Marketing and IT; Cats and Dogs

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective, first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

 

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

Of course I am generalizing for a comedic effect here, but I see communication breakdowns like this all the time in business environments, especially between the marketing and IT teams. You think men are from Mars and women are from Venus? I think IT folks are from Vulcan and marketing people are from Betazed (if you didn’t get this, find a Trekkie around you and ask).

Now that we are living in the age of Big Data where marketing messages must be custom-tailored based on data, we really need to find a way to narrow the gap between the marketing and the IT world. I wouldn’t dare to say which side is more like a dog or a cat, as I will surely offend someone. But I think even non-Trekkies would agree that it could be terribly frustrating to talk to a Vulcan who thinks that every sentence must be logically impeccable, or a Betazed who thinks that someone’s emotional state is the way it is just because she read it that way. How do they meet in the middle? They need a translator—generally a “human” captain of a starship—between the two worlds, and that translator had better speak both languages fluently and understand both cultures without any preconceived notions.

Similarly, we need translators between the IT world and the marketing world, too. Call such translators “data scientists” if you want (refer to “How to Be a Good Data Scientist”). Or, at times a data strategist or a consultant like myself plays that role. Call us “bats” caught in between the beasts and the birds in an Aesop’s tale, as we need to be marginal people who don’t really belong to one specific world 100 percent. At times, it is a lonely place as we are understood by none, and often we are blamed for representing “the other side.” It is hard enough to be an expert in data and analytics, and we now have to master the artistry of diplomacy. But that is the reality, and I have seen plenty of evidence as to why people whose main job it is to harness meanings out of data must act as translators, as well.

IT is a very special function in modern organizations, regardless of their business models. Systems must run smoothly without errors, and all employees and outside collaborators must constantly be in connection through all imaginable devices and operating systems. Data must be securely stored and backed up regularly, and permissions to access them must be granted based on complex rules, based on job levels and functions. Then there are constant requests to install and maintain new and strange software and technologies, which should be patched and updated diligently. And God forbid if anything fails to work even for a few seconds on a weekend, all hell will break lose. Simply, the end-users—many of them in positions of dealing with customers and clients directly—do not care about IT when things run smoothly, as they take them all for granted. But when they don’t, you know the consequences. Thankless job? You bet. It is like a utility company never getting praises when the lights are up, but everyone yelling and screaming if the service is disrupted, even for a natural cause.

On the other side of the world, there are marketers, salespeople and account executives who deal with customers, clients and their bosses, who would treat IT like their servants, not partners, when things do not “seem” to work properly or when “their” sales projections are not met. The craziest part is that most customers, clients and bosses state their goals and complaints in the most ambiguous terms, as in “This ad doesn’t look slick enough,” “This copy doesn’t talk to me,” “This app doesn’t stick” or “We need to find the right audience.” What the IT folks often do not grasp is that (1) it really stinks when you get yelled at by customers and clients for any reason, and (2) not all business goals are easily translatable to logical statements. And this is when all data elements and systems are functioning within normal parameters.

Without a proper translator, marketers often self-prescribe solutions that call for data work and analytics. Often, they think that all the problems will go away if they have unlimited access to every piece of data ever collected. So they ask for exactly that. IT will respond that such request will put a terrible burden on the system, which has to support not just marketing but also other operations. Eventually they may meet in the middle and the marketer will have access to more data than ever possible in the past. Then the marketers realize that their business issues do not go away just because they have more data in their hands. In fact, their job seems to have gotten even more complicated. They think that it is because data elements are too difficult to understand and they start blaming the data dictionary or lack thereof. They start using words like Data Governance and Quality Control, which may sound almost offensive to diligent IT personnel. IT will respond that they showed every useful bit of data they are allowed to share without breaking the security protocol, and the data dictionaries are all up to date. Marketers say the data dictionaries are hard to understand, and they are filled with too many similar variables and seemingly conflicting information. IT now says they need yet another tool set to properly implement data governance protocols and deploy them. Heck, I have seen cases where some heads of IT went for complete re-platforming of their system, as if that would answer all the marketing questions. Now, does this sound familiar so far? Does it sound like your own experience, like when you are reading “Dilbert” comic strips? It is because you are not alone in all this.

Allow me to be a little more specific with an example. Marketers often talk about “High-Value Customers.” To people who deal with 1s and 0s, that means less than nothing. What does that even mean? Because “high-value customers” could be:

  • High-dollar spenders—But what if they do not purchase often?
  • Frequent shoppers—But what if they don’t spend much at all?
  • Recent customers—Oh, those coveted “hotline” names … but will they stay that way, even for another few months?
  • Tenured customers—But are they loyal to your business, now?
  • Customers with high loyalty points—Or are they just racking up points and they would do anything to accumulate points?
  • High activity—Such as point redemptions and other non-monetary activities, but what if all those activities do not generate profit?
  • Profitable customers—The nice ones who don’t need much hand-holding. And where do we get the “cost” side of the equation on a personal level?
  • Customers who purchases extra items—Such as cruisers who drink a lot on board or diners who order many special items, as suggested.
  • Etc., etc …

Now it gets more complex, as these definitions must be represented in numbers and figures, and depending on the industry, whether be they for retailers, airlines, hotels, cruise ships, credit cards, investments, utilities, non-profit or business services, variables that would be employed to define seemingly straightforward “high-value customers” would be vastly different. But let’s say that we pick an airline as an example. Let me ask you this; how frequent is frequent enough for anyone to be called a frequent flyer?

