Analytics Isn’t Reporting

Today, virtually all organizations have challenges in effectively leveraging analytics to drive business performance. Odds are pretty good that when you read that statement, you thought of at least one example in your organization. Perhaps you thought about the systemic contribution that analytics is making or a frustration you’ve had with analytics performance. If so, you’re hardly alone.

Today, virtually all organizations have challenges in effectively leveraging analytics to drive business performance.

Odds are pretty good that when you read that statement, you thought of at least one example in your organization. Perhaps you thought about the systemic contribution that analytics is making or a frustration you’ve had with analytics performance. If so, you’re hardly alone.

Here’s my home base for thinking about “analytics” in your organization.

“The promise of marketing analytics isn’t esoteric, or abstract — it’s fundamentally simple — analytics generates evidence of problem or opportunity that can be used to drive a specific business impact.”

Yet marketing analytics all too often fails to live up to its full potential. When it comes to the Web, almost a decade after the advent of mass adoption of Web analytics platforms like Google Analytics, engagement and conversion rates are still struggling to make methodical progress forward, and bring the business to materially greater profitability.

One of the biggest errors in strategy is the inadvertent substitution of “reporting,” or even “dashboards,” for a robust analytics process. It helps to first appreciate how subtle that difference is and why it happens:

  1. Analytics Is Interesting. Analytics can be intellectually stimulating, but some individuals and organizations spend too much time in the rapture of how interesting all that data can be. I was recently at an event where a smart young woman had a name badge on that said “I love data” below her name. I was tempted to write “I make money with the data” under my own.

    While I’ll be the first to express a life-long affair with the database and discovering “interesting” things in the data, that’s just not enough. So we have to monitor when analytics isn’t producing the evidence we need to affect change and deliver a business impact. While that can take a tremendous amount of work, the purpose itself must remain clear to create value.

  2. Reports Don’t Always Have the Right Questions Behind Them. Most of us came up in business generating and reading reports. I confess that I remember craving a report we used to call “the blue book” (if you still remember paper). I looked forward to every week when I ran my business line off of it in a large company that razed many a forest generating blue books. Thankfully, they email them now — but these reports are the same static, one-dimensional view of the business, many years later.

    The problem comes when we see our “standard reports” as the answer, even if the question we should be asking has changed.

    When you’re dealing with fickle consumers, and infinite choice is a click away, those questions sometimes change faster than “reporting standards” can realistically keep up with.

  3. The Relevancy Is Gone. Better than 80 percent of the time, I see marketing organizations with ample “stats” on their historical activity — yet they often fundamentally lack a strategic big picture and framework to consistently improve marketing and business decision-making. Frequently, the same organizations struggled with aligning the technical implementation of analytics and metrics required to drive business growth.

  4. Continuous Business Improvement Sometimes Requires a Cultural Shift. Cultural shifts of any size aren’t trivial, of course. I recently attended an all-day digital commerce strategy summit at a large brand I’ve done strategy work with during the past year. Dozens of staff, vendors and executives attended. The ultimate revelation for some of these executives who made the six-figure investment in the event was, “this requires patience, and is very methodical and testing-based” — it took a huge amount of effort, resources and time. To the credit of the executive who sponsored this event, a necessary cultural shift was recognized. While all in attendance knew intuitively about “test-optimize-learn” and had a large investment in their analytics software platform — she recognized that her organization was playing catch-up culturally — an achievement in itself.

5. Prioritization Is Key. Many large and more traditional organizations have very deep roots in a task- and reporting-based culture. This stifles Data Athletes from doing their jobs. Prioritization is key. As the old saying goes, “If everything is a priority, nothing is a priority.” Executive sponsors need to make choices on where to dial effort back; focus can then be applied to build a point of view based on evidence, and the opportunity to create and discover the context of opportunity and problems.

Forward vs. Backward Analysis.
Very frequently, I’ve helped organizations that started analytics processes or programs by looking “backward” at tactical reports; these reports can only show if a past tactic has or hasn’t worked. You cannot tell if a different tactic or mix of tactics would have done better, and by how much. Worse yet, the very volume of these “reports” often obscures the bigger picture. The solution … Look forward.

