Tag Archives: BI

Omni-Channel Cost Shifting

One of the great benefits customer lifecycle programs bring to the party is unearthing cross-divisional or functional profitability opportunities that otherwise would fall into the cracks between units and not be addressed.  What I think most managers in the omni-channel space may not realize (yet) is how significant many of these issues can be.

To provide some context for those purely interested in the marketing side, this idea joins quite closely to the optimizing for worst customers and sales cannibalization discussions, but is more concerned with downstream operational issues and finance.  Cost shifting scenarios will become a lot more common as omnichannel concepts pick up speed.

Shifty Sales OK, Costs Not?

Why is cost shifting important to understand?  Many corporate cultures can easily tolerate sales shifting between channels because of the view that “any sale is good”.  On the ground, this means sourcing sales accurately in an omni-channel environment requires too much effort relative to the perceived benefits to be gained.  Fair enough; some corporate cultures simply believe any sale is a good sale even if they lose money on it!

Cost shifting  tends to be a different story though, because the outcomes show up as budget variances and have to be explained.  In many ways, cost shifting is also easier to measure, because the source is typically simple to capture once the issue surfaces.  And as a cultural issue, people are used to the concept of dealing with budget variances.

Here’s a common case:

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Choosing the Size of Control Groups

The following is from the December 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

 Q:  I am a big fan of your web site and read your Drilling Down book. Great work!

A:  Thanks for the kind words!

Q:  I was wondering if you could help me picking the right control group size for a project of ours?  The population is 25 million telco customers that for which we want to do a long term impact analysis (month by month) in regards to revenue increase versus control group.  The marketing initiatives are mix of retention, lifecycle and tactical/seasonal activities.  We want to measure revenue increase through any of the marketing activities compared to control group.

A:   Great project, this is the kind of idea that can really improve margins if you can find out which specific tactics drop the most profit to the bottom line.

Q:   I have searched the web for some help and found calculators that say: On 25 million and smallest expected uplift of 0.1% and highest likely rate of > 5% the calculator gives 250k (1%).  Is that sufficient to calculate the net impact on the remaining base?  Would be very grateful if you could give me your thoughts.

A:  Well, it could be and might not be…

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Hacking the RFM Model

The following is from the May 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  First of all thank you for your help.  I have some questions I would be pleased if you answer them for me.

A:  No problem!

Q:  1. RFM analysis – is it possible to use some other ranking technique rather than quintiles? Using quintiles for bigger databases will cause many tied values, isn’t it a problem?

A:  Sure, you can use it any way it works best for you.  There is no “magic” behind quintiles, you can use deciles or whatever works best. It’s the idea of ranking by Recency, Frequency, and Value that is the key concept in the model.

I’ve seen dozens and perhaps hundreds of variations on the core RFM model, depending on how you classify a “variation”.  One change that’s common is changing the scaling, as you mention above, to accommodate the size of the database.  Smaller databases use quartiles or even tertiles.  Larger databases, choose the ordered distribution that meets the need.

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Got Discount Proneness?

Discount Proneness is what happens when you “teach” customers to expect discounts.  Over time, they won’t buy unless you send them a discount.  They wait for it, expect it.  Unraveling this behavior is a very painful process you do not want to experience.

The latest shiny object where Coupon Proneness comes into play is the “shopping cart recapture” program.  Mark my words, if it is not happening already, these programs are teaching customers to “Add to Cart” and then abandon it, waiting for an e-mail with a discount to “recapture” this sale – a sale that for many receiving the e-mail, would have taken place anyway. 

The best way to measure this effect is to use a Control Group.

When I hear people talking about programs like this (for example, in the Yahoo analytics group) what I hear is “the faster you send the e-mail, the higher the response rate you get”.

That, my friends, is pretty much a guarantee that a majority of the people receiving that e-mail would have bought anyway.  Hold out a random sample of the population and prove it to yourself.  There is a best, most profitable time to send such an e-mail, and that time will be revealed to you using a controlled test.  The correct timing is almost certainly not within 24 or even 48 hours.

