Multichannel Marketing Metrics
with Akin

In the two years since publishing the Multichannel Metrics book, the face of marketing has changed drastically.

We are entering a critical crossroads in 2010.

By 2013, will we look back and find that this was the year when marketers from online and other marketing teams first realized how similar their goals have become and took steps towards integration across camps?

Or will we look back and find that the camps remained ignorant of each other and instead set in stone silo’d technologies making integration more difficult than ever? 

What do you mean, web and direct marketers’ goals are aligned now?

Marketers have had no choice.

Greatness has been thrust upon them!

Direct marketers

1. Have seen marketing dollars beginning to shift from offline to online
2. Finally see clearly that a large portion of interactions with their customers has moved online. For example, according to anecdotal feedback from several older European banks, 25% to 50% of their clients use online banking now. Some financial institutes acquire the majority, if not all, customers online.

Online marketers

1. Have had to abandon their silo’d, website-centric thinking because their websites are now only one component of their total web presence. Other presences include social media, mobile sites, behaviorally targeted ads and personalized emails.
2. They may continue to treat the offline as a step child for another couple years. Yet, they are already adopting multi-online-channel marketing practices for the purpose of integrating all these web presences.
3. The latter has required them to move from their traditional focus on aggregate level metrics and dashboards to looking at data about individuals across website, mobile, social, and advertising. This experience with individual level data will also make it easier for them to integrate their customers’ offline interactions down the road.

As a result, both online and direct marketers are now pursuing multi-channel data integration at the level of individual prospects and customers. Both camps do this for the purposes of

1. behavioral targeting,
2. better understanding of marketing ROI.

The technology gap is closing

While all vendors talked the talk for 360 degree views, the reality was different. Web analytics data was far removed from a direct marketers’ access.

• After all, the data are owned by the web team who couldn’t care less about individual level data at that time.
• The data are also hosted remotely at SaaS based web analytics vendors that prioritized reporting and good looking charts over granular data. As a result, data feeds (while available) would come with no SLAs. A feed might or might not arrive at the agreed time of night. That made it too unreliable for driving interactive (let alone real time) marketing programs.

Meanwhile, web marketers could integrate analytics based targeting into email marketing only by bridging the gap between several vendors and paying for integration services.

These technology gaps are now increasingly closing.

• Omniture is positioning its online marketing suite along with integrations with ESP partners through its Genesis program.
• Unica is positioning both
  • Its eMessage and Interact products for email and web personalization integrated with Unica’s web analytics and campaign management products for enterprise clients
  • Its recently launched Interactive Marketing OnDemand product where SaaS based customers use web analytics, email, and web personalization within a single application and UI.

The crossroads

This alignment of methods, goals, and technology represents our arrival at a crossroads.

(note added on March 12th): Maybe the word alignment is too much said.  But methods, goals, and technology are now more parallel and similar than ever.

But will we leave these crossroads into an integrated future or will we set in stone two silo’d multichannel worlds between online and direct marketing teams?

That is the big question.

—

In part 2 we will look at direct marketers vs. digital. Then in part 3 we will review where multichannel web marketing stands in 2010.

Tags: Business Metrics, Direct Marketing, multichannel marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics by akin
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I have to say, I am growing increasingly annoyed with the silo’d nature of the discussion that seems to still be dominating our web analytics industry.

We have been so silo’d that, for example, even something as adjacent to web analytics as audience measurement and its vendors (i.e. the comScore, Hitwise, Compete of the world) seem to appear more like second class citizens in our discussions. Meanwhile, 90% of the chatter among web analytics vendors, consultants, and bloggers seems to focus only on core web analytics topics and vendors.

A symptom that should give us pause is that most of our guru authors and bloggers – who are such rock stars to us web analytics people – are utterly unknown outside our little niche. Forget offline marketers, not even other online marketers know them!

Surely that isn’t because “the others” are all stupid and don’t understand performance optimization.

