Any Time Is Search Time for Consumers

At a baseball game the other day, I couldn’t help but notice how many people in my seating area were busy looking at their phones, phablets or tablets. Baseball, with its languorous pace, provides spectators plenty of extra time to search online, check their email, send texts and engage with social media. It seems no one near me at the game was wasting a single moment of this valuable screen time. Savvy sports marketers already know this and regularly encourage social media use, providing hashtags and URLs almost everywhere.

At a baseball game the other day, I couldn’t help but notice how many people in my seating area were busy looking at their phones, phablets or tablets. Baseball, with its languorous pace, provides spectators plenty of extra time to search online, check their email, send texts and engage with social media. It seems no one near me at the game was wasting a single moment of this valuable screen time. Savvy sports marketers already know this and regularly encourage social media use, providing hashtags and URLs almost everywhere. Go to any sporting event and see for yourself just how much online activity is going on all around you. It would be a fair to say almost everybody is constantly online with a mobile device.

This highly distracted behavior is not confined to sporting events. This behavior is the new norm. It is pervasive. Google has recognized this and has adjusted their algorithm to give a boost to mobile friendly sites. There are several clear signals for ecommerce site owners in this shift to mobile. With limited search real estate available on smaller screens and search rankings increasingly difficult to secure, each organic search click becomes more important. They must not be wasted. It is imperative that a site catch the surfer on their first search and direct their attention directly to the product they want with minimal effort; otherwise that searcher may very well move on to another site or to some other online activity. Are you making it as easy as possible for all your visitors to find just what they want almost instantly? That should be the goal.

If your site were perfectly optimized—an ideal, hypothetical, situation, every searcher would conduct a search and find just the right product on the very first try. It doesn’t work that way even in fairy tales. It took Goldilocks three tries to find the “just right” porridge. Are you effectively supporting the customer’s quest through your navigation, and does Google understand how your navigation supports the user? If you cannot answer this in the affirmative, you need to adjust your proverbial sails to catch the wind.

Ask yourself whether your faceting supports a second more refined search query. For example, someone searching for “batting helmets” might want to refine their search to reflect the user (youth or adult), a brand or price preference, or the whether the helmet is for slow pitch softball or high-velocity hardball. Your navigation and its faceting should support this searcher behavior. Does your site make it easy for the first time visitor to quickly find additional options when they arrive from a search engine, or must they go through numerous clicks to see them?

Your navigation should act as a secondary search tool. Google has recognized the value of the navigation, and through site links allows site owners to communicate key navigational elements. We can expect to see Google continue to make efforts to compress more useful information into less space in the search listing in an effort to satisfy the user more quickly. Give your Google listings a quick sanity check and see if they conform to how users look for your products. One quick tip is to review your two and three word phrases and see if they show up when and where you would expect them. Search and shop your own site the next time you are sitting at a ball game with spare screen time. You’ll be surprised at what you might find out.

The Purpose-Driven Brand

Since the beginning of time to this very moment, we humans have been driven by purpose. Consciously and unconsciously, we seek meaning in our lives and the need to actively make a difference and leave a personal legacy of good when we move on from this existence. Jung addresses this in his Individuation process and so, too, do modern and past psychologists and researchers of human behavior drivers.

Since the beginning of time to this very moment, we humans have been driven by purpose. Consciously and unconsciously, we seek meaning in our lives and the need to actively make a difference and leave a personal legacy of good when we move on from this existence. Jung addresses this in his Individuation process and so, too, do modern and past psychologists and researchers of human behavior drivers.

Rick Warren, founder of The Saddleback Ministries, and best-selling author, discovered just how powerful our need and drive for purpose is when he wrote, “The Purpose-Driven Life: What on Earth Am I Here For?” Written in 2003, this book became the bestselling hardback non-fiction book in history, and is the second most-translated book in the world, after the Bible.

Today’s consumer seeks purpose outside of the traditional methods of religion, volunteerism, and random acts of kindness toward friends and strangers. Many of us, in fact most of us, seek to further our sense of purpose with our choices at the grocery store, online shopping carts and more. According to research by Cone Communications and Edelman, consumers in the U.S. are more likely to trust a brand that shows its direct impact on society (opens as a PDF). Others, upwards of 80 percent, are more likely to purchase from a company that can quantifiably show how it makes a difference in people’s lives—beyond just adding to the investment portfolio of a very select few.

According to the Merriam Webster dictionary, purpose is defined as:

: the reason why something is done or used
: the aim or intention of something
: the feeling of being determined to do or achieve something

Consumers are not just expecting big business to define a social purpose for the brand, they are demanding it by how they are making purchasing and loyalty choices. Edelman’s “Good Purpose Study,” released in 2012 and covering a five-year study of consumers worldwide shows:

  • 47 percent of global consumers buy brands that support a good cause atleast monthly, a 47 percent increase in just two years.
  • 72 percent of consumers wouldrecommend a brand that supports a good cause over one that doesn’t, a 39 percent increase since 2008
  • 71 percent of consumers would help a brand promote its products or services if there is a good cause behind them, representing a growth of 34 percent since 2008
  • 73 percent ofconsumers would switch brands ifa different brand of similar quality supported a good cause, which is a 9 percent increase since 2009

Another research group, Cone Communications, showed that 89 percent of consumers are likely to switch brands to one that is associated with a good cause if price and quality are similar; and 88 percent want to hear what brands are doing to have a real impact, not just that they are spending resources toward a cause.

This new state of consumerism doesn’t just show people still have a heart and soul, it is a big flag to brands in all industries to integrate CSR or Corporate Social Responsibility into their brand fiber, customer experience and marketing programs.

I interviewed William L. “Toby” Usnik, Chief CSR Officer for Christie’s in New York City, who maintains that CSR has moved far beyond writing a check and then emotionally moving on from a cause or community in need. It is about a brand’s purpose being bigger than developing its return to shareholders. Validating Usnik is a recent article published in the March 21, 2015, edition of The Economist, quoting Jack Welch of GE fame as saying “pursuing shareholder value as a strategy was ‘the dumbest idea ever.’ ” While that might be debatable, it is becoming less and less debatable, per the statistics above that show how defining a brand’s purpose in terms of the social good it delivers to communities related to its business is anything but “dumbest”—and rather, is getting smarter and smarter by the day.

Charting new territory in his role as Chief CSR Officer for Christie’s, Usnik’s first step was to define CSR as it relates to human psychology and the values of the Christie’s brand. For Usnik, it starts with building a brand’s purpose around Maslow’s hierarchy of needs and helping your constituents get closer to self-actualization, or that state of reaching a higher purpose for a greater good.

