4 Customer Experience Lessons From Your Wait Staff

It’s not very often I give a 40% tip at a restaurant, and it’s not because I’m cheap. But the other night was a different kind of customer experience, with lessons for marketers about successful customer engagement strategies.

It’s not very often I give a 40% tip at a restaurant, and it’s not because I’m cheap. But the other night was a different kind of customer experience, with lessons for marketers about successful customer engagement strategies.

It was a crazy night and hard to find a restaurant that could take our party of seven. We were stressed and so was the wait staff, who had plenty of reasons to be stressed and just go through the motions to get the job done.

This young man was different. He replaced the stress of the night with a calm smile. He asked about our evening and totally empathized with our “issues,” never mentioning his own. He told us in advance that the cook was a little behind but he would do his best to get us served quickly.

He noticed we were hungry and it was late so while our order was being processed, he brought us a complimentary appetizer; nothing expensive, but enough to tide us over. When he brought us our order, it was faster than we expected  given the crowd, and it was clear he had listened to our special needs, made sure that everything was just as we asked it to be, and had even gone a step further to make sure my gluten-free daughter was not exposed to any gluten indirectly.

And throughout dinner, he checked in. With a smile. And asked how we liked our food, did we have any other needs or even suggestions? His conversations were relevant and cheerful to the point that we left feeling like we had made a new friend.

It was a pleasure to leave him a $40 tip.

Waiting tables may seem like a simple process that is simply a routine. Yet there are a lot of insights to gain here for customer engagement strategies and success. Let’s change this scenario to a sales call:

  • You have a customer who is stressed, tired and needs a solution soon.
  • You have a big backlog or a long queue of customer orders.
  • Timing is critical for this customer and, if you can’t deliver, they may go elsewhere.
  • They have some very specific needs that have to be met with precision.

Implementing What We Learned

So how do you “serve” them to get them to purchase,then to satisfaction and loyalty?

Recognize upfront and immediately a customers’ stress, anxiety, and needs. Empathy for those needs and issues goes a long way. We connect with people who are like us, understand our pain and concerns, and give us even a little hope that they will be resolved.

While challenges and obstacles can’t be overcome in a day in most cases, find a way to minimize the pain (or hunger) with a small service or added value while they wait for the full resolution. Be transparent about any shortcomings you might have for providing the products or services needed and assure customers you will do your best to meet their needs. Regardless of your business, you can almost always find something to ease the process. If your wait time is longer than normal, offer a discount for their patience, which gives them a reason to stay with you vs. shop for a faster solution.

Follow up. It is simply amazing how much money is left on the table (and I don’t mean tips) after sparking interest in your products and then not following up personally with a close. With all of the emails and ads and messages we are exposed to daily, while multitasking at home or at work, we simply do not respond to messages of interest immediately. But when someone calls us a few days later to see if we have all we need, we often go for the order, just like we often go for dessert when we had no intention to before being asked if we wanted that tiramisu or chocolate mousse vs. the fresh key lime pie?

Conclusion

Pay attention next time you have a really good waiter and watch how subtlety and skillfully they earn a good tip by following these simple steps above. Try the same kind of touchpoints in your customer journey and watch your sales and stickiness take off!

Data, Data Everywhere: Nary a Bargain to Find?

Stephen Yu’s recent and extremely thought-provoking piece on AI started me wondering once again about the dangers of data overload and whether we’ll ever really, really understand the purchasing decisions people make, how they make them and be able to track them accurately.

Data mining
“Big_Data_Prob,” Creative Commons license. | Credit: Flickr by KamiPhuc

Stephen Yu’s recent and extremely thought-provoking piece on AI started me wondering once again about the dangers of data overload and whether we’ll ever really, really understand the purchasing decisions people make, how they make them and be able to track them accurately.

Because today’s machines gobble data and — like my dog eats anything he can get jaws around — we marketers seem to search for more and more bytes in the hope that sifting through this mega data will hold the keys to the holy grail of maximum profitability. Perhaps it will. But as a disciple of Lester Wunderman, I can’t let go of his oft-expressed prescient warning that “Data is an expense. Knowledge is a bargain.”

