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SmartData Collective > Big Data > Data Mining > Online to Offline Conversions
Data Mining

Online to Offline Conversions

AkinArikan
AkinArikan
8 Min Read
Online to Offline Conversions
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
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(This post is part of a series on the state of multichannel metrics today, one year after the book came out.)

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Believe it or not, one of the downsides of the WWW is that it can actually produce too many leads that are unqualified.

Too many leads? How could that be a problem?

Well, …

  • How can Sales and Marketing sift through all the pebbles to find the nuggets, i.e. the serious and profitable buyers that they should jump on first without losing time to cold leads?
  • How to help the sales and marketing teams be more intelligent in what they are going to say to a hot web lead when they do get in touch?

Automobile industry was the original innovator here

As you may imagine, the car industry has been aching for a solution to these questions more urgently than anyone else. After all, the research shopping phenomenon is most pronounced here.

That is, almost everybody researches their next car online. But in all my tours throughout Europe and US I have only met two people who actually entered their credit card online and ordered their car for pick up in the dealership. The rest continued their shopping in the dealerships, i.e. offline.

Me too, I remember, I was up-sold and cross-sold the old fashioned way by a used car sales man in the dealership.

Back in 2006 already, some of the most remarkable presentations that I have ever witnesses at the eMetrics Marketing Innovation summit came from car manufacturers on this very subject.

Ford and Volkswagen

As presented by Ford and Volkswagen, their online teams struck a partnership with their sales and marketing teams for delivering better and more actionable leads.

Their efforts included many action items including wonderful traditional web analytics for improving advertising, landing pages, and web site experience.

But also included was an effort to listen to prospective buyers much more closely and turn the signals that buyers are sending through their click behavior into more intelligent responses offline.

The sausage making process

Through a patchwork of product solutions (from web analytics to data mining and sales force automation), web behavior data was fed into a profiling and scoring process:

  • Profiling
    • Based on their behavior (e.g. in the product configurator), does the buyer seem interested in the brand or already in a particular model?
    • Are there particular accessories that the buyer is looking for?
    • If the prospect is registered what did they specify about their current ownership?
    • Etc.
  • Scoring
    • Based on the online behavior (e.g. recency, frequency) how close does the prospect seem to a buying decision?
    • Based on the profile, how profitable a buyer do we have at hand?

Then Volkswagen would interpret the profile to send the brochure that would be most likely to be helpful, e.g. focused on attracting the buyer to the brand vs. providing more info on a specific model.

Likewise, Ford would aim to give an indication to the dealership how hot a lead seems to be so that they would time and prioritize their sales outreach accordingly.

Business Results

Volkswagen found very high correlation between those touched by their marketing effort vs. closed car sales. But at the time of their presentation in 2006 they had not done controlled testing yet. In other words, at that time they could not yet prove that the buyers wouldn’t have purchased anyway.

Ford found that leads ranked as “hot” were six times more likely to purchase than “cold” leads and still twice as likely as the average lead. So the business case for making the sales team more effective is very strong. The case for customers is strong too, i.e. the ones most urgently in need of assistance are likely to be served first.

The morale

This innovation made sense to many other industries as well. Namely, in all considered purchases where the sales cycle used to be with a live sales person before the WWW, but where the process now tends to start with self-service research online.

Offline, in the store, you used to simply watch shoppers’ behavior to assess their readiness and interests. Should you leave them alone or are they looking like they could use assistance?

Online, the same customer service idea applies. So why would you ignore all those signals that the clicks are sending!

In the next post we’ll go over two modern adaptations of this idea. One in real estate and the other in B2B high tech.

»

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