“Moving customers upwards through Maslow’s hierarchy of needs is critical to address,” says Usnik. “Customers of all ages, and especially Millennials, are moving toward a state of self-actualization and looking to define their purpose and place in communities and the world. They seek relationships with brands that are doing the same within their own value set. As a result, any business today needs to ask itself, ‘What is the impact of our activities on each other, the community, the workplace, customers and the planet?’ “

Defining your brand’s purpose and corresponding CSR efforts is the first step to developing emotional and psychological bonds with internal and external customers. When you make your CSR actionable by engaging others in your cause, you can build passion and loyalty that not only define your brand, but also your profitability. Coke defines its brand through its happiness campaign that involves delivering free Coke and other items, like sports equipment and toys, to villages around the world, and through water sanitization programs.

Tom’s Shoes, an example that is known to most as one of the pioneers in philanthropic branding, went from $9 million to $21 million in revenue in just three years by being a “purpose-driven brand” that enables people to give back to others simply by making a purchase. With a cost of goods sold of $9 and a sale price of more than $60, that is not hard to do.

At Christie’s CSR, is a big part of CRM. According to Usnik, Christie’s helps many of its customers sell high-value works of art. Many customers then donate the proceeds to social causes that align with their personal values or passions. By helping customers turn wealth into support for charitable causes, they actually create strong emotional bonds with customers, rooted in empathy and understanding—which is far more critical for securing lifetime value than points and reward programs.

In just 2014, $300 million in sales were facilitated through Christie’s that benefited non-profit organizations. Additionally, Christie’s regularly volunteers its charity auctioneers to nonprofit events. And in 2014, he estimates they’ve raised $58 million for 300 organizations.

The key to successful branding via CSR programs and purpose-driven strategies that transcend all levels of an organization and penetrate the psyche of we humans striving to define our role in this world is sincerity. Anything less simply backfires. Brands must be sincere about caring to support worthwhile causes related to their field, and they must be sincere when involving customers in charitable giving.

Concludes Usnik, “You can’t fake caring. If you pretend to care about a cause you align with, or a cause that is important to your customer, [you] won’t succeed. Caring to make a difference must be part of your culture, your drive and your passion at all levels. If you and your employees spend time and personal energy to work closely with your customers to make a difference for your selected causes and those of your customers, you are far more likely to secure long-term business and loyalty and overall profitable client relationships.”

Takeaway: The five primary drivers of human behavior, according to psychologist Jon Haidt of the University of Virginia and author of “The Happiness Hypothesis,” are centered around our innate need to nurture others, further worthy causes, make a difference in the world, align with good and help others. When brands can define themselves around these needs, we not only influence human behavior for the greater good, we can influence purchasing behavior for the long-term good of our individual brands. And per the Edelman research, 76 percent of customers around the world say its okay for brands to support good causes and make money at the same time. So define your purpose, build your plan, engage your customers and shine on!

Exciting New Tools for B-to-B Prospecting

Finding new customers is a lot easier these days, what with innovative, digitally based ways to capture and collect data. Early examples of this exciting new trend in prospecting were Jigsaw, a business card swapping tool that allowed salespeople to trade contacts, and ZoomInfo, which scrapes corporate websites for information about businesspeople and merges the information into a vast pool of data for analysis and lead generation campaigns. New ways to find prospects continue to come on the scene—it seems like on the daily.

Finding new customers is a lot easier these days, what with innovative, digitally based ways to capture and collect data. Early examples of this exciting new trend in prospecting were Jigsaw, a business card swapping tool that allowed salespeople to trade contacts, and ZoomInfo, which scrapes corporate websites for information about businesspeople and merges the information into a vast pool of data for analysis and lead generation campaigns. New ways to find prospects continue to come on the scene—it seems like on the daily.

One big new development is the trend away from static name/address lists, and towards dynamic sourcing of prospect names complete with valuable indicators of buying readiness culled from their actual behavior online. Companies such as InsideView and Leadspace are developing solutions in this area. Leadspace’s process begins with constructing an ideal buyer persona by analyzing the marketer’s best customers, which can be executed by uploading a few hundred records of name, company name and email address. Then, Leadspace scours the Internet, social networks and scores of contact databases for look-alikes and immediately delivers prospect names, fresh contact information and additional data about their professional activities.

Another dynamic data sourcing supplier with a new approach is Lattice, which also analyzes current customer data to build predictive models for prospecting, cross-sell and churn prevention. The difference from Leadspace is that Lattice builds the client models using their own massive “data cloud” of B-to-B buyer behavior, fed by 35 data sources like LexisNexis, Infogroup, D&B, and the US Government Patent Office. CMO Brian Kardon says Lattice has identified some interesting variables that are useful in prospecting, for example:

  • Juniper Networks found that a company that has recently “signed a lease for a new building” is likely to need new networks and routers.
  • American Express’s foreign exchange software division identified “opened an office in a foreign country” suggests a need for foreign exchange help.
  • Autodesk searches for companies who post job descriptions online that seek “design engineers with CAD/CAM experience.”

Lattice faces competition from Mintigo and Infer, which are also offering prospect scoring models—more evidence of the growing opportunity for marketers to take advantage of new data sources and applications.

Another new approach is using so-called business signals to identify opportunity. As described by Avention’s Hank Weghorst, business signals can be any variable that characterizes a business. Are they growing? Near an airport? Unionized? Minority owned? Susceptible to hurricane damage? The data points are available today, and can be harnessed for what Weghorst calls “hyper segmentation.” Avention’s database of information flowing from 70 suppliers, overlaid by data analytics services, intends to identify targets for sales, marketing and research.

Social networks, especially LinkedIn, are rapidly becoming a source of marketing data. For years, marketers have mined LinkedIn data by hand, often using low-cost offshore resources to gather targets in niche categories. Recently, a gaggle of new companies—like eGrabber and Social123—are experimenting with ways to bring social media data into CRM systems and marketing databases, to populate and enhance customer and prospect records.

Then there’s 6Sense, which identifies prospective accounts that are likely to be in the market for particular products, based on the online behavior of their employees, anonymous or identifiable. 6Sense analyzes billions of rows of 3rd party data, from trade publishers, blogs and forums, looking for indications of purchase intent. If Cisco is looking to promote networking hardware, for example, 6Sense will come back with a set of accounts that are demonstrating an interest in that category, and identify where they were in their buying process, from awareness to purchase. The account data will be populated with contacts, indicating their likely role in the purchase decision, and an estimate of the likely deal size. The data is delivered in real-time to whatever CRM or marketing automation system the client wants, according to CEO and founder Amanda Kahlow.

