Multichannel Marketing Metrics
with Akin

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

The previous post examined a B2B example of integrating web analytics into predictive analytics and the data warehouse for online to offline conversion optimization. Now, let’s look at one more example. This time from B2C at the hand of a nifty client of Unica’s in the real estate industry

For competitive reasons, the company asked that their names not be published. As you can tell from that, these analytics are of a caliber that helps this company compete on analytics.

What is a Marketer to do these days!

Marketers get flak from consumers when they spam or interrupt them at unwelcome moments.

And so a marketer may lean back from push marketing and switch to pull marketing, i.e. let consumers drive and self-service their needs.

But then it won’t take long before the marketer will get flak from the CEO for not being pro-active and driving as much business as they could.

Dammed if you do, dammed if you don’t, it may seem.

Intelligent middle ground

This real estate industry company is choosing a clever middle ground by feeding web analytics data into predictive modeling.

They are using web analytics (in this case Unica’s NetInsight in two ways:

First, the traditional way, i.e. reports and analytics to make web site and advertising better.

But secondly, web analytics at the level of individual visitors to their web site. Key behavioral data is accessed in the web analytics data mart including visitor:

• recency
• frequency
• length on site
• number of pages viewed
• key site events that may signal significant engagement

This behavioral data is fed into Unica’s predictive analytics module.

Besides web analytics data, the predictive analysis is also fed with data from the customer registration database, e.g. stated real estate buying / selling intentions and parameters.

The sausage making

A linear regression model predicts the likelihood for each visitor to be serious about a real estate transaction in upcoming weeks. Rank ordering registered site visitors by this probablity allows the company to reach out to the top candidates at each moment in time.

More precisely, the analytics are used to prioritize who will be contacted by email campaigns vs. who is ripe for an outbound sales call. The sales force automation system used by the company literally bubbles hot and ripe leads up to the top so they get addressed first.

The results

The targeted approach makes both the customer service team and the outbound campaign management efforts that much more successful.

To be precise, the company achieved 22% lift out of their predictive models.

Multichannel metrics pay off in real Dollars (Euros, Yens, …).

The implementation phase

Let me finish with a big thank you to marketing services provider, Amberleaf, who were the services company that integrated multiple Unica products ranging from web analytics to predictive modeling, campaign management, and lead management.

Our real estate industry client has had access to a web analytics web data mart for many years already. But connecting the dots between web and customer analytics into marketing and sales management applications still required an extra step.

It took the combination of a visionary CEO and an experienced, yet affordable, systems integrator to pull it off.

Tags: Customer Centricity, Direct Marketing, Marketing Disciplines, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics, multichannel marketing, targeting // Add Comment »

(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.

Tags: Business Metrics, Customer Centricity, Direct Marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics, multichannel marketing, targeting // Add Comment »

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

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.

Tags: Customer Centricity, Direct Marketing, Marketing Disciplines, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics, multichannel marketing, targeting // 3 Comments »

Just about a year ago, the mutlichannel marketing metrics book was published. Since then I have had a chance to tour through Europe and US to speak with many marketers from online and more traditional sides of the house.

Here is what I am finding as to how far companies have come in the past year.

• Online - offline integration has not yet become mainstream
• But there are more and more examples of companies implementing something and those who do have great business results to show for
• It doesn’t always have to be online and offline integration. Lucrative enough for online marketers can be to integrate click data with online customer data. Business cases that I am aware of also look very strong.

I will share sample business ideas that companies have implemented in a coming series of blog posts.

Before then I should point out some things that haven’t changed yet.

• The question about anonymous visitors and cookie deletion remains one of the most frequent ones that I am asked
• Privacy considerations come up frequently
• Marketers wonder why they should look at web analytics data closely now even if they haven’t yet used other data sources that are available to them to the fullest potential.

Which industries are doing the best job at this?

The leaders today seem to be in the area of highly considered purchases such as automotive, group travel, telco, real estate, B2B high-tech, etc. Common to these industries is that

• Before the WWW, buyers used to get all their advice from a live sales person whereas today most initial research occurs online in a DIY fashion.
• The buying process often crosses online and offline channels, e.g.
  • Awareness is prompted via TV commercials
  • Research is performed online
  • The purchase often occurs offline

In the coming posts we’ll look at examples from acquisition marketing, persuasion, re-marketing, cross-sales, and retention / win-back.

Check the following posts that continue this thread:

1. Online to Offline Conversions
2. A modern example from B2B, National Instruments
3. An example from a B2C, real estate web site

Tags: Uncategorized // Add Comment »

If you missed the May 19 WAA Webcast with Kevin Hillstrom and Jim Novo, you can replay it any time on demand.

By the way, do you think it will be 5 or more like 10 years before all TV will be much like the Internet?

That is to say, you will turn on the tube and a big bing or google box will appear in the middle of the screen. You’ll type in “Kevin  & Jim webcast”  and get your multichannel marketing fix while sipping a cup of old fashioned tea.

Unless, of course, you see my PPV (pay-per-view) ad show up towards the right of your TV screen and click on it to read this blog.

Meanwhile,  recommendations will appear at the bottom of the screen that are targeted to your remote control behavior.

Hopefully, something better than “Meet exciting online and offline marketers in <your city>”.

Tags: Brand Marketing, Business Metrics, Customer Centricity, Direct Marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, Uncategorized, Web Analytics, targeting // 2 Comments »