That is, if you care about Profits over Sales, and trust me, somebody at your company does.  They just have not told you yet!

When you give away margin you do not have to give away on a sale, that is a cost.  Unless you are including that cost in your campaign analysis, you are not reflecting the true financial nature of the campaigns you are doing.  If you are an analyst, that’s a problem.

If you are using cart recapture campaigns, please do a controlled test sooner rather than later.  Because once your customers have Discount Proneness, it will be very painful to fix.

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Heavy Lifting

Another eMetrics (Toronto) has passed and I have to say this:  Web Analysts and Marketers proved once again they are up to the task of continuously improving the Productivity of their efforts!

At the same time, (and as I expressed during the sessions on the analytical culture), I fear that many in the web analyst community are becoming very “inwardly focused”.  They tend to talk more among themselves about the pennies they are making / saving while tripping over the dollars that are right there to be had if they reached out to other analytical disciplines in the company or measurement community.

Many among us knew this was a danger from our BI experiences.  If all you ever do is talk to each other about new shiny objects, your contribution to the business effort can suffer.  BI struggles every day with this weight, the challenge of being labeled “really smart but irrelevant”.  I don’t think we want this to happen to WA.

So with this backdrop, some of the conversations I heard at eMetrics Toronto about certain measurement practices were disturbing.  For example, it seems very few people are measuring their customer contact efforts properly, and in time this lack of analytical rigor is going to damage the WA effort for all practitioners.

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Use Discounts for Customer Retention?

The following is from the March 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  Most CRM experts agree that discount is a terrible way to attract new customers.  They seem to all agree that these “transaction buyers” are money-losing customers and have no loyalty.

A:  I think using discounts profitably for customer acquisition depends a lot on your “Brand Personality” and your business model.  That said, often people screw this up and attract the wrong kind of customer.

Q:  But, I have seen a  lot of different opinions on the use of discounts to increase loyalty and retention among current customers.  I have seen experts contradicting themselves on this subject saying that discount is a terrible way to reward gold customers or to move up customers to a “better segment” and after some time they contradict themselves mentioning a successful discount case study (points are a common method used).  Jim, what is your opinion about using discounts as a weapon in a retention program?

A:  First, we have to define “discount”.  Price discounts have the effect of reducing margins, but so do “better service” ideas like “VIP phone lines” and loyalty programs.  So you can take your discount on the top line or the operational line, the fact is it costs money to provide good service to best customers in hopes of keeping them.  I mean, what’s the $10 million you spent on a CRM system?  Choose your poison, it costs money to retain customers.

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Off the Marketing Richter Scale

Man, what a month in Marketing land.

First, you have one of the largest Ad Agencies in the world admitting their business model is broken, because agencies are not in charge of the fundamentals of Branding – service, innovation, engagement, and execution.  I would add the same thing could often be said of the client side; MarCom people spend way too much time on “Com” and not enough on “Mar” – is it time for a realignment?

Then, in an even more spectacularly unexpected move, you have C-Level folks at 2 gargantuan Advertising Agencies (though both part of WPP) co-writing an article declaring that Brand and Response are the Same.  Here’s the opener: “the value that brands bring to a company’s total business value is exaggerated.”

Holy Branding Batman, that’s one heck of a thing to say for an Ad Agency, know what I mean?  But they are absolutely right, the nature of a Brand has changed, this ain’t the 1960’s.

This is how they get to “the singularity”:

“What was once sales is now enhancing the brand expe­rience, because through direct marketing technology and strategies, a brand can reinforce its ability to listen, customize and learn from the consumer. This is not just direct marketing, its direct engagement with every potential customer, sometimes at the moment they’re introduced to the brand.  In fact, in a world of compressed consumer decision-making, direct response is now a potent form of brand­ing.”