Just to whet your appetite further, have a look at CrowdFlower, a start-up company in San Francisco, which sends your customer and prospect records to a network of over five million individual contributors in 90 countries, to analyze, clean or collect the information at scale. Crowd sourcing can be very useful for adding information to, and checking on the validity and accuracy of, your data. CrowdFlower has developed an application that lets you manage the data enrichment or validity exercises yourself. This means that you can develop programs to acquire new fields whenever your business changes and still take advantage of their worldwide network of individuals who actually look at each record.

The world of B-to-B data is changing quickly, with exciting new technologies and data sources coming available at record pace. Marketers can expect plenty of new opportunity for reaching customers and prospects efficiently.

A version of this article appeared in Biznology, the digital marketing blog.

Beyond RFM Data

In the world of predictive analytics, the transaction data is the king of the hill. The master of the domain. The protector of the realm. Why? Because they are hands-down the most powerful predictors. If I may borrow the term that my mentor coined for our cooperative venture more than a decade ago (before anyone even uttered the word “Big Data”), “The past behavior is the best predictor of the future behavior.” Indeed. Back then, we had built a platform that nowadays could easily have qualified as Big Data. The platform predicted people’s future behaviors on a massive scale, and it worked really well, so I still stand by that statement.

In the world of predictive analytics, the transaction data is the king of the hill. The master of the domain. The protector of the realm. Why? Because they are hands-down the most powerful predictors. If I may borrow the term that my mentor coined for our cooperative venture more than a decade ago (before anyone even uttered the word “Big Data”), “The past behavior is the best predictor of the future behavior.” Indeed. Back then, we had built a platform that nowadays could easily have qualified as Big Data. The platform predicted people’s future behaviors on a massive scale, and it worked really well, so I still stand by that statement.

How so? At the risk of sounding like a pompous mathematical smartypants (I’m really not), it is because people do not change that much, or if so, not so rapidly. Every move you make is on some predictive curve. What you been buying, clicking, browsing, smelling or coveting somehow leads to the next move. Well, not all the time. (Maybe you just like to “look” at pretty shoes?) But with enough data, we can calculate the probability with some confidence that you would be an outdoors type, or a golfer, or a relaxing type on a cruise ship, or a risk-averse investor, or a wine enthusiast, or into fashion, or a passionate gardener, or a sci-fi geek, or a professional wrestling fan. Beyond affinity scores listed here, we can predict future value of each customer or prospect and possible attrition points, as well. And behind all those predictive models (and I have seen countless algorithms), the leading predictors are mostly transaction data, if you are lucky enough to get your hands on them. In the age of ubiquitous data and at the dawn of the “Internet of Things,” more marketers will be in that lucky group if they are diligent about data collection and refinement. Yes, in the near future, even a refrigerator will be able to order groceries, but don’t forget that only the collection mechanism will be different there. We still have to collect, refine and analyze the transaction data.

Last month, I talked about three major types of data (refer to “Big Data Must Get Smaller“), which are:
1. Descriptive Data
2. Behavioral Data (mostly Transaction Data)
3. Attitudinal Data.

If you gain access to all three elements with decent coverage, you will have tremendous predictive power when it comes to human behaviors. Unfortunately, it is really difficult to accumulate attitudinal data on a large scale with individual-level details (i.e., knowing who’s behind all those sentiments). Behavioral data, mostly in forms of transaction data, are also not easy to collect and maintain (non-transaction behavioral data are even bigger and harder to handle), but I’d say it is definitely worth the effort, as most of what we call Big Data fall under this category. Conversely, one can just purchase descriptive data, which are what we generally call demographic or firmographic data, from data compilers or brokers. The sellers (there are many) will even do the data-append processing for you and they may also throw in a few free profile reports with it.

Now, when we start talking about the transaction data, many marketers will respond “Oh, you mean RFM data?” Well, that is not completely off-base, because “Recency, Frequency and Monetary” data certainly occupy important positions in the family of transaction data. But they hardly are the whole thing, and the term is misused as frequently as “Big Data.” Transaction data are so much more than simple RFM variables.

RFM Data Is Just a Good Start
The term RFM should be used more as a checklist for marketers, not as design guidelines—or limitations in many cases—for data professionals. How recently did this particular customer purchase our product, and how frequently did she do that and how much money did she spend with us? Answering these questions is a good start, but stopping there would seriously limit the potential of transaction data. Further, this line of questioning would lead the interrogation efforts to simple “filtering,” as in: “Select all customers who purchased anything with a price tag over $100 more than once in past 12 months.” Many data users may think that this query is somewhat complex, but it really is just a one-dimensional view of the universe. And unfortunately, no customer is one-dimensional. And this query is just one slice of truth from the marketer’s point of view, not the customer’s. If you want to get really deep, the view must be “buyer-centric,” not product-, channel-, division-, seller- or company-centric. And the database structure should reflect that view (refer to “It’s All About Ranking,” where the concept of “Analytical Sandbox” is introduced).

Transaction data by definition describe the transactions, not the buyers. If you would like to describe a buyer or if you are trying to predict the buyer’s future behavior, you need to convert the transaction data into “descriptors of the buyers” first. What is the difference? It is the same data looked at through a different window—front vs. side window—but the effect is huge.

Even if we think about just one simple transaction with one item, instead of describing the shopping basket as “transaction happened on July 3, 2014, containing the Coldplay’s latest CD ‘Ghost Stories’ priced at $11.88,” a buyer-centric description would read: “A recent CD buyer in Rock genre with an average spending level in the music category under $20.” The trick is to describe the buyer, not the product or the transaction. If that customer has many orders and items in his purchase history (let’s say he downloaded a few songs to his portable devices, as well), the description of the buyer would become much richer. If you collect all of his past purchase history, it gets even more colorful, as in: “A recent music CD or MP3 buyer in rock, classical and jazz genres with 24-month purchase totaling to 13 orders containing 16 items with total spending valued in $100-$150 range and $11 average order size.” Of course you would store all this using many different variables (such as genre indicators, number of orders, number of items, total dollars spent during the past 24 months, average order amount and number of weeks since last purchase in the music category, etc.). But the point is that the story would come out this way when you change the perspective.

Creating a Buyer-Centric Portrait
The whole process of creating a buyer-centric portrait starts with data summarization (or de-normalization). A typical structure of the table (or database) that needs to capture every transaction detail, such as transaction date and amount, would require an entry for every transaction, and the database designers call it the “normal” state. As I explained in my previous article (“Ranking is the key”), if you would like to rank in terms of customer value, the data record must be on a customer level, as well. If you are ranking households or companies, you would then need to summarize the data on those levels, too.