Let’s just assume that we are going through an exercise of defining a frequent flyer for an airline company, not for any other travel-related businesses or even travel agencies (that would deal with lots of non-flyers). Granted that we have access to all necessary data, we may consider using:

1. Number of Miles—But for how many years? If we go back too far, shouldn’t we have to examine further if the customer is still active with the airline in question? And what does “active” mean to you?

2. Dollars Spent—Again for how long? In what currency? Converted into U.S. dollars at what point in time?

3. Number of Full-Price Ticket Purchases—OK, do we get to see all the ticket codes that define full price? What about customers who purchased tickets through partners and agencies vs. direct buyers through the airline’s website? Do they share a common coding system?

4. Days Between Travel—What date shall we use? Booking date, payment date or travel date? What time zones should we use for consistency? If UTC/GMT is to be used, how will we know who is booking trips during business hours vs. evening hours in their own time zone?

After a considerable hours of debate, let’s say that we reached the conclusion that all involved parties could live with. Then we find out that the databases from the IT department are all on “event” levels (such as clicks, views, bookings, payments, boarding, redemption, etc.), and we would have to realign and summarize the data—in terms of miles, dollars and trips—on an individual customer level to create a definition of “frequent flyers.”

In other words, we would need to see the data from the customer-centric point of view, just to begin the discussion about frequent flyers, not to mention how to communicate with each customer in the future. Now, it that a job for IT or marketing? Who will put the bell on the cat’s neck? (Hint: Not the dog.) Well, it depends. But this definitely is not a traditional IT function, nor is it a standalone analytical project. It is something in between, requiring a translator.

Customer-Centric Database, Revisited
I have been emphasizing the importance of a customer-centric view throughout this series, and I also shared some details regarding databases designed for marketing functions (refer to: “Cheat Sheet: Is Your Database Marketing Ready?”). But allow me to reiterate this point.

In the age of abundant and ubiquitous data, omnichannel marketing communication—optimized based on customers’ past transaction history, product preferences, and demographic and behavioral personas—should be an effortless routine. The reality is far from it for many organizations, as it is very common that much of the vital information is locked in silos without being properly consolidated or governed by a standard set of business rules. It is not that creating such a marketing-oriented database (or data-mart) is solely the IT department’s responsibility, but having a dedicated information source for efficient personalization should be an organizational priority in modern days.

Most databases nowadays are optimized for data collection, storage and rapid retrieval, and such design in general does not provide a customer-centric view—which is essential for any type of personalized communication via all conceivable channels and devices of the present and future. Using brand-, division-, product-, channel- or device-centric datasets is often the biggest obstacle in the journey to an optimal customer experience, as those describe events and transactions, not individuals. Further, bits and pieces of information must be transformed into answers to questions through advanced analytics, including statistical models.

In short, all analytical efforts must be geared toward meeting business objectives, and databases must be optimized for analytics (refer to “Chicken or the Egg? Data or Analytics?”). Unfortunately, the situation is completely reversed in many organizations, where analytical maneuvering is limited due to inadequate source data, and decision-making processes are dictated by limitations of available analytics. Visible symptoms of such cases are, to list a few, elongated project cycle time, decreasing response rates, ineffective customer communication, saturation of data sources due to overexposure, and—as I was emphasizing in this article—communication breakdown among divisions and team members. I can even go as far as to say that the lack of a properly designed analytical environment is the No. 1 cause of miscommunications between IT and marketing.

Without a doubt, key pieces of data must reside in the centralized data depository—generally governed by IT—for effective marketing. But that is only the beginning and still is just a part of the data collection process. Collected data must be consolidated around the solid definition of a “customer,” and all product-, transaction-, event- and channel-level information should be transformed into descriptors of customers, via data standardization, categorization, transformation and summarization. Then the data may be further enhanced via third-party data acquisition and statistical modeling, using all available data. In other words, raw data must be refined through these steps to be useful in marketing and other customer interactions, online or offline (refer to “‘Big Data’ Is Like Mining Gold for a Watch—Gold Can’t Tell Time“). It does not matter how well the original transaction- or event-level data are stored in the main database without visible errors, or what kind of state-of-the-art communication tool sets a company is equipped with. Trying to use raw data for a near real-time personalization engine is like putting unrefined oil into a high-performance sports car.

This whole data refinement process may sound like a daunting task, but it is not nearly as painful as analytical efforts to derive meanings out of unstructured, unconsolidated and uncategorized data that are scattered all over the organization. A customer-centric marketing database (call it a data-mart if “database” sounds too much like it should solely belong to IT) created with standard business rules and uniform variables sets would, in turn, provide an “analytics-ready” environment, where statistical modeling and other advanced analytics efforts would gain tremendous momentum. In the end, the decision-making process would become much more efficient as analytics would provide answers to questions, not just bits and pieces of fragmented data, to the ultimate beneficiaries of data. And answers to questions do not require an enormous data dictionary, either; fast-acting marketing machines do not have time to look up dictionaries, anyway.

