The Day Marketers Became ‘Big Brother’

Data collection is transactional. Before Google and social media, transactions were, for the most part, financial. But now they’re personal. Every friend, family member, like, love, click, view, search, post, follow, preference, location and comment is a piece of transactional data that can be exploited not only for commercial purposes, but for political purposes, as well.

Every breath you take; Every move you make; Every bond you break; Every step you take,

I’ll be watching you

Every single day; Every word you say; Every game you play; Every night you stay,

I’ll be watching you

It’s a bit ironic, and somewhat prescient, that a band named The Police sang those lyrics in the year before 1984. We’ve finally found the Holy Grail of one-to-one marketing … but do we like it?

Back in the ’90s, I was working with a client using credit bureau customer data to build models of people who were likely to be interested in home equity loans. Some people would chide me about invading people’s privacy for commercial purposes, but I would always respond:

“We’re not interested in the personal information points about John Q. Public; we’re only interested in the fact that he belongs to a segment of people who meet a specific set of financial criteria. We market to that entire segment of people without paying attention to any one of single individual’s personal, customer data points. I don’t care about your specific home value or mortgage balance; I only care that you belong to that group of people who would qualify for a home equity loan.”

But now I’m creeped out.

Customer Data Is Integral to the Credit Economy

Customer data collection is transactional. Before Google and social media, transactions were, for the most part, financial. But now they’re personal. Every friend, family member, like, love, click, view, search, post, follow, preference, location and comment is a piece of transactional data that can be exploited not only for commercial purposes, but for political purposes, as well.

And we give it up so willingly!

In order to participate in the credit economy, we tacitly agreed to have our financial transaction data stored and monitored. (Let’s not get into how that worked out recently, but the value exchange seemed reasonable). Now, we willingly give up reams of personal transaction data, and the value exchange is quite different. We get to access pictures of friends with their food, children and pets, we get accurate turn-by-turn directions to wherever we’re going, and we get that leather messenger bag we looked at online to follow us around the Internet for weeks on end.

Can marketers still make the argument, “we’re not interested in the personal information points about John Q. Public; we’re only interested in the fact that he belongs to a segment of people who meet a specific set of criteria”? Even if now, that segment is a segment of one?

And the customer data collectors? Like The Police, they say, I’ll be watching you.

What do you think? Comments welcome.

The Secret Sauce for B2B Loyalty Marketing

Who’s likely to be your valuable customer? What will their value be in next few years? How long will they continue to do business with you? Which ones are in vulnerable positions, and who’s likely to churn in next three months? Wouldn’t it be great if you could identify who’s vulnerable among your valuable customers “before” they actually stop doing business with you?

B2B loyalty
“business-agreement,” Creative Commons license. | Credit: Flickr by Kevin Johnston

Properly measuring customer loyalty is often a difficult task in multichannel B2B marketing environment. The first question is often, “Where should we start digging when there are many data silos?” Before embarking on a massive data consolidation project throughout the organization, we suggest defining the problem statements by breaking down what customer loyalty means to you first, as that exercise will narrow down the list of data assets to be dealt with.

Who’s likely to be your valuable customer? What will their value be in next few years? How long will they continue to do business with you? Which ones are in vulnerable positions, and who’s likely to churn in next three months? Wouldn’t it be great if you could identify who’s vulnerable among your valuable customers “before” they actually stop doing business with you?

Marketers often rely on surveys to measure loyalty. Net Promoter Score, for example, is a good way to measure customer loyalty for the brand. But if you want to be proactive about each customer, you will need to know the loyalty score for everyone in your base. And asking “everyone” is too cost-prohibitive and impractical. On top of that, the respondents may not be completely honest about their intentions; especially when it comes to monetary transactions.

That’s where modeling techniques come in. Without asking direct questions, what are the leading indicators of loyalty or churn? What specific behaviors lead to longevity of the relationship or complete attrition? In answering those questions, past behavior is often proven to be a better predictor of future behavior than survey data, as what people say they would do and what they actually do are indeed different.

