Multichannel Marketing Metrics
with Akin

So much has been written about our little world of web analytics, that it is getting increasingly difficult to come up with useful new contributions.

But the Beyond Web Analytics trio, Adam Greco, James Dutton, and Rudi Shumpert, managed to do just that. Their series of podcasts featured past guests such as Gary Angel, Jim Sterne, John Lovett, Josh Manion, Greg Dowling, and others.

So, I was honored to be next as their guest. In the podcast that Rudi published today, we discussed many recipes for web analysis ranging from the web analytics wizards, to the desire for ever smarter web analytics tools, and more.

I must admit, I feel a bit tickled like Elmo to see podcasts on our nerdy subject show up within cool places such as iTunes.
Listen online here.

Tags: targeting, Web Analytics by akin
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In a recent post readers and I mulled over the fact whether Amazon really is that cool with their customer analytics and interactive marketing as we keep saying in our industry. Or whether their real secret to success is that they simply offer the cheapest price.

To that effect, Jared Waxman was kind enough to leave another thorough comment as an ex-Amazon’ian. (or do they call themselves Amazones?) Thought, I’d point you to it so it doesn’t go under in its location within a past post.

Thanks much to Jared and Ned and others who commented.

Tags: Customer Centricity, Direct Marketing, multichannel marketing, targeting by akin
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For all that buzz around Amazon’s sophisticated analytics and its targeted book recommendations, it is worth asking in Kevin Hillstrom’s priceless, heretic style: Is it just hype or does it really make the big difference for their business?

Do we really buy from Amazon because of the recommendations, personalized emails, the behavioral targeting through widgets?

Or do we buy from Amazon because they always have the cheapest price? (Not to the least because of the 3d party vendors and used books that are linked in.)

If they stopped being the least expensive would we still be buying from Amazon?

In other words, are they really competing on analytics? Or are they competing on price?

What is our willingness to pay extra for the kind of “marketing as a service” that Amazon has perfected?

Of course … this question isn’t really about Amazon, in the end. Much rather I am trying to double check what the true value of sophisticated analytics and targeted marketing are. All hype aside.

Separate things: What you will buy vs. where you will buy it

There is no doubt that Amazon is the go-to place for doing your research on books.

But being the greatest place for researching books doesn’t necessarily mean that people will buy the books there if they can get them cheaper elsewhere.

I imagine we all go to many web sites to research what car, electronics, gear, etc. we should buy. Where we will buy the item that we settle on tends to be a different question though. We might check on eBay or Craigslist, for example.

So are we giving too much credit to Amazon’s sophisticated analytics and marketing?

The answer …

As Anil Batra was joking yesterday when I saw him at the OMMA Metrics and Measurement in San Francisco, a typical consultant’s answer to such a question could be: “That depends on … what it depends on.”

It depends on …

Of course, I don’t have Amazon’s data. But I think that the answer will indeed differ by buyer segment:

1. High value prospects that buy books frequently and in higher quantities will likely appreciate the convenience and time savings. But given that these people buy so many books they would also be the segment that could get the biggest total savings by being disloyal to Amazon.
2. Infrequent buyers that are strapped for time will value the convenience over a few bucks of savings per book.
3. Infrequent buyers that are strapped for money but not for time would be more likely to put in the extra 5 minutes for buying the book at the cheapest vendor. Most books aren’t big ticket items. So this effect will likely be much less pronounced than, say, with electronics. But it would be there to a certain degree.

In the end, maybe the answer has not all that much to do with frequency but is a function of

• how much money vs. time buyers can save.
• how much value the individual puts on money vs. time

Time is money and money can buy time

The more time Amazon can save its book researchers, the more of them would buy their books on Amazon (assuming price is fixed). Still, those buyers who value money a lot more than convenience and time would be the hardest, if not impossible, to keep.

Anything else they could do?

Barnes and Noble has (or had) essentially a frequent buyer card. You could pay an annual fee and would get x % discounts on anything you buy.

But that really is just a return to the strategy of competing on price.

Could Amazon withhold access to book research features unless a buyer … purchased something in the last 12 months or joined some kind of for-pay club? That would be a return to subscription based content. Seems like it would backfire badly.

Bottom-line

It appears that the true value of all those analytics and targeted marketing for the retailer are in drawing the crowd into their (online) store. They get a shot at making sales (and cross-sales) that they wouldn’t otherwise have.

But converting researchers into buyers requires more than just targeted marketing. It also requires convenience, a “good enough” price, and of course customer satisfaction with previous transactions.

How would you go about measuring the value of targeted marketing effort XYZ?

If you have to measure ROI of targeted marketing effort XYZ you would probably do it through controlled testing.

That would be easy for Amazon’s targeted emails.