Analytics Should Be Forward-Looking. It’s driven not only by analyzing the past, but by creating a framework for planning and creating future performance. In other words, what to test, how to test it, and how to use the results of those tests to drive continuous improvements in the business.

In short, analytics done well creates visibility into what you should be doing and suggests the delta with what you are currently doing. Think about the aforementioned necessity for prioritization — Analytics done well helps you set those priorities.

Analytics professionals and and the executive team must all work together according to one principle:

Analytics is the process of identifying truths from data.
These truths inform decisions that measurably improve business performance.

Analytics Must Be Purpose-Driven.
Here’s a simple approach to create focus and align the specific implementation of analytics to serve you and your business growth:

  • Your business’s Purpose drives specific Business Objectives.
  • Those Business Objectives, in turn, inform Goals.
  • Your Goals are tracked via KPIs.
  • The KPIs are continuously compared against Benchmarks.

It’s easy to dive into the weeds, get lost in the data, lose patience with the process, and begin a bottom-up approach. This deceptively simple framework I’ve suggested will help you take a top-down approach to analytics that ensures you are measuring the right things — correctly. When you do, you will become a true analytics-driven organization.

Doing so will help your organization grow faster, more consistently and reliably — and that makes for a valuable and happier organization. Be a Data Athlete, not an analytics nerd — and you’ll make all the difference in your organization.

I Am Fascinating – Even My Hotel Thinks So

You know that age-old scenario with the man stuck in the labyrinth, who can’t find his way out? Well, there’s an online version of that—it’s the registration page that tells you there’s an error and you cannot continue, except the error is not with you, it’s with them

You know that age-old scenario with the man stuck in the labyrinth, who can’t find his way out? Well, there’s an online version of that—it’s the registration page that tells you there’s an error and you cannot continue, except the error is not with you, it’s with them.

Recently, I was shopping for a hotel in the San Diego area as I am planning to attend the DMA’s Annual Conference in October. Booking through the DMA’s site would ensure me a group rate, so I started perusing my options, sorting them by price.

One of the least expensive options was a hotel I had never heard of, but considering the property was only a 5 minute walk from the convention center, it was worth a closer look and the ad copy really intrigued me. Rather than simply extoling the hotel’s many features, I was given a peek at my life as a guest at their hotel: “When you are whisked up to your room, you’ll look out over the city, feeling like you belong here and that San Diego’s world is your oyster.” Sold! (Oh, and nice job getting me to picture myself as a happy customer.)

But then I began the booking process and a funny thing happened. After entering my guest details and confirming the rate and date, I was prompted to add my loyalty program ID number. Never one to pass up a deal, I clicked on the drop down menu to see if they would give me points with my favorite airline. Alas, my sole choice was the Kimpton InTouch loyalty program. Since I had never heard of it, I closed out of the menu. But it seems that InTouch was now selected, and I was unable to un-select it unless I put in my member number.

Abandon the transaction entirely? Another might have, but I—being the intrepid and inquisitive marketer that I am—jumped onto my second screen and researched the Kimpton InTouch program. (Did I mention I’m not one to pass up a deal?) It provided a simple registration form and the hope of instant use. But rather than getting a formulaic “welcome” email with membership number, a clever thing happened at the end of my registration process—a virtual membership card appeared on my screen, with my new InTouch loyalty number AND a downloadable V-card for Outlook. Genius!

In a split second, I downloaded and saved the V-card into my Outlook Contacts, and was delighted to know I would now have this number at my fingertips whenever booking with Kimpton again. And if the San Diego experience turned out to be as fabulous as promised, it was highly likely I would.

A simple copy from one screen and pasted to the other, and my booking process was back on track.

But what was equally interesting about the Kimpton InTouch registration form was this statement and request near the bottom of the form:

We love being fans and friends of our members. Please help us stay InTouch with you.

