Category Archives: Marketing thru Operations

New RFM: Segment Wireless Customers by Behavior or Demos?

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.

If you’ve never actually segmented customers by behavior, it can be a bit tricky exercise.

Just remember this: if improved customer retention is the goal, you want to segment with ideas and data that will lead to metrics specialized in driving successful retention programs. For example, demographics are descriptive of the customer; segmenting by demographics will group customers by description. This segmentation does not really indicate anything about their behavior, so will rarely give you the tools or leverage needed to change behavior. If you want to change behavior, segment using behavior.

Make sense? Then on to the Drillin’ …

Q: I have taken up a new assignment in this new financial year in my company, a Cellular / Mobile connections provider. I would like some direction from you; also I have suggested your book to my management.

A: Well, thanks for the plug on the book and I’ll give the “direction” a try!

Q: Objective: To create loyal customers who become brand evangelists

Areas covered:

  1. To drive customer loyalty to ensure 80% of the customers recommend brand to others.
  2. Customer Behaviour Profiling: Create an action oriented customer profile, use profiles to create marketing & service programs to retain & increase value of customers.
  3. Predictive Marketing / Promotions: To predict the likelihood of future events based on customer models & to predict the profitability of a promotion to encourage customers to do what we want them to do & achieve the highest ROI (Return on investment). Predict when a customer is about to defect / leave us.
  4. LifeTime Value: To find what a customer is worth in the future and based on this to find how much you could spend on retaining them & still make a profit.

Please reply on how to start this activity?

A: Yikes! That’s a pretty long list of “areas to be covered”, you are going to be very busy! Some of it sounds pretty familiar too, like I’ve read it on my web site you might want to get that book after all!

The creation of retention programs always starts with customer segmentation, you have to understand the behavior you have before you can create programs to modify behavior.

That probably means starting with #4, LTV. You want to look at LTV by segment.

Get records of defected customers, put them in a spreadsheet or database, and determine:

  • Average length of time as subscriber
  • Average spending over that time
  • % of this spend that is considered “profit”, which you can use as a proxy for LTV.

In the beginning, you can use a company “profit” average for LTV until you get more sophisticated. In communications, the number often used is EBITDA Margin; ask your finance people what you should use to determine % of spending that is LTV.

Once finding the average, it is time to segment by different dimensions and determine the same 3 variables above for all the different customer segments. For example:

Spending quintiles – highest 20%, high 20%, middle 20%, low 20%, lowest 20% of
defected customers; what is average length of subscription, spend, LTV?

Product / service bundling – identify different levels of service / tiers / add on services of defected customers; what is average length of subscription, spend, LTV?

Source of customer – which ads / offers / selling methods originally attracted the defected customer; what is average length of subscription, spend, LTV?

Geography – using transmitter locations or other natural boundaries dividing the defected customers; what is average length of subscription, spend, LTV?

Hardware – group defectors by type of phone or terminal or other hardware; what is average length of subscription, spend, LTV?

Contract details – if contracts vary widely as to their basic nature and terms, group defectors by contract type; what is average length of subscription, spend, LTV?

After running these studies you should have enough data to logically and critically construct your other 3 initiatives in profitable ways.

Jim

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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: Managing Customer Value Like an Investment Portfolio

Analytical Culture, Customer Experience, DataBase Marketing, Digital Analytics, Driller Q & A, Marketing / Tech Interface, Marketing Research, 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.

Do you manage your own investments in the stock market? If you do, you probably have used technical indicators like moving average of prices or up / down volume balances or similar to make investment decisions. And if so, guess what? This approach to investment portfolio management is very similar to the management of customer value, it’s really all about the metrics and the source of changes to those metrics. We can so some Drilling’ if you like …

Q:  I have been enjoying reading your tutorials.  I am interested in the financial planning market particularly and have developed an application for segmentation of market and clients by attitudinal factors.  Having provided my clients (advisers) with the tools to turn the qualitative data into quantitative measures and slice and dice their client base appropriately, the next question from them is “How do I use this and what to do with the information?.”

A:  You betcha, that’s the hard part.  A common question when people get into analysis; the “what do I do with this” should come first so the metrics produce an actionable outcome…

Q:  I would be interested in providing links on my web space to access your papers and content. Do you have any content or case study examples for marketing and client servicing for the financial planning industry?

A:  Well, I don’t think I have a page on my site specifically on this area, but let’s create one, OK?  I’ll include this example on my blog and it will go up on my site.

Characteristics and attitudes are interesting but frequently not particularly actionable because they are not “behaviors.”  When people speak of “doing something,” they are typically thinking of increasing or decreasing a behavior of the customer.  If you are trying to figure out what to do about a behavior, you really need to use behavioral metrics, which will tell you “who” to do something to and “when” you should do it for best results.

Continue reading New RFM: Managing Customer Value Like an Investment Portfolio →