Category Archives: Newsletters

Commerce Channel Management

Brand Management, Driller Q & A, Newsletters

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

Q:  We are a manufacturer with a cool product not really on the net and right now, but we are entering stores.  I wonder, is it wise to try to sell on the net before retail markets have the products or not – is it better to hold off until the retail markets first have the products and then launch them onto the net?  Does the net really help sell products or does it create copy cats?  Trying to find the best way to go – any advice would be greatly appreciated.

A:  Great question.  Answer is “it depends” and it difficult to be more specific without knowing more about the product and your marketing plans.  But in general, if you want to optimize the profitability of the product launch and you are paying for media, you should think about these choices as a “chain” or series of events each with a specific but interconnected strategy for each channel.

An example would be DRTV or infomercial products, which generally are launched at a higher price into the spot TV channel (cable networks, etc.).  Here sales are made at a very high margin but the volume is generally low; the Objective is to generate awareness and hopefully make a profit, but breaking even is OK because you essentially have the media “free” and that will help drive the next step.

Based on all the awareness you have generated with TV spots, you then can go to the TV shopping channels and say, “Look, people know this product because we have already pre-sold it for you.  We will let you sell it at a lower price if you will drive volume. And that’s typically exactly what happens; most of the profit on the product is made here.

From the spot TV, the audience knows the product sells for $19.95 or whatever, so when it is offered at $14.95 on the shopping channel they think it’s a great deal and the volume is tremendous.  Typically, the spot TV would still be running at this stage, though sales from that channel will have peaked.

Once sales get soft in the TV shopping channel, you then introduce the product online and in stores.  This is essentially “end of lifecycle” for the product, where you are simply trying to make sure you don’t get stuck with any.  You sell that at cost plus to the onliners / retailers and they blow them out at $9.95 or so.  You don’t end up wearing the inventory and everybody is happy because the spot TV / TV shopping has generated plenty of awareness, people pounce on the product, and it moves very quickly through retail.  Typically no TV would be running at this stage because you couldn’t sell any at the original price.

Now, I’m not saying you should follow this model.  But what I am saying is the decision you are trying to make is more complex than “should we”, it involves understanding which channel can do what for you and at what price.

For example:

You said you are “entering stores”, but did not say if you / the stores are running any media to support this effort.  If you are not running any media then I would get on the web and sell the product for retail price or higher.  This generates some awareness / demand / trial but preserves the margins of the retail partner, and hopefully your direct profits will cover costs.  You basically get “free media” from the web (as in the spot TV example above) and the retail folks will love it because it will drive sales in their channel.

If you / the retailers are doing a lot of paid media support, then I would not sell on the web until sales through retail get soft.  Then you are in a position to undersell them or liquidate on the web based on the awareness you have generated offline.  This doesn’t mean you should not have a web site, you should, and it should tell people which retail outlets they can buy the product in.

On the other hand, if there is a razor / razor blade model built into the product (think a doll with add-on sets of clothing), you could sell the primary razor product and some of the blades in retail, then develop more targeted / segmented / rare blade offerings that are exclusive to the web for online stores.

Again, it’s very difficult to make the “right” judgment on this question not knowing anything at all about the product, whether there are supplemental / follow-on products, whether there are continuity pieces involved (collections) and so forth; and especially not knowing what the nature of the retail relationship is.

But I think you get the general idea.  You play the strengths of the channels off each other, generally in some sequential way, depending on what the marketing / media plan is and the characteristics of the product.  That is, if you are interested in optimizing media spend versus sales.  If you have an unlimited media / PR budget, then sure, sell it everywhere!

Hope that helps!

Jim

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What’s the Frequency?

Analytics Education, DataBase Marketing, Driller Q & A, Measuring Engagement, Newsletters, Web Analytics

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

Q: I ordered your book and have been looking at it as I have a client who wants me to do some RFM reporting for them.

A: Well, thanks for that!

Q: They are an online shoe shop who sends out cataloges via the mail as well at present. They have order history going back to 2005 for clients and believe that by doing a RFM analysis they can work out which customers are dead and Should be dropped etc. I understand Recency and have done this.

