When I first got into Marketing, it was product-focused. As market technology became more prevalent - first as Database Marketing, then as CRM - the insights forced a shift to becoming customer-focused. And that made sense - and produced a lot of revenue.

I’ve been pondering the customer-focus with the advent and expansion of social media. Advances in crawling, text mining, and aggregation tools have brought us as close to one-to-one marketing as we’ve ever been. So that got me to thinking, if we truly know who the customer is, then isn’t it time to tell them about our products?

Think about it. We became customer focused because we didn’t know much about the customer. Now that we know so much more, perhaps we should try to sell them something too. After all, if we know so much let’s just tell them about our products and services.  Just a thought.

For more inquisitive insight, visit me at the DMA in October- Booth #1204

Just this week, I was on the receiving end of the best B2B campaign I’ve seen in years – and had to fire off this posting to you without delay, given the valuable marketing lessons to be learned from it.

A seemingly hand addressed brown paper envelope was delivered to me.

Inside… a hotel key and a note offering a web address. It intrigued me – had I won a prize? (Of course I knew I hadn’t. But nevertheless, I was impressed by the mystery. How could anyone not be?)

So I took the bait and visited the site. It was a hotel micro-site inviting me to review a simple hotel restaurant menu and “construct” my perfect meal. Very cool interactive content.

Once I completed this menu , the true nature of the campaign was revealed. Both mailer and site were a demonstration of the prowess of VLG - clearly a very interesting interactive agency! VLG pointed out that I had not only engaged with the site, but revealed critical information about myself along the way (steak, not chicken!).

Moments after completing the site’s online form I received a call from VLG.

So what can you take away from this top-flight campaign? Five key components:

1. Accurate Targeting: I lead sales and marketing at AccuData. I’m the perfect target for this agency

2. Attention-Getting Opener: Marketers know unusual mail pieces have a better chance of being opened. Boxes are the best but a hand-addressed brown paper envelope is very good.

3. Irresistible call-to-action: the hotel key was a unique way to spur the desired response… my visit to the URL.

4. Show, don’t tell: The mailer didn’t tell me what VLG could do. It showed me the proof in a memorable way. And that made all the difference.

5. Fast follow-up: By calling just moments after my site visit, VLG reached me at the peak of my interest when they were still top-of-mind. Definitely a best practice.

Was the campaign expensive? Sure. But VLG could never have driven that response from me with email or a more traditional mail piece. B2B direct marketing can often support more expensive promotional costs because of the relatively large value of a sale.

Check out another great mail piece I’ve reviewed: “Dear Mr. Goff” isn’t even close - let’s get variable

There was an interesting article in a recent Wall Street Journal. It discussed a study that concluded eating chocolate is linked to depression. ‘How is this relevant to me’, you may ask?

The objective of the model was gain insight as to what foods may contribute to depression. But that’s not important for this blog post. What is important is the process and methodology used to arrive at a conclusion.

The study indicated that several factors were introduced into a statistical model to see what impact each factor had on the stability, reliability, and predictability of the model. What the model discovered is that introducing foods other than chocolate had little or no impact on the model’s metrics. That’s exactly the same process we use to build and evaluate our predictive response and purchase models.

Like researchers, we too seek out factors that ultimately contribute to our understanding of who may buy something – or may not.

A couple of days ago I read an article about how data from social networking sites is being used by marketers. The article discussed how chatter amongst consumers is being tracked by marketers. And they are tracking who is chatting with whom - essentially the social network for that particular product or service.

Medical research on behavior modification concludes that behavior modification is achieved through social influences. And that got me thinking: impressive gains can be made if marketers can find a way to harness the social ‘chatter’ that I view as an indication of behavior.

Analytically, this means exciting times are upon us as social media-based data is now becoming available. The ability to add this data to the already existing banks of demographic and lifestyle data will enable highly refined target marketing.

I’ve always said that the objective of marketing is to invoke an emotion in the prospect. Hopefully that emotion is to buy your product or service. The best way to invoke that emotion is by connecting your message to their behavior.

Back when I first got into this business, I applied MicroVision codes to my customer database, did a frequency analysis, figured out which cluster codes my company was primarily selling to, bought prospects with those same codes, and got reasonably strong results. But that was almost 20 years ago when few marketers were doing anything like that.

Why was it successful back then? I surmise that so few marketers were doing any type of targeting that I was getting to prospects better than my competitors.

Interestingly, some marketers still use this technique and until recently have met with some degree of success.

A current client of ours fell into that category. They came to us because their response rates have been consistently dropping.