Data Roadmap—Phased Approach
For the effort to build a consolidated marketing data platform that is analytics-ready (hence, marketing-ready), I always recommend a phased approach, as (1) inevitable complexity of a data consolidation project will be contained and managed more efficiently in carefully defined phases, and (2) each phase will require different types of expertise, tool sets and technologies. Nevertheless, the overall project must be managed by an internal champion, along with a group of experts who possess long-term vision and tactical knowledge in both database and analytics technologies. That means this effort must reside above IT and marketing, and it should be seen as a strategic effort for an entire organization. If the company already hired a Chief Data Officer, I would say that this should be one of the top priorities for that position. If not, outsourcing would be a good option, as an impartial decision-maker, who would play a role of a referee, may have to come from the outside.

The following are the major steps:

  1. Formulate Questions: “All of the above” is not a good way to start a complex project. In order to come up with the most effective way to build a centralized data depository, we first need to understand what questions must be answered by it. Too many database projects call for cars that must fly, as well.
  2. Data Inventory: Every organization has more data than it expected, and not all goldmines are in plain sight. All the gatekeepers of existing databases should be interviewed, and any data that could be valuable for customer descriptions or behavioral predictions should be considered, starting with product, transaction, promotion and response data, stemming from all divisions and marketing channels.
  3. Data Hygiene and Standardization: All available data fields should be examined and cleaned up, where some data may be discarded or modified. Free form fields would deserve special attention, as categorization and tagging are one of the key steps to opening up new intelligence.
  4. Customer Definition: Any existing Customer ID systems (such as loyalty program ID, account number, etc.) will be examined. It may be further enhanced with available PII (personally identifiable information), as there could be inconsistencies among different systems, and customers often move their residency or use multiple email addresses, creating duplicate identities. A consistent and reliable Customer ID system becomes the backbone of a customer-centric database.
  5. Data Consolidation: Data from different silos and divisions will be merged together based on the master Customer ID. A customer-centric database begins to take shape here. The database update process should be thoroughly tested, as “incremental” updates are often found to be more difficult than the initial build. The job is simply not done until after a few successful iterations of updates.
  6. Data Transformation: Once a solid Customer ID system is in place, all transaction- and event-level data will be transformed to “descriptors” of individual customers, via summarization by categories and creation of analytical variables. For example, all product information will be aligned for each customer, and transaction data will be converted into personal-level monetary summaries and activities, in both static and time-series formats. Promotion and response history data will go through similar processes, yielding individual-level ROI metrics by channel and time period. This is the single-most critical step in all of this, requiring deep knowledge in business, data and analytics, as the stage is being set for all future analytics and reporting efforts. Due to variety and uniqueness of business goals in different industries, a one-size-fits-all approach will not work, either.
  7. Analytical Projects: Test projects will be selected and the entire process will be done on the new platform. Ad-hoc reporting and complex modeling projects will be conducted, and the results will be graded on timing, accuracy, consistency and user-friendliness. An iterative approach is required, as it is impossible to foresee all possible user requests and related complexities upfront. A database should be treated as a living, breathing organism, not something rigid and inflexible. Marketers will “break-in” the database as they use it more routinely.
  8. Applying the Knowledge: The outcomes of analytical projects will be applied to the entire customer base, and live campaigns will be run based on them. Often, major breakdowns happen at the large-scale deployment stages; especially when dealing with millions of customers and complex mathematical formulae at the same time. A model-ready database will definitely minimize the risk (hence, the term “in-database scoring”), but the process will still require some fine-tuning. To proliferate gained knowledge throughout the organization, some model scores—which pack deep intelligence in small sizes—may be transferred back to the main databases managed by IT. Imagine model scores driving operational decisions—live, on the ground.
  9. Result Analysis: Good marketing intelligence engines must be equipped with feedback mechanisms, effectively closing the “loop” where each iteration of marketing efforts improves its effectiveness with accumulated knowledge on a customer level. It is very unfortunate that many marketers just move through the tracks set up by their predecessors, mainly because existing database environments are not even equipped to link necessary data elements on a customer level. Too many back-end analyses are just event-, offer- or channel-driven, not customer-centric. Can you easily tell which customer is over-, under- or adequately promoted, based on a personal-level promotion-and-response ratio? With a customer-centric view established, you can.

These are just high-level summaries of key steps, and each step should be managed as independent projects within a large-scale initiative with common goals. Some steps may run concurrently to reduce the overall timeline, and tactical knowledge in all required technologies and tool sets is the key for the successful implementation of centralized marketing intelligence.

Who Will Do the Work?
Then, who will be in charge of all this and who will actually do the work? As I mentioned earlier, a job of this magnitude requires a champion, and a CDO may be a good fit. But each of these steps will require different skill sets, so some outsourcing may be inevitable (more on how to pick an outsourcing partner in future articles).