It then asked for my URL/Website/Blog and Twitter handle. Certainly this boutique hotel group was not planning to visit my company’s website and follow me on Twitter? Or was it?

It’s now a week later and Kimpton Hotels is not following me on Twitter, but for a brief moment I felt like the most interesting customer in the world. On the other hand, what is Kimpton planning to do with this information? Tweet me after my stay? Encourage me to tweet about my experience while a guest?

Check back with this column in October and find out. I’ll be impressed if Kimpton comes through with something that makes me feel like the most interesting customer in their world.

Who’s Your Scapegoat?

I find it interesting that machines and procedures often become scapegoats for “human” errors. Remember the time when the word “mainframe” was a dirty word? As if those pieces of hardware were contaminated by some failure-inducing agents. Yeah, sure. All your worries will disappear along with those darn mainframes. Or did they?

I find it interesting that machines and procedures often become scapegoats for “human” errors. Remember the time when the word “mainframe” was a dirty word? As if those pieces of hardware were contaminated by some failure-inducing agents. Yeah, sure. All your worries will disappear along with those darn mainframes. Or did they? I don’t know what specific hardware is running behind those intangible “clouds” nowadays, but in the age when anyone can run any operating system on any type of hardware, the fact that such distinctions made so much mayhem in organizations is just ridiculous. I mean really, when most of computing and storage are taken care of in the big cloud, how is the screen that you’re looking at any different than a dummy terminal from the old days? Well, of course they are in (or near) retina display now, but I mean conceptually. The machines were just doing the work that they were designed to do. Someone started blaming the hardware for their own shortcomings, and soon, another dirty word was created.

In some circles of marketers, you don’t want to utter “CRM” either. I wasn’t a big fan of that word even when it was indeed popular. For a while, everything was CRM this or CRM that. Companies spent seven-figure sums on some automated CRM solution packages, or hired a whole bunch of specialists whose titles included the word CRM. Evidently, not every company broke even on that investment, and the very concept “CRM” became the scapegoat in many places. When the procedure itself is the bad guy, I guess fewer heads will roll—unless, of course, one’s title includes that dirty word. But really, how is that “Customer Relationship Management” could be all that bad? Delivering the right products and offers to the right person through the right channel can’t be that wrong, can it? Isn’t that the whole premise of one-to-one marketing, after all?

Now, if someone overinvested on some it-can-walk-on-the-water automated system, or just poorly managed the whole thing, let’s get the record straight. Someone just messed it all up. But the concept of taking care of customers with data-based marketing and sales programs was never the problem. If an unqualified driver creates a major car accident, is that the car’s fault? It would be easier to blame the internal combustion engine for human errors, but it just ain’t fair. Fair or not, however, over-investment or blind investment on anything will inevitably call for a scapegoat. If not now, in the near future. My prediction? The next scapegoat will be “Big Data” if that concept doesn’t create steady revenue streams for investors soon. But more on that later.

I’ve seen some folks who think “analytics” is bad, too. That one is tricky, as the word “analytics” doesn’t mean just one thing. It could be about knowing what is going on around us (like having a dashboard in a car). Or it could be about describing the target (where are the customers and what do they look like?). Or it could be about predicting the future (who is going to buy what and where?). So, when I hear that “analytics” didn’t work out for them, I am immediately thinking someone screwed things up dearly after overspending on that thing called “analytics,” and then started blaming everything else but themselves. But come on, if you bought a $30,000 grand piano for your kids to play chopsticks on it, is that the piano’s fault?

In the field of predictive analytics for marketing, the main goals come down to these two:

  1. To whom should you be talking, and
  2. If you decided to talk to someone, what are you going to offer? (Please don’t tell me “the same thing for everyone”.)

And that’s really it. Sure, we can talk about products and channels too, but those are all part of No. 2.