Now, this summarization (or de-normalization) is not a process of eliminating duplicate entries of names, as you wouldn’t want to throw away any transaction details. If there are multiple orders per person, what is the total number of orders? What is the total amount of spending on an individual level? What would be average spending level per transaction, or per year? If you are allowed to have only one line of entry per person, how would you summarize the purchase dates, as you cannot just add them up? In that case, you can start with the first and last transaction date of each customer. Now, when you have the first and last transaction date for every customer, what would be the tenure of each customer and what would be the number of days since the last purchase? How many days, on average, are there in between orders then? Yes, all these figures are related to basic RFM metrics, but they are far more colorful this way.

The attached exhibit displays a very simple example of a before and after picture of such summarization process. On the left-hand side, there resides a typical order table containing customer ID, order number, order date and transaction amount. If a customer has multiple orders in a given period, an equal number of lines are required to record the transaction details. In real life, other order level information, such as payment method (very predictive, by the way), tax amount, discount or coupon amount and, if applicable, shipping amount would be on this table, as well.

On the right-hand side of the chart, you will find there is only one line per customer. As I mentioned in my previous columns, establishing consistent and accurate customer ID cannot be neglected—for this reason alone. How would you rely on the summary data if one person may have multiple IDs? The customer may have moved to a new address, or shopped from multiple stores or sites, or there could have been errors in data collections. Relying on email address is a big no-no, as we all carry many email addresses. That is why the first step of building a functional marketing database is to go through the data hygiene and consolidation process. (There are many data processing vendors and software packages for it.) Once a persistent customer (or individual) ID system is in place, you can add up the numbers to create customer-level statistics, such as total orders, total dollars, and first and last order dates, as you see in the chart.

Remember R, F, M, P and C
The real fun begins when you combine these numeric summary figures with product, channel and other important categorical variables. Because product (or service) and channel are the most distinctive dividers of customer behaviors, let’s just add P and C to the famous RFM (remember, we are using RFM just as a checklist here), and call it R, F, M, P and C.

Product (rather, product category) is an important separator, as people often show completely different spending behavior for different types of products. For example, you can send me fancy-shmancy fashion catalogs all you want, but I won’t look at it with an intention of purchase, as most men will look at the models and not what they are wearing. So my active purchase history in the sports, home electronics or music categories won’t mean anything in the fashion category. In other words, those so-called “hotline” names should be treated differently for different categories.

Channel information is also important, as there are active online buyers who would never buy certain items, such as apparel or home furnishing products, without physically touching them first. For example, even in the same categories, I would buy guitar strings or golf balls online. But I would not purchase a guitar or a driver without trying them out first. Now, when I say channel, I mean the channel that the customer used to make the purchase, not the channel through which the marketer chose to communicate with him. Channel information should be treated as a two-way street, as no marketer “owns” a customer through a particular channel (refer to “The Future of Online is Offline“).

As an exercise, let’s go back to the basic RFM data and create some actual variables. For “each” customer, we can start with basic RFM measures, as exhibited in the chart:

· Number of Transactions
· Total Dollar Amount
· Number of Days (or Weeks) since the Last Transaction
· Number of Days (or Weeks) since the First Transaction

Notice that the days are counted from today’s point of view (practically the day the database is updated), as the actual date’s significance changes as time goes by (e.g., a day in February would feel different when looked back on from April vs. November). “Recency” is a relative concept; therefore, we should relativize the time measurements to express it.

From these basic figures, we can derive other related variables, such as:

· Average Dollar Amount per Customer
· Average Dollar Amount per Transaction
· Average Dollar Amount per Year
· Lifetime Highest Amount per Item
· Lifetime Lowest Amount per Transaction
· Average Number of Days Between Transactions
· Etc., etc…

Now, imagine you have all these measurements by channels, such as retail, Web, catalog, phone or mail-in, and separately by product categories. If you imagine a gigantic spreadsheet, the summarized table would have fewer numbers of rows, but a seemingly endless number of columns. I will discuss categorical and non-numeric variables in future articles. But for this exercise, let’s just imagine having these sets of variables for all major product categories. The result is that the recency factor now becomes more like “Weeks since Last Online Order”—not just any order. Frequency measurements would be more like “Number of Transactions in Dietary Supplement Category”—not just for any product. Monetary values can be expressed in “Average Spending Level in Outdoor Sports Category through Online Channel”—not just the customer’s average dollar amount, in general.

Why stop there? We may slice and dice the data by offer type, customer status, payment method or time intervals (e.g., lifetime, 24-month, 48-months, etc.) as well. I am not saying that all the RFM variables should be cut out this way, but having “Number of Transaction by Payment Method,” for example, could be very revealing about the customer, as everybody uses multiple payment methods, while some may never use a debit card for a large purchase, for example. All these little measurements become building blocks in predictive modeling. Now, too many variables can also be troublesome. And knowing the balance (i.e., knowing where to stop) comes from the experience and preliminary analysis. That is when experts and analysts should be consulted for this type of uniform variable creation. Nevertheless, the point is that RFM variables are not just three simple measures that happen be a part of the larger transaction data menu. And we didn’t even touch non-transaction based behavioral elements, such as clicks, views, miles or minutes.

The Time Factor
So, if such data summarization is so useful for analytics and modeling, should we always include everything that has been collected since the inception of the database? The answer is yes and no. Sorry for being cryptic here, but it really depends on what your product is all about; how the buyers would relate to it; and what you, as a marketer, are trying to achieve. As for going back forever, there is a danger in that kind of data hoarding, as “Life-to-Date” data always favors tenured customers over new customers who have a relatively short history. In reality, many new customers may have more potential in terms of value than a tenured customer with lots of transaction records from a long time ago, but with no recent activity. That is why we need to create a level playing field in terms of time limit.

If a “Life-to-Date” summary is not ideal for predictive analytics, then where should you place the cutoff line? If you are selling cars or home furnishing products, we may need to look at a 4- to 5-year history. If your products are consumables with relatively short purchase cycles, then a 1-year examination would be enough. If your product is seasonal in nature—like gardening, vacation or heavily holiday-related items, then you may have to look at a minimum of two consecutive years of history to capture seasonal patterns. If you have mixed seasonality or longevity of products (e.g., selling golf balls and golf clubs sets through the same store or site), then you may have to summarize the data with multiple timelines, where the above metrics would be separated by 12 months, 24 months, 48 months, etc. If you have lifetime value models or any time-series models in the plan, then you may have to break the timeline down even more finely. Again, this is where you may need professional guidance, but marketers’ input is equally important.

Analytical Sandbox
Lastly, who should be doing all of this data summary work? I talked about the concept of the “Analytical Sandbox,” where all types of data conversion, hygiene, transformation, categorization and summarization are done in a consistent manner, and analytical activities, such as sampling, profiling, modeling and scoring are done with proper toolsets like SAS, R or SPSS (refer to “It’s All About Ranking“). The short and final answer is this: Do not leave that to analysts or statisticians. They are the main players in that playground, not the architects or developers of it. If you are serious about employing analytics for your business, plan to build the Analytical Sandbox along with the team of analysts.

