Category Archives: Analytics Education

Branding vs. Direct Marketing Metrics

Analytical Culture, Analytics Education, Brand Management, DataBase Marketing, Driller Q & A, Measuring Engagement

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

Topic Overview

Hi again folks, Jim Novo here.

Oh dear. A marketer caught between branding and direct. Each approach has it’s own data and metrics that either can be important or not to the folks working with the other approach. Can the measurment of success using these two approaches be reconcilled? It’s possible, but does that make sense if the “success outcomes” are radically different? Gonna be a deep Drillin’…

Q:  We constantly try to quantify the value of web sites as a branding vehicle.  The thing that keeps gnawing at me is we will often report the average time spent on site.  This seems like it should have a value we could wrap into our ROI, but as it is, it stands largely on its own.  

Are you aware of, or have any thoughts on, how we might put an actual value to this?  Is it enough to show lift without respect to time, and to talk about return visits in terms of frequency models, or is there some way to drill down to a fundamental value of what a person-second on your site could be worth (obviously the content of the site will impact how much of that value you actually got)? 

A:  I’ve done a bunch of work like this and personally, I think you measure branding with branding metrics and direct with direct metrics.  If the CPG people understand the value of advertising in terms of brand affinity, recall, intent to purchase, and so forth, then it seems to me that is what you measure.  They have already made the “final connection” between these metrics and ROI, so it’s not really up to the marketer to make those connections.  They believe increasing intent to purchase = advertising worked.  And I’m not sure you really can make a connection, because the “units” you are measuring are different and the math ultimately fails.

Here’s why.  Traditional advertising has never been judged by the “value of the customer,” it is judged by the “value of the media.”  The customer is “reach” and has no individual value; individual customers are totally exchangeable as long as the reach is the same.  Any single person is irrelevant; it does not matter what they do or don’t do.  If there is no “customer,” I’m not sure how you would ever get to ROI.  It is assumed from reach comes sales, and this is proven using branding metrics, not ROI.

Q:  I’ve gone back and forth on this and approached it from a few different angles For example, determine cost of 1 second of TV advertising per person.  You could use this information to calculate how much it would have cost to communicate the total person-seconds you had on your site in a particular month, but this is fraught with problems as you might guess, and am looking for another point of view.

Continue reading Branding vs. Direct Marketing Metrics →

Customer Segmentation: Tangible vs Intangible Cost, Let Data Define Segments

Analytics Education, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement

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

Topic Overview

Hi again folks, Jim Novo here.

This month in the newsletter we answer questions on the nitty gritty of the actual discovery work by taking a very deep look into the whys and hows of segmenting customers. Straight-up and to the point, put on those data shoes and Let’s do some Drillin’!

Q:  Hi Jim, I’m a great fan of your work!

A:  Well, thanks for your kind words.

Q:  I have a basic question for you.  We are an online retailer and thus use email as the primary marketing communication channel (we do use Direct Mail to our best customers around holidays).

A:  Those are smart choices.  I’ve seen some stats on using direct mail to drive lapsed online customers either back online or into a store that are very encouraging, real money-makers for retail.  Definitely worth testing, though in both cases, the product mix averaged higher ticket than your category typically does.

Q:  However, we don’t have a set customer segmentation technique and thus no specific customer segments.  One outside consultant, a statistician, had suggested looking at a new customer’s activity in the first 30 days and then classifying them into High Spender, Frequent Transactor, etc. segments.  Not sure how well it works.

A:  That’s quite unusual, I think.  It would work in the first 30 days, but I think you would have to re-classify every 30 days using a scheme like that.  Considering web-only behavior, the typical retail lifecycle beyond 2nd purchase (many buy only one time) is a ramping to a peak and then a more gradual, but still steep, falloff in purchases.  The model above would not take this into account, and while the initial label might be accurate, it soon would not be.  That’s not to say these kinds of models don’t work, but it usually takes years of testing and study to perfect them.  “Data miners” often believe the numbers will simply tell them things like this, but they don’t take into account the human behavioral and other mitigating factors which may not be in the data.  

For example, Recency and Latency are really “meta-data” about customer behavior; they are data created from other data.  You can’t just look at the first 30 days of transactions and give a customer a label; customers have LifeCycles and you drive the highest ROI when you take advantage of knowing these cycles and acting on them to increase profits.

Q:  I feel that we target our customers primarily by their category purchases, and not by any kind of behavioral model.

Continue reading Customer Segmentation: Tangible vs Intangible Cost, Let Data Define Segments →

New RFM: Customer Retention in “Subscription” Businesses

Analytical Culture, Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement, ,

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

Topic Overview

Hi again folks, Jim Novo here.

How do you measure likelihood of customer defection when purchase behavior is highly orchestrated or executed due to repetitive billings? Yea, it’s a bit more complicated because “orders” really can’t express any kind of behavioral change, can they? So, you have to find indicators other than sales to provide the triggers. The Drillin’ the Drillin’ …

Q:  Jim, first let me say that I am enjoying your book VERY MUCH!!  Nicely done, and a nice job of integrating it with the CRM paradigm, 1-to-1 etc… I’m reading very slowly and finished the Latency Metric Toolkit.

A:  Great!  Thanks for the kind words.

Q:  I had a couple of questions on the Latency toolkit and the Latency tripwire, especially as it applies to environments with built in cycles for repeat purchases.

I am in a business where our resources are quarterly based, i.e. customers purchase our resource use them for a quarter and re-purchase the next quarter’s resource.  That is, we have a built in pattern, where customers would purchase our resources each quarter.  I was wondering how well I can use Latency with this type of built in cycle or if I would have any problems applying your Latency concepts to it, maybe they apply that much more readily?   In our case we try to call most folks who haven’t purchased within 2 weeks of a new quarter beginning.

A:  Right, a subscription-type business.  This is also an issue with utilities and other like businesses who bill about the same amount each month or have contracts for service (like wireless).  The answer is if the revenue generation really doesn’t represent anything to do with the behavior, then you simply look for other parameters to profile.  For example, a friend of mine was responsible for analyzing the likelihood of subscription renewal in a business that provided the content online.   Increasing Latency of visit was a warning flag for pending defection, and they triggered their most profitable campaigns based on last visit Recency.  In wireless, the correlations are found in payment Latency and age of phone.

Continue reading New RFM: Customer Retention in “Subscription” Businesses →