But the case that should not be is the IT team or the analytics team solely dictating the whole process. Creating a central depository of marketing intelligence is something that sits between IT and marketing, and the decisions must be made with business goals in mind, not just limitations and challenges that IT faces. If the CDO or the champion of this type of initiative starts representing IT issues before overall business goals, then the project is doomed from the beginning. Again, it is not about touching the core database of the company, but realigning existing data assets to create new intelligence. Raw data (no matter how clean they are at the collection stage) are like unrefined raw materials to the users. What the decision-makers need are simple answers to their questions, not hundreds of data pieces.

From the user’s point of view, data should be:

  • Easy to understand and use (intuitive to non-mathematicians)
  • Bite-size (i.e., small answers, not mounds of raw data)
  • Useful and effective (consistently accurate)
  • Broad (answers should be available most of time, not just “sometimes”)
  • Readily available (data should be easily accessible via users’ favorite devices/channels)

And getting to this point is the job of a translator who sits in between marketing and IT. Call them data scientists or data strategists, if you like. But they do not belong to just marketing or IT, even though they have to understand both sides really well. Do not be rigid, insisting that all pilots must belong to the Air Force; some pilots do belong to the Navy.

Lastly, let me add this at the risk of sounding like I am siding with technologists. Marketers, please don’t be bad patients. Don’t be that bad patient who shows up at a doctor’s office with a specific prescription, as in “Don’t ask me why, but just give me these pills, now.” I’ve even met an executive who wanted a neural-net model for his business without telling me why. I just said to myself, “Hmm, he must have been to one of those analytics conferences recently.” Then after listening to his “business” issues, I prescribed an entirely different solution package.

So, instead of blurting out requests for pieces of data variables or queries using cool-sounding, semi-technical terms, state the business issues and challenges that you are facing as clearly as possible. IT and analytics specialists will prescribe the right solution for you if they understand the ultimate goals better. Too often, requesters determine the solutions they want without any understanding of underlying technical issues. Don’t forget that the end-users of any technology are only exposed to symptoms, not the causes.

And if Mr. Spock doesn’t seem to understand your issues and keeps saying that your statements are illogical, then call in a translator, even if you have to hire him for just one day. I know this all too well, because after all, this one phrase summarizes my entire career: “A bridge person between the marketing world and the IT world.” Although it ain’t easy to live a life as a marginal person.

Sex and the Schoolboy: Predictive Modeling – Who’s Doing It? Who’s Doing it Right?

Forgive the borrowed interest, but predictive modeling is to marketers as sex is to schoolboys. They’re all talking about it, but few are doing it. And among those who are, fewer are doing it right. In customer relationship marketing (CRM), predictive modeling uses data to predict the likelihood of a customer taking a specific action. It’s a three-step process.

Forgive the borrowed interest, but predictive modeling is to marketers as sex is to schoolboys.

They’re all talking about it, but few are doing it. And among those who are, fewer are doing it right.

In customer relationship marketing (CRM), predictive modeling uses data to predict the likelihood of a customer taking a specific action. It’s a three-step process:

1. Examine the characteristics of the customers who took a desired action

2. Compare them against the characteristics of customers who didn’t take that action

3. Determine which characteristics are most predictive of the customer taking the action and the value or degree to which each variable is predictive

Predictive modeling is useful in allocating CRM resources efficiently. If a model predicts that certain customers are less likely respond to a specific offer, then fewer resources can be allocated to those customers, allowing more resources to be allocated to those who are more likely to respond.

Data Inputs
A predictive model will only be as good as the input data that’s used in the modeling process. You need the data that define the dependent variable; that is, the outcome the model is trying to predict (such as response to a particular offer). You’ll also need the data that define the independent variables, or the characteristics that will be predictive of the desired outcome (such as age, income, purchase history, etc.). Attitudinal and behavioral data may also be predictive, such as an expressed interest in weight loss, fitness, healthy eating, etc.

The more variables that are fed into the model at the beginning, the more likely the modeling process will identify relevant predictors. Modeling is an iterative process, and those variables that are not at all predictive will fall out in the early iterations, leaving those that are most predictive for more precise analysis in later iterations. The danger in not having enough independent variables to model is that the resultant model will only explain a portion of the desired outcome.

For example, a predictive model created to determine the factors affecting physician prescribing of a particular brand was inconclusive, because there weren’t enough dependent variables to explain the outcome fully. In a standard regression analysis, the number of RXs written in a specific timeframe was set as the dependent variable. There were only three independent variables available: sales calls, physician samples and direct mail promotions to physicians. And while each of the three variables turned out to have a positive effect on prescriptions written, the “Multiple R” value of the regression equation was high at 0.44, meaning that these variables only explained 44 percent of the variance in RXs. The other 56 percent of the variance is from factors that were not included in the model input.

Sample Size
Larger samples will produce more robust models than smaller ones. Some modelers recommend a minimum data set of 10,000 records, 500 of those with the desired outcome. Others report acceptable results with as few as 100 records with the desired outcome. But in general, size matters.

Regardless, it is important to hold out a validation sample from the modeling process. That allows the model to be applied to the hold-out sample to validate its ability to predict the desired outcome.

