Should You Really Bother With Personalization?

If you weren’t a marketing professional, you’d probably find it hard to believe that there is a debate of sorts in rather large organizations about personalization in marketing. In many cases, it’s less of a debate than an absence of one — or serious consideration, or a plan to get there.

Database & CRMIf you weren’t a marketing professional, you’d probably find it hard to believe that there is a debate of sorts in rather large organizations about personalization in marketing. In many cases, it’s less of a debate than an absence of one — or serious consideration, or a plan to get there.

You’d probably say or think something such as, “It’s common sense … why wouldn’t they do it?!”

Yes, the great “unwashed” have high expectations of the brands that wish to share in their discretionary income.

When IBM’s Watson can carry on a conversation of sorts with Bob Dylan in a TV commercial, and beat a grandmaster in chess, why can’t it send a postcard reminding you to get your oil changed?

These all sound like reasonable expectations to the “Valued Customer,” as we’re referred to by the non-personalized brands we have all engaged with. Don’t they seem reasonable?

For the longest time, Amazon stopped sending “people who bought X also bought Y” emails. Granted, Amazon’s personalization is part of it’s “all of the above” strategy — the company literally invests in everything it sees value in.

Moving Beyond the Basics

Yet most organizations have limited dollars and are fixated on “covering the basics” in their outbound communication. As a result, personalization has been limited in the vast majority of cases. The low costs and high performance of emailing have kept many brands from investing in higher-value touches with consumers — even as the CRM waves hit ever higher crests.

Why? Personalization alone doesn’t add enough value. And the reason is that personalization without relevancy doesn’t work. The basics of personalization in email marketing have been around for years. The consumer is accustomed to it and, in some cases, may expect it.

Relevancy, however, is harder to come by.

“Thank you for your purchase, Mike” works. “Mike, to get the best performance from your new time-trial bike, try using Rock N Roll Gold oil to protect your chain from rust and dirt.” works better.

In order to accomplish this level of personalization and relevancy, you’d need to know a few things. You would need the dexterity with this data to get it into your communication easily. If you know I’ve purchased a time-trial bike, then you need to know about bicycles. This could be challenging for mass/big box retailers like Amazon or Jet. But consider that Amazon is already providing “video shorts” on the categories you spend time in — and obviously, the ideas in those videos fit perfectly with their inventory.

Relevancy can also mean things that personalization isn’t often used for. More often than not, personalization still means “insert variable here.” On the other hand, relevancy can mean very important things that shape and influence a customer relationship … like recognition.

Simple and Powerfully Effective

I’ve found that a simple “thank you” message to the most valuable customers a brand has, thanking them for being loyal, and choosing them ― even without an offer ― generated incremental sales upon open and in the following several days.

Think it doesn’t pay to show the customers you know them and appreciate them? Think that’s not relevant? Think again.

Yet, when was the last time you had any brand thank you? Brands I’ve spent tens of thousands of dollars with have provided not-so-much as an email’s-worth of recognition.

How about the “profitability problem” that comes with one-time buyers? Does your favorite brand thank you when you make that all-important first repeat purchase? These individuals are categorically more profitable, and materially more likely than “one-and-done” buyers to buy the brand again and again. A little recognition establishes the context of their relationship — and the fact that they even have one with the brand … goes a long way.

Personalization can be easy without being valuable to the customer. Relevancy can be more challenging, especially if you don’t have your data house in order. Relevancy requires a strategy — but relevancy works, big-time.

Putting Relevancy and Personalization Into Action

Some examples of relevancy in action …

  • Announcing a Sale in a category consumers bought in previously, when they “missed” an expected purchase
  • Social Proof — don’t just say it’s the best product, show them how many stars it was ranked and by how many people. Show them a review from someone most like them. This is more doable than many brands realize — and the “content” is simply … free.
  • Localize — Instead of promoting product, promote Gene Smith in the golfing department (to folks who bought golf equipment). Not only will the employee morale increase, your customers can be nudged into multichannel buying. Think that doesn’t make a difference? Multichannel buyers spend more and spend more often in virtually every category.
  • Product Recommendations are “old news” — but they still work. Don’t leave this out. But combine it with social proof and watch conversion climb.

