Customers Move From Catalog To Online To Retail

I've been telling you that Multichannel Forensics continually indicate that customers move from online to retail, glad to see others are also observing this relationship:

• IBM Research Suggests Customers Prefer To Move From Online To Retail, Not From Retail To Online, via Direct Magazine.

When you know that customers move from Catalog to Online, then from Online to Retail, then use Online to research future Retail activity, you view your multichannel marketing activities very differently than you view them through the multichannel marketing best practices we're currently being taught.

And long term, for those with a retail presence, the e-commerce channel is dwarfed by the "internet as a research channel" concept. The direct marketing community and web analytics community isn't ready for this reality.

If you are a retailer who has run simulations illustrating long-term customer migration, you've probably observed something like this (comparing an online/direct customer to a retail customer --- click on the image to enlarge it):



Again, you're likely to see this type of trend if you are Gap or J. Crew or Eddie Bauer or Best Buy or Ann Taylor or any brand with an online/direct and retail channel. Customers seem to migrate from online to retail. When that happens, they are much less likely to buy online, much more likely to research online. This changes how you view your website.

Fresh Data And Storytelling

Back in 1990, you'd feed a 5.25 inch floppy disk into your IBM-AT desktop computer. You'd fire up SPSS. At the bottom of the screen ticked the number of records that were being processed .... 20 records ... 40 records ... 60 records ... 80 records.

Today, I loaded a large dataset, wrote six hundred lines of code, and began processing the information. At the bottom of the screen, I could visualize the records as they were being processed ... 200,000 records ... 400,000 records ... 600,000 records ... 800,000 records. Thank you, Acer, AMD and SPSS for providing a fun computing environment.

It's a blast to see new data, information you haven't been exposed to before. The data at Nordstrom seldom changed during my six years there. Sure, occasionally the annual retention rate would vary (maybe 67.2% one year, then 69.3% the next year --- if you are at Macy's or Neiman Marcus or Saks, sorry, those aren't the actual numbers). New customers might vary by fifty thousand verses forecast. There were seldom huge surprises. Such is the case when a business consistently meets or exceeds expectations.

When you get to see new data from a new company, there is a sense of exhilaration. It is like opening up a box of puzzle pieces. You find the corners and the border pieces first. Each piece fits into another piece. Eventually, the pieces provide a path for you to get to the end of the assembly process.

Each line of code produces reporting --- the reporting tells a story. Existing customers are retained, lapsed customers repurchase, new customers feed the future growth of a business. Within minutes, the lifeblood of a company is evident on your computer monitor.

Looking across years, you can visualize the decisions that executives made, decisions that caused increases or decreases in customer counts. These increases or decreases drove subsequent decisions, which drove increases or decreases in customer counts. Within an hour, Multichannel Forensics illustrated what happened, and forecast what is likely to happen in the future. Suddenly, there is a story to tell.

There is a huge difference between Data Mining and Storytelling. Data Mining seeks to explain the data. Storytelling is an art form that translates information in a way Executives can digest, understand, and act upon.

Data Mining has a place without Storytelling. Data Mining coupled with Storytelling yields potential. Data Mining and Storytelling that speaks directly to a current, future or perceived Executive need (as defined by the Executive) causes change.