Important First Steps

1. Define Your Outcome. What do you want the model to do for your business? Predict likelihood to opt-in? Predict likelihood to respond to a particular offer? Your objective will drive the data set that you need to define the dependent variable. For example, if you’re looking to predict likelihood to respond to a particular offer, you’ll need to have prospects who responded and prospects who didn’t in order to discriminate between them.

2. Gather the Data to Model. This requires tapping into several data sources, including your CRM database, as well as external sources where you can get data appended (see below).

3. Set the Timeframe. Determine the time period for the data you will analyze. For example, if you’re looking to model likelihood to respond, the start and end points for the data should be far enough in the past that you have a sufficient sample of responders and non-responders.

4. Examine Variables Individually. Some variables will not be correlated with the outcome, and these can be eliminated prior to building the model.

Data Sources
Independent variable data
may include

  • In-house database fields
  • Data overlays (demographics, HH income, lifestyle interests, presence of children,
    marital status, etc.) from a data provider such as Experian, Epsilon or Acxiom.

Don’t Try This at Home
While you can do regression analysis in Microsoft Excel, if you’re going to invest a lot of promotion budget in the outcome, you should definitely leave the number crunching to the professionals. Expert modelers know how to analyze modeling results and make adjustments where necessary.

How to Create High Performing Sweepstakes for Lead-Gen Efforts

OK, I know what you’re thinking … viable leads typically don’t come from sweepstakes and contests. And when not done correctly, that’s exactly right. However, just as any online direct response tactic, this one is no different. Over the years, sweepstakes marketing has become refined through testing and targeting. And since the boom in social media, sweepstakes are more popular than ever. But before you embark on this tactic, there are a few core concepts to know—as well as best practices.

OK, I know what you’re thinking … viable leads typically don’t come from sweepstakes and contests.

And when not done correctly, that’s exactly right.

However, just as any online direct response tactic, this one is no different. Over the years, sweepstakes marketing has become refined through testing and targeting. And since the boom in social media, sweepstakes are more popular than ever.

But before you embark on this tactic, there are a few core concepts to know—as well as best practices.

The Precursors

It’s important to get to know your list to help determine its value and how much you are willing to give away for a lead, such as:

  • What is your average conversion time (how long does it take someone to move from a lead to a buyer—30, 60, 90-plus days?)
  • What is the lifetime value (LTV) per buyer?
  • What is your average revenue per name?
  • What is your average cost per lead (CPL)?

Conversion Time. Monitor a group of new names (perhaps by campaign) who come on your file and see at what point, at what percent and for what dollar amount your leads convert to buyers. This will help you know how much and how long it takes a lead to convert. Let’s say you have a pay-per-click campaign and, in the first 30 days, 20 percent of the leads convert and the average unit sale is $50. This shows you your time threshold for getting a sale. You’ll know when to anticipate revenues and can manage your budget accordingly.

LTV. You take the total your buyers purchased: Let’s say over five years, this group collectively spent $100,000, and divide that amount by number of buyers (let’s say its 500). Your LTV is $200. This will show you the potential long-term opportunity for a buyer’s worth, as well as the loss (if the customer leaves your list).

Rev Per Name. This is more for the current buyers on your file not long term, as with LTV. Take the total your buyers spend at 30, 60 and 90 days; and at each time point, divide that amount by the number of buyers. So let’s say at 30 days, your newest names bring in collectively $10,000 and there are 1,000 buyers. That is a $10/rev per name. This will show you current buyer worth and your threshold for acquisition costs.

Cost Per Lead. When you’re doing an acquisition effort, how much does it cost you per name? Take the cost of the media buy and divide by the number of leads that came in. This will tell you how much you typically spend to bring in a new name. Ideally, you want to keep you cost per lead much lower than your revenue per name and LTV. I like to hover between $5 and $25 CPL. CPLs will be different by channel. However, if you bring in a lead at $50 and you know, based on your list performance, that name will spend $75 in the first 6 months, you can afford to take an initial loss.

The Offer

What are you going to give away? The value of the giveaway should be something that won’t be viewed as too good to be true by users as well, as one you can earn back (based on the aforementioned list criteria and in a certain time period). So knowing your giveaway threshold is important.

In addition to being realistic and appealing, the offer should also be relevant and interesting to your target prospect.

I’ve seen random sweepstakes offers on the Web, as I’m sure you have. One in particular, a publishing company, featured an offer: “Win a free iPad.”

This makes zero sense to me in so many ways …

Unless this publishing company is uploading an app on the iPad with a free online subscription to one of their publications, I don’t see the relevance for the end-user. This publisher will likely wind up with thousands of leads, but they will be unqualified, irrelevant people looking for a free electronic device and not in the other information products they offer.

Plus there’s an out-of-pocket cost for the product and shipping of the product.

This, in my opinion, is typical of the “old” sweepstakes offers where little strategy and direct response knowledge seemed to go into planning the campaign.

However, one website I discovered in my research for this article seems to hit the nail on the head and offer something synergistic to their leads, as well as qualifies the lead for future potential sales via cross-sell and upsell efforts.