Personalization is important, but not without building relevancy to an ever-greater level. Consider the simple fact that the less relevant your communications are with your customer, the less they’ll find your brand relevant … and irrelevancy is where brands go to wither and die.

The Bottom Line: Relevancy Is the Value in Personalization

The upside to personalizing can be real. While blanket, low-grade personalization may have become passé, an authentic dialog based on relevancy is an investable business strategy. This requires having your data, your strategy and your knowledge of the customer, and the numerous cohorts that undoubtedly exist in every business.

The bottom line is simple: Personalization without a workable strategy may not be a good business value, and therefore may not be warranted.

Delivering relevancy to your customer experience, however, is priceless.


Great Businesses Are Built on Great Customers

Most business leaders want to run an enterprise that is truly “great.” Some are satisfied with running a “good” business, and in many circumstances there is nothing wrong with that.

CEOMost business leaders want to run an enterprise that is truly “great.” Some are satisfied with running a “good” business, and in many circumstances there is nothing wrong with that.

Over the long haul however, there’s a problem with just being “good.” As Jim Collins will tell you, businesses over time break one way or the other. Eventually, the few become great, and the rest will ultimately — go away.

Consider the fact that of the largest and most venerable businesses in the Fortune 500 in 1955, about 90 percent don’t even exist anymore. For most executives, this is a sobering reality worth consideration and reflection.

“… of the largest and most venerable businesses in the Fortune 500 … about 90 percent don’t even exist anymore …”

Here are just a few iconic brands that today seem like dinosaurs — or perhaps more appropriately, the fossil records of dinosaurs:

  • American Motors
  • Brown Shoe
  • Studebaker
  • Collins Radio
  • Detroit Steel
  • Zenith Electronics
  • National Sugar Refining

All are Fortune 500 businesses — the biggest most powerful brands — and none of which exist today.

There are a number of reasons companies become great. They do one thing — very well. They are relentlessly focused on the customer. Or, as Warren Buffet says, “on delighting the customer.” They face the harshest unvarnished current realities and take action to change them. They have great people who can deliver on a great vision. Many of these points are well-developed in Jim Collins’ seminal work “Good to Great.

On “delighting customers,” there is another perspective — as evidence suggests that is great companies don’t only delight their customers, but also have great customers.

This has traditionally been couched as “knowing the customer,” or being driven to “service the customer.” Yet our experience shows there is another crucial dimension — knowing the customer also means knowing who the customer is and is not.

In our work with dozens of brands spanning two decades, we’ve consistently found that almost all brands are carried by a surprisingly small number of customers; usually between 10 percent and 25 percent that generate the vast majority of revenue and profit — quite literally up to 75 percent.

‘Great Customers’ Have Fringe Benefits

So while “great customers,” it seems, can carry good companies, great companies have identified a product or service that deeply satisfies and, therefore, attracts a materially larger proportion of “great customers.”

That satisfaction can be best measured effectively through simple approaches like Net Promoter Score, where a brand simply asks “how likely are you to recommend us to a friend or colleague?”

Research has illustrated that evangelistic customers are a deep well of profit for a brand, as they attract those like them through referral. Yet many growing brands struggle to acquire customers cost-effectively and at-scale. Customer value/quality, which we might consider a proxy for a great “customer fit,” is second-string to gross revenue. How can a brand decide to focus on customer auality when gross revenue is the No. 1 requirement?

The answer, as it stands, is fairly simple.

The right customers for any brand are the ones who exhibit behaviors that truly value the brand, its unique value proposition, products and services. These are customers who bring not only the much-needed gross revenue, but the profits generally reserved for truly great companies. That affinity is ultimately expressed through trial, repeat purchase, higher order size and referrals from other customers who are bound to spend similarly.

While it’s evident that these behaviors are correlated highly with great products, customer service, pricing and distribution — the unique value proposition is defined in the eye of that customer — which can be objectively defined, targeted, acquired and grown.