Modeling is also beneficial, as it fills inevitable data gaps, as well. No matter how much data you may have collected, you will never know everything about everyone in your base. Models are tools that make the most of available data assets, summarizing complex datasets into forms of answers to questions. How loyal is the Company XYZ? The loyalty model score will express that in a numeric form, such as a score between one and 10 for every entity in question. That would be a lot simpler than setting up rules by digging through a long data dictionary.

Our team recently developed a loyalty model for a leading computing service company in the U.S. The purposes of the modeling exercise were two-fold:

  1. Find a group of customers who are likely to be loyal customers, and
  2. Find the “vulnerable” segment in the base.

This way, the client can treat “potentially” loyal customers even before they show all of the signs of loyalty. At the opposite end of the spectrum, the client can proactively contact vulnerable customers, if their present or future value (need a customer value model for that) is high. We would call that the “valuable-vulnerable” segment.

We could have built a separate churn model more properly, but that would have required long historical data in forms of time-series variables (processes for those can be time-consuming and costly). To get to the answer fast with minimal data that we had access to, we chose to build one loyalty model, making sure that the bottom scores could be used to measure vulnerability, while the top scores indicate loyalty.

What did we need to build this model? Again, to provide a “usable” answer in the shortest time, we only used the past three years of transaction history, along with some third-party firmographic data. We considered promotion and response-history data, technical support data, non-transactional engagement data and client-initiated activity data, but we pushed them out for future enhancement due to difficulties in data procurement.

To define what “loyal” means in a mathematical term for modeling, we considered multiple options, as that word can mean lots of different things. Depending on the purpose, it could mean high value, frequent buyer, tenured customers, or other measurements of loyalty and levels of engagement. Because we are starting with the basic transaction data, we examined many possible combinations of RFM data.

In doing so, we observed that many indicators of loyalty behave radically differently among different segments, defined by spending level in this instance, which is a clear sign that separate models are required. For other cases, such overarching segments, they can be defined based on region, product line or target groups, too.

So we divided the base into small, medium and large segments, based on annual spending level, then started examining other types of indicators of loyalty for target definition. If we had some survey data, we could have used them to define what “loyal” means. In this case, we mixed the combinations of recency and frequency factors, where each segment ended up with different target definitions. For the first round, we defined the loyal customers with the last transaction date within the past 12 months and total transaction counts within the top 10 to 15 percent range, where the governing idea was to have the target universes that are “not too big” or “not too small.” During this exercise, we concluded that the small segment of big spenders was deemed to be loyal, and we didn’t need a model to further discriminate.

Stephen Yu's B2B loyalty marketing chart
Credit: Stephen H. Yu

As expected, models built for small- and medium-level spenders were quite different, in terms of usage of data and weight assigned to each variable. For example, even for the same product category purchases, a recency variable (weeks since the last transaction within the category) showed up as a leading indicator for one model, while various bands of categorical spending levels were important factors for the other. Common variables, such as industry classification code (SIC code) also behaved very differently, validating our decision to build separate models for each spending level segment.

The 10 Most Effective Tips for Customer Reactivation

Are you looking for the best ways to reactivate dormant customers and reduce churn? Here’s a roundup of the 10 most effective practices today, in both business and consumer markets. Consider which of these may be the most applicable to your business, your customers and your objectives.

Are you looking for the best ways to reactivate dormant customers and reduce churn? Here’s a roundup of the 10 most effective practices today, in both business and consumer markets. Consider which of these may be the most applicable to your business, your customers and your objectives. And don’t forget to set aside some budget for ongoing retention and reactivation marketing. It’s the best money you can spend.

1. Move Quickly

The longer a customer is inactive, the more likely an eventual defection. Early action is arguably the single most effective technique in reactivation marketing. But, you can take this principal a step further if you examine customer behavior patterns to predict inactivity before it even starts. For example, if purchase frequency slows, or order size shrinks, inactivity is likely to follow. Analyze the characteristics of your purchase cycle.   Anomalies in a particular customer’s behavior may indicate a problem that, with early intervention, can be addressed.