Take skin care company, Dermagist. Their sweepstakes offer is for lead generation, touts a “$200 shopping spree,” and is featured on their website and Facebook page. The tactics they are using can be applied to most any industry.

Leads have to “register” by liking Dermagist’s Facebook page, as well as post on Dermagists’ Facebook page why they love the product. Winners are chosen monthly and given a promo code worth $200 toward anything in their store. No purchase is necessary.

What I Like …

The offer is ongoing, so it’s a continuity of new leads (email addresses) coming in on a monthly basis to help build the list and offset any attrition.

The prize is realistic, targeted and qualifies the recipient based on relevant interest—it’s appealing to those interested in skincare products and is a great way to get repeat and referral sales.

Leads have to “register” by liking Dermagist Facebook page, as well as post on their Facebook wall why they love the product. This strategy helps with social media engagement (boosting page “likes,” visibility and credibility), as well as product awareness.

I also liked that on the website’s sweepstakes registration page, last month’s winner’s name was posted. This helps reinforce contest legitimacy.

Location, Location, Location

Where you promote your sweepstakes is equally important for targeting and relevance.

There’s the obvious, such as having a banner ad, header content or interstitial on the website’s home page mentioning the promotion.

You can also promote it on your business’ Facebook page organically (through fan page timeline and wall posts), through apps, as well as through targeted ads and boosted posts, selecting audiences in the Newsfeed that are like-minded with your target customer.

Tabsite has a variety of Facebook-friendly apps for contests and sweepstakes (photos, trivia and more).

A word of caution: If you are promoting a sweepstakes on Facebook, make sure to follow its guidelines or your campaign may run the risk of getting shut down.

Promoting it organically with search engine marketing is another tactic, such as with free online press releases.

And, of course, if your budget allows, you can promote your sweepstakes through targeted media buys (banner ads, email list rental) and pay-per-click. These costs should be factored into the overall campaign effort and cost per lead.

So when you start thinking about your acquisition efforts and how sweepstakes may be used, know that through the evolution of the consumer and Internet marketing in general, this is not your father’s sweepstakes anymore.

Being a creative and strategic marketer will help you take this strategy to a whole new, high-performing level.

It’s All About Ranking

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

Yes, “choice” is the keyword in all of these questions. And if you picked Paris over other places as an answer to the last question, you just made a choice based on some ranking order in your mind. The world is big, and there could have been many factors that contributed to that decision, such as culture, art, cuisine, attractions, weather, hotels, airlines, prices, deals, distance, convenience, language, etc., and I am pretty sure that not all factors carried the same weight for you. For example, if you put more weight on “cuisine,” I can see why London would lose a few points to Paris in that ranking order.

As a citizen, for whom should I vote? That’s the choice based on your ranking among candidates, too. Call me overly analytical (and I am), but I see the difference in political stances as differences in “weights” for many political (and sometimes not-so-political) factors, such as economy, foreign policy, defense, education, tax policy, entitlement programs, environmental issues, social issues, religious views, local policies, etc. Every voter puts different weights on these factors, and the sum of them becomes the score for each candidate in their minds. No one thinks that education is not important, but among all these factors, how much weight should it receive? Well, that is different for everybody; hence, the political differences.

I didn’t bring this up to start a political debate, but rather to point out that the decision-making process is based on ranking, and the ranking scores are made of many factors with different weights. And that is how the statistical models are designed in a nutshell (so, that means the models are “nuts”?). Analysts call those factors “independent variables,” which describe the target.

In my past columns, I talked about the importance of statistical models in the age of Big Data (refer to “Why Model?”), and why marketing databases must be “model-ready” (refer to “Chicken or the Egg? Data or Analytics?”). Now let’s dig a little deeper into the design of the “model-ready” marketing databases. And surprise! That is also all about “ranking.”

Let’s step back into the marketing world, where folks are not easily offended by the subject matter. If I give a spreadsheet that contains thousands of leads for your business, you wouldn’t be able to tell easily which ones are the “Glengarry Glen Ross” leads that came from Downtown, along with those infamous steak knives. What choice would you have then? Call everyone on the list? I guess you can start picking names out of a hat. If you think a little more about it, you may filter the list by the first name, as they may reflect the decade in which they were born. Or start calling folks who live in towns that sound affluent. Heck, you can start calling them in alphabetical order, but the point is that you would “sort” the list somehow.

Now, if the list came with some other valuable information, such as income, age, gender, education level, socio-economic status, housing type, number of children, etc., you may be able to pick and choose by which variables you would use to sort the list. You may start calling the high income folks first. Not all product sales are positively related to income, but it is an easy way to start the process. Then, you would throw in other variables to break the ties in rich areas. I don’t know what you’re selling, but maybe, you would want folks who live in a single-family house with kids. And sometimes, your “gut” feeling may lead you to the right place. But only sometimes. And only when the size of the list is not in millions.

If the list was not for prospecting calls, but for a CRM application where you also need to analyze past transaction and interaction history, the list of the factors (or variables) that you need to consider would be literally nauseating. Imagine the list contains all kinds of dollars, dates, products, channels and other related numbers and figures in a seemingly endless series of columns. You’d have to scroll to the right for quite some time just to see what’s included in the chart.