Admittedly, when this was first expressed, data was one hell of a lot more expensive to keep and handle than it is today and shaking knowledge out of it was very difficult. But that’s hardly the point. Our trade press is now overflowing with titles like “Planning and Measuring Social Media Campaigns” (Sysomos), the “Email Marketing Metric You May Not Know” and unnumbered guides to the customer journey. But I’m still waiting for the definitive article that leaves all of the peripheral data by the side of the road and presents a usable and believable knowledge-based metric model to measure the cost of each step in the journey from awareness through to final purchase. In today’s multi-media environment, that’s the metric model we are all waiting for. Will we ever get it? Will AI provide it? I’m not so sure.

There is historically a different focus between top management whose attention is quite sensibly on macro numbers and operational marketers who know that it is the micro numbers that spotlight big opportunities. The ROMI, the return on the total marketing investment, is the bottom line for both: How much did we earn for how much marketing money invested? Simple.

But at what milestones in the customer journey did the momentum toward purchase increase and at what others did the potential customer take a turn away from purchase and why? That’s the type of data we need if we are to optimize our practice and it will surely impact the ROMI. Sadly in many cases, we will never know.

Recently, some of my Brazilian colleagues created a very strong email campaign as the first stage in persuading well-segmented prospects to clickthrough to a website to register interest and gain a price advantage in making a major purchase. The client reported that while the website was receiving a lot of activity, only a tiny fraction came as the expected clickthrough from the emails. The client was understandably angry and it didn’t make any sense.

Every adult Brazilian has a unique CPF number, which is regularly requested and used to identify the individual in financial transactions. It’s rather like an American Social Security number. Because my colleagues were fortunate enough to have the CPFs of the prospects to whom the emails had been sent and as registration on the website also required a CPF, it was a relatively easy task to compare the two groups to determine how many of the registrants had been sent the emails, even if they hadn’t availed themselves of the clickthrough option. It turned out to be a happily large percentage.

While research has been undertaken to determine why, any measurement of the relation of emails to registrations and their cost would have been both misguided and meaningless. If the marketers had decided to stop using the emails because, as they said, ”emails didn’t generate any response,” they would have been making a critical error.

Perhaps that’s a long way around the issue of just why, with all of the enormous data and sophisticated tools at our disposal, we just can’t develop a meaningful metric model that reliably tracks the prospect along the path to becoming a customer. And it argues that while AI will certainly add valuable knowledge, getting inside the head of a prospect and truly understanding his/her actions is a long way off.

Google Opens the Door to the Trusted Stores Program

Google has changed the requirements for its Trusted Stores program to make it easier for stores to join the program. What does this promise for the consumer, for merchants taking advantage of the offer, particularly those who went through the initial vetting process necessary to obtain the designation, as well as for Google? When Google first set up its Trusted Stores program, it provided a level of purchase protection for consumers and a conversion enhancement incentive for merchants displaying the Trusted Stores badge. The program badge provides consumers a level of confidence prior to purchase, and for consumers opting-in at time of purchase, a free purchase protection program; whereby, Google promises to intervene if there was an issue with the purchase. To display the Google Trusted Stores badge, the merchant had to submit feeds with shipping and cancellation information to prove that the merchant met specific levels of shipping and customer satisfaction performance set by Google.

Google has changed the requirements for its Trusted Stores program to make it easier for stores to join the program. What does this promise for the consumer, for merchants taking advantage of the offer, particularly those who went through the initial vetting process necessary to obtain the designation, as well as for Google? When Google first set up its Trusted Stores program, it provided a level of purchase protection for consumers and a conversion enhancement incentive for merchants displaying the Trusted Stores badge. The program badge provides consumers a level of confidence prior to purchase, and for consumers opting-in at time of purchase, a free purchase protection program; whereby, Google promises to intervene if there was an issue with the purchase. To display the Google Trusted Stores badge, the merchant had to submit feeds with shipping and cancellation information to prove that the merchant met specific levels of shipping and customer satisfaction performance set by Google.