My goal as a database designer has always been serving the analysts and statisticians with “model-ready” datasets on silver platters. My promise to them has been that the modelers would spend no time fixing the data. Instead, they would be spending their valuable time thinking about the targets and statistical methodologies to fulfill the marketing goals. After all, answers that we seek come out of those mighty—but often elusive—algorithms, and the algorithms are made of data variables. So, in the interest of getting the proper answers fast, we must build lots of building blocks first. And no, simple RFM variables won’t cut it.

Is Your Content Fresh, Frequent and Unique?

Today, your content plays a much larger role in getting top search results than ever before; therefore, it may be time to adjust your SEO content. In September 2013, Google unveiled Hummingbird, the single largest revamp of its basic search algorithm in more than 10 years. The intent of this major change was to improve the speed and precision of the processing. It was also designed to address the changes in searcher behavior as search volumes continue to shift from desktop computers to mobile devices.

Today, your content plays a much larger role in getting top search results than ever before; therefore, it may be time to adjust your SEO content. In September 2013, Google unveiled Hummingbird, the single largest revamp of its basic search algorithm in more than 10 years. The intent of this major change was to improve the speed and precision of the processing. It was also designed to address the changes in searcher behavior as search volumes continue to shift from desktop computers to mobile devices.

Hummingbird uses signals derived from the query and the user’s behavior to assist in delivering a result that quickly and precisely answers what the user really wants to find. When users search on mobile devices, they are frequently asking specific questions in conversational language: “Where is the nearest flower shop?” or “How many miles to … ?” Hummingbird was designed to address these natural language questions and provide specific and precise answers. To be found relevant, your content must address the needs of searchers for real information.

Although Hummingbird is expected to impact 90 percent of searches, many marketers are unaware of its influence on their search traffic. No significant shifts in Web traffic were reported worldwide after its launch. This is because the impact on most well-optimized sites was negligible. This should not be interpreted as a license to maintain the status quo on your search efforts. As users become more accustomed to receiving quality results from their conversational search queries, they will expect content that is honed to specifically address the questions that they form into queries.

To meet these expectations, your content should present answers to the types of questions that might be posed in a search query. It should be rich in useful information that is presented clearly. If you expect your content to appear near the top of the search results, it must meet these three criteria: fresh, frequent and unique. Over time, we can expect to see steadily improving search results for sites that understand and actualize these content requirements.

Fresh content does not necessarily mean that all of your content must be new. If you previously developed, as part of your search program, evergreen pieces, such as “frequently asked questions” or how-to articles, you should revisit them and check how long they have been on your site. Would they benefit from an update or a revision, or just a reformatting? For Google, fresh content is better than stale content. Just as no one really wants to read the stale magazines in the doctor’s waiting room; they don’t want the digital equivalent delivered in response to their search queries. Google obliges this by screening for the newest, freshest content. Now is the time to refresh those evergreen content pieces, even if you have not seen a negative shift in your search volumes. You may be able to capture additional visitors who are seeking answers to those questions that you have cleverly addressed.

Because frequency is another criterion used to evaluate the value of your content, you should be sure to have a schedule for adding more content and for refreshing older pieces. Take a lesson from the success of blog sites. Those with frequent posts of fresh content are rewarded with more search traffic than those with just a few stale posts. Consider how you might apply the same principles to content additions to your website.

Your content must also be unique—not just an aging chestnut. Avoid stale recitations or rehashes of information. Ask yourself: “Does this provide something that is new, unique—or is it just content for the sake of content?” For search success in the future, you will need to pay close attention to your content strategy and deliver fresh, frequent and unique content.

McKinsey Thinks Bland, Generic Loyalty Programs Are Killing Business – And They May Be Right!

A recent Forbes article by McKinsey, “Making Loyalty Pay: Six Lessons From the Innovators,” showed loyalty program participation has steadily increased during the past five years (a 10 percent annual rate of growth), with the average household now having almost 25 memberships. For all of that growing popularity, there are huge questions for marketers: Are the programs contributing to increased sales? And what is the impact of loyalty programs on enterprise profitability?

A recent Forbes article by McKinsey, “Making Loyalty Pay: Six Lessons From the Innovators,” showed loyalty program participation has steadily increased during the past five years (a 10 percent annual rate of growth), with the average household now having almost 25 memberships. For all of that growing popularity, there are huge questions for marketers: Are the programs contributing to increased sales? And what is the impact of loyalty programs on enterprise profitability?

Overall, companies with loyalty programs have grown at about the same rate as companies without them; but there is variance in performance value among industries. These programs produce positive sales increases for hotels, for example, but negative sales impact on car rental, airlines and food retail. And, companies with higher loyalty program spend had lower margins than companies in the same sector which do not spend on high-visibility loyalty programs.

McKinsey has noted that, “Despite relative underperformance in terms of revenue growth and profitability, over the past five years, market capitalization for companies that greatly emphasize loyalty programs has outpaced that of companies that don’t.” This, as they see it, may be indicative of hope among companies with programs that long-term customer value can be generated.

Within the McKinsey report, several strategies are offered for helping businesses overcome the negatives often associated with loyalty programs. Key among these are:

  • Integrate Loyalty Into the Full Experience
    Companies can link the loyalty program into the overall purchase and use experience. An example cited in the article is Starbucks, which has created its program to reflect the uniqueness of its café experience. Loyalty is built into the program by integrating payments and mobile technology, which appeals to its target audience.
  • Use the Data
    This may be the most important opportunity represented by loyalty programs. Data collected from the programs can offer competitive opportunities. Tesco, the largest supermarket chain on the planet, has been doing loyalty program member number-crunching for years through DunnHumby. Similarly, Caesars Entertainment has rich databases on its high-rolling program members. One retailer has combined its loyalty program with a 5 percent point-of-sale discount, building volume from its highest-value customers. In another well-documented example, a retailer has used its loyalty program data to identify future mothers before other chains, thus targeting offers to capture both their regular spend and new category purchases as buying habits evolve.
  • Build Partnerships
    As stated on so many occasions, organizations that build trust generate stronger, more bonded, customer behavior. This applies to loyalty programs as well, where there is ample opportunity to build cross-promotion for customers with non-competing products and services. In the U.K., Sainsbury, the major supermarket competitor of Tesco, has partnered with Nectar, a major loyalty coalition. Nectar has more members than Tesco, and participants can collect rewards across a large number of non-competing retailers. Through partnership, Sainsbury’s offers customers a broader and deeper value proposition; and Nectar also generates data from coalition partners, which it uses to better target promotions to customers.
  • Solve Customer and Industry Pain Points
    Numerous customer behavior studies have shown that people will gravitate to, and pay more for, better service. A perfect example of this is Amazon Prime, where additional payment gets customers faster delivery and digital tracking. This is good for Amazon (estimates are that members spend more than four times more with Amazon than non-members), its customers, and its suppliers, who also get access to Prime customers and the positive rub-off of affiliating with a trusted brand.
  • Maximize Difference Between Perceived Value and Real Cost
    Often, program elements can represent high perceived value without adding much in the way of bottom-line cost to the sponsor. The example cited is Starwood Hotels and Resorts where, through its Starwood Preferred Guest (SPG) program, there is a focus on personal leisure travel rewards for high-spending frequent guests.
  • Allocate Loyalty Reinvestment to the Most Valuable Customers
    Many companies have only recently come to the realization that some customers are more valuable than others; and, to be successful, loyalty programs need to target the higher revenue customers. In 2010, Southwest Airlines revamped its loyalty program to make rewards more proportional to ticket price; and this has better targeted the most profitable customers, as well as enabled the airline to adopt a loyalty behavior metric that is closely tied to actual revenue generation.