In situations like that, how nice would it be if some analyst threw in just two model scores for responsiveness to your product and the potential value of each customer, for example? The analysts may have considered hundreds (or thousands) of variables to derive such scores for you, and all you need to know is that the higher the score, the more likely the lead will be responsive or have higher potential values. For your convenience, the analyst may have converted all those numbers with many decimal places into easy to understand 1-10 or 1-20 scales. That would be nice, wouldn’t it be? Now you can just start calling the folks in the model group No. 1.

But let me throw in a curveball here. Let’s go back to the list with all those transaction data attached, but without the model scores. You may say, “Hey, that’s OK, because I’ve been doing alright without any help from a statistician so far, and I’ll just use the past dollar amount as their primary value and sort the list by it.” And that is a fine plan, in many cases. Then, when you look deeper into the list, you find out there are multiple entries for the same name all over the place. How can you sort the list of leads if the list is not even on an individual level? Welcome to the world of relational databases, where every transaction deserves an entry in a table.

Relational databases are optimized to store every transaction and retrieve them efficiently. In a relational database, tables are connected by match keys, and many times, tables are connected in what we call “1-to-many” relationships. Imagine a shopping basket. There is a buyer, and we need to record the buyer’s ID number, name, address, account number, status, etc. Each buyer may have multiple transactions, and for each transaction, we now have to record the date, dollar amount, payment method, etc. Further, if the buyer put multiple items in a shopping basket, that transaction, in turn, is in yet another 1-to-many relationship to the item table. You see, in order to record everything that just happened, this relational structure is very useful. If you are the person who has to create the shipping package, yes, you need to know all the item details, transaction value and the buyer’s information, including the shipping and billing address. Database designers love this completeness so much, they even call this structure the “normal” state.

But the trouble with the relational structure is that each line is describing transactions or items, not the buyers. Sure, one can “filter” people out by interrogating every line in the transaction table, say “Select buyers who had any transaction over $100 in past 12 months.” That is what I call rudimentary filtering, but once we start asking complex questions such as, “What is the buyer’s average transaction amount for past 12 months in the outdoor sports category, and what is the overall future value of the customers through online channels?” then you will need what we call “Buyer-centric” portraits, not transaction or item-centric records. Better yet, if I ask you to rank every customer in the order of such future value, well, good luck doing that when all the tables are describing transactions, not people. That would be exactly like the case where you have multiple lines for one individual when you need to sort the leads from high value to low.

So, how do we remedy this? We need to summarize the database on an individual level, if you would like to sort the leads on an individual level. If the goal is to rank households, email addresses, companies, business sites or products, then the summarization should be done on those levels, too. Now, database designers call it the “de-normalization” process, and the tables tend to get “wide” along that process, but that is the necessary step in order to rank the entities properly.

Now, the starting point in all the summarizations is proper identification numbers for those levels. It won’t be possible to summarize any table on a household level without a reliable household ID. One may think that such things are given, but I would have to disagree. I’ve seen so many so-called “state of the art” (another cliché that makes me nauseous) databases that do not have consistent IDs of any kind. If your database managers say they are using “plain name” or “email address” fields for matching or summarization, be afraid. Be very afraid. As a starter, you know how many email addresses one person may have. To add to that, consider how many people move around each year.

Things get worse in regard to ranking by model scores when it comes to “unstructured” databases. We see more and more of those, as the data sources are getting into uncharted territories, and the size of the databases is growing exponentially. There, all these bits and pieces of data are sitting on mysterious “clouds” as entries on their own. Here again, it is one thing to select or filter based on collected data, but ranking based on some statistical modeling is simply not possible in such a structure (or lack thereof). Just ask the database managers how many 24-month active customers they really have, considering a great many people move in that time period and change their addresses, creating multiple entries. If you get an answer like “2 million-ish,” well, that’s another scary moment. (Refer to “Cheat Sheet: Is Your Database Marketing Ready?”)

In order to develop models using variables that are descriptors of customers, not transactions, we must convert those relational or unstructured data into the structure that match the level by which you would like to rank the records. Even temporarily. As the size of databases are getting bigger and bigger and the storage is getting cheaper and cheaper, I’d say that the temporary time period could be, well, indefinite. And because the word “data-mart” is overused and confusing to many, let me just call that place the “Analytical Sandbox.” Sandboxes are fun, and yes, all kinds of fun stuff for marketers and analysts happen there.

The Analytical Sandbox is where samples are created for model development, actual models are built, models are scored for every record—no matter how many there are—without hiccups; targets are easily sorted and selected by model scores; reports are created in meaningful and consistent ways (consistency is even more important than sheer accuracy in what we do), and analytical language such as SAS, SPSS or R are spoken without being frowned up by other computing folks. Here, analysts will spend their time pondering upon target definitions and methodologies, not about database structures and incomplete data fields. Have you heard about a fancy term called “in-database scoring”? This is where that happens, too.