What Has Changed and Who Benefits?
To increase merchant enrollment, Google has announced that it is dropping the requirement that stores submit shipping and cancellation information. To be a member of the program, a store must do a sales volume of 200 orders per month, so the program will still exclude the casual on-line merchant fulfilling just a few orders a day. The merchant must also provide assurances that email inquiries are addressed within one day. When the program was initiated some merchants were reluctant to join because they did not want to provide Google, an ad-selling monolith, business sensitive information on their order volumes. Google has removed this barrier.

A change benefitting all merchants in the program is that they can now custom position the badge on their site and display it on their https pages. Previously, it had to appear in the lower right hand corner of the home page. An additional carrot to attract new enrollees is that reviews garnered through the Trusted Stores program will help advertisers, using Google’s AdWords program, qualify for review extensions that boost click through rates by offering the consumer merchant quality assurances.

Google’s intent is clear. The ad giant wants to enroll more high volume merchants in its program. A greater number of stores would enhance Google’s position as a shopping resource. By dropping the data sharing requirement, Google removes a clear and significant barrier to participation. Surely, a merchant with a stellar performance rating, attaining the coveted review extensions for AdWords and enjoying enhanced click through rates will be more willing to purchase more AdWords, a clear win for Google. To industry watchers, this move is not just a program change or a way to gain more AdWord sales. It is a strategic move to counter the growing influence of Amazon as a commerce source for consumers and to consolidate Google’s position as a source of trusted reviews versus other review platforms. This program change is a signal of things to come and bears careful watching.

1-Trick Ponies and Customer Loyalty Behavior

About 30 years ago, Paul Simon wrote a song entitled “One-Trick Pony.” The song describes a performing pony that has learned only one trick, and he succeeds or fails with the audience based on how well he executes it. As Simon conveys in the lyrics: “He’s got one trick to last a lifetime. It’s the principal source of his revenue.”

About 30 years ago, Paul Simon wrote a song entitled “One-Trick Pony.” The song describes a performing pony that has learned only one trick, and he succeeds or fails with the audience based on how well he executes it. As Simon conveys in the lyrics: “He’s got one trick to last a lifetime. It’s the principal source of his revenue.”

For a long time, I’ve seen this song and its message as something of a metaphor for what challenges many companies endeavoring to create customer loyalty behavior and more effective customer loyalty programs.

A key reason companies have a difficult time achieving stronger customer loyalty is they fail to provide full value and emotional relationship fundamentals. They focus on satisfying customers exclusively through basic rational and functional benefits, which is often too benign and passive an approach to create lasting value.

Mostly, they emphasize single-element or minimal element tactical approaches with customers, such as pricing, merchandise, loyalty cards or points-based programs, without determining (either before programs are launched or after they are up and running) whether this is sufficient motivation for building a long-term relationship. Smart marketers know, for instance, being a low-cost provider can be a trap and that only overall perceived value will prevail. In the United States, chain discount retailers like Caldor, Bradlees, Jamesway, Value City, Ames and Filene’s are either in trouble or have gone out of business, while Target, Costco and Walmart, with strong brand equity and high perceived value, have sustained.

Being a low-cost provider means that brand and customer strategies get little emphasis, and they require little investment. Let’s be honest. Cutting costs seems safe. The downside is it usually does not produce much loyalty (customer or staff), strategic differentiation or profitability.

In a 1980 Harvard Business Review article by William Hall (written, parenthetically, about the same time Simon wrote “One-Trick Pony), he reported study results comparing companies that competed on differentiated customer value vs. companies that competed principally on cost. On any important measure—return on equity, return on capital, and annual revenue growth—companies delivering both rational and relationship value beat the price competitors every time.

Customers can almost always locate cheaper products or services. Ultimately, they will invest a greater share of their purchase dollars with suppliers who create stronger emotional bonds and deliver superior perceived value. Competing on price, or any other single dimension, may pull away customers from other suppliers in the short run, but it will be difficult to keep them for long. Price is rarely a sufficient “barrier to exit,” and is more often an invitation to churn.

The same thing often holds true for incentive programs. Many consumers participate in programs like supermarket bonus clubs and airline frequent flyer programs, but they aren’t particularly effective at producing greater loyalty for any one airline or any one supermarket chain. Customers are often members of several programs, and the most active users tend to be those who would have been frequent purchasers, anyway. The incentive and reward structure more often benefits the already loyal rather than increasing loyalty. Gift programs, travel, dining, entertainment, merchandise, and cash award programs, and other plateau and pre-selected response stimulus programs are having an increasingly difficult time breaking through the clutter to provide unique, differentiated customer value.

