal or cost less to serve?  What part of the marketing mix is each customer most sensitive to?  LTV (as described above) gives no implications for strategy.  The only strategy is to offer and promote to the high valued customers.

 

PREDICTIVE ANALYSIS

How would LTV change using predictive analysis instead of descriptive analysis?  First note that while LTV is a future-oriented metric, descriptive analysis uses historical (past) data and the entire metric is built on that, with assumptions about the future applied unilaterally to every customer.  Prediction will specifically thrust LTV into the future (where it belongs) by using independent variables to predict the next time until purchase.  Since the major customer behavior driving LTV is timing, amount and number of purchases, a statistical technique needs to be used that predicts time until an event.  (Ordinary regression predicting the LTV amount ignores timing and number of purchases.)

Survival analysis is a technique designed specifically to study time until event problems.  It has timing built into it and thus a future view is already embedded in the algorithm.  This removes much of the arbitrariness of typical (descriptive) LTV calculations.

So, what about using survival analysis to see which independent variables, say, bring in a purchase?  This decreasing time until purchase tends to increase LTV.  While survival analysis can predict the next time until purchase, the strategic value of survival analysis is in using the independent variables to CHANGE the timing of purchases.  That is, descriptive analysis shows what happened; predictive analysis gives a glimpse of what might CHANGE the future.

Strategy using LTV dictates understanding the causes of customer value: why a customer purchases, what increases / decreases the time until purchase, probability of purchasing at future times, etc.  Then when these insights are learned, marketing levers (shown as independent variables) are exploited to extract additional value from each customer.  This means knowing that one customer is say sensitive to price and that a discount will tend to decrease their time until purchase.  That is, they will purchase sooner (maybe purchase larger total amounts and maybe purchase more often) with a discount.  Another customer prefers say product X and product Y bundled together to increase the probability of purchase and this bundling decreases their time until purchase.  This insight allows different strategies for different customer needs and sensitivities, etc.  Survival analysis applied to each customer yields insights to understand and incent changes in behavior.

This means just assuming the past behavior will continue into the future (as descriptive analysis does) with no idea why, is no longer necessary.  It’s possible for descriptive and predictive analysis to give contradictory answers.  Which is why “crawling” might be detrimental to “walking”.

If a firm can get a customer to purchase sooner, there is an increased chance of adding purchases–depending on the product.  But even if the number of purchases is not increased, the firm getting revenue sooner will add to their financial value (time is money).

Also a business case can be created by showing the trade-off in giving up say margin but obtaining revenue faster.  This means strategy can revolve around maximization of cost balanced against customer value.

The idea is to model next time until purchase, the baseline, and see how to improve that.  How is this carried out?  A behaviorally-based method would be to segment the customers (based on behavior) and apply a survival model to each segment and score each individual customer.  By behavior is typically meant purchasing (amount, timing, share of products, etc.) metrics and marcom (open and click, direct mail coupons, etc.) responses.

 

AN EXAMPLE

Let’s use an example.  Table 1 shows two customers from two different behavioral segments.  Customer XXX purchases every 88 days with an annual revenue of $43,958, costs of $7,296 for a net revenue of $36,662.  Say the second year is exactly the same.  So year 1 discounted at 9% is NPV of $33,635 and year 2 discounted at 9% for two years is $30,857 for a total LTV of $64,492.  Customer YYY has similar calculations for LTV of $87,898.

TABLE 1  
CUSTOMER DAYS BETWEEN PURCHASES ANNUAL PURCHASES TOTAL REVENUE TOTAL COSTS NET REV YR 1 NET REV YR 2   YR1 DISC YR2 DISC LTV AT 9%
XXX 88 4.148 $43,958 $7,296 $36,662 $36,662 $33,635 $30,857 $64,492
   
YYY 58 6.293 $62,289 $12,322 $49,967 $49,967   $45,842 $42,056 $87,898

 

The above (using descriptive analysis) would have marketers targeting customer YYY with > $23,000 value over customer XXX.  But do we know anything about WHY customer XXX is so lower valued?  Is there anything that can be done to make them higher valued?

Applying a survival model to each segment outputs independent variables and shows their effect on the dependent variable.  In this case the dependent variable is (average) time until purchase.  Say the independent variables (which defined the behavioral segments) are things like price discounts, product bundling, seasonal messages, adding additional direct mail catalogs, offering online exclusives, etc.  The segmentation should separate customers based on behavior and the survival models should show how different levels of independent variables drive different strategies.

Table 2 below shows results of survival modeling on the two different customers that come from two different segments.  The independent variables are price discounts 10%, product bundling, etc.  The TTE is time until event and shows what happens to time until purchase based on changing one of the independent variable.  For example, for customer XXX, giving a price discount of 10% on average decreases their time until purchase by 14 days.  Giving YYY a 10% discounts decreases their time until purchase by only 2 days.  This means XXX is far more sensitive to price then YYY–which would not be known by descriptive analysis alone. Likewise giving XXX more direct mail catalogs pushes out their TTE but pulls in YYY by 2 days.  Note also that very little of the marketing levers affect YYY very much.  We are already getting nearly all from YYY that we can, no marketing effort does very much to impact the TTE.  However, with XXX there are several things that can be done to bring in their purchases.  Again, none of these would be known without survival modeling on each behavioral segment.

 

Table2
  xxx yyy
VARIABLES TTE TTE
price discount 10% -14 -2
product bundling  -4 12
seasonal message   6 21
5 more catalogs  11 -2
online exclusive -11  3

 

Table 3 below shows new LTV calculations on XXX after using survival modeling results.  We decreased TTE by 24 days, by using some combinations of discounts and bundling and online exclusives, etc.  Note now the LTV for XXX (after using predictive analysis) is greater than YYY.

 

TABLE 3
CUSTOMER DAYS BETWEEN PURCHASES ANNUAL PURCHASES TOTAL REVENUE TOTAL COSTS NET REV YR 1 NET REV YR 2   YR1 DISC YR2 DISC LTV AT 9%
XXX 64 5.703 $60,442 $10,032 $50,410 $50,410 $33,635 $30,857 $88,677
   
YYY 58 6.293 $62,289 $12,322 $49,967 $49,967   $45,842 $42,056 $87,898

 

What survival analysis offers, in addition to marketing strategy levers, is a financial optimal scenario, particularly in terms of costs to market.  That is, customer XXX responds to a discount.  It’s possible to calculate and test what is the (just) needed threshold of discounts to bring a purchase in by so many days with the estimated level of revenue.  This ends up being a cost / benefit analysis that makes marketers think about strategy.  This is the advantage of predicative analysis–giving marketers strategic options.