Honestly, I don’t know exactly why we seem to be such a silo’d breed. It is probably just a function of specialization in the workplace. Web analysts handle web analytics tools, multivariate testing, voice of customer, and maybe participate in behavioral targeting. But

• Who owns audience measurement / competitive intelligence? Probably a shared function with marketing/PR?
• Who owns social media monitoring? More often the “social media manager” or PR rather than the web analyst?
• Who owns search optimization tools? SEO and PPC teams, of course. (And they too can be separated from each other in larger organizations)
• Who owns email marketing? The direct or customer marketing functions.
• Who owns ad servers and behavioral targeting networks? The online marketing or media team
• Who owns site performance? IT
• Who owns the replay stuff? Web developers?

If there is any way out of this strange situation it is probably to be found in embracing the different aspects of web marketing in a more balanced fashion instead of losing ourselves in increasingly nuanced web analytics details that seem esoteric and boring to people outside our niche.

Might we find bigger gains in 2010 by looking more for a breadth-first approach vs. continuing our deep dive?

Take search marketing as an example

The diagram below shows the search marketing funnel starting from potential visitors, i.e. users of the search engines (or their content networks). The search marketer aims to acquire them on site and then lure them deeper into the funnel to engage, persuade, and convert.

The diagram then lists the different categories of marketing tactics and technologies that are involved in moving prospects through the funnel. Let’s take a deeper look at each category.

1. Audience measurement and influence

This category includes more items than one might think at first glance, namely the following.

2. Search marketing

This category includes the most obvious items associated with search marketing optimization:

3. Landing page management, and 4. site management

These categories include similar items that I shall list together here. But it makes sense to keep them as two categories because the vendors/tools for landing page management are sometimes not the same ones as those used for managing content on the rest of the site.

Not to even mention product recommendations, product reviews, etc.

Is that all?

No, there is much more that is critical. Search visitors will often not convert on their first visit. So re-marketing is essential.

More importantly, maybe, the customer life cycle doesn’t end with the first purchase. That is in fact when the work of the customer marketer only begins and the life cycle continues with on-boarding, growing lifetime value, attrition risk detection, and win back. Some additional tactics and technologies that are involved on the online channels include the following:

5. Interactive Marketing

Do we really need all of that … stuff … to optimize search marketing?

If your goal is merely to improve search marketing, e.g. PPC, you need nothing more than a Google AdWords account while paying attention to the built-in couple of metrics. But if you are after optimization, then the above are truly all part of the funnel or chain. Each of these pieces are truly needed and will pay for themselves.

And we are supposed to integrate that with web analytics?

As a supporting function and nerve center, web analytics has the potential to glue most of these elements together. When done right this could make your web analytics people some of the best known employees across all of these teams.

But you would be forgiven if you are thinking that integrating all of these functions with web analytics could be too big of an effort and cost. That is precisely why vendors such as Omniture and Unica are building out online marketing suites.

Today, not all of the above are available (and integrated) within one vendor’s suite. But that day will come because there is a real need by marketers.

Tags: Multichannel Marketing Measurement, Multichannel marketing metrics, Search marketing optimization, Web Analytics by akin
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“The best things in life are free”, they say, and organic search traffic might seem free at first glance.

But in truth, as you will know, organic search isn’t free at all. It requires hard SEO work upfront before you can rank well for highly coveted keywords.

This wizard is for any marketer looking to get more business results (traffic + outcomes) from organic search (and who doesn’t?). It walks the analyst through a series of steps for increasing results, e.g. by identifying the keywords to prioritize for SEO, doing the on-site and off-site SEO, and optimizing your web marketing for organic search visitors.

Click here to start the SEO wizard Click here for a bird’s eye view, i.e. summary flow chart

This wizard is the third example of an expert system that I got a chance to work on now. The wizards aim to help web analysts (and in this case also search engine marketers) with their complex work. Earlier releases were the wizards for PPC optimization and troubleshooting a drop in website conversions.

Couple comments and observations:

• As always I would like to point out that these wizards can neither replace the need for experienced SEMs nor web analysts. The goal of the wizards is just to make their work more systematic.
• A good example of why this is true may be the following. Namely, for any specific website, an experienced SEO can probably point out the 20% of the steps in this wizard that will help achieve 80% of the results. In contrast, in the wizard all recommendations seem to be of equal priority / importance. That is a bit of a flaw with this SEO and also the PPC wizard, I have to concede.

As I am not an SEM myself (neither SEO nor PPC), this wizard will only become truly exciting with the help of user comments that can be added to any step of the wizard.