2. Segment Your Dormant Customers, and Treat Them Differently

As marketers well know, different customers have different needs, and represent different levels of value to the firm. Applying segmentation is a key success factor in the reactivation effort, just as it is elsewhere in marketing.   Consider such segmentation variables as:

  • Original acquisition source media, like email, SEM, direct mail, display advertising, event, or telemarketing.
  • Channel usage. This can be communications channels like email or telephone. Or it can be purchase channel preferences, like retail store, tablet, mobile, or desktop computer.
  • Product usage.
  • Customer value, using indicators like RFM, cumulative margins, or intent signals.
  • Inactivity length, typically divided by months or years, depending on the purchase cycle in your business.

3. Deepen Your Understanding of the Dormant Customer

There are a number of approaches you can take, among them:

  1. Analyze behavioral patterns, looking for insights. For example, you may notice that an unusually large order is followed by a period of inactivity, and hypothesize that the customer is not getting ready to leave—she just has all the product she needs for a while.
  2. Use data append to gather more information about the customer. Your database marketing partner can add data points to your customer record that will suggest effective reactivation strategies. Demographic, lifestyle and attitudinal data are especially revealing.
  3. Consider some research, using an outbound telephone call, or a focus group, to gain additional insights into the reasons for the inactivity.

4. Communicate Through Different Channels

Thanks to marketing automation, email communications have become very easy to deploy, and there’s no question that email is effective for current customer communications. But relying entirely on email may annoy lapsed customers, not to mention leave you exposed to possible spam traps. So don’t forget the other options available—telephone, postal mail, mobile, retargeted display advertising, social media, your website — and add them to the mix to broaden your reach and keep your customers interested in your messaging. If your customer records are incomplete, ask your database marketing partner to append additional elements to allow communications through these other channels.

5. Use Proven Offers

Once you’ve determined that the inactivity is not a customer service problem, then the essential tool for reactivation is a motivational offer. Discounts are widely used by marketers today—because they work. But consider additional offers that have proven to be effective in reactivation marketing, such as:

Does Your Content Work? Advanced KPIs for Google Analytics

You spend tons of time making sure every word in your blog posts is perfect, but are you measuring the performance of these posts effectively?

Google Cabinet MCheck out even more about personalization and artificial intelligence with FUSE Enterprise.

You spend tons of time making sure every word in your blog posts is perfect, but are you measuring the performance of these posts effectively?

Whether you’re a Google Analytics magician or a certified beginner, GA is integral when evaluating the performance of any website (including small blogs).

For many just starting out in Google Analytics, digging through your plethora of data to unearth actionable insights is no small feat. To save your soul (and your time!) this post will walk you through how to create my go-to advanced segment: Engagement/Post.

This GA segment is simple, quick and applicable to any type of blog or business with content-focused KPIs.

Without further ado, here’s how you can take advantage of the unique segment I created to measure user engagement on my blog.

Kia blog post GA segment

What Is Google Analytics Advanced Segmentation?

Google Analytics Advanced Segments isolate specific types of traffic within your reporting views for deeper analysis. Segments essentially allow you to view GA data that follows your specified criteria. There are five ways to customize segments; by:

  • Demographics
  • Technology
  • User Behavior
  • Date of First Visit
  • Traffic Source

In addition to this list, GA provides the ability to program your parameters with conditions and/or sequences under the “Advanced” tab within the segment editor. This gives you the added flexibility of setting multiple conditions (which we’ll explore later) for your segments.

Using Advanced and Custom Segments in Google Analytics

In any view, segments can be found at the top of the screen underneath the header that contains the report’s name, your selected date range and the options for sharing. To remove/edit/share segments, toggle its settings by clicking the arrow next to each box.

kia blog post GA view

GA offers pre-set segments, such as:

  • Converters
  • Non-converters
  • Direct Traffic
  • Mobile Traffic
  • Etc.

Take a look at these later on, if you’re interested in using Google’s system segments.

Creating the ‘Engagement/Post’ Segment

My go-to segment, Engagement/Post, is unique because it gives you a refined look at the performance of specific content rather than an overall peek at website traffic.

Kia blog post Engagement/Post segment
Here’s how I define Engagement when creating the Engagement/Post segment

Next, you’ll create a condition that excludes traffic from your categorical website pages (example: home page, about us, etc.). Because it’s super-important to analyze these separately. This is because user intent and behavior varies, depending on where they are on your site. Bundling all activity without distinguishing between the pages that matter most is a sure-fire way to fudge up a good GA analysis.

