Category Archives: DataBase Marketing

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 →

New RFM: Predicting Student Churn

DataBase Marketing

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.

Only attempt to control what you can actually get control of. In a massive operational system, sometimes everybody agrees they would like to improve this or that customer retention metric. But the reality is, because of the way the system is structured / managed / operated, it will be nearly impossible to improve the specific metric. Such is the case with a University. So you gotta carefully evaluate and choose what metrics you think you have a chance to control. Sound difficult? Sure it is. But difficulty hasn’t stopped us yet, has it fellow Drillers?

Q: It is clear that retention of students is a complex issue. The students’ satisfaction with the university will be partly determined by their experience during their first semester with the university. I have identified that each service encounter will contribute to the overall impression that the student has of the university. Some encounters are ‘moments of truth’ and will have a major impact on the student’s perceptions of the university.

A: Hmm, interesting.

You just got very lucky, I happen to have first-hand experience on this topic, which is very rare, as not many educational institutions are thinking this way. You should be congratulated for making this connection, though it will be a difficult battle dealing with the university administration on making changes, in my experience.

Q: I would much appreciate if you could advice me on the retention strategy and what approach the university should take to retention. Also, any ideas on management of moments of truth, particularly what enhances and detracts from the customers’ encounters with the university.

A: Please consider this old business maxim: Only attempt to control what you can actually control; otherwise you will end up not having an affect on anything.

It very well may be that the various “touchpoints” exist and can be defined, but can you reasonably control any of them? Which ones, and how will you control them? This is where you need to focus your efforts.

In my experience, a university is not the kind of place where you can undertake a “customer service education program” with employees and expect compliance at all the touchpoints. So what you have to do is pick the major points of influence where you know you can exert some control and seek to prove your case with facts and testing.

Continue reading New RFM: Predicting Student Churn →

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 →