Some of the online incentive programs have positively increased transactions, mostly among younger, female and active surfing potential buyers. To keep these incentive promotions from being one-trick ponies, they must be carefully targeted to the right consumers and at the right time. These programs must have four effective elements: ability to attract prospects to the website and, once there, to generate consideration, preference, and purchase. Getting infrequent buyers to purchase more often, or frequent buyers to place larger orders through the use of incentives, will hinge on how well companies leverage their customers’ profiles. Even more basic, it must be well-understood what customers perceive as value and what it will take to optimize their repeat purchases. The essentials for bricks and mortar product and service providers are virtually the same.

Generic, cookie-cutter and “me, too” discounts or incentives don’t do particularly well at increasing overall customer “share of wallet,” because they don’t sufficiently reward the customer for their enhanced purchase activity over time. All that’s really required to meet the customer halfway is infusion of some targeted, personalized elements to the incentive program to make them more attractive and beneficial.

The first step is to segment customers who should receive different incentives. This can be done through both basic data analysis and applied, or pilot, customer research. For example, for large customers who purchase infrequently, the company might have determined that, if they offer special discounts made within the near future, say 60 or 90 days, these customers would find that attractive. Customers who purchase frequently but in low volume amounts might be offered a discount on their next order, so long as it is larger than their last order. The array of potential loyalty program offerings can be customized based on identified needs.

What about incentives for customers who are both frequent and large volume purchasers? Well, start by saying “thank you” to them. Few things are more appreciated than thanks, and few companies express their gratitude as much as they should. Many forget to thank their customers altogether. This is especially critical for Web-based companies, or ISPs and cable companies, where the purchase experience is frequently virtual rather than personal. Thanked customers are more likely to go out of their way to provide positive referrals and testimonials.

Paul Simon’s song lyrics conclude: ” … the bag of tricks it takes to get me through my working day.” Companies would be well-served to have a bag of experience and customer loyalty tricks, using disciplined research and customer data to identify them, rather than relying on only one—price—to get them through.

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.

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.

#INLINE-CHART#

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.

Help! I’m Being Stalked by a Bathtub!

As a marketing agency, we’re always recommending different media channels to our clients depending on the product, the target audience demographics, marketing goals, etc. And, like many of you, I thought online retargeting was a clever way of “helping” to remind browsers that since they had been interested in a product/service at one point, they might still be interested in making a purchase from that site, so a little tap on the shoulder seemed like a clever way to stay top of mind. Until it happened to me.

As a marketing agency, we’re always recommending different media channels to our clients depending on the product, the target audience demographics, marketing goals, etc. And, like many of you, I thought online retargeting was a clever way of “helping” to remind browsers that since they had been interested in a product/service at one point, they might still be interested in making a purchase from that site, so a little tap on the shoulder seemed like a clever way to stay top of mind. Until it happened to me.

Retargeting, for those of you who may not know, involves having an advertiser drop a cookie into the consumer’s browser which enables the advertiser to follow that consumer around and display an ad for the advertiser after they’ve left the original site.

The logic is sound, the process is relatively simple, and it seems to make good marketing sense. Before it happened to me, I equated it to shoe shopping. I visit a store and see a pair of shoes I like. I try them on, but since I haven’t really looked in a lot of other shoe stores yet, I decide to put off the purchase until I’ve looked at all my options. But in the back of my head a little voice keeps whispering, “Those black patent kitten heels were perfect—even if they were $100 more than you wanted to spend.” I may or may not go back to that first store to get them but I do think about those shoes for quite a while—and with my luck, I return to the store only to find they are now sold out in my size.

But if I was shopping online and the shoes I liked were at Retailer A, I’m now seeing ads for those shoes no matter where I cruise on the Internet. Yep. Those black patents are now stalking me. Not whispering, but shouting out to “come back!”

However, I must confess that my recent stalking incident was not about shoes at all, but about bathtubs.