It would be harder for their book recommendations because you would wonder where they disappeared to if you fell into the control group.

Oh … but you could make them deliberately untargeted. Say you find that dinosaur book that the individual bought and shipped to somebody as a gift 3 years ago and recommend more dinosaur books.

Tags: Customer Centricity, Direct Marketing, targeting, Web Analytics by akin
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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.)

In a subscription based business model, e.g. mobile phone service, you have two ways to make a customer:

1. gain a new one
2. renew an existing one, i.e. retain them

When Unica’s clients from the mobile phone carrier industry report about their work they invariably start by describing how vital #2 is to their industry.

The market for mobile phone services is so saturated that the only way to gain a new client is to take them away from a competitor.

Sounds like a place for marketing innovation.

A few innovative Telcos have hit on ways to combine online and offline worlds in order to improve their success rate with retention marketing.

As I learned from one of my colleagues last year, here is how one company went about it.

Online-offline integrated analytics for detecting attrition signals

At a large US mobile phone carrier, traditional retention marketers were working on predicting which customers were about to leave for the competition. These clients would be included in retention marketing efforts.

Originally, the statisticians had been going after this job the old fashioned way, i.e. trying everything from a customer’s contract details to transactions (i.e. usage) and demographics details to find something that would predict attrition.

But the only variables that showed any influence were the age of the subscriber’s phone device and the amount that they paid on the last bill.

Not exactly enough to catch someone in the hot act when they are about to walk out through the door.

Yet, it was going OK.

To put it in numbers, the marketers were able to reach 70% of customers at risk of leaving by contacting 40% of the possible audience. So their predictive models were giving them some amount of lift.

But wait a minute … If someone is thinking about switching would they not likely be coming to the web site and doing something on there that deviates from their usual click behavior?

Might they not be checking available promotions or upgrades or ways to strike a deal?

The idea seemed so promising that the statisticians gave it a go.

They took a chunk of historical web data for registered clients. They paired that up with the same customers’ historical churn data in order to train a predictive model (along w/ the offline data).

And what they found was impressive

Indeed there were predictive click behaviors on their web site but it wasn’t intuitive.

• Clients on a low subscription contract would have one kind of online signal that revealed their intention, e.g. address change.
• Clients on a higher rate plan however turned out to send a different signal with their clicks.

The numbers rewarded them.

Now, when contacting 40% of the potential list they were able to reach an extra 15% responses for a total of 85% of potential responses.

That doesn’t just means lots of stamps and mailers saved.

It means foremost saving the cost of special discounts that they would have extended unnecessarily to clients who weren’t thinking about leaving anyway.

Highly worthwhile.

Real time?

Most of the online-offline integration case studies that you may have read about in this series were of interactive nature, i.e. online click behavior would prompt action within a short period of time.

Here we have an example of how a company first took a historical chunk of data to train their model. No real time needed here.

But now that the model has been trained, fresh web analytics data would be fed to it regularly in order to keep predicting current customers at risk.

The morale

This is yet another strong business case for integrating online and offline analytics.

No wonder the case is strong. After all

• The case for competing on analytics is strong.
• The case for using behavioral data is strong
• Click data is a rich lather of behavioral data

It is time for the lollygaggers to stop acting surprised and jump on board!

Tags: Customer Centricity, Direct Marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, targeting, Web Analytics by akin
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Other than “How about cookie deletion?” the second biggest question that I have received in the past year when discussing the topic of online-offline integration is the question about privacy.

• Will it be OK with privacy regulations if I integrate click data from web analytics with customer data in order to improve the relevance of my marketing communications?
• More importantly, will it be OK with web site visitors’ expectations?

The regulations side is usually a short answer for me. Mind you, the regulations seem rather cumbersome to read. But the bottom-line boils down to:

• Have a clear privacy policy on the site
• Make it as easy to opt-out as possible, ideally a single click
• Extra credit, if in addition to opt-out you allow the individual to set their own preferences of how they’d like to be contacted and on what topics
• In countries where it is required, work with opt-in

To me, the bigger question seems about site visitors’ expectations.

It may seem we are wearier of being tracked than ever. There is always a big outcry when Facebook et al announce a move towards ad targeting.

But in reality, we are much more public with our lives than ever. especially in our social networks. See this article for instance.

So what is it about this privacy thing that we really want?

The following examples help me.

Privacy in a store

We hate walking into a small store if the sales person is too much in our face and doesn’t let us browse the items on our own. Maybe we fear getting pressured into buying something before we are ready. Heck, we may well be browsing for entertainment and not thinking of buying anything at the moment. And the shop keeper that is in our face makes us feel bad about ourselves.

But we also hate being in a big box retail store and not being able to find someone to answer our questions when we are ready to ask them.