Look forward to hearing what people think and hope that this proves helpful.

Tags: Multichannel Marketing Measurement, Multichannel marketing metrics, Search marketing optimization, Web Analytics by akin
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This Thursday, Angie Brown from Elsevier is presenting live in a webinar within the Web Analytics Association’s webcast series. She is going to share some very clever examples of her web analytics work at the publishing giant.

I had the chance to listen to Angie’s presentation live at Unica’s annual customer conference earlier this year in Paris. Her anecdotes about using web analytics to inform offline and other strategies showcase that the insights go much further than just to improve the website or advertising.

As the webcast is sponsored by Unica, I will get to follow Angie’s featured presentation with a few words on Unica’s view of next-generation use of analytics.

Register here to attend live on Thursday at noon US ET or to receive the recording link after the live event.

Tags: Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics by akin
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Social media are challenging and humbling. The discussion of how marketers should use them is often clouded by vague recommendations, niche anecdotes, and buzzword mania.

The confusion has also impacted marketing analysts. More than ever they are facing questions of what can be measured and how. But very often there is neither a clear business goal nor action plan behind the question.

In order to select meaningful analytics for Facebook, LinkedIn, Twitter, blogs and videos, the key (as always) is to start with the business goals that your company is pursuing with each social media marketing effort.

But, as Jim Novo pointed out months ago, your team must come clean on what these business goals really are.

• For example, just because you are blasting out messages on Twitter doesn’t mean that you are really doing viral or social marketing. You are just running a reach and direct response campaign much like any other form of spam or PR.
• In that case you should also measure success with very similar metrics to those used for spam and PR, as Jim pointed out.

While there are wonderful lists of available metrics for social media overall, I didn’t come across a list yet that is grouped by the various business goals companies may be pursuing with their social media campaigns.

So, therefore, here is the grouped list that I would like to encourage.

The overview reveals that social media measurement require combining data from multiple sources:

1. Typical web analytics metrics such as unique visitors or views, click-throughs, and outcomes such as conversions, revenues, profits.
2. Data about social media participants such as their demographic profile or number of friend/follower connections. This information would typically be available from the APIs of social media platforms or from social media monitoring solutions
3. Social media monitoring trends such as the volume of chatter around specified topical keywords as well as sentiment

If you are a Unica customer, ask your account manager for our technical paper on how to measure metrics such as the above using Unica NetInsight and social media monitoring solutions.

Tags: Multichannel Marketing Measurement, Multichannel marketing metrics, Social media metrics, Web Analytics by akin
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This wizard is for any marketer looking to get more results from their search engine pay-per-click (PPC) campaigns (and who doesn’t?). It walks the analyst through a series of steps for increasing results, e.g. by eliminating wasted spending, identifying missed opportunities, improving the persuasion process, or optimizing budget allocation.

Click here to start the wizard Click here for a bird’s eye view summary flow chart

The wizard is the second example of an expert system that aims to help web analysts with their complex work. Just at the beginning of this month I had released the first wizard, namely for troubleshooting a drop in conversions on a website.

Couple comments and observations:

• Just like a clinical decision support system isn’t meant to replace doctors, this wizard has no way of replacing the need for experienced web analysts, search marketers, or helpful consultants.
  • Doing the optimization still requires significant time and attention.
  • The wizard is just like a good adviser that has helpful hints but won’t do any of the work for you.
• While releasing the troubleshooting wizard I had been wondering whether the idea of an expert system would lend itself to problems that aren’t “troubleshooting” in nature but more of general “optimization” nature.
  • It turns out the idea makes total sense for optimization as well.
  • But you can easily see a big difference if you compare the bird’s eye view of the PPC wizard vs. troubleshooting wizard. Namely, the latter is a decision tree whereas this new one is more an exploration of various optimization areas.
• I still highly recommend the use of automated paid search management solutions such as SearchForce or Marin software. But if you review the wizard you will see that many areas for optimizing paid search involve tasks that cannot be automated.

I am not an SEM myself. So, this wizard will only become truly exciting with the help of user comments that can be added to any step of the wizard.

Look forward to hearing what people think and hope that this proves helpful.

Tags: multichannel marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, Search marketing optimization, 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 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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