April 4, 2007
How Not to Be Successful in Direct Marketing
Here are the leading ways to NOT differentiate yourself in direct marketing:
1) Build a massive prospect database. I have had a front-row seat for a number of DB builds over the years, and have come away convinced that large-scale prospect databases are only for the largest DM companies. I think that most DM companies need a prospect datamart (i.e. consisting of a representative sample of the prospect universe) combined with a well-defined campaign execution process.
2) Use standard compiled data. The vast majority of DM companies use the exact same compiled data to produce extract lists. These names are tired, and the data is highly inaccurate. To gain an advantage, seek out vendors that claim to have unique data, along with the standard data.
3) Overuse vertical lists. While this can vary greatly by product, I tend to think that large DM marketers should carefully test any vertical lists before integrating them into an overall strategy. In particular, it is important to understand whether the vertical names (which are usually far more expensive than compiled names) would have been sourced anyway via another channel.
4) Use un-benchmarked modeling. I believe that 60% of firms claiming to offer 'analytics and modeling' do not know what they are doing. By this, I mean that they produce over-fit (or otherwise flawed) models that do not work well in practice -- although they may look great on the development data. All serious DM companies should get an independent assessment of their modeling from time to time.
5) Look forward, not backward. I frequently come across DM companies that spend little, or no, time analyzing what worked and did not work on past campaigns. Important items such as creative, message, offer, etc. can be optimized for the future based on past results.
Best in class DM companies focus on an analytical approach to campaign management.
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Posted by BeyeBLOGS at 2:45 PM | Comments (1)
March 28, 2007
How to Select a Modeling Vendor
As an analytics vendor for the past fifteen years, including the last ten years as the head of two different modeling/analytics companies, I have some definite thoughts regarding best practices in selecting a provider.
1) Competency -- I believe that only about a third of companies that sell analytical services are consistently competent. By this I mean that these companies always (or almost always) produce analytic solutions that are competitive with what the best vendors would produce using the same data. This impression comes from numerous validations, assessments and benchmarking projects I have been involved with over the years. How to you gauge competency? Here are a few thoughts:
a). The best way is to have the vendor develop a test model, where some of the data is held out as a validation sample. I would expect that the results would be consistent between the development and validation samples.
b). The product should, in most cases, make business sense. In line with this, each attribute should be logical and the 'signs' should match -- meaning that an attribute that has a positive correlation with what you are trying to predict should not have negative points.
c). I would make sure that the vendor does NOT use a purely automated modeling system. With the current state of the technology, I firmly believe that an experienced modeler will produce a better solution. The 'art of modeling' has yet to be automated.....
2) People -- While many/most vendors have some experienced people on their staff, it is important to assess the experience and reputation of the people that will actually work on the project. I would recommend working with a smaller vendor that can provide an experienced team, rather than selecting a larger company that will assign a relatively inexperienced team.
3) Interest -- Based on my experience, you are always better off with a vendor that views your account as a 'big deal'. If you are not a big deal to your vendor, getting relevant attention is going to be a problem.
4) Reputation -- While word of mouth can sometimes be misleading, it is at least directionally correct. If a vendor has a spotty reputation, it is important to find out why.
Of course, all of the above assumes that there is a strong and synergistic relationship in place between you and the modeling vendor that you select.
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Posted by BeyeBLOGS at 2:45 PM | Comments (1)