Really, we want the person to be right there — magically — just when we need their help but not before. And we love it if they understand us so well that they can recommend just what we will benefit from buying.

Privacy in a restaurant

We hate when the waiter is too much in our face, especially after we are done with the meal. Maybe we fear pressured in vacating the table for the next guests.

Just as much we hate it when the waiter is nowhere to be found when we need the check or want to order something (else).

The waiter should just — magically — refill the glasses as soon as they are empty. They need to be right there with the desert menu and our check just when we want it.

Magic???

How does it work in those stores and restaurants that do this well? Is it magic?

No magic.

The perfect shop keeper and waiter are super observant. They put a web analytics tool to shame when it comes to tracking our behavior.

But they aren’t in our face about it.

And they don’t pressure us into buying something or ordering an appetizer along with the expensive main course.

They are at our service.

And yet they still do bring us the best cross-sales offer at the best time.

Marketing so relevant that it feels like a service

I still cringe when I hear that marketing should be so relevant that it feels like a service. At first glance it seems a cheesy thing to say. It seems a utopian dream of techies like me.

But wait.

How about all those educational webinars on the web that I love to attend and learn from?

Guess what! The people doing them (e.g. me, myself) aren’t altruistic at all. Their purpose is purely marketing. They cost a ton of money, by the way. Yet, it is a service and doesn’t feel like marketing at all unless the speaker is too salesy.

There are other examples too:

• How about book recommendations on Amazon
• Movie recommendations on Netflix?

They tend to be quite relevant and not at all in your face. Ignore them easily if you want.

In fact, haven’t you come to expect and demand that any product page on a retailer’s web site will contain information on accessories that go with the item?

So, it can be done

These examples prove that marketing, in the ideal cases, can:

1. feel like a service
2. be not in your face and not pushy

My take away is that the combination of click and customer data, if used the right way, can absolutely enable service oriented marketing. But if you abuse it for span, you will cause all of us marketers to look bad and to lose out.
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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.)

Tags: Business Metrics, Customer Centricity, multichannel marketing, Multichannel Marketing Measurement, targeting, Uncategorized, Web Analytics by akin
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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.)

The previous post focused on a travel company that listens to their customers’ online behavior so to get their cross-sales – or last minute – offers to those who are most likely to care.

Now how about the retail banking industry?

Retail banks and credit card companies have been great innovators in the use of analytics for direct and relationship marketing. For example, the case of CapitalOne is well described in the book Competing on Analytics.

For good reasons:

1. Banking is a relationship business.
2. Banks know embarrassingly much about their customers based on all those transactions and other data
3. And what is the profit from a new bank customer at the moment when they first open their account? Negative, probably, until over the life time of the relationship the bank makes back their initial costs (and then some)
4. Banks have learned that they are most successful with cross-selling products to customers within the first 30 to 90 days of the relationship. After that, customers seem to go on auto pilot with their relationship.
5. Banks also have learned that new customers are most at risk in the beginning of their relationship and may spring back to their previous bank if something goes wrong.

So banks plan out all their customer communications very carefully by predicting which offer or message sent to which customer at what time is most likely to amount to maximized net present value.

(You might joke that we wouldn’t have the stupid mortgage crisis today if the direct marketing departments had been in charge of their banks)

So why is it then that many banks are such laggards when it comes to online offline integration?

Few industries have it easier.

• You visit your bank’s web site frequently
• You are almost always logged into your account when you go to your bank’s web site.
• There is a big incentive for not deleting your cookie so that you won’t have to go through all those annoying security questions to “register” your computer.
• Quite likely you even clicked the “remember my user ID” option so that even the bank’s home page greets you with “Hi Joe, welcome back”

All your bank’s products are outlined online so you have it easy to answer your own questions.

Looking for a car loan or savings account?

Likely you check rates online.

So … hellooo … is anyone listening?

Customer 1:  European bank cross-sells savings accounts

It seems a no-brainer.

The bank, with the help of a nifty online marketing consultant, mined their web analytics and customer data for clients who

• have a checking account
• but no savings account
• YET who have recently been on the web site looking at savings rates.

All it took was a feed from web analytics looking at web pages relating to savings accounts and keyed by login-name of customers.

For extra credit, they could have extended the campaign to those who weren’t logged in but whose cookie was previously registered during an authenticated session.

According to the consultancy, the campaign was easy to do and very lucrative. So they are expanding the program.

Customer 2: Stock brokerage looks at web data to cross-sell options-trading

Another no-brainer.

Options-trades are a great way for an online stock brokerage to increase transaction volume and to lock in clients into a tighter relationship.

But options are risky and somewhat more complicated than trading stocks. So they aren’t everyone’s cup of tea.

Who is ready to receive the cross-sales offer?