And what comes out of the Analytical Sandbox and back into the world of relational database or unstructured databases—IT folks often ask this question—is going to be very simple. Instead of having to move mountains of data back and forth, all the variables will be in forms of model scores, providing answers to marketing questions, without any missing values (by definition, every record can be scored by models). While the scores are packing tons of information in them, the sizes could be as small as a couple bytes or even less. Even if you carry over a few hundred affinity scores for 100 million people (or any other types of entities), I wouldn’t call the resultant file large, as it would be as small as a few video files, really.

In my future columns, I will explain how to create model-ready (and human-ready) variables using all kinds of numeric, character or free-form data. In Exhibit A, you will see what we call traditional analytical activities colored in dark blue on the right-hand side. In order to make those processes really hum, we must follow all the steps that are on the left-hand side of that big cylinder in the middle. Preventing garbage-in-garbage-out situations from happening, this is where all the data get collected in uniform fashion, properly converted, edited and standardized by uniform rules, categorized based on preset meta-tables, consolidated with consistent IDs, summarized to desired levels, and meaningful variables are created for more advanced analytics.

Even more than statistical methodologies, consistent and creative variables in form of “descriptors” of the target audience make or break the marketing plan. Many people think that purchasing expensive analytical software will provide all the answers. But lest we forget, fancy software only answers the right-hand side of Exhibit A, not all of it. Creating a consistent template for all useful information in a uniform fashion is the key to maximizing the power of analytics. If you look into any modeling bakeoff in the industry, you will see that the differences in methodologies are measured in fractions. Conversely, inconsistent and incomplete data create disasters in real world. And in many cases, companies can’t even attempt advanced analytics while sitting on mountains of data, due to structural inadequacies.

I firmly believe the Big Data movement should be about

  1. getting rid of the noise, and
  2. providing simple answers to decision-makers.

Bragging about the size and the speed element alone will not bring us to the next level, which is to “humanize” the data. At the end of the day (another cliché that I hate), it is all about supporting the decision-making processes, and the decision-making process is all about ranking different options. So, in the interest of keeping it simple, let’s start by creating an analytical haven where all those rankings become easy, in case you think that the sandbox is too juvenile.

Facebook’s Timeline for Brands: A Facebook Performance Opportunity

Facebook’s new Timeline for Brands enables marketers to foster engagement with participants. This engagement can equal Facebook performance. Brands can separate themselves from the competition by using real-time Facebook engagement data and insights to optimize their brand pages for performance.  

Facebook recently announced the launch of Facebook Timeline for Brands, or new profile pages for brands on the social networking site. New features of brand pages include the following:

  • pages are much more visual as brands have the opportunity to use large cover photos and videos to promote themselves;
  • brands can now prominently feature their most important tabs at the top of their pages;
  • brands can pin key posts to the top of their pages for up to seven days (i.e., they can highlight important posts for a longer time period); and
  • similar to Twitter, brands can privately message fans (and vice versa), helping Facebook become a more powerful customer service tool

The new pages are the hub for your brand on Facebook. All of your brand’s Facebook activities, ads and posts originate from your brand page. The brand page is also the key place for you and your fans to communicate, enabling you to foster stronger customer relationships.

Brands now have a platform on Facebook for complete experience optimization — i.e., engaging participants through sights, sounds, words, interactions, ads, games and apps, all in one easy-to-find place. Facebook noted that it wants Timeline for Brands to bring back the relationship between the customer and shopkeeper. The updated brand pages provide a platform for brands to engage with customers on a more personal and relevant level than probably any other platform, including the brand’s own website.

The same day Facebook launched Timeline for Brands, it also announced its new real-time Page Insights. Real-time insights are a game changer as marketers used to have to wait 48 hours for Facebook data.

Facebook Product Manager David Baser recently talked to AdAge about what real-time insights means for brands seeking performance through Facebook pages. Baser maintained that engagement can equal performance if brands are able to leverage real-time participant data to quickly optimize brand pages. For instance, if a brand knows that a certain post is driving a significant number of likes, comments or shares, that brand can quickly pin that post to the top of its brand page.

The new brand pages and real-time insights give brands the opportunity to understand how well they’re interacting with their users and how responsive customers are to the brand. These engagement metrics don’t necessarily directly equate to performance (i.e., sales and leads), but they can help a brand understand its ability to increase the likelihood of performance — e.g., conversions, new customers, improved customer loyalty and increased average order size.

The like button isn’t the only Facebook engagement metric of interest to marketers. Facebook also now reports on various engagement metrics centered on actions. These include the “People Talking About This” metric, which incorporates likes, comments, shares, tags, check-ins and event RSVPs, and the “Engaged Users” metric, which incorporates clicks on links, photos and video views. Performance marketers are focused on collecting and analyzing this engagement data to inform brand page content, make real-time brand page optimization decisions and increase the chance of performance. Brands should consider the following when analyzing their Facebook marketing strategy:

  1. Test specific posts (videos, polls, etc.) around new products, promotions and events.
  2. Collect engagement data.
  3. Measure changes in customer behavior (e.g., sales, leads, new-to-file customers, order size, etc.) based on the data.

Facebook’s new Timeline for Brands enables marketers to foster engagement with participants. This engagement can equal Facebook performance. Brands can separate themselves from the competition by using real-time Facebook engagement data and insights to optimize their brand pages for performance.