Tag Archives: Analytical Culture

Intra-Company Promotional Risks

Analytical Culture, Analytics Education, DataBase Marketing, Driller Q & A, Marketing thru Operations, ,

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.

Eating Your Own is not a great idea. Yet in many large companies, different divisions literally try to steal customers / sales from each other using the common customer database. Sure, everyone gets real excited over the plans to merge databases across the company and get the “full view” of the customer, and it makes perfect sense. Perfect sense – if you’re also institutionally prepared for wild battle over customer ownership and value stealing to come. This is particularly true (and difficult to deal with) in multi-channel retail environments.

Got your Drillin’ shows on? Let’s take a stroll through subsidy cost land …

Q:  Hi Jim,

Our company has 8 divisions and we completed the integration of all the customer databases a couple of years ago.  We have the 360 degree view of the customer, at least as far as sales transactions, across the entire company in one database.

A:  Congratulations!  However, I note not joy, but some kind of concern in you voice.  I’m just waiting for the “But…”

Q:  The database services group I am part of is under IT.  We respond to requests from the different divisions for customer analysis and the creation of promotional lists for email, direct mail, and telephone campaigns.  It’s interesting because our group is finally directly involved with increasing the profitability of the business and we have some input, which makes the job more rewarding.  I picked up your book because I thought reading it might increase our ability to contribute.

A:  Well, again, congratulations!  But I’m still getting that nagging feeling from your tone.  Still waiting for the “But…”

Q:  I’ve got a two part question for you:

1.  What I am seeing is the different divisions promote to “best customers” of the company as a whole, or even try to target best customers of another division for their campaigns.  It seems to me that contacting these same people over and over from the different perspectives of the divisions is not optimizing customer value, and might actually be irritating to the customers (I know it would be irritating to me).  

The contact frequency across all divisions to the same customer can reach 4 – 6 times a month through various media (phone, mail, e-mail).  Also, there is no customer retention effort going on that I am aware of, it appears it is all acquisition oriented but the main targets are customers of other divisions.

A:  Oh boy, the database marketing pendulum has swung the other way, from “we don’t know what to do with all this data” to “we’re really maximizing that database asset”. You are correct to be concerned about this issue.  Talk about “push marketing”… the intent of each division is “pull” because of the targeting but the result is “push” because of the volume and “noise” created at the customer level.  Too much too fast.

Continue reading Intra-Company Promotional Risks →

Difference between RF(M) Scores & LifeCycle Grids?

Analytical Culture, Analytics Education, 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.

Both RF(M) scoring and Lifecycle Grids use the same key predictive metrics – Recency and Frequency. So what’s the difference? RFM is a predictive “snapshot” at a specific point in time; LifeCycle Grids are more like a “movie” designed to be predictive over different periods of time. Another way to think of this: RFM is tactical, LifeCycle Grids are strategic.

You dig? Let’s Drill …

Q:  We’re a telecom company trying to get a handle on customer churn and defection, so we can come up with some programs that will hopefully extend customer participation.  We live in the no contract space, offering a service that’s an add on to wireless phone service, so we don’t have a good indicator as to when the customer relationship might end.

A:  Ah, yes.  Your business model is “built for churn”, as I said on my blog the other day.  The behavior then is more like retail, where independent decisions are made in an ongoing way, deciding again and again to purchase.

Q:  I think your LifeCycle Grids method will show best what is happening to our customers.  If using this method, there doesn’t seem to be any reason to do the RF scoring as customers are just going into cells based on where they fall in the Recency and Frequency spectrum.  Is that correct?  Is there any real  difference between RF scoring and the LifeCycle Grids approach?

A:  You are partially correct, they are two versions of the same idea – both are scoring using Recency and Frequency. The traditional RF(M) scoring where customers are ranked against each other is a “relative” scoring method used primarily for campaigns – it is tactical, an allocation of resources model. 

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Is Your Digital Budget Big Enough?

Analytical Culture, Digital Analytics, Measuring Engagement

At a high level, 2014 has been a year of questioning the productivity of digital marketing and related measurement of success.  For example, the most frequent C-level complaint about digital is not having a clear understanding of bottom-line digital impact. For background on this topic, see articles here, here, and here.

I’d guess this general view probably has not been helped by the trade reporting on widespread problems in digital ad delivery and accountability systems, where (depending on who you ask) up to 60% of delivered “impressions” were likely fraudulent in one way or another.  People have commented on this problem for years; why it took so long for the industry as a whole to fess up and start taking action on this is an interesting question!

If the trends above continue to play out, over the next 5 years or so we may expect increasing management focus on more accurately defining the contribution of digital – as long as management thinks digital is important to the future of the business.

If the people running companies are having a hard time determining the value of digital to their business, the next logical thought is marketers / analysts probably need to do a better job demonstrating these linkages, yes?  Along those lines, I think it would be helpful for both digital marketers and marketing analytics folks to spend some time this year thinking about and working through two of the primary issues driving this situation:

1.  Got Causation?  How success is measured

In the early days of digital, many people loved quoting the number of “hits” as a success measure.  It took a surprisingly long time to convince these same people the number of files downloaded during a page view did not predict business success ;)

Today, we’re pretty good at finding actions that correlate with specific business metrics like visits or sales, but as the old saying goes, correlation does not imply causation.

If we move to a more causal and demonstrable success measurement system, one of the first ideas you will encounter, particularly if there are some serious data scientists around, in the idea of incremental impact or lift.  This model is the gold standard for determining cause in much of the scientific community.  Personally, I don’t see why with all the data we have access to now, this type of testing is not more widely embraced in digital.

Continue reading Is Your Digital Budget Big Enough? →