My husband and I are remodeling a bathroom, so I’ve spent quite a bit of time searching for the perfect bathtub online. Yesterday I actually placed an expensive bathtub in my shopping cart and proceeded to check out, but at the 11th hour started thinking that maybe my contractor could purchase the same tub for a better price. So I abandoned my cart. And in the process, it seems, launched obsessive tracking behavior that could only be rivaled by a professional stalker.

No matter what site I visited while researching client-related work, bathtubs kept appearing. Some were in the upper right hand corner of the page, so as I scrolled down the page they would disappear from view. Whew!

Others seemed to travel down the page with me … tumbling tub over tub with prices flashing, offers blazing and the lure of a long, hot soak compelling me to glance … nay linger … on the designer tub dangling within the reach of a mouse click.

But since I had no intention of completing the purchase transaction without the nod from my contractor, the ads seemed to get more annoying than helpful as the day went on. At one point, a colleague was looking over my shoulder while we were reviewing some online research. After looking at the page for about five minutes, she pointed to the tub ad and commented, “That tub reminds me—did you finish remodeling your bathroom yet?”

Intellectually I understand why retargeting is so valuable. Statistics show that 95 percent of users leave a site without making a transaction, and the ones retargeted are 70 percent more likely to complete a purchase, so it makes perfect sense to retarget.

However the default setting for most retargeting platforms is 30-90 days, so if you’re planning to include retargeting in your marketing mix, think carefully about cookie duration and ad fatigue. Because right now, my fatigue is only off-set by the dream of a long, hot soak in my new tub—cookie-free.

Emails That Target Customer Behavior Without Using Big Data

The ever increasing volumes of data used by companies like Target, Walmart and Amazon to carefully target their customers is cumbersome and difficult to manage. Analyzing patterns to find the right trigger that will motivate an individual to buy requires gifted statisticians that combine art and science into marketing magic. But what if you are not quite ready to use big data in your business? Can you still reap some of the benefits?

The ever increasing volumes of data used by companies like Target, Walmart and Amazon to carefully target their customers is cumbersome and difficult to manage. Analyzing patterns to find the right trigger that will motivate an individual to buy requires gifted statisticians that combine art and science into marketing magic. But what if you are not quite ready to use big data in your business? Can you still reap some of the benefits?

Fortunately for companies that don’t have a team of statisticians standing by, customer behavior and activity can be used to increase sales without the challenges that come with big data. It’s as simple as watching for specific activity or changes in customer behavior and being prepared with a customized response to encourage people to buy.

If this is your first venture into customer behavior marketing, start with the people who are the easiest to identify. Seasonal and discount shoppers are relatively easy to recognize because they have very specific buying patterns. Creating customized marketing for them increases their response and reduces costs. The dual benefits make this a logical place to begin.

Seasonal shoppers are the people who purchase items at specific times of the year. Traditional RFM (recency, frequency, monetary value) analytics flag them as top buyers shortly after a purchase and then systematically move them down the value chain. When they place the next order, they move back to the top and flow down again. Creating a marketing plan that sends materials when they are most likely to buy reduces marketing costs without affecting sales.

Discount shoppers only buy when there is a sale. This segment can be further divided into subsets based on how much discount is required to get the sale. If the marketing is properly tailored, this group of people serves as inventory liquidators. Minimizing the non-sale direct mail pieces they receive and heavily promoting sales increases revenue while reducing costs.

Both groups respond well to promotional emails. Capturing email addresses should be standard operating procedure. It is especially critical for seasonal and discount shoppers because they tend to be more impulsive than other segments. The emails that remind seasonal shoppers that it is that time again and tell discount buyers about the current sales are economical and effective.

The next step after targeting shopper segments is adding specific product category information based on the individual’s shopping history. When my daughter was younger, my shopping behavior with American Girl included two orders per year for regular priced items and sale purchases in between. The two full price orders were placed just before Christmas and her birthday. Sale purchases were impulse driven and triggered by emails announcing clearance items.

Bitty Baby was the category of choice in the early years of buying from American Girl. The shift to the character dolls didn’t happen until my daughter was nine. She received her first Bitty Baby at two. During nine years of systematic purchases, no one recognized that I only ordered certain things at specific times. How much would your company save if your marketing was tailored to customer purchasing patterns?