Could it be those clients who are not yet authorized for options trading but who have recently been on the web page studying the process of how to sign up for options trading?

You betcha!

So what are the couch potatoes waiting for?

Legitimately, banks want to make sure that they stay within the accepted norms for privacy. The online-offline integration feels very new to them. So bankers are quick to throw up their arms and say that their hands are tied by the privacy rules of their organizations.

Are banks really that concerned about our privacy though?

Every night banks crunch through our daily account transactions to watch for unusually large deposits so that they can quickly bring us a cross-sales offer before we move the money elsewhere.

How is that for privacy!

Personally, I think that some of the arguments that we hear today are similar to what happened when the first train came out. Namely, it was said that traveling at the never-before-heard speed of 20 MPH was not healthy for human beings!

No doubt, we will see more banks review the opportunities very carefully. Innovators are already tapping into the integrated data. Others will follow.

In some future edition of Competing on Analytics we may well hear about some bank that rode the opportunity out to their advantage.

Tags: Customer Centricity, multichannel marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, targeting, Web Analytics by akin
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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.)

The previous posts focused on companies accelerating conversions from online to offline. Here now comes an example of cross- or repeat-sales. This time from the travel industry.

The company is Collette Vacations, a provider of global travel and escorted tours to more than 150 destinations. 91 years old, the company is far from old fashioned.

Other than their multichannel approach to marketing, what does Collette have in common with the previous three businesses that we examined (automotive, B2B, and real estate)?

All 4 examples are in the category of highly considered purchases. I.e. Collette’s tours to Antarctica, or Oberammergau aren’t something you book in a rush. Much more typical is that customers research their options online, watch available videos, sign up for a live presentation, and eventually book after clarifying all their questions, maybe by email, chat, or phone.

Last minute travel

Now, something that I learned from Jukka, at The Mileage company, is that travel business is very tricky business.

You wouldn’t guess from the outside.

But one of the many tricky aspects is that travel products are perishable goods just like tomatoes at the grocer.

You can’t sell a hotel room one day after it has been standing empty.

So what to do when Collette has vacation seats to Barcelona on sale and they need to find buyers quickly?

Should they spam their entire email list as a marketer would be tempted to do? Collette thought better.

Protect the future value of your email list

Email marketing is tricky business too. Email too often and while initially you may get results, eventually you burn the attention of your recipients. After all, except for cases when a tour sells out completely, there would always be last minute sales to promote.

If you were a spammer you would find reason to send spam every day. Yet, your future emails would likely remain unopened or go into the spam folder.

So, a year ago, Collette decided not to fall into that trap. Instead they connected their web analytics with their email campaign management system.

When, for instance, Barcelona vacations are on sale they have targeted the announcement to the segment of web site users that have recently spent time browsing web pages related to Mediterranean vacation options but haven’t recently been purchasing travel.

Not surprising that this segment is much more likely to care about the promotional announcement than the average population.

The sausage making

But how to connect web site visitors to their email addresses?

Collette’s web site permits registering online in return for something of value, e.g. the ability to save a wish list of vacation destinations. Along with online registration, it is best practice to save the login (or persistent cookie) that is created in conjunction with the provided contact information.

That info forms the basis of being able to listen to an individual’s clicks so to know better about the kinds of vacations that they may find of interest.

As Collette’s privacy policy states, customers have many options to opt-out. But if they do remain opted-in, “Knowing how you use the site enables us to better tailor our content and services to most effectively suit your needs.”

The Results

The official case study with further details on Collette’s implementation is available for download. Among some of the results that Collette shared is that their flagship, “welcome” campaign performs 30% above industry average.

The Morale

The hardest thing about this is not at all the technology in my opinion. But it is for the business to come up with ideas that entice visitors to register with their accurate email information. What value are you going to provide to your clients so that they will give you the email address where they actually do check their messages?

And since cookies are deleted eventually and people use multiple devices to browse the Internet it isn’t even enough to get a registration only once. Rather, the business needs to entice visitors to login periodically so to keep the data trail alive.

One would wish this was easier.

But what I like about this challenge is that it keeps marketing real. We have to provide value to the customer for the permission to include them in our analytics and the permission to communicate.

Two-way value has always been the basic ideal behind CRM.

Tags: Customer Centricity, Direct Marketing, multichannel marketing, Multichannel marketing metrics, targeting, Web Analytics by akin
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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.)

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, Multichannel Marketing Measurement, Multichannel marketing metrics, targeting, Web Analytics by akin
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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.)

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, Multichannel Marketing Measurement, Multichannel marketing metrics, targeting, Web Analytics by akin
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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.)

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, Multichannel Marketing Measurement, Multichannel marketing metrics, targeting, Web Analytics by akin
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