No. 1 is relatively simple. Let’s say you have an opportunity to talk to 1 million people, and let’s assume it will cost about $1 to talk to each of them. Now, if you can figure out who is more likely to respond to your offer “before” you start talking to them, you can obviously save a lot of money. Even with a rudimentary model with some clunky data, we can safely cut that list down to 1/10 without giving up much opportunity and save you $900,000. Even if your cost is a fraction of that figure, there still is a thing called “opportunity cost,” and you really don’t want to annoy people by over-communicating (as in “You’re spamming me!”). This has been the No. 1 reason why marketers have been employing predictive models, going back to the punchcard age of the ’60s. Of course, there have been carpet-bombers like AOL, but we can agree that such a practice calls for a really deep pocket.

No. 2 gets more interesting. In the age of ubiquitous data and communication channels, it must become the center of attention. Analytics are no longer about marketers deciding to whom to talk, as marketers are no longer the sole dictators of the communication. Now that it is driven by the person behind the screen in real-time, marketers don’t even get to decide whether they should talk to them or not. Yes, in traditional direct marketing or email channels, “selection” may still matter, but the age of “marketers ranking the list of prospects” is being rapidly replaced by “marketers having to match the right product and offer to the person behind the screen in real-time.” If someone is giving you about half a second for you to respond, then you’d best find the most suitable offer in that time, too. It’s all about the buyers now, not the marketers or the channels. And analytics drive such personalization. Without the analytics, everyone who lands on some website or passes by some screen will get the same offer. That is so “1984,” isn’t it?

Furthermore, the analytics that truly drive personalization at this level are not some simple segmentation techniques either. By design, segmentation techniques put millions of people in the same bucket, if a few commonalities are found among them. And such common variables could be as basic as age, income, region and number of children—hardly the whole picture of a person. The trouble with that type of simplistic approach is also very simple: Nobody is one-dimensional. Just because a few million other people in the same segment to which I happen to be assigned are more “likely” to be into outdoor sports, should I be getting camping equipment offers whenever I go to ESPN.com? No siree. Someone can be a green product user, avid golfer, gun owner, children’s product buyer, foreign traveler, frequent family restaurant visitor and conservative investor, all at the same time. And no, he may not even have multiple personalities; and no, don’t label him with this “one” segment name, no matter how cute that name may be.

To deal with this reality, marketers must embrace analytics even more. Yes, we can estimate the likelihood measures of all these human characteristics, and start customizing our products and offers accordingly. Once complex data variables are summarized into the form of “personas” based on model scores, one doesn’t have to be a math genius to know this particular guy would appreciate the discount offer for cruise tickets more than a 10 percent-off coupon for home theater systems.

Often people are afraid of the unknowns. But that’s OK. We all watch TV without really understanding how HD quality pictures show up on it. Let’s embrace the analytics that way, too. Let’s not worry about all the complex techniques and mystiques behind it. Making it easy for the users should be the job of analysts and data scientists, anyway. The only thing that the technical folks would want from the marketers is asking the right questions. That still is the human element in all this, and no one can provide a right answer to a wrong question. Then again, is that how analytics became a dirty word?

Winner of the 2012 Presidential Election: Data

Now that the contentious 2012 election has finally ended, we get a chance to look back and assess what happened and why. Regardless of who you voted for, it’s impossible not to acknowledge that the real winner of the 2012 election was data.

Now that the contentious 2012 election has finally ended, we get a chance to look back and assess what happened and why. Regardless of who you voted for, it’s impossible not to acknowledge that the real winner of the 2012 election was data.

For the first time in history, this election demonstrated the power of using analytics and numbers crunching for politics. What I find most remarkable is the rapid evolution of this change. If you look back just a few years ago, Karl Rove was widely regarded as the political mastermind of the universe. Rove’s primary innovation was the use of highly targeted direct mail campaigns to get out the evangelical and rural vote to win the 2004 election for George W. Bush. Fast-forward a few short years, and not only did Rove’s candidate lose, but the master strategist was reduced to challenging his network’s numbers geeks live on the air, only to be rebuffed.

In every way, the old guard was bested by a new generation of numbers crunchers, nerds and data geeks who leveraged data science, analytics, predictive modeling and a highly sophisticated online marketing campaign to poll, raise money and get out the vote in an unprecedented manner.

