tag:blogger.com,1999:blog-18668583.post5162902295534507236..comments2023-04-11T05:52:47.102-07:00Comments on Adventures from Kathmandu to San Francisco by Sandeep Giri: Can Analytics Influence Direct Marketing Creative Process?Sandeep Girihttp://www.blogger.com/profile/03619852381418350234noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-18668583.post-44879841869980593522007-05-16T10:56:00.000-07:002007-05-16T10:56:00.000-07:00I have an blog entry regarding segmentation. I tho...I have an blog entry regarding segmentation. I thought of sharing here.<BR/><BR/>http://analyticsbhups.blogspot.com/2007/05/customer-focussed-versus-customer.html<BR/><BR/>BhupendraBhupendrahttps://www.blogger.com/profile/11979831334460992121noreply@blogger.comtag:blogger.com,1999:blog-18668583.post-4464553543649297122007-05-16T10:54:00.000-07:002007-05-16T10:54:00.000-07:00This comment has been removed by the author.Bhupendrahttps://www.blogger.com/profile/11979831334460992121noreply@blogger.comtag:blogger.com,1999:blog-18668583.post-58932748504963358092006-10-04T16:48:00.001-07:002006-10-04T16:48:00.001-07:00We had a couple of other comments from some collea...We had a couple of other comments from some colleagues at work on this topic that are worth sharing:<br /><br />=================================================<br />I like his definition/analysis of “segmentation”<br /><br />This multifaceted aspect of “segmentation” is why I get uncomfortable with prospects who want us to segment their data without a clear description of business purpose the results will be used for.<br /><br />His point on being in a high churn group not translating into a message is spot on. IOW, being AtRisk identifies likely candidates for retention marketing but says nothing about how to communicate with them. > is well aware of this. The messaging in the Retention trial is primarily age/gender driven with some tweaking of message based on usage and/or tenure.<br /><br />=================================================<br /><br />Yup, as I’ve said before “segmentation” = grouping, nothing more, and nothing more useful without additional intelligence around it. Picking a handful of universal segmentation schemes that are useful and possible with only (transactions and behavioral) data is difficult, but something I’d like to see baked with more thought towards what has worked in the past and the marketing implications for future applications. Every segmentation we provide should point clearly to marketing actions and carry these implications as part of our offering, rather than being a group that exists solely because we can slice it in the data.<br /><br />Some quick examples of making segmentations actionable and attractive to a prospective customer:<br /><br />* At-risk groups should be limited to three groups – high risk, low risk, neutral<br />* FM segments (frequency and monetary spend to date) should be aggregated in a different way – probably a matrix of 2 frequency levels vs. 2 monetary levels for:<br />-- HighF – HighM -> Reward and keep them happy<br />-- HighF – LowM -> Moderate up-sell effort<br />-- LowF – HighM -> Continue to market, cross-sell<br />-- LowF – LowM -> Drop from marketing after recency falls offSandeep Girihttps://www.blogger.com/profile/03619852381418350234noreply@blogger.comtag:blogger.com,1999:blog-18668583.post-30356475570185357942006-10-04T16:48:00.000-07:002006-10-04T16:48:00.000-07:00TrackBack URL for this entry:
http://www.typepad.c...TrackBack URL for this entry:<br />http://www.typepad.com/t/trackback/6265834<br /><br />This is exactly what I'd hoped to get out of blogging -- conversations! Thanks for elaborating on my post.<br /><br />In our line of work (predictive analytics side of the house), we often run predictive models to rank customers/prospects by their propensity to either respond to direct marketing, or to spend, or to even attrite. The result is a prioritzed list of customers/prospects in terms of who should you focus on contacting first.<br /><br />In other words, this segmentation tells you who to contact, not necessarily how to contact.<br /><br />Now, with adequate past campaign data (both send and response), you can build models that will predict which particular tactics will work best, i.e. performance of different offers, media (direct mail vs email vs telephone), message frequency (propensity to respond on subsequent contacts), etc. So, within a segment, you can find further breakdowns of tactical segments.<br /><br />IMHO, this is where the creative process needs to be aligned with the analytics. Predictive models can generate a prioritized list of customers/prospects for a particular marketing goal, and even break it down into tactical segments based on past marketing data.<br /><br />With this, the "creative" team should have a much better insight to its audience, their preferences, and of course, the overall goal of the marketing campaign -- which should help crafting the most compelling message to the audience.Sandeep Girihttps://www.blogger.com/profile/03619852381418350234noreply@blogger.comtag:blogger.com,1999:blog-18668583.post-39035996368334076222006-10-03T19:06:00.000-07:002006-10-03T19:06:00.000-07:00Sandeep: this is some great insight into a common ...Sandeep: this is some great insight into a common problem in DM. I've expanded on your post in my blog:<br />http://adelino.typepad.com/adelino_marketing/2006/10/customer_segmen.html<br /><br />Adelino de AlmeidaAnonymoushttps://www.blogger.com/profile/09966220982023278812noreply@blogger.com