What about targeting people who haven’t purchased from a specific category?

The ability to predict what people want before they know it is one of the advantages of analyzing trends and activity in big data. Before moving to that level, start with the information that shoppers are providing. This trigger email from Amazon was sent two weeks after I searched for soda can tops on their site without purchasing.

The email avoids the creepy factor by saying, “are you looking for something in our Kitchen Utensils & Gadgets department? If so, you might be interested in these items.” Instead of, “because we noticed that you spent 14.34 minutes searching for soda can tops you may be interested in the ones below.”

The best practices included in this email are:

  • It doesn’t share how they know that the shopper is interested in a specific category or item.
  • The timing from the original search to email generation is long enough to allow time to purchase, but not so long the search is forgotten.
  • It makes accessing the items easy by providing multiple links.
  • The branding is obvious with links to my account, deals and departments.

Targeting customer behavior can become very complicated very quickly. Starting simple with specific segments and activity allows you to test and build on the lessons learned. The return on investment is quick and may surprise you.

How to Get Engaged Prospects to Buy

“How do we get customers engaged on our blog and other social media to buy or transact with us? How do we make that leap?” It’s a common question and you’re not alone in asking it. Here’s my answer: Getting engaged sales prospects to consider a purchase or actually transact is easy if you return to trusty, time-tested, proven basic direct response practices.

“How do we get customers engaged on our blog and other social media to buy or transact with us? How do we make that leap?”

It’s a common question and you’re not alone in asking it. Here’s my answer: Getting engaged sales prospects to consider a purchase or actually transact is easy if you return to trusty, time-tested, proven basic direct response practices.

  1. Solving customers’ problems
  2. Designing to sell (planning social experiences to provoke customer responses that connect to the sales funnel)
  3. Translating (discovering customer need as it evolves and using this knowledge to improve response and conversion rate)

How to Sell by Solving Problems
Making things like blogs, YouTube videos, Facebook, Twitter and the like actually sell challenges us to trust traditional instincts—to evolve, not reinvent. The social aspects of attracting, nurturing and earning a purchase are already known. Successful social sellers are designing interactions (“conversations”) in ways that solve customers’ problems. This approach makes it easy to help customers guide themselves toward products and services.

Solving customers problems has always worked! It’s a simple, effective way to produce awareness, interest, desire and purchase behavior. Providing answers to customers’ questions remains the best way to effectively coax or nurture customers toward making a purchase. Social media is inherently interactive, making this process even easier to accomplish.

The key is using this familiar process, not figuring out what time of the week earns more Twitter retweets (or other nonsensical yet popular recommendations we often hear).

Get Customers to Ask Questions That Connect to Products
Making social media sell for you is a matter of facilitating, and then connecting, question-and-answer oriented, digital conversations to helpful products and services whenever they’re relevant. It’s an old idea that you can leverage to drive sales with “new,” social media.

Think about it in your own life. Have you ever found yourself suddenly more equipped to make a purchase based on knowledge you suddenly became aware of? Think about it in your business, outside the Internet. Do you publish whitepapers, magazine articles, or other self-diagnosis tools to help customers become more clear on problems, avoid risk, or exploit unseen opportunities? Are you doing it in ways that occasionally connect with your products or services?

Beware: Just like cranking out whitepapers or information-dense brochures, earning sales takes more. Success requires relevancy and earning response from customers. That means making a habit of inducing customer behavior with every tweet, post, or update you make on social platforms. And that takes a plan, a designed system of question-and-answer driven interactions.

Beware of the Digital Charlatans
As I discuss in the June edition of Target Marketing, beware. Paradigm shifts and “total game-changers” are a goldmine for gurus and self-appointed experts pushing flash-in-the-pan software, books (Full disclosure: I wrote a social media book) and consulting services. There’s nothing wrong with making a living, but beware of misguided advice designed to scare otherwise rational business people into making irrational, hasty investments and spending money on ideas that don’t work.

Successful social sellers understand that the difference between fooling around on social media and selling with it relies on a return to the basics.