Of course, this can all be done in a variety of ways without using advanced segments (think: filters, views, content groupings, etc.). But segmentation in GA is a foolproof way of validating this type of traffic data without getting your hands too dirty.

Kia blog post Engagement segment detail
Your GA segment should look something like this now

This advanced segment will allow you to better understand which content drives the most engaged users on your site. Compare it against other segments for best results.

Explore the Solutions Gallery

Kia blog post GA gallery

The GA Solutions Gallery is for those interested in importing dashboards, custom reports and segments into their own GA accounts. Essentially, this platform serves as a forum for sharing user-generated GA solutions.

The Solutions Gallery is perfect for beginners, because there aren’t any major commitments or heavy setup involved with importing. For pros, check out the GA Solutions Gallery if you’re looking for specific, detailed segments that align with common KPIs.

Feeling lucky? Upload or create your own solution to share publicly for reuse in the gallery.

Recommended Dashboards for Content Marketers

  • The Content Analysis Dashboard provides you with insights that help evaluate the efficiency of your content. The dashboard widgets show the pages that are underperforming or overperforming so you can adjust your strategy accordingly.
  • The Engagement and Loyalty Dashboard helps you analyze traffic growth over time to improve loyalty and engagement with your content.

You can also create your own dashboard in Google Analytics under the “Customization” tab. The tab is great for everyday GA users who wish to make shortcuts, craft custom reports and receive alerts.

Marketers, what’s your opinion on my Engagement/Post segment? Did you implement it, or did you find another segment that matched your needs in the Solutions Gallery?

Learn even more about the convergence of technology and branded content at the FUSE Enterprise summit. Artificial intelligence and personalization will be featured among many other techniques and technologies.

Drive Your Buyer’s Lifecycle, Increase Revenue and Retention

The process of acquiring and sifting traffic into engaged, and ultimately buying, prospects is critical to your customer acquisition efforts. Managing your audience is often referred to as the early stage of the “Customer Journey.” In this post, we’ll focus on the core and most pivotal part of your relationship

The process of acquiring and sifting traffic into engaged, and ultimately buying, prospects is critical to your customer acquisition efforts. Managing your audience is often referred to as the early stage of the “Customer Journey.” In this post, we’ll focus on the core and most pivotal part of your relationship with the consumer — purchasing from your brand.

Based on some years of experimentation and measurement, we can share a simplified and highly actionable approach that can make a difference in how you value and grow value among customers. This is the buyer lifecycle.

Mike Ferranti chartProspects: Before They Are Customers
Prospects, of course, come from many places: word of mouth and direct visits to your website and to your retail stores. Advertising and search drives them to on- and off-line points of sale. Prospects can be those who simply signed up on that ever-larger email signup popup on your homepage, or those who put items in a cart and “almost” purchased, but abandoned.

But prospects can also be those who we leverage statistical intelligence to hand-pick. Not just look-alikes but the “buy-alike” prospects with the highest potential value. See my prior column called “The Most Important CRM Metric You Might Be Missing.”

All of these prospects have the same thing in common, they have not purchased, and a level of investment and communications will be required to drive them to the next step. This cannot be overlooked without consequence. Prospects, regardless of the level of engagement or targeting, have a massive, and in some cases, a predictable difference from the buyers you seek to drive incremental sales from — they lack the most powerful signal of all behaviors — actually spending with your brand. Commonsensical enough, perhaps — but the prospect ‘batch and blast’ marketing that pervades retail emailers typically makes the challenge harder. Customer Intelligence is required to target, learn and test your way into viable prospect conversion strategies. We reiterate this point as it is often assumed that prospects, when contacted, will just buy — and they don’t. The bar is higher (see “Bigger is Better: How to Scale Up Customer Acquisition Smarter” for how to target the right customers, and the sophistication your competitors may be leveraging already).