Loyalty programs continue to grow, but they are also tending to become more closely integrated with brand-building and multichannel customer experience optimization. But, there is also lots of commoditization and passivity were these programs are concerned—sort of the “If You Build It, They Will Come” syndrome at work. And, of course, there’s a mini contra movement among some retail chains, where they have removed established loyalty programs—or never initiated them in the first place—in favor of everyday low prices and more efficient performance.

I Am the Judge of You

Pointing the finger has never been so easy … and so anonymous. I suppose it’s human nature to feel (and act on) the need to take pot shots at others—whether it’s their point of view, their creations or their behavior. But to be able to do so without the fear of repercussion seems to be a growing trend. And as the owner of a product or service, it’s never been more infuriating

Pointing the finger has never been so easy … and so anonymous.

I suppose it’s human nature to feel (and act on) the need to take pot shots at others—whether it’s their point of view, their creations or their behavior. But to be able to do so without the fear of repercussion seems to be a growing trend. And as the owner of a product or service, it’s never been more infuriating.

Many small business owners complain about the power of Yelp, and understandably so. But the concept is actually brilliant. Interact with a business and, whether your experience was good or bad, you have a very large forum where you can share the love (or not). The fatal flaw is that you can do so without the business owner having the ability to correct the situation because, inevitably, pot shots are done from behind the shield of anonymity.

My Dad always used to say, “If you don’t have anything nice to say, don’t say anything at all.” I believe in the concept of healthy debate, so I don’t necessarily agree with my Dad, but to have a healthy debate, you need to know the enemy.

Many sites (like this one) require you to log in before you can post a comment. However you can log in with your gmail or yahoo account … and if your user name is not your actual name, it’s easy to start the attack without your boss, co-workers, spouse or clients judging you for your aggressive behavior and unsportsmanlike conduct.

The behavior is not limited to consumer sites like Yelp. On business-to-business sites like this one, there are lots of negative posts from unknown readers, and I wonder, what do they hope to accomplish??

I was recently planning a trip to Mexico and visited several travel sites trying to get the inside scoop on hotels and restaurants. While I was delighted with the many insights like “try to stay on the 4th floor or higher because the thumping beat from the dance floor will keep you awake until midnight,” I was also stunned by the spewing rants from individuals who have logged in with names like “CrabbyinNJ.”

How do we, as brand ambassadors, overcome these customer feedback challenges?

First, and foremost, train AND empower those who are on the front lines of customer engagement to act like the customer—is—always—right. Granted, you can never please all the people all of the time, but sometimes a lot of customer sympathy and a few “my apologies!” can go a long way to diffuse a situation. There is nothing more infuriating than having an issue and the person serving you is either indifferent or plainly unequipped to help solve your problem.

Second, don’t just send blanket “How did we do?” emails to every customer after an interaction. If the customer has had an issue, there should be a place to flag that issue in your customer database, so it can be quickly followed up on by someone who is in authority. Many situations can be rectified before the individual decides to go into a public forum to publicly skewer you and your business.

Third, listen to complaints and actually try to think about ways you may be able to change your policies or procedures in order to ensure the issue doesn’t repeat itself.

Finally, circle back to those customers who had an issue, got it resolved satisfactorily, and ask them if they’d be willing to write about the incident. I hear many business owners say they’re worried that if the customer “advertises” they got something for free or at a deeper discount as a way to try and resolve the issue, it will set the stage for a future customers demanding the same thing. My response is that if, as a rule of business, you treat people the way they want to be treated in the first place—with respect, concern and understanding—you shouldn’t have a problem.

As for those who slap others from behind the shield of anonymity (and you know who you are), man up.

Following the Breadcrumbs to Guide People Through the Path to Purchase

 

Marketing is about service; it’s about helping a company identify and fulfill the needs, wants and desires of consumers. Throughout most of its history, marketing has focused on the needs of the marketer and the marketer’s company. We’ve been shareholder-centric, company-centric and product-centric. We’ve organized our companies to be engineering-driven, sales-driven or marketing-driven. In other words, we’ve been self-absorbed and focused on our needs and our offerings and what we want to accomplish. This inward focus must change. To execute effectively, brands most certainly need to maintain an inward focus on all of the activities above. However, they also need to create and hone their mobile marketing capabilities. That is, train their people, invest in technology and develop processes to achieve their goals in the new mobile reality. 

 

Move aside purchase funnel and make room for the path to purchase. Perhaps you’ve noticed the headlines of late: “Marketing is Changing,” “Mobile Advertising is More Effective Than Desktop Advertising,” “CIOs Now Report to the CMOs (Or Should),” “It Is About Being Mobile First,” and so on.

All of these headlines, and countless more, are referring to an inalienable truth today: social norms and people’s behaviors are changing, and as a consequence so is the practice of marketing.

Marketing is about service; it’s about helping a company identify and fulfill the needs, wants and desires of consumers. Throughout most of its history, marketing has focused on the needs of the marketer and the marketer’s company. We’ve been shareholder-centric, company-centric and product-centric. We’ve organized our companies to be engineering-driven, sales-driven or marketing-driven. In other words, we’ve been self-absorbed and focused on our needs and our offerings and what we want to accomplish. This inward focus must change.

To execute effectively, brands most certainly need to maintain an inward focus on all of the activities above. However, they also need to create and hone their mobile marketing capabilities. That is, train their people, invest in technology and develop processes to achieve their goals in the new mobile reality.

