Online to Offline Conversions – A modern example from National Instruments

(This post is part of a series on the state of multichannel metrics today, one year after the book came out.)

The previous post paid homage to the original innovators behind the online-to-offline conversion improvement idea, namely the automobile industry. Now, let’s look at a modern adaptation from the B2B, high tech, sector at National Instruments.

National Instrument’s Michelle Rutan and I presented this case together at the eMetrics Marketing Optimization Summit in San Jose this May. By popular demand, the session will be repeated live, online, namely in the WAA”s upcoming webcast on July 21st.

The challenge

Take these stats in:

• The company’s web site has 1 million unique URLs.
• Visitors reach the site through one of 500,000 monthly keywords.

Add to that the fact that there are more than 6,000 part numbers of National Instruments (NI) products. So what we have at hand is a great challenge for managing a long, long tail of web pages, keywords, and products.

In this complex situation, how can the sales and marketing team get insight into what prospects are interested in? Imagine being a sales person and asking your account whether they’d be interested in one of 6,000 products today?

Hey, how about a 200 MhZ Wave form generator for you today? Would you like Fast, Flexible 6.6 GHz RF Instrumentation with that?

Or imagine being a marketing person crafting an email that will be perceived as relevant.

Hey, we got to Wave form generators for the price of one today?

Boy, that seems hard.

The general idea

Much like car buyers, many of NI’s buyers use the web site to do their own product research. So NI said to themselves, why should we ignore the click signals that buyers are sending us through their keyword searches and content views?

As a side note, there is one great advantage for this in B2B that some companies leverage. Namely, buyers need not even be registered for their clicks to be attributed to a specific prospect. Often it suffices to translate the IP address into the company’s domain name to know that somebody from company X has been visiting the site.

“What happens on the web site, stays on the web site”, no more.

You could say that in web marketing 1.0 the web site was a silo by itself. Prospects might do their research but marketers and sales teams would pretty much ignore them until they email or call in.

At NI, the marketing team has been pondering for a long time already how they might turn the data on visitors’ behavior into better customer service.

• Surely, a recent and frequent visitor to the product (not support) section of the site is more likely to be an active prospect.
• Surely, a visitor who is deeply engaged with product pages (again, not tech support pages) is likely to be an active prospect.

But identifying active prospects is only part pf the equation. With a long tail of products the question for sales and marketing remains what these prospects are interested in.

Getting to the essence of a prospect’s interests

Profiling visitors’ interests based on their web behavior turned out to be much easier said than done.

NI experimented with several methods of mapping keywords and web content to the products that the pages relate to. Turns out this isn’t so easy at all. Several methods were tried and eliminated.

• Do each of 1 million URLs really contain the necessary information for mapping them to their content?
• Can you conceive of a way to map that many URLs to their essential contents manually — and keep the mapping up to date?

Not so easy.

Then Michelle Rutan had another totally cool idea of how content and keywords could be mapped out. Applying this new mapping to her web analytics data, she generated a test segment of prospective buyers for a particular product and geo area.

The Results

The test campaign was launched as an email and achieved a whopping 44% open rate and 25% click-through rate!

This is great testament to the fact that recipients perceived the content as relevant.

The data is still so fresh that resulting sales information was not available (not that it would be publicly available anyway).

The million dollar question:How did Michelle map web behavior to prospects’ essential interests?

Since this is entirely Michelle’s idea, it isn’t for me to reveal her innovative approach. But if you are curious, you can hear from her directly by tuning into the upcoming webcast.

Michelle will present from the business perspective. Then I will run through a lab case of how this idea can be turned into reality by integrating web analytics into the data warehouse, BI, or CRM system.

I know, Michelle and I look forward to your questions.