To be sure, an analysis of your prospect base, which in a great many organizations is actually called the “email file” — another issue, in itself ― will help you determine who is likely to buy and who is not. This can be achieved by considering engagement measures, like opening and clicking your emails, visiting the website and micro-conversions. While these behaviors are correlated with the move from prospect to buying, it is not uncommon for the “average” prospect files to contain too many records of individuals who will never buy — they are lookers, not buyers. They may lack the means, intent or occasion to buy — or they may have experienced some change in their lifestage that moved them out of the market for your product. The opportunity is in identifying the highest value prospects and investing more thoughtfully in converting them.

5 Data-Driven Marketing Catalysts for 2016 Growth

The new year tends to bring renewal, the promise of doing something new, better and smarter. I get a lot of calls looking for ideas and strategies to help improve the focus and performance of marketers’ plans and businesses. What most organizations are looking for is one or more actionable catalysts in their business.

The new year tends to bring renewal and the promise of doing something new, better and smarter. I get a lot of calls looking for ideas and strategies to help improve the focus and performance of marketers’ plans and businesses. What most organizations are looking for is one or more actionable marketing catalysts in their business.

To help you accelerate your thinking, here is a list of those catalysts that have something for everyone, some of which can be great food for thought as you tighten up plans. This year, you will do well if you resolve to do the following five things:

  • Build a Scalable Prospect Database Program. Achieving scale in your business is perhaps the greatest challenge we face as marketers. Those who achieve scale on their watch are the most sought-after marketing pros in their industries — because customer acquisition is far from cheap and competition grows more fiercely as the customer grows more demanding and promiscuous. A scientifically designed “Prospect Database Program” is one of the most effective ways great direct marketers can achieve scale — though not all prospecting databases and solutions are created equally.

A great prospecting database program requires creating a statistical advantage in targeting individuals who don’t already know your brand, or don’t already buy your brand. That advantage is critical if the program is to become cost-effective. Marketers who have engaged in structured prospecting know how challenging it is.

A prospect database program uses data about your very best existing customers: What they bought, when, how much and at what frequency. And it connects that transaction data to oceans of other data about those individuals. That data is then used to test which variables are, in fact, more predictive. They will come back in three categories: Those you might have “guessed” or “known,” those you guessed but proved less predictive than you might have thought, and those that are simply not predictive for your customer.

Repeated culling of that target is done through various statistical methods. What we’re left with is a target where we can begin to predict what the range of response looks like before we start. As the marketer, you can be more aggressive or conservative in the final target definition and have a good sense as to how well it will convert prospects in the target to new customers. This has a powerful effect on your ability to intelligently invest in customer acquisition, and is very effective — when done well — at achieving scale.

  • Methodically ID Your VIPs — and VVIPs to Distinguish Your ‘Gold’ Customers. It doesn’t matter what business you are in. Every business has “Gold” Customers — a surprisingly small percentage of customers that generate up to 80 percent of your revenue and profit.

With a smarter marketing database, you can easily identify these customers who are so crucial to your business. Once you have them, you can develop programs to retain and delight them. Here’s the “trick” though — don’t just personalize the website and emails to them. Don’t give them a nominally better offer. Instead, invest resources that you simply cannot afford to spend on all of your customers. When the level of investment in this special group begins to raise an eyebrow, you know for certain you are distinguishing that group, and wedding them to your brand.

Higher profits come from leveraging this target to retain the best customers, and motivating higher potential customers who aren’t “Gold” Customers yet to move up to higher “status” levels. A smart marketing database can make this actionable. One strategy we use is not only IDing the VIPs, but the VVIP’s (very, very important customers). Think about it, how would you feel being told you’re a “VVIP” by a brand that matters to you? You are now special to the brand — and customers who feel special tend not to shop with many other brands — a phenomenon also known as loyalty. So if you’d like more revenues from more loyal customers, resolve to use your data to ID which customers are worth investing in a more loyal relationship.

  • Target Customers Based on Their Next Most Likely Purchase. What if you knew when your customer was most likely to buy again? To determine the next most likely purchase, an analytics-optimized database is used to determine when customers in each segment usually buy and how often.

Once we have that purchase pattern calculated, we can ID customers who are not buying when the others who have acted (bought) similarly are buying. It is worth noting, there is a more strategic opportunity here to focus on these customers; as when they “miss” a purchase, this is usually because they are spending with a competitor. “Next Most Likely Purchase” models help you to target that spending before it’s “too late.”