Since today’s consumer spends the majority of their time on or being influenced by mobile devices and mobile-enhanced media, they’ve begun to expect one-to-one personalized treatment. It’s imperative that marketers turn their primary focus away from themselves and towards people (a word rarely used to define consumers). Marketers must take their focus away from shuttling the “consumer” down the proverbial purchase funnel cattle shoot and direct it toward guiding and helping people along their individualized path to purchase.

Below is a side-by-side illustration of the purchase funnel, resting on the base of loyalty and advocacy, and the new path to purchase.

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The stages and steps along the purchase funnel and path to purchase are similar, but subtle differences exist. The purchase funnel is a linear view of the world through the marketer’s eyes and the marketer’s goals, while the path to purchase is a nonlinear view, with many branches. It’s a view of the world through an individual’s senses and how they go through their day, week, year or life satisfying their wants and desires.

These two views of the world, the purchase funnel and the path to purchase, aren’t at odds with each other. Rather, they’re simply a view of the world through different lenses.

To be clear, the path to purchase isn’t the purchase funnel laid on its side as it’s often portrayed. People don’t care about “brand awareness” like marketers do. People care about expressing their needs or responding to conscious and unconscious exposure. Marketers need to understand that value is created between the customer’s view and their own.

For marketers to be successful in the age of the mobile-led digital economy, it’s important to look at the world through both lenses, the purchase funnel and the path to purchase. They need to be able to step into the shoes of the people they serve (their customers) and will service (their prospects).

Putting yourself in people’s shoes isn’t easy nor is it comfortable, but it must be done. The first step is to build empathy and understanding. A helpful way to do this is to understand behavior, ideally by talking with and listening to your customers directly, as explained by Jonathan Stephen from JetBlue in a recent webinar I did with him.

Another way is to conduct primary research and review secondary research. For example, to help you understand the behavior of people along the path to purchase, I encourage you to read the xAd Mobile Path To Purchase research.

In 2012 and 2013, xAd conducted studies in the U.S. and the U.K. to evaluate mobile’s role along the path to purchase for the travel, auto, restaurant, retail, gas and convenience, banking and finance, and insurance industries.

What the studies showed, not surprisingly, was that mobile has an impact but its impact varies and its use differs along each step of a person’s journey along the path to purchase. The insights from the xAd studies and related industry efforts are valuable breadcrumbs to understanding people today, their behavior (especially when it comes to mobile) and for putting mobile at the heart of modern-day marketing strategies.

I know it’s hard and change is never easy, but as marketers we must continually relearn our trade and adapt to the changing conditions of the marketplace.

Loyalty Programs? We Don’t Need No Stinkin’ Loyalty Programs!

Without fear of (much) argument, it’s a fair statement to say that all companies want, and try to generate and achieve, optimum loyalty from their customer bases. They should want this, because study after study shows the financial rewards of having loyal customers. Some companies reach this goal through superior value delivery, built on quality products and services, and positive, consistent customer experiences. For the past several decades, many companies have relied on customer loyalty cards or programs, by which they can track purchase behavior and give rewards for repeat and volume buying activity.

Without fear of (much) argument, it’s a fair statement to say that all companies want, and try to generate and achieve, optimum loyalty from their customer bases. They should want this, because study after study shows the financial rewards of having loyal customers. Some companies reach this goal through superior value delivery, built on quality products and services, and positive, consistent customer experiences. For the past several decades, many companies have relied on customer loyalty cards or programs, by which they can track purchase behavior and give rewards for repeat and volume buying activity.

Customer loyalty programs are especially popular among retailers. During the years, retailers have found these programs to be powerful business tools within their highly competitive markets. But some retailers have completely disavowed loyalty programs, either never initiating them in the first place or canceling them, in favor of reduced pricing. In fact, this has become something of a trend. What’s behind it?

Let’s start with the biggest retailer—Walmart. The company has long claimed that a loyalty program isn’t needed because its prices are so low. Walmart believes that loyalty programs can, indeed, provide excellent information about customers who participate; however, as one Walmart executive put it: ” … some of the loyalty programs are very expensive, and we don’t think that serves everyday low cost and everyday low price.” Lower-than-competition everyday prices has been Walmart’s merchandising and marketing mantra since its inception. But, at least for groceries and sundry products, that often isn’t the case. Supermarket chains like Save-A-Lot and Aldi’s, neither of which has a loyalty program, will often beat Walmart’s item-for-item pricing by a significant margin. And other competitors can use their loyalty programs to selectively pick products, and individual customers, to offer pricing—which undermines Walmart.

As for generating customer purchase data, Walmart has a “scan & go” app for mobile devices, which allows customers to scan their own items as they shop; and this provides the company with valuable information on what customers are purchasing, the length of time they’re shopping in the store, and what offers and coupons might drive future purchases. Walmart uses additional methods of understanding individual customer purchases. One of these is Walmart credit cards. Another is reloadable MasterCard and Visa debit cards. A third is “Bluebird,” a prepaid debit card which functions as Walmart customers’ alternative to having a checking account, with which they can make deposits, pay bills—and shop at Walmart. Like Tesco is already doing in the U.K, Walmart has been considering development of its own bank, which would provide even more customer data.

Asda, a Walmart-owned supermarket chain in the U.K, also has no loyalty program. It’s the second-largest supermarket company, behind Tesco; and, as in the U.S., newer low-priced chains, such as Aldi, are actively competing with Asda. In place of a loyalty program, Asda believes it provides customers with what they want most, a “great multichannel retail experience.” The chain, according to executives, focuses on the key fundamentals: prices, quality, convenience and service. Alex Chrusczcz, Asda’s head of insights and pricing, offers two explanations of how the organization is endeavoring to build customer loyalty:

  • “Aspire to treat customers equally, or you’ll create a fractured brand and shopping experience. If you have someone paying one price and another customer with a coupon paying a different price, the perception of the brand is becoming fractured. Make sure it’s consistent.”
  • “Be pragmatic in terms of technology and analytics. They aren’t a silver bullet. Use these tools and combine them with the experience of your team.”

From my perspective, the second explanation is common sense; however, the first statement is really questionable—even counterintuitive, if a subordinating goal of loyalty behavior is to help drive customer-centricity. Simply put, all customers are not equal in value; and marketing strategies which treat them as such often create lower revenue.

In the U.S., regional supermarket chain Publix has no loyalty program. The company doesn’t have, as a result, the ability to track, at a household level, what customers are and aren’t purchasing in their stores. What Publix does, instead of loyalty cards, is try different alternative approaches to build sales. One of these, for example, was to test a program where shoppers could set up an online account where they could digitally clip coupons; and then, in the Publix store, the discounts they’d set up online could be automatically applied by typing in their phone numbers. Publix also has a BOGO program for their own brands, and accepts competitors’ coupons in their stores.