On the subject of polling, I was intrigued by Nate Silver’s incredibly accurate FiveThirtyEight blog that used a sophisticated system to synthesize dozens of national polls in a rolling average to predict the actual election results. In the run-up to the election, he even received a lot of flak from various pundits who claimed he was wrong basing on their perception on voter “enthusiasm,” “momentum” and other non-scientific observations. At the end of the day, however, data won out over hot air and punditry big time. Silver’s final tally was absolutely dead on, crushing most other national polls by a wide margin.

I especially love his Nov. 10 post in which Silver analyzes the various polls and shows which ones fared the best and which ones weren’t worth the paper they were printed on. It’s shocking to see that the Gallup Poll—in many people’s mind the oldest and most trusted name in polling—was skewed Republican by a whopping 7.2 points when averaged across all 11 of their polls. Ouch. For an organization that specializes in polling, their long-term viability must be called into question at this point.

One thing I find highly interesting when looking at the various poll results is that when you examine their methodologies, it’s not too surprising that Gallup fell flat on its face, relying on live phone surveys as the primary polling method. When considering that many young, urban and minority voters don’t have a landline and only have a cellphone, it doesn’t take a rocket scientist to conclude any poll that doesn’t include a large number of cellphones in its cohort is going to skew wildly Republican … which is exactly what happened to Gallup, Rasmussen and several other prominent national polls.

Turning to the Obama campaign’s incredible Get Out The Vote (GOTV) machine that turned out more people in more places than anyone could have ever predicted, there’s no doubt in anyone’s mind that for data-driven marketers, the 2012 U.S. election victory was a watershed moment in history.

According to a recent article in Time titled “Inside the Secret World of the Data Crunchers Who Helped Obama Win,” the secret sauce behind Obama’s big win was a massive data effort that helped him raise $1 billion, remade the process of targeting TV ads, and created detailed models of swing-state voters that could be used to increase the effectiveness of everything from phone calls and door-knocks to direct mailings and social media.

What’s especially interesting is that, similarly to a tech company, Obama’s campaign actually had a large in-house team of geeks, data scientists and online marketers. Composed of elite and senior tech talent from Twitter, Google, Facebook, Craigslist and Quora, the program enabled the campaign to turn out more volunteers and donors than it had in 2008, mostly by making it it simpler and easier for anyone to engage with the President’s reelection effort. If you’d like to read more about it, there’s a great article recently published in The Atlantic titled “When the Nerds Go Marching In” that describes the initiative in great detail.

Well, looks like I’m out of space. One thing’s for sure though, I’m going to be very interested to see what happens in coming elections as these practices become more mainstream and the underlying techniques are further refined.

If you have any observations about the use of data and analytics in the election you’d like to share, please let me know in your comments.

—Rio

Death by Whitepaper

As a B-to-B marketer, you should be very familiar with the strategy of whitepapers. But that doesn’t mean you are designing or using them appropriately for your business. I should know, as I’ve seen, read, created, written and rewritten literally hundreds of them. And I’ve often been so bored after the first paragraph that I wonder why I bothered to download the document.

As a B-to-B marketer, you should be very familiar with the strategy of whitepapers. But that doesn’t mean you are designing or using them appropriately for your business. I should know, as I’ve seen, read, created, written and rewritten literally hundreds of them. And I’ve often been so bored after the first paragraph that I wonder why I bothered to download the document.

According to Wikipedia, a whitepaper is an authoritative report or guide that helps solve a problem. They are typically used to educate readers and help them make a decision.

In the early 1990’s, marketers started to leverage whitepapers as a way to present information about a particular topic that was of interest to a marketer’s target audience, but written in a voice that sounded like a third-party, subject matter authority. It may or may not have even mentioned the marketer’s product or service. Instead, it provided in-depth, useful information that helped readers solve a problem or expand their understanding of an issue.

In 2012, whitepapers have often been used as the lazy marketer’s brochure-ware: A forum where the product/service attributes are extolled, at length.