The approach requires building a model that is statistically validated and then tested. Once that’s done, we have a capability that is consistently very powerful.

  • Target Customers Based on Their Next Most Likely Product or Category. We can determine the product a customer is most likely to buy “next.” An analytics-ready marketing database (not the same as a CRM or IT warehouse/database) is used to zero-in on the customers who bought a specific product or, more often, in a specific category or subcategory, by segment.

Similar to the “Next Most Likely Purchase” models, these models are used to find “gaps” in what was bought, as like-consumers tend to behave similarly when viewed in large enough numbers. When there is one of these gaps, it’s often because they bought the product from a competitor, or found an acceptable substitute — trading either up or down. When you target based upon what they are likely to buy at the right time, you can materially increase conversion across all consumers in your database.

  • Develop or Improve Your Customer Segmentation. Smart direct marketing database software is required to store all of the information and be able to support queries and actions that it will take to improve segmentation.

This is an important point, as databases tend to be purpose-specific. That is, a CRM database might be well-suited for individual communications and maintaining notes and histories about individual customers, but it’s probably not designed to perform the kind of queries required, or structure your data to do statistical target definition that is needed in effectively acquiring large numbers of new customers.

Successful segmentation must be done in a manner that helps you both understand your existing customers and their behaviors, lifestyles and most basic make up — and be able to help you acquire net-new customers, at scale. Success, of course, comes from creating useful segments, and developing customer marketing strategies for each segment.

No One Is One-Dimensional

If anyone says to your face “You’re one-dimensional,” you would be rightfully offended by such statement. It would almost sound like “You are so simple that I just figured you out.”

If anyone says to your face “You’re one-dimensional,” you would be rightfully offended by such statement. It would almost sound like “You are so simple that I just figured you out.” Along with that line of thinking, you should be mad at most marketers, as they treat consumers as one-dimensional subjects. Even advanced marketers who claim that they pursue personalized marketing routinely treat customers as if they belong to “1” segment along with millions of other people. Sort of like drones with similar characteristics. Some may title such segments with other names, like “clusters” or “cohorts.” But no matter. That is how personalization works most times, and that is why most consumers are not impressed with so-called personalized messages.

Here is how segments are built through cluster analysis. Unlike regression models, clusters are built without clear “target” (or dependent) variables (refer to “Data Deep Dive: The Art of Targeting”). Considering all available variables, statisticians group the universe with commonly shared characteristics. A common analogy is that they throw spaghetti noodles on the wall, and see which ones stick together. Analysts can control the number of segments and closeness (or “stickiness”) of resultant groups. I have seen major banks grouping their customers into six to seven major segments. Most commercial clustering products by data compilers maintain 50 to 60 segments or cohorts (I am not going to name names here, but I am sure you have heard of most of them). I was personally involved in a project where we divided every town in the U.S. into 108 distinctive clusters using consumer, business and geo-demographic variables. The number of segments may vary greatly, depending on the purpose.

Once distinctive segments are created through a mathematical process, then the real fun begins. The creators get to describe characteristics of each segment in plain English, and group smaller segments into higher-level “super” clusters. Some creative companies name each cluster with whimsical titles or dominant first names of each cluster (for copyright reasons, I wouldn’t use actual names, but again, I’m sure marketers have heard about them). To identify dominating characteristics of people within each cluster, analysts use various measurements to compare them against the whole universe. For instance, if a cluster shows an above-average index of post-college graduates, then they may call it “highly educated.” If analysts see a high index-value of luxury car owners, then they may label the whole cluster with some luxurious-sounding name.

Segmentation is an age-old technique and, of course, it still has its place in marketing. Let me make it clear that using segments for target marketing is much better than not using anything at all. It also provides a common language among various players in marketing, binding clients and vendors together. Marketing agencies, who cannot realistically create an unlimited number of copies, may prepare a set number of creatives for major segments that their clients are targeting. With descriptions of segments in front of them, copywriters may write as if they are talking to the target directly. Surely, writing copy for a “Family-oriented young couple with dual income” would be easier than doing so for some anonymous target.

