Shortly after my post two weeks ago about Advanced Attribution in Adobe Insight, industry evangelist & author Avinash Kaushik contributed his thoughts in a post titled Multi-Channel Attribution: Definitions, Models and a Reality Check.

I was thrilled when reading his post, since he led right into my topic for this post, which I was working on at the time.

Avinash outlined three distinct types of attribution opportunities:

  1. Multi-Channel Attribution, Online to Store (MCA-O2S)
  2. Multi-Channel Attribution, Across Multiple Screens (MCA-AMS)
  3. Multi-Channel Attribution, Across Digital Channels (MCA-ADC)

Of the first type, Online to Store (MCA-O2S), he says “It’s mandatory.” I agree completely. One of the breakout sessions I presented at the recent Adobe Digital Marketing Summit covered how we solved this exact problem last year with one leading retailer. At Summit we told the story of our solution that allows marketers to “understand the offline impact… driven by online marketing and advertising” (Avinash’s words, but what we accomplished last year at this client.)

But let’s get back to this post…

My other Summit breakout was with Best Buy, titled “Mobile is the glue that connects digital to offline channels.”

Integrating Web & Mobile data with Store data at Best Buy

Last year, we focused on finding new ways of integrating digital (Web & mobile) channel data into the existing Adobe Insight installation that contains all in-store purchase data available for analysis.

For several years, Best Buy has used Adobe Insight in two separate installations, one that includes all bestbuy.com and mobile channel data, and another that includes all customer & point of sale (POS) data. On several occassions, we’ve talked about the value of “MCA-O2S” and the need to combine the two datasets.

Consequently, in 2011, we did just that. In fact, we built a solution that includes all three of Avinash’s attribution opportunities: Online to Store (O2S), Across Multiple Screens (AMS), and Across Digital Channels (ADC). Score!

We started with extracts from the .com & Mobile dataset, then ran those extracts through a process to map the Web Visitor IDs to in-store Customer IDs, and then loaded these revised log files into the Customer / Point of Sale analysis cluster. By doing so, we enriched the store channel sales data with the .com & Mobile channel data for the matched customers.

Initial Results of Analysis at Best Buy

As part of our Summit breakout, Chris Moroz, Associate Manager for Digital Analytics at Best Buy, presented the initial results of the analysis.

Many of the insights were covered last month in an article on Internet Retailer, “Mobile Adds a New Dimension to a Customer’s Value.” Unfortunately, though, the Internet Retailer article focuses more on the supposition that mobile “adds to” a customer’s value. Right after I shared the article, Chris at Best Buy sent me a quick note, saying “It’s too bad [the article] didn’t include the purpose of the presentation and that with your help we’re finding ways to connect all customer touch points. I thought that was the bigger story.”

In fact, what Best Buy was really showcasing at Summit is a solution that provides full “Online to Store (O2S)” customer view and attribution. The solution provides a deep understanding of a store shopper who also interacts with Best Buy via emerging channels like the mobile site & mobile apps.

Some of what we proven in the “O2S” dataset at Best Buy were hypotheses, like “On any given day, a BestBuy.com visitor is more likely to purchase in store.” However, the surprise was in how much more likely – much more than expected. One fourth (25%) of the mobile site visitors make a store purchase within 2 weeks.

In fact, for those mobile site visitors who made a store purchase within 2 weeks, there were some surprising findings on areas of the site that were leading indicators of a store purchase. Site content like “Store Locator / Finder” might be expected to be a strong indicator of an upcoming store purchase. But surprising categories show up as well, like “Home Theater Accessories” (26% of visitors to this area made a store purchase within 2 weeks) or “Computing: Network” (25%).

Very significantly, customers who used mobile and shopped in store have a 25% higher Lifetime Value (LTV) than non-mobile customers. Customers who are valuable to Best Buy use the mobile channel!

With QR code scans in Best Buy stores (on the “Fact Tag” price labels that connect to product details on the mobile site) jumping 120% this last holiday season, the full 360° view of the customer, Web, mobile, store, & more, is essential to companies like Best Buy.

As we presented at Summit, mobile is truly a key part of the glue that Best Buy uses to connect the full customer picture via Adobe Insight. The rich value they’ve found in this comprehensive customer view – from the geographic information on where customers are accessing certain mobile experiences (our store? Or a competitor’s store?), and how the Web & mobile activity influences store purchases –  is now indispensible.


A Unique Solution with Adobe Insight

I truly believe that we offer a unique solution (software and services) that connects the digital channels (marketing touches, mobile, Web, etc.) with the offline store & account data. With the ability to integrate data from any structured log source and with solutions like the Adobe Consulting Unified Customer Process to align all data under a unified view of the customer, retailers stand to benefit strongly from Adobe Insight now – and as new data from channels prove their value in the future.

Comment & Tweet

I’d love to hear your take on this in the comments below, or via Twitter at @Halbrook. In addition, if you have any questions about Adobe Insight specific to retail & travel, let me know, and I can discuss with your in detail, connect you to the right resources, or perhaps even craft a future blog post around the answer to your question!

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