I love it when you talk that way.

Let’s be clear on this.

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Sales or Profits?

Seems the previous post (Best Seller Gone Bad) really hit home for people; perhaps we should drill into  this a bit.  So:

1.  Is the impact of your work evaluated against Sales or Profits?  (example)

2.  Do you think this evaluation approach is correct for your job and company?  Why? 

3.  Would you change this evaluation method if you could?

4.  What is holding you back from trying to make this change?

Personally, I always choose Profits if I can; the leverage is so much higher than Sales.  It’s much easier to generate $5 in Profits than $5 in Sales for any given $1 in budget, because there is generally so much waste in the Marketing system.

Update: OK, how about answering this question – when your work performance is evaluated, what percentage of this measurement is based on qualitative factors?  quantitative factors?

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NCDM 08: What was Hot?

With two very intense days of 5-track sessions going at NCDM 08, it was difficult to pay attention to everything that was going on.  Still, by picking up handouts from sessions I did not go to, I could get a sense of what the hot topics were this year:

1.  Web Plays Data Friendly – Almost every session had a web component in it, the BI folks are getting it done.  The data is coming off the web / out of web analytics and into the data warehouse so everybody knows what is really going on. 

Much of this work is being done by the big service bureaus, not the companies themselves, as far as I could tell.  Web Intelligence, baby.

2.  Media Mix Models – which typically show online Display advertising as a poor choice for allocating marketing budget to when you are also running offline media; the yield is quite poor versus almost every other media.  Search, as you might expect, Rocks on Productivity, though TV still rules for Productivity and moving the needle.

The implication here is you’d be much better off running TV to generate / amplify Search behavior than running Display to do the same.  Offline for Awareness, Online for Intent / Desire

3.  Contact Optimization – I’ve written before about what ultimately happens when you don’t have a Contact Strategy.  At some point, BI will measure the bottom line impact of every division in the company pounding customers with the division’s own contact strategy (Hint: you are driving your customers crazy).  Then, move all customer contact to a centralized model which controls contact by source of new customer and value generated, measured through controlled testing.

This movement should not be surprising, given the whole “customer in control” and “social” movements which onliners give so much lip service to but never take action on.  Well, not never, but rarely.

4.  Measuring Engagement – yes, Engagement.  OFFLINE, as well as online.  The overwhelming message was this: it does not make any sense, and actually costs you money, to keep pounding your customers with any kind of communication when they don’t respond / interact.  You can measure dis-Engagement, and when you see it, you should stop communicating – online or offline. 

And these folks proved it, over and over, with real math.  See related #3 above.  If you’d like to see a detailed e-mail example of this concept, check out this case study.

And of course:  Models and more models.  Like Hierarchical Bayesian and Disaggregate Discrete Choice.

Boy, I love it when you talk like that.


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Sherlock Holmes Problem

I think this is probably the last Learning and Teaching issue in Online Marketing (series starts here) before attempting to evaluate and summarize the challenge.  I would like to receive comments from you on the Sherlock Holmes Problem.

“There are two types of minds — the mathematical and what might be called the intuitive. The former arrives at its views slowly, but they are firm and rigid; the latter is endowed with greater flexibility and applies itself simultaneously to the dive.”

Blaise Pascal

In his post How the Skills of a Night Auditor Translate into Web Analytics, Christopher Berry explores a notion we have wrestled with a lot while developing the WAA’s Certified Web Analyst Test – can you teach someone to be curious in a “business analytics” way?  Or are people just born with / socialized into this skill set?  How do you measure and test someone for “analytical curiosity”?

We have referred to these issues internally on the Education Committee as the “Sherlock Holmes” problem.  The issue is not the ability to read and interpret reports, or write up findings, or anything like that.  It’s the ability to see coincidences or oddities in the data, to conceptualize linkage or relationships others don’t see, to follow the data trail (or blaze it) right down to Root Cause.

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