However, the trouble begins when marketers start using such a “descriptive” tool for targeting purposes. Just because there is a higher-than-average index value of a certain characteristic in a segment, is it justified to treat thousands, or sometimes millions, of people in the target group the same way? Surely, not everyone in the “luxury” segment is about luxury automobiles or vacations. It is just that the cluster that someone happened to have belonged through some statistical process has a higher-than-average concentration of such folks.

Then how do we overcome such shortcomings of a popular method? I suggest we reverse the way we look at the behavioral indices completely. The traditional method defines the clusters first, and then the analysts put descriptions looking at various behavioral and demographic indices. For promotions for specific products or services, they may examine more than 50, sometimes more than a few hundred index values. Only to label everyone in a segment the same way.

Instead, for targeting and personalization, marketers should commission independent models for every type of behavioral or demographic characteristic that may matter for their campaigns. So, instead of using one “luxury segment,” we should build multiple models. For example, for a travel industry like airlines or cruise lines, we may consider the following series of model-based “personas”:

  • Foreign vacationers
  • Luxury vacationers
  • Frequent business travelers
  • Frequent flyers
  • Budget-conscious travelers
  • Family vacationers
  • Travelers with young children
  • Frequent theme park visitors
  • Bargain-seekers
  • Adventure-seekers
  • Wine enthusiasts
  • Gourmets
  • Brand-loyal travelers
  • Point collectors
  • etc.

This way, we can describe “everyone” in the target universe in a multi-dimensional way. Surely, not everyone is about everything. That is why we need a system under which one person may score high in multiple categories at the same time. We all have tendencies to be bargain seekers, but everyone has a different threshold for it (i.e., what length of trouble would you go through for a 10 percent discount?). If you have multiple descriptors for everybody, you can find the most dominant characteristics for one person at a time. Yes, one may have high scores in “luxury vacationers,” “frequent flyers” and “frequent business travelers” models, but which characteristic has the highest score for “him”?

Imagine having assigned scores for these “personas” for everyone. I may score nine out of nine in “frequent flyer” (and that is for certain, as I am writing this on a plane again), score six out of nine in “luxury vacation,” and score two out of nine in “family vacationers” (as my kids are not young anymore). If you have one chance to show me something that resonates with me this second, what would be the offer? Even a machine can decide the outcome with a scoring system like this. Now imagine doing it for millions of people, all customized.

Last month, I wrote that personalization is not an option anymore, and further, marketers should aspire to personalize their messages for most people, most times, through all channels, instead of personalizing only for some people sometimes through some channels (refer to “Road to Personalization”). Because “personas” based on statistical models will not have any missing values, we can achieve that ambitious goal with this technique.

With new modeling techniques and software, this is just a matter of commitment now. We are not operating in the 80s anymore, and it is time to move ahead from simple segmentation methods. Yes, using segments would be much better than no targeting at all. But with a few more tweaks, we can build more than 20 personas in the same time that we would spend for developing segments using a clustering technique, which isn’t exactly cheap even nowadays.

Another downside of a clustering technique is that, once the statistical work is done, it is very difficult to update the formula without changing existing marketing schemas. By nature, segments are very static. It is no secret that even some data compilers chose to stay with old models, as they are afraid of creating inconsistencies with newly updated ones. Some are more than a decade old.

Conversely, it is very easy to update personas, as it is not much different from refitting the models one at a time. And we don’t have to update the whole series every time, either. Just watch out for the ones that do not validate very well over time. With real machine learning techniques around the corner, we can even consider automating the whole process, from model update to deployment of messages through every channel.

The hard part would be imagining the categories of personas, but I suggest starting small with essential categories, and then keep building upon them. Surely, teenage apparel companies would have a very different list than business service companies that sell their services to other businesses. Start with obvious ones, like bargain seekers, high-value customers and specific key product targets.

Connecting personas to actual creatives will require some work in the beginning, too. However, if you plan the categories with set creatives in mind from the get-go, it won’t be so difficult. Again, start small and see how it goes, along with some A/B testing. Ten categories will be plenty for many businesses. But having more than 100 personas won’t take up much space in supporting databases, either. Once the system gets stable, marketers can automate much of the process, as most commercial software can take these personas like any other raw variable.

