Kevin Hillstrom: MineThatData

Exploring How Customers Interact With Advertising, Products, Brands, and Channels, using Multichannel Forensics.

April 15, 2009

A Multichannel Forensics Discovery

One of the issues many of you have with Multichannel Forensics happens with "start-up micro-channels".

Here's the deal. Let's say you have a Twitter presence. And you do a really nice job of 140 character merchandising, whatever that is.

Maybe 500 customers purchase from Twitter in the course of a year. And of the 500 customers, 250 will purchase again next year ... 200 via e-commerce, 75 via Twitter (the overlap of 25 customers is the hallowed "Multichannel Customer").

Now you have your e-commerce channel. You had 50,000 customers who purchased via e-commerce last year ... of them, 25,000 will purchase again next year, 24,500 via e-commerce, 1,000 via Twitter (the overlap of 500 being "Multichannel").

This leads us to the Migration Probability Table.



Online Twitter




Repurchase Rate Company 50.0% 50.0%

Online 49.0% 40.0%

Twitter 2.0% 15.0%




Repurchase Index Online 98.00% 80.00%

Twitter 4.00% 30.00%

The index for the Online Channel to Twitter is 4% ... Isolation Mode. The index for Twitter to the Online Channel is 80% ... Transfer Mode.

This scenario is unacceptable to those who promote the emerging channel. The emerging channel is growing and thriving, while the existing channel fails to drive enough customers to the emerging channel to allow the emerging channel to truly grow at a breathtaking rate.

Many of you working in emerging channels have asked me to re-think the concept of emerging channels, as the Multichannel Forensics framework seems to limit the importance of the emerging channel.

Tomorrow, we'll explore a discovery that should benefit those who are advocates of emerging channels.

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