After a brief conversation with the client it occurred to me that it might be more ‘ROI’ effective to build a response model - something the client had previously considered but concluded it would be too expensive. I was able to illustrate how the economics of a response model favorably compared to the economics of cluster code marketing for the size of their direct mail campaigns.

For clients that are doing rather large scale direct mail, the unit cost of doing a model drops dramatically when compared with the variable cost of doing cluster code marketing.

It is something to consider if you’re not currently doing any sort of predictive model-based marketing.

I’ve been in this business since before database marketing was called database marketing (let alone CRM). When Don Peppers and Martha Rogers et.al. first introduced the concept of 1:1 marketing, it more or less remained a concept. Why? Because at the time, neither the technology nor the data existed to cost-effectively implement such programs.

As I read the current issue of DM News it is clear that more and more marketing services are rapidly moving toward this concept of 1:1 marketing. They are acquiring technology companies. They are acquiring analytical companies. They are acquiring creative services agencies.  Why? There are four reasons:

1) The technology to process nano bytes of data has been refined and costs reduced. 

2) The depth and breadth of behavioral data is vastly improved and readily available - both from traditional sources and now from the social media channels.

3) Modeling software can now swiftly process millions of calculations. 

4) Variable Laser printing technology, PURLs and dynamic web page capabilities.

Although the ability to converge data, technology and analytics has existed in marketing for decades, it has not been able to be as precise as 1:1 because of the cost and time involved.  That has changed.  Additionally, the cost of personalization far outweighed the benefit.

Now with laser printing technology, PURLs, dynamic web page capabilities, integrated with behavioral data and predictive model analytics,  it is now faster and cheaper to bring personalization to the doorstep (or desktop) of each individual in your targeted market.

Why is this important? Recently I’ve developed several regional models. The results clearly indicate that there are regional differences in any given market and that incremental gains are achieved when marketing is targeted regionally rather than generically and broadly.

If you take this a step further than it is reasonable to assume that by targeting on a 1:1 basis yet more incremental gains will be achieved over a regional model. 

Consider a general store from the 1800s – the kind of store you might have seen on the Frontier in early 1800s. In those days, this was virtually the only place you could buy supplies. When you walked in, you’d find piles of blankets, bags of wheat and bottles of “snake oil.”

The shopkeeper would know you because it’s the only place in town. He would know what you bought, how much you bought and how you paid — he even knew your family. In fact, the clerk knew ALL about you because you lived in a small town and bought everything you needed at the store; furthermore, the shopkeeper was a prominent figure in town, right in the middle of all the town gossip.

Of course, there were no computers but he DID have a ledger book, which held the account balances for everyone in town — in those days, folks were able to buy on credit just like we do today. But this was a smart shopkeeper and there was much more in the ledger. I’ve seen several 1800s-era ledger books. One held a variety of notations about customers, like their likes and dislikes, their children’s birthdays, even a notation about a customer’s horse that had died (“might need new tack”).

In essence I was looking at a 1800s-style customer database and a really comprehensive 360-degree customer profile. But more importantly, the shopkeeper was able to interact with each customer on the shop floor, using all the information available in real time.

Three points:

1. What many of us are trying to do today — craft personalized communications and understand our customer — is literally a concept and practice that has been around as long as merchants have been doing business.

2. What changed over time was the scale of commerce and hence the complexity of what we now call “database marketing” or “CRM” across thousands and even millions of customers.

3. Although the technology needed to collect and manage data at this scale developed, particularly since 1995, marketers have generally NOT been able to make the data usable on the shop floor. The “marketing data” was typically not made operational because of a missing link between the data in the back-end marketing database and the shop floor. In the case of the general store the linkage was there - because the shopkeeper managed the data in the ledger and interacted with the customers.

Here is an example of how this linkage can work today, although VERY few companies are doing it:

I recently boarded a plane, as I’ve done hundreds of times. The flight attendant approached me just after takeoff to offer me a small bottle of champagne and thank me for attaining elite flight status. Wow! Now of course I had received a mailing from the airline — the direct marketing team had done their job well. They knew I achieved the flight status, ran a “trigger” campaign, and mailed me a nice thank-you note. I was able to board early due to my flight status, which of course is great. But emotionally, these benefits paled in comparison to that little bottle of champagne and the in-plane thank you.

In this case, the airline extended its customer data, and my flight status in particular, to the airplane and created an in-plane program to make the data operational. This is hard to do and I only had this experience once. But some companies are all about developing real-time, operational database marketing programs. Casinos for example (maybe I’ll talk about this in another post).

How do you do it?

In most cases, the marketing database exists, as does the customer facing POS and other systems. What’s missing is simply the linkage between the two and this is where web services come in. Today it’s quite straightforward to develop web services that link “back-end” customer databases to front line systems.