Sometimes they are poorly designed, or not designed at all—just pages upon pages of text (“because,” as one client informed me, “they’re supposed to be white papers”). She wasn’t kidding.

I particularly hate it when a marketer designs a whitepaper with a full-color, full-bleed, front cover (thanks for soaking up all my printer toner!). As a result, I carefully print beginning on page 2, which often means the contact information for the company which was on the front cover (website, sales contact, phone number and email address) are not included with my whitepaper when printed.

It seems that whitepapers are a lost art. So here are a few tips on whitepaper best practices that every good B-to-B marketer should follow:

  1. Start planning a whitepaper topic by identifying your target’s pain point, or determine a timely issue that would interest your target. It should NOT be focused on your company’s product/service benefits, however those could be woven into your story as a support to your point-of-view, or to demonstrate a solution to an issue.
  2. Make sure it’s well researched, with footnoted facts and figures that support the point you’re making. Include the most current data to keep your topic timely.
  3. Your writer should be an experienced whitepaper writer, not necessarily a copy writer or the named author. It’s most important that the paper is well written, well presented and interesting. It should NOT include sensational headlines, exclamation points or product demos.
  4. Include an Executive Summary: A pithy, 100-word-or-less overview that allows readers to scan and determine if they’re interested in reading more.
  5. Break up reader monotony by including well-crafted subheads, large call-outs (interesting statistics or quotes), visuals (that support the copy), charts/graphs or even icons. Eyes need a resting place when they read a long document and visuals help retain interest.
  6. Number your pages please (so much easier when the reader forwards it up the food chain and includes a note that says to the CEO, “some interesting insights on page 4, 2nd paragraph”). After all, isn’t that your ideal scenario?
  7. At the end of the paper, include an “About the Author” to provide credibility. Your author credentials don’t need to include the name of a high school or favorite pet, but they should include years of experience, where/how they gained their knowledge, the names of articles/books they’ve written, etc.
  8. Include a short paragraph about your company, positioning it in the most relevant light as it relates to the topic. Include a link to a relevant page on your website to learn more (i.e., www.xyzcompany/resources), and an 800 number and email address. You’d be surprised how many people actually want to learn more after reading a helpful whitepaper.
  9. Make sure it’s easily navigable when viewed digitally, but can also be easily printed. And, please don’t bleed my toner dry by including lots of black or lots of bleeds.

Deciphering Big Data Is Key to Understanding Buyer’s Journey

Long before a sale is won or lost, customers and prospects embark on what can be called the “buyer’s journey.” This journey is a complex evolution spanning the entire lifecycle of the customer-vendor relationship, beginning with identification of the underlying business issue or need, and culminating in vendor selection.

Long before a sale is won or lost, customers and prospects embark on what can be called the “buyer’s journey.” This journey is a complex evolution spanning the entire lifecycle of the customer-vendor relationship, beginning with identification of the underlying business issue or need, and culminating in vendor selection.

Along the way, the prospect engages in a wide breadth of activities. Some are internal, such as winning over key stakeholders, building internal consensus and acquiring the necessary budget; while others are externally facing. For example, market research, engaging with colleagues in similar firms to share experiences, and of course contacting salespeople for product demos and pricing negotiation.

I do not claim to have coined the term ‘buyer’s journey.’ For more information on it, you can check out a great article by Christine Crandell that appeared on Forbes.com earlier this month. Among other things, Crandell does a great job explaining how social media can be leveraged to better connect with and understand the buyer’s journey, particularly during times when prospects are not engaged with your sales team. What’s especially interesting about the concept of the buyer’s journey is that prospects are actually unengaged with your firm during the vast majority of this process. Engagement only begins when prospects start their market research and contact a salesperson—usually not before.

Now how does this relate to database marketing? Well, it does in two huge ways. On a strategic basis, any marketer worth his or her own salt knows that effective marketing depends getting your message in front of qualified prospects as inexpensively as possible. In order to do this effectively, identifying how prospects are researching the marketplace is key. Why? Because this is where your prospects are spending much of their time, this is where you need to have your brand appearing front and center. So, from a marketing spend point of view, without a doubt this is where you’re going to get the most bang for your buck.

