I’ve been getting a good amount of email asking for tricks to improve response rates for email and direct mail. 

I’m going to give-up one of my favorite tricks in this post, but before I do I hope you have read Andrew Russo’s post “A new era of predictive analtyics.”  He reminds us that response rate isn’t really the important metric - it’s about “adjusted response,” ultimately the EBITDA generated by a campaign.

Here goes:  I’m about to turn 43 and I can tell you that my ability to read the small type typically used in emails and some direct mail pieces is quickly eroding.  The truth is I don’t often put on my reading glasses just for your email or direct mail and it goes in the bin - I’m just not that into you!

So what to do?  It’s simple really.  Append age to your list or pull it from your house file.  Simply increase the font size a bit for those over 40.  Simple to do, and you will see results if you are dealing with customers or prospects over 40 or so.

Now some of you know that in the case of direct mail there might be an impact to printing costs.  This is just something you have to watch, perhaps adjust your copy to ensure that the increased font size doesn’t result in incremental production costs.

Marketers are overly focused on building predictive response models and AccuData’s current focus is to help move our clients beyond them.

Most direct marketers build predictive response models, but how about ROI?  After building literally hundreds of models for clients we’ve realized that optimizing response and optimizing ROI is not always the same thing.  In fact we often see that the ROI associated with the top performing response decile is LESS than the ROI of mid-deciles. The trick is to look beyond the initial sale and consider factors such as returns, margins and the post-campaign behavior of the responder.

AccuData has developed a proprietary methodology to address this question – The AccuData Value-Adjusted Optimization Model. The model is comprised of two submodels:  1) a response optimization model and 2) a revenue maximization model.  The first model is more of the traditional approach of marketers – maximize response rate.  The second model seeks to maximize near-term value (NTV).  NTV is a subset of customer lifetime value.  However, rather than projecting value over a customer’s product-buying lifetime, NTV projects out over 24 – 36 months.  This approach lends itself to reliability and stability.

I’m in the process of writing a white paper on this topic and I’ll address this topic again once it’s released.