This approach allows data-enabled operational programs like:

1. Distributing coupons online or through the mail and then monitoring redemptions in real time, providing data to yet other real-time CRM trigger programs

2. Creating data-enabled experiences on planes, hotel rooms, restaurants

3. Linking to social media campaigns, like recording Facebook enrollments and reacting via email in real time.

This is really fun stuff.

Conventional wisdom has it that if you target market to prospects that look like your clients, you will have great success.  Conventional wisdom also has it that by doing something - anything - different you will likely see a change in performance - hopefully a positive change.  All of this is true and I have experienced it many times myself.  However, this approach merits a closer look to understand what the impact is on acquisition costs and therefore your return on marketing investment.

In a traditional marketing campaign, customer attributes such as demographics, lifestyles and behaviors are compared to the same attributes in the market.  If they see a high presence of prospects that share the attributes as their customers, they market to them. Ultimately this technique generates new customers as intended.  However, there may also be a higher number of nonresponders which, of course, they’d like to avoid.

If you consider that on a good day you may get a .5% response rate, then clearly 99.5% of the prospects will be non-responders.  Hence by merely comparing attributes of customers to a market - without regard to how those attributes are integrated to tell a fuller story - may inhibit your ability to gain an incrementally higher rate of  responders above the number of new customers that were acquired.

For example, let’s assume that your customers’ average age is between 35 and 45.  So, you buy a list of prospects that are age 35-45.  Unbeknownst to you, although that demographic likely contains many propsective responders, it may also have a higher ratio of nonresponders to responders.  The end result is that although you get many new customers, you may also get a higher number of nonresponders and consequently your acquisition cost increase.

How do you get around this?

A predictive response model considers how a full plate of customer attributes is integrated to better differentiate the potential responders from nonresponders.  By eliminating those that have a low probability of responding, you increase your overall average response rate thereby lowering your overall acquisition costs.

Although a predictive response model also adds incremental expense, your marketing campaigns may be large enough you need to carefully weigh the financial impact of the savings from not marketing to the low-probability-of-response prospects against the cost of the model.  You will be surprised at how cost-effective a predictive response model may be.

The prevailing perception among many marketers is that all buyers of your product or service are alike.  Hence they typically focus on age or income or other basic segmenting factors.  The fact is that there are regional influences and behaviors that clearly earn a place in differentiating those that will buy from those that don’t.  For example, in some recent models I’ve built, there were buyers from both urban and rural areas.  The rural buyers tended to like Gardening and Fishing.  If we were to apply that natioanlly - without regional geographical recognition - we would erroneously use those attributes for targeting purposes. We would completely miss densely populated areas that likely do not offer Fishing and Gardening opportunities.  Clearly, the opportunities offered in an urban setting attract a certain market segment whereas the opportunities offered in a rural setting offer different opportunites. 

Recognizing these important regional differences will increase the probability of a purchase.

The AccuData team is home after a very productive week at the DMA and I have to say that this year’s show was a great success.  This seemed to be a far more serious crowd than last year.  Perhaps it was San Diego vs. Vegas or maybe CFOs simply didn’t approve non-essential travel.  While the reason is a mystery, the floor and the sessions were filled with a healthy crowd looking for solutions and to learn.  Good stuff.

The Brain enjoyed the show as well and several hundred t-shirts and Twitter followers later he’s developed quite a following.  Next year we may have to provide a bit of security for him - there were some unruly groupies and a couple of reporters that tried to sneak into his hotel room.  I just saw his last Tweet - I guess the show was a bit much and he’s tired.  So no new Tweets for at least a week I’d guess.  We’ll see.

Booths are always fun to evaluate.  On the “what were they thinking” scale, one company bought a 10×20 space and had only a chair and small round cocktail table along with minimal signage.   But this company DID hire an actor, dressed and painted to look like a bronze statue, to jab people as they passed by the booth, scaring some of them, amusing others and baffling me.  But this company’s booth traffic seemed good and I hope the show went well for them.  None of the booths really wowed me but there were many good ones and it was clear that most companies put a good deal of effort into their show presence.

AccuData certainly did and while we’ll see how leads and deals play out, the show went well.  I just wish The Direct Marketing Association would allow show exhibitors to do more direct marketing pre-show.  The delegate list was released late, email and telemarketing are prohibited and there are relatively few DMA-sponsored mechanisms for selling.  I don’t get it.  Perhaps this is why the DMA is facing so much emerging competition for mind share and marketing dollars from other associations and companies creating new forums for learning and selling.