Now, of course, this is far easier said than done. It’s going to take a ton of market research, including customer interviews, focus groups, industry insight and general analysis to identify how your customers researched the marketplace prior to making a purchase. Did they attend key industry trade shows or events? Do they belong to specific peer or networking groups? What publications do they subscribe to? Whatever the answers to these questions are … well this is where you need to be.

Another key to deciphering the buyer’s journey is understanding how the prospect is engaging with your firm across all Key Performance Indicators (KPIs). This understanding can only be arrived at through a deep analysis of every touchpoint between you are your customers. The best way to achieve this is to identify and extract customer and prospect data wherever it may reside. There are no shortcuts here. For large organizations, it can be located in an email broadcast tool, CRM, ERP, Marketing Automation Solution or purpose-built Master Data Management (MDM) Hub, among other places.

Now, of course, this means extracting and sifting through tons and tons of data—everything ranging from garden variety campaign analytics to purchasing history, from personal attributes to company insight, from demographic data to psychographic profile. Tracking, archiving and sorting out all this information is big business. In fact, many in the industry are now referring to this reality as ‘Big Data,’ as companies track and store vast troves of information that they need to make sense out of. In addition to the physical IT infrastructure required to capture and store the information, making sense out of it often requires technical expertise. Without wanting to veer off topic, if this sounds interesting then I suggest turning to NPR, where an interesting and in-depth story on Big Data aired on November 29, 2011.

As I was saying, once the data is extracted, you need to make sense out of it. Paramount to this task is the process of creating robust user profiles replete with detailed demographic, psychographic and, of course, (for B2B) firmographic information—in effect, multi-dimensional user profiles—and mapping it back to KPIs that help identify engagement patterns and behavior central to the buyer’s journey.

Once user profiles have been established, this is where the fun parts comes in, as marketers leverage this information to create compelling offers that speak to the various customer segments. The good news is that recent technological innovations have made this job much easier and more effective. Using marketing automation tools, it’s now possible to broadcast varying sophisticated drip marketing campaigns to various segments of your database—segments that can now easily be created using complex rules based on both list attributes and user engagement. What’s more, the marketing message itself—email creative, direct mail piece, landing page, and so on—can now be highly personalized based on profile data, resulting in higher response rates, reduced media costs and, of course, improved customer satisfaction.

I hope this all makes sense. Any comments or feedback are welcome.

5 Interesting Things I Learned This Week

2. Been flogged online? The best way to deal with negative reviews that come along with being more visible in the blogosphere may come from an unlikely source: a section on Yelp’s Business Owner’s Guide titled “Responding to Reviews.”

My blog post this week is a culmination of a few interesting tidbits I learned this week:

1. More retailers are experimenting with social media, despite the fact that social media tactics are still experimental at best and returns are hazy. In fact, according to Fiona Swerdlow, head of research at Shop.org — who presented the opening keynote at Retail Online Integration’s Retail Marketing Virtual Conference & Expo (RMV) — 80 percent of respondents to a recent survey from Shop.org are pursuing the channel because they believe it’s a great time to experiment and learn more.

2. Been flogged online?
The best way to deal with negative reviews that come along with being more visible in the blogosphere may come from an unlikely source: a section on Yelp’s Business Owner’s Guide titled “Responding to Reviews.”

This great tip came via Eric Anderson, vice presdient of emerging media at White Horse Interactive during his RMV presentation titled “Live Retail Website Lab.”

When crafting your message to customers, Yelp advises keeping the following three things in mind:

  1. Your reviewers are your paying customers.
  2. Your reviewers are human beings with (sometimes unpredictable) feelings and sensitivities.
  3. Your reviewers are vocal and opinionated (otherwise, they wouldn’t be writing reviews).