Some retailers do more than emphasize the sales and service fundamentals. They build genuine passion for, and bonding with, the brand by creating a more human, emotional connection. And, though there are few organizations like this, retailers such as Trader Joe’s are the exception that proves the rule. Trader Joe’s has no customer loyalty program. What they have is enthusiasm, achieved through differentiated, every-changing customer experiences, enhanced by upbeat, helpful employees. This has enabled Trader Joe’s to generate sales per square foot that are double the sales per square foot of Whole Foods. So, another way of stating that Trader Joe’s creates loyalty behavior without a program is to say: The shopping experience is, defacto, the loyalty program.

Now, we come to retailers which had customer loyalty programs, usually of long-standing, and elected to discontinue them. Actually, much of this has been done by one organization, Cerberus Capital Group, the early 2013 purchaser of multiple regional retail supermarket chains from Supervalu (Shaw’s, Acme, Star, Albertson’s and Jewel-Osco). Calling the new positioning “card-free savings,” and reflective of the first strategy stated above by Asda, each of the chains issued statements with themes like “We want buying to be simple for all, so that every (name of company) customer gets the same price whether a loyalty card has been used or not.” Additionally, and again like Asda, these chains have said they will go back to the basics: clean stores, well-stocked shelves, reduced checkout time, clearly marked sale items and creation of a more customer-focused culture. Some of their executives have also theorized that the chains will now adopt a more local-level approach, rather than customer-level, to their decision-making, and that individual store managers will now be more actively involved in driving successful performance.

So, the chains acquired by Cerberus appear to believe that “sunsetting,” or eliminating these programs, is a calculated risk and that they would still find good ways of providing value to retain more loyal customers, as well as incentives for those with the potential to move from purchase infrequency. Most analysts, however, felt that Cerberus eliminated the programs largely because the chains they purchased were either not mining card data, or not effectively analyzing and applying this material for better marketing and merchandising, thus making the loyalty systems too expensive to maintain.

Cerberus has entered into takeover discussions with California-based Safeway, which also owns Vons and Pavilion. If this sale takes place, it’s a good bet that these chains will also drop their reward cards, because Cerberus-owned supermarkets clearly don’t need, or want, no stinkin’ loyalty programs.

Bad Thing! Or Why Segmentation by Consumer Attitudes May Be Dangerous

For years, B-to-B and B-to-C marketers have relied on attitudinal segmentation research to help them group their current customer base, and potential customers as well, for communication, promotion, marketing and experience initiatives. The thesis has been that, by asking a small, but meaningful, set of attitudinal questions, they would be able to develop an index, algorithm or framework equation that ranked these consumers by propensity to buy, both near-term and long-term.

For years, B-to-B and B-to-C marketers have relied on attitudinal segmentation research to help them group their current customer base, and potential customers as well, for communication, promotion, marketing and experience initiatives. The thesis has been that, by asking a small, but meaningful, set of attitudinal questions, they would be able to develop an index, algorithm or framework equation that ranked these consumers by propensity to buy, both near-term and long-term.

These frameworks—they’re arithmetic, so we can’t call them “models”—typically include questions regarding the importance of elements like value for money, acting with the consumer’s interests in mind, credit and payment terms, having knowledgeable employees, offering products which will meet the consumer’s needs, and the like. From these questions, basic segment categorization can be determined; and, once these three, four or five segments are established, we’ve often seen marketers go on to build assumptive plans and conduct further, more detailed, research around them.

The goal of these approaches is to produce attitudinal segments, which the questions can predict with high accuracy, often in the 80 percent or 90 percent range. This creates what economists would call a “post hoc ergo propter hoc” situation, Latin for “after this; therefore, because of this.” It is a logical fallacy, essentially saying that A occurred (the responses to the attitudinal questions); and then B occurred (the cuts, or segments, of consumers). Thus, A caused B. Once the B, or segment creation, stage has been established, further fallacies, such as creating reliable marketing, operational and experiential strategies around these supposed propensities, can be built. It’s a classic situation, where correlation is thought to be the same as causation. As your economics or stat professors may have told you, correlation and causation are far from being identical concepts.

As a consultant and analyst, I’ve seen this result of this application of research and analytics play out on a firsthand basis on multiple occasions. Here’s a recent one. A client in the retail office products market had been using an attitudinally derived element importance question framework for small business market segmentation purposes. The segment assumptions went unquestioned until followup qualitative research was conducted to better shape and target their planned marketing and operational initiatives. Importance of certain products and reliable service were identified in the research as key areas of focus and opportunity for the office products retailer; but, in the qualitative research, power of both focus areas appeared, anecdotally, to be consistent across all segments. And, even though implied supplier roles were suggested to build purchases, this was much more “leap of faith”-based on the established quantitative research segment personas than actual qualitative research findings.

There are related issues with what we can describe as quasi-behavioral measures, such as single question metrics (likelihood to recommend to a friend or colleague or the amount of service effort required on the part of a consumer); or traditional customer loyalty indices (where future purchase intent is included, but also attitudinal questions such as overall satisfaction). It’s not that they don’t offer some segmentation guidance. They do—on a macro or global level; but they tend to be less effective on a granular level, especially where elements of customer touchpoint experience are involved.

And, they tend to have limitations as predictors of segment behavior, a key business outcome for marketers and operations management. When compared to research and analysis techniques, such as customer advocacy and customer brand-bonding, which are contemporary, real-world frameworks built on actual customer experience—high satisfaction scores, high index scores and high net recommendation scores produced likely future purchase results (in studies across multiple industries) which were often 50 percent to 75 percent lower than advocacy or brand bonding frameworks. I’d be happy to provide proof for anyone interested in reviewing the findings.

So, that’s the scenario. The challenge, and potential danger, for marketers and those responsible for optimizing customer experience is that these attitudinal and quasi-behavioral questions are just that—attitudes and quasi-behaviors. Attitudes are fairly superficial feelings, and tend to be both tactical and reactive. And, because they are so transitory, their predictive value is often unstable and unreliable. Quasi-behaviors are also open to many similar challenges. More importantly, attitudes and quasi-behaviors are not behaviors, such as high probability downstream purchase intent based on actual previous purchase, evidence of positive and negative word-of-mouth about a brand based on prior personal experience, and brand favorability level based on experience. These are especially valuable in understanding competitive set, and they have real, and very stable, predictive and analytical value for marketers.

As Jaggers, the lawyer, said to Pip in Charles Dickens’, “Great Expectations,” take nothing on its looks; take everything on evidence. There’s no better rule.” For marketers, that’s excellent shorthand for taking everything on behavior, and perceptions based on documented personal experience, rather than attitudes and quasi-behaviors.