A: OK, that’s a great start…

Q: But on frequency there appears to be lots of conflicting information – one book I read says you should do it over a time period as an average and others do it over the entire lifecycle of a client.

A: You can do it either way, the ultimate answer is of course to test both ways and see which works better for this client.

Q: Based on the client base and that the catalogues are seasonal my client reckons a client may decide to make a purchase decision every 6 months. My client is concerned that if I go by total purchases , some one who was  really buying lots say two years ago but now buys nothing could appear high up the frequency compared to a newer buyer who has bought a few pairs, who would actually be a better client as they’re more Recent Do I make sense or am I totally wrong?

A: Absolutely make sense. If you are scoring with RFM though, since the “R” is first, that means in the case above, the “newer buyer who has bought a few pairs” customer will get a higher score than the “buying lots say two years ago but now buys nothing” customer.

So in terms of score, RFM self-adjusts for this case. The “Recent average” modification you are talking about just makes this adjustment more severe.  Other than testing whether the “Recent average” or “Lifetime” Frequency method is better for this client, let’s think about it for a minute and see what we get.

The Recent average Frequency approach basically enhances the Recency component of the RFM model by downgrading Frequency behavior out further in the past. Given the model already has a strong Recency component, this “flattens” the model and makes it more of a “sure thing” – the more Recent folks get yet even higher scores.

What you trade off for this emphasis on more recent customers is the chance to reactivate lapsed Best customers who could purchase if approached.  In other words, the “LifeTime Frequency” version is a bit riskier, but it also has more long-term financial reward. Follow?

So then we think about the customer. It sounds like the “make a purchase decision every 6 months” idea is a guess as opposed to analysis.  You could go to the database and get an answer to this question – what is the average time between purchases (Latency), say for heavy, medium, and light buyers?  That would give you some idea of a Recency threshold for each group, where to mail customers lapsed longer than this threshold gets increasingly risky, and you could use this threshold to choose parameters for your period of time for Frequency analysis.

Also, we have the fact these buyers are (I’m guessing) primarily online generated.  This means they probably have shorter LifeCycles than catalog-generated buyers, which would argue for downplaying Frequency that occurred before the average threshold found above and elevating Recency.

So here is what I would do. Given the client is already pre-disposed to the “Recent Frequency” filter on the RFM model, that this filter will generally lower financial risk, and that these buyers were online generated, go with the filter for your scoring.

Then, after the scoring, if you find you will in fact exclude High Frequency / non-Recent buyers, take the best of that excluded group – Highest Frequency / Most Recent – and drop them a test mailing to make sure fiddling with  the RFM model / filtering this way isn’t leaving money on the table.

If possible, you might check this lapsed Frequent group before mailing for reasons why they stopped buying – is there a common category or manufacturer purchased, did they have service problems, etc. – to further refine list and creative. Keep the segment small but load it up if you can, throw “the book” at them – Free shipping, etc.

And see what happens. If you get minimal  response, then you know they’re dead.

The bottom line is this: all models are general statements about behavior that benefit from being tweaked based on knowledge of the target groups. That’s why there are so many “versions” of RFM out there – people twist and  adopt the basic model to fit known traits in the target populations, or to better fit their business model.

Since it’s early in the game for you folks and due to the online nature of the customer generation, it’s worth being cautious. At the same time, you want to make sure you don’t leave any knowledge (or money!) on the table. So you drop a little test to the “Distant Frequents” that is “loaded” up / precisely targeted and if you get nothing, then you have your answer as to which version of the model is likely to work better.

Short story: I could not convince management at Home Shopping Network that a certain customer segment they were wasting a lot of resources on – namely brand name buyers of small electronics like radar detectors – was really worth very little to the company. So I came up with an (unapproved) test that would cost very little money but prove the point.

I took a small random sample of these folks and sent them a $100 coupon – no restrictions, good on anything. I kept the quantity down so if redemption was huge, I would not cause major financial damage.

With this coupon, the population could buy any of about 50% of the items we showed on the network completely free, except for shipping and handling.

Not one response.

End of management discussion on value of this segment.

If you can, drop a small test out to those Distant Frequents and see what you get. They might surprise you…

Good luck!

Jim

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PRIZM Clusters Not as Predictive as Behavior

DataBase Marketing, Driller Q & A, Newsletters

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

PRIZM Clusters Not as Predictive as Behavior

Q: I am on an interesting project (and my first DB Mktg one): the client has a large loyalty program, and loves his PRIZM clusters. However, when I told him a little more about Recency and suggest that we spread all members across based on it, he was surprised to see that his PRIZM segments were not a predictive indicator at all!

A: Yes, and here is something many people don’t realize about PRIZM and other geo-demo programs, including census-driven. They were developed for site location – where should I put my Burger King, where should I put my mall? They are incredibly useful for this. However, think about all the sample size discussions for web analytics related to A/B testing, and now imagine what your PRIZM cluster looks like.

In most cases, you are talking about 1 or maybe 2 records in a geo location – what is the likelihood these households reflect the overall “label” of the PRIZM cluster? Combine this with the fact that for customer analysis, demographics are generally descriptive or suggestive but not nearly as predictive as behavior and you have a bit of a mess.

Here’s a test for you. It only requires rough knowledge of your neighbors, so should not be very difficult (for most people!)

1. What is your “demographic”?
2. If you were to walk around the block and knock on doors, how many households would you find that are “in your demographic”?

Right. Maybe a handful, unless you live in a brand new housing development or other special situation.  Now think about walking your zip code, or walking out 10 blocks or so from your house in any direction, and knocking on doors. Do you find most of these people are in the same demographic as you are? Did you ever find the “cluster average” neighbor?

We certainly know from web analytics that dealing with “averages” can be very dangerous indeed. So too with taking a demographic “average” of a zip or other area and tying it to a specific household. The model falls apart at the household level of granularity.

So now what to you think of all those websites and services that claim to know demographics based on a zip code they captured?

Now, if you think about an e-commerce database, with most records being one of a very few in a zip or cluster, you can see how the cluster demos would really break down at the household level.

Again, nothing wrong with using these geo-demo programs for what they were intended to be used for. When you are looking for a mall location or doing urban planning they can be very helpful. But the match rates at the individual household level are poor.

Couple this with the fact that e-commerce folks are usually looking for behavior from customers, and the fact demographics are not generally predictive of behavior by themselves, and you have yourself analytical stew.

Better than nothing?  Absolutely, and for customer acquisition, sometimes all you can get. Best you can be? Not if you have the behavioral records of customers. In fact, what we often see is a skew in the demographics being called “predictive” when the underlying behaviorals are driving action.

In other words, let’s say a series of campaigns generates buyers with a particular demo skew. A high percentage of these Recent responders then respond to the next promotion. If you look just at the demos, you would see a trend and declare the demos are “predictive” of response, even though they are incidental to the underlying Recency behavior.

I suspect something like this was going on with your client. Not looking at behavior, over time the client becomes convinced that the PRIZM clusters are predictive, when for some reason they are simply coincident in a way with the greater power of the behavioral metrics. Given the client has behavioral data, that should be the first line of segmentation.

Q: After reading you for some years, I now understand how one must be very careful with psycho-demographics.

A: Well, at least one person is listening!  And now you have seen how this works right before your very own eyes.

I think this situation is really a function of Marketers in general being “brought up” in the world of branding / customer acquisition. Most Marketers come up through the ranks “buying media” or some other marketing activity that focuses on demographics to describe the customer. And most of the college courses and reading material available focus on this function, so even the IT-oriented folks in online marketing end up learning that demographics are really important. And they can be, when you don’t know anything about your target.

Then the world flips upside down on you, and now people are looking at customer marketing, and that’s a whole different ballgame. The desired outcome is “action” that can be measured and the “individual” is the source of that outcome, as opposed to “impressions” and “audience”.

In the past, if your tried and true weapon of choice for targeting was demographics, that is what you reach for as you enter into the customer marketing battle.Problem is, it’s just not the best weapon for that particular marketing engagement.

Jim

Get the book at Booklocker.com

Find Out Specifically What is in the Book

Learn Customer Marketing Concepts and Metrics (site article list)

Download the first 9 chapters of the Drilling Down book: PDF