So, if your marketing team is committed enough to have purchased personalization engines for various channels, get out of the old segmentation method and consider building model-based personas. After all, no one is one-dimensional, and everyone deserves personalized offers and messages in this day of abundant data and machine power. This is not 1984 anymore.

Email Segementation: Make Your List More Than the Sum of Its Parts

Segmentation is also one of the most powerful and often under-utilized features of email automation applications. Though automation makes the process simpler, many marketers are put off by overhead in the form of upfront work required to develop and deploy rules and testing scenarios that result in more effective targeting and conversion. Should they bother?

Segmentation is the process of grouping names within your list into like interests, position in the buying cycle, demographics or other criteria relevant to your business.

Segmentation is also one of the most powerful and often under-utilized features of email automation applications. Though automation makes the process simpler, many marketers are put off by overhead in the form of upfront work required to develop and deploy rules and testing scenarios that result in more effective targeting and conversion. Should they bother?

Simply put: The answer is a resounding yes.

Using forms and engagement tracking, marketers can collect more information than ever before, and advanced data collection—progressive profiling—lowers form abandonment while acquiring new data through the querying of only data that has not yet been collected. When forms alone are not enough, email messages can be designed to A/B or multivariate test whole groups in order to garner specificity that leads to segmentation.

Segmenting lists using all of this type of data means you can selectively choose your most active (or profitable) groups, deselect the inactive, and develop campaigns designed to specifically reengage those who still hold promise. Data combined with automation means we benefit from better conversions and our prospects and leads benefit from messages in which they are truly interested. Targeted emails translate to better ROI in virtually every study.

Not only does segmentation make money through higher conversions, it saves money, too. When audiences are not separated into segments and are sent generic messages, open rates are lower. According to a study from MarketingSherpa, segmented emails get 50 percent more clicks than their untargeted counterparts.

Despite all the benefits of segmentation, not all marketers are onboard. For instance, Experian found that even though targeted email campaigns have a 40 percent higher open rate, 80 percent of marketers email the same content to an entire group.

Are businesses and marketers overcomplicating the process? Segmentation can be as simple or as complex as fits your needs, but customizing the process and making it unique to your business can give you the edge over competitors.

6 Steps to Segmentation

  1. Set a quantifiable and measurable goal for your campaign.
  2. Ensure your list contains enough names that it will still result in meaningful data, even after segmentation.
  3. Create segments using any data important to your business, such as: behavior, demographics, position in the sales funnel, and so on.
  4. Identify the most valuable segments—those that present the greatest opportunities.
  5. Create targeted messaging specifically designed to engage each segment.
  6. Track and measure results.
  7. When you treat new and current subscribers in the same manner and send them the same messages, you are missing one of the most important ways to nurture your lead to purchase. Segmentation can be as simple as dividing your list into new and current leads, but other ideas include:
  • Age
  • Gender
  • Marital status
  • Income
  • Occupation
  • Education
  • Presence of children
  • Owner vs. renter
  • Length of residence
  • Lifestyle segmentation
  • Past purchase
  • Last visit to website
  • Pages visited at website
  • Resources downloaded

Explicit data are demographics such as company size, industry segment, job title and geographic location.

Implicit data are the recipient’s actions or interactions, such as those who open, click, download a resource, watch a video, visit your website, share your content, and so on.

For some businesses, even though they have a large list, the list does not contain enough data to enable meaningful segments—but all is not lost. Many companies provide list-append services that allow you to add data to your current list by matching on a unique bit of data you do have, such as the email address.

Another segmentation idea is to identify those within your list who are returning customers and those with the highest value order. These two groups are generally the most valuable to your company and therefore warrant especially targeted messaging and hand-holding.

Segments can even be divided further into sub-segments, and those sub-segments divided again, and so on. However, creating relevant content for each segment is not without effort, so it’s best to not subdivide your list to the point where there are not enough names in the sub-segment to justify the work required.

With segmentation; you can greatly improve message relevance; set up better A/B and multivariate testing; target your audience with subject lines, designs, and images that resonate with the individual; and acquire higher click-thru and sales rates.

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

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

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

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

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

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

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

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

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

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

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.