3. The Interactive Advertising Bureau announced guidelines designed to standardize the information that ad networks and exchanges provide to advertisers and agencies. Here are the six new guidelines:

  • Transparency should exist for inventory sources, publisher relationships, content types and ad placement details.
  • Advertisers should be presented with content categories that are universally defined in the industry.
  • Categories of illegal content should be defined or labeled. For example, content that infringes a copyright should be marked as prohibited for sale.
  • Under the industry organization’s provisions, ad networks should rate content for audience segments.
  • Data disclosure terms should be outlined for offsite behavioral targeting and third-party data.
  • Companies should provide for IAB training of appointed compliance officers in each certified network or exchange.

4. Email’s influence over multichannel purchasing is powerful, according to a study from e-Dialog. The majority of consumers (58 percent) surveyed said they’ve been driven to make a purchase in a store or over the phone by a marketing email. And while websites are the preferred place for consumers to opt in, they’re also very willing to subscribe to email messages offline — e.g., when placing a catalog order (46 percent), at the point of sale (29 percent) or via SMS text message (13 percent).

5. More than 50 percent of consumers have come to expect personalized merchandising, starting with a personalized homepage. Furthermore, 77 percent of shoppers will make an additional purchase when presented with personalized recommendations.

These findings came via a report from MyBuys, a provider of personalized recommendations for multichannel retailers, titled “Consumer Insights into Multi-Channel Interactions: Practical Tools for Profitable Selling.” For the report, MyBuys commissioned the e-tailing group to survey 1,000 consumers to gain insights into how shoppers interacted with personalized merchandising and where they expected to see personalized recommendations.

Did you learn anything interesting this week that you’d like to share? Post it here.

Behavioral Targeting Industry Needs Further Delineation

I received an interesting press release the other day from ValueClick Media that recapped a recent behavioral targeting panel that took the stage at the Hard Rock Hotel in Chicago.

The panel featured an industry analyst (David Hallerman, senior analyst, eMarketer), a behavioral targeting product expert (Joshua Koran, vice president, targeting and optimization, ValueClick, Inc.), a brand marketer (Julian Chu, Director of Acquisition Marketing, Discover) and an interactive agency executive (Sam Wehrs, Digital Activation Director, Starcom).
 

I received an interesting press release the other day from ValueClick Media that recapped a recent behavioral targeting panel that took the stage at the Hard Rock Hotel in Chicago.

The panel featured an industry analyst (David Hallerman, senior analyst, eMarketer), a behavioral targeting product expert (Joshua Koran, vice president, targeting and optimization, ValueClick, Inc.), a brand marketer (Julian Chu, Director of Acquisition Marketing, Discover) and an interactive agency executive (Sam Wehrs, Digital Activation Director, Starcom).

What I found most interesting about the release was that fact the group discussed and agreed on the need for delineation between the different approaches to behavioral targeting.

“While it is important to understand the difference between retargeting – which Hallerman referred to as “reactive” – and the more complex models, the panel agreed it is also critical to understand the differences within the more sophisticated group of behavioral targeting approaches, and Joshua Koran shared three designations: “clustering,” “custom business rules” and “predictive attributes,” the release said.

The “clustering” approach assigns each visitor to one and only one segment while the “custom business rules” approach offers marketers the ability to target visitors who have done X events in Y days, with Boolean operators of “and.” “or,” and “not.” Finally, the “predictive attributes” approach automates the assignment of interest categories based on the visitor activities that best correlate with performance; thus, the system is continuously learning to identify multiple interest attributes per visitor.

Another notable takeaway was the need for a focus on the customer experience and the corresponding importance of demonstrating value to customers when serving behaviorally targeted ads.

According to the release Julian Chu offered three questions marketers must address to make behavioral targeting a valuable experience for customers instead of merely serving the ads, which would unavoidably become customer annoyance: How are you going to do it? Where is it going to happen? What is going to happen at that time?

Presented as part of ValueClick Media’s ongoing Media Lounge education event series, this event – The Changing Behavioral Targeting Landscape – as well as the discussion itself underscored the importance of education relative to this increasingly important online advertising technique.

Food for thought!