Strategic Implications of Adding Higher-Value Customers

The implications of achieving high-value customer growth are much more than adding good customers alone. Growing the depth and breadth of high-value customers is a requirement in making a company a fundamentally superior business to its competitors and peers.

Consider the following chart where an organization’s “right customer” (AKA, Most Valuable Buyers) is acquired scientifically, rather than acquiring customers en masse with minimal consideration — or without consideration of the quality of the customer in the first place.

That is to say that the customers organizations are adding are disproportionately more valuable than the “average” customer in their customer base at the start of the period.

Graphic for Mike Ferranti's post

In this real-field, proven example, a “Most Valuable Buyer (MVB)”-targeted campaign produced customers with 420 percent, or 4.2x, the revenue than that of the average buyer in their customer base. While this surely is impressive, there is a strategic implication beyond the high return on high-quality customer acquisition.

As the volume of high-value customers increases, the percentage of “MVBs” in the customer base continues to grow. The rate at which their sales and profit volume grows is faster than the rate of customer growth alone.

Not Just Profitability, Profit Volume

The key to growth and longevity is developing a competitive advantage that is not easily replicated in the marketplace. The secret is not to cut costs as many companies often do in the short-term; rather, it is to grow profit volume. In the example above, we can see the dramatic scaling of profits as the percentage of high-value customers, or “MVBs,” increases. As the brand continues to acquire higher value and higher-performing customers, the total profit volume grows, as well.

How do you grow profit volume? The formula is simple. Find the “right customers” who are high-value for your business and continue to get more of them at-scale. Those customers will be unique to you and distinct from your competitors. Over time, this formula becomes a sustainable, competitive advantage for your business. At that point, your business moves from an average, or “good company,” to the profit profile of a “great” company with “great customers.”

Like Jim Collins concluded many years ago, “Those that become great stay, and those who don’t — go away.”

Blockchain Is Eating Commerce

Blockchain is a technology that has the potential to become a disruptive force in the ever-more digital economy. Its potential value — coupled with friends, clients and business partners asking about it — led me to publish this outline and answers to many of the questions I’ve been fielding. It’s something every Data Athlete will want to understand.

BlockchainYou may not be familiar with blockchain. Many “in-the-know” digital folks aren’t terribly familiar with blockchain; what it is, or how it works. I was surprised by how few were.

Blockchain is a technology that has the potential to become a disruptive force in the ever-more digital economy. Its potential value — coupled with friends, clients and business partners asking about it — led me to publish this outline and answers to many of the questions I’ve been fielding. It’s something every Data Athlete will want to understand.

Blockchain Starts With Bitcoin

Blockchain is essentially a distributed database, which means it’s like the database you know that lives on your server or in the cloud — except that it’s spread copies of itself around the Internet or network. A distributed database has the benefits of fault tolerance and transparency — more than one “node” on the network has a copy of the data. Blockchain also utilizes strong cryptography that prevents changes to the transactions content — they are permanent.

These characteristics were developed to support the exchange of Bitcoin, the now famous crypto-currency that is being used worldwide to facilitate a myriad of transactions.

Bitcoin is said to concern banking institutions and governments alike — as its decentralized nature means no one nation owns or controls it. Bitcoin and its underlying Blockchain are like the “MP3 of currency” in the early ’90s. summarizes the power of its decentralization:

“Sending Bitcoins across borders is as easy as sending them across the street. There are no banks to make you wait three business days, no extra fees for making an international transfer, and no special limitatons on the minimum or maximum amount you can send.”

So in order for Bitcoin to be a “free” and universal currency, it could not be centrally managed or controlled; hence, blockchain was created first — Bitcoin actually started the following year.

Furthermore, each and every Bitcoin has a copy of every transaction/exchange it was ever involved in. All of the data on that chain is distributed to every blockchain-distributed journal (or database) across the Web.

What Is Blockchain Used for Today?

Blockchain’s most prevalent usage is in Bitcoin. But remember, it’s an encrypted, distributed database. And so, blockchain technology also securely moves and stores host money, titles, deeds, music, art, scientific discoveries, intellