into Profits with a Spreadsheet
The Guide to Maximizing Customer Marketing ROI
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Visitor Conversion, Initial and Repeat Drilling Down
Newsletter
# 35
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0-1%
conversion |
7% |
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1-2%
conversion |
15% |
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2-3%
conversion |
17% |
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3-5%
conversion |
19% |
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5-10%
conversion |
16% |
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11-20%
conversion |
6% |
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20+%
conversion |
3% |
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Didn’t
know conversion |
14% |
Source: E-Tailing Group's 1Q 2003 research of
200 online commerce sites
I have a new client, a major name brand retailer in the US, that averages .51% on visits. One of my first clients, a small specialty retailer, now averages 9.1% on visits. So the averages are all over the map, and tend to increase as the niche you are playing in gets smaller. More competition, lower conversion. There are always exceptions. Amazon's conversion rate was reported in 2001 to be near 15%. Another report in 1999 said 8.3%.
The exact nature of how Amazon conversion was measured in these cases is unknown; it probably was estimated from public records. Obviously, if you measure conversion rate using unique visitors instead of visits (I prefer visits), the rate goes higher because the base you start from is lower.
My guess is that the 15% number is on unique visitors and the 8.3% number is on visits. But then you get to have a long discussion on defining a "visit" or "unique visitor." What is the time period for defining unique, the past year? Does entering an affiliate store where the book art / description is pulled from the Amazon site count as a visit, even though the "visitor" is not at the Amazon site?
So the best thing to do is pick a measurement that makes sense for your site, stick with it, and use your own data as a benchmark, rather than comparing it to anyone else. The combination of your products, web site technology, and traffic streams is most likely unique to you and the best measure of success is your own benchmark. The E-Metrics book covers the measurements and techniques you can use on a website which are most likely to lead you to converting visitors into buyers at a higher rate; it's all about the "front end" of the customer conversion issue.
None of the stats above break out new versus repeat customers, and
this can obviously make a big difference. You want your
first-time buyer customer to come back and buy again; these "back
end" kinds of issues covered in the Drilling
Down book. You can't manage this issue unless you measure
it, and my guess is that for your business, the primary repeat
purchase metric you should start with is Latency. You can read
about Latency in this free
tutorial on the web site.
Latency triggers off the time between customer events, so for example,
the number of weeks between purchasing the first sim and the second
sim, the second sim and the third sim, etc. Look at your
multi-buyers and measure the average time between purchases; this is
where you start.
Let's say it is 8 weeks. Set up a "sniffer" or report run that looks at every customer each week, and flags those customers whose last purchase was 9 weeks ago; send these people a promotion (1 extra week for "slippage"). If you send the promotion before this, you risk giving discounts to people who would have bought anyway. If you send it too long after this threshold, you risk the customer has already defected and is no longer interested.
The above is a very "blunt force" example of Latency; it doesn't take into account different "pacing" by different customer segments. So for example, customers on the "Cisco track" might have an average Latency between courses of 6 weeks; customers on the "IBM track" might have an average Latency of 10 weeks between courses. You don't have to figure out why this is true, it's enough to know it is true.
So the second step is to look at multi-buyers by segment to determine Latency, and proceed as above. If you choose a segmentation scheme like "track" and you don't see any clear patterns, you may have the wrong segment scheme. You should see a "bell curve" around the average Latency. If there are a too many "outliers," or the bell curve is lopsided, you have a piece of it but there is something else going on.
For example, the truest segment might be "track by price." If you now look at Latency that way and get a near perfect bell curve, you nailed it. Other variables might be occupation, country of origin, and age.
Latency is covered in the Drilling Down book, but it is the same
material that is at the link above. Another of the major
models in the book is Recency, it is also available as a free
tutorial on the web site.
The third major behavior model is called RFM, and then there is a
hybrid of all these models I developed called LifeCycle Grids.
Those two are covered only in the Drilling
Down book (hey, I can't give it all away free, I'd have no
business!) but you can read an overview of these models here.
Hope that helps!
Jim
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That's it for this month's edition of the Drilling Down newsletter. If you like the newsletter, please forward it to a friend! Subscription instructions are top and bottom of this page.
Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them right along to me, along with any other questions on customer Valuation, Retention, Loyalty, and Defection here.
'Til next time, keep Drilling Down!
- Jim Novo
Copyright 2003, The Drilling Down Project by Jim Novo. All rights reserved. You are free to use material from this newsletter in whole or in part as long as you include complete attribution, including live web site link and/or e-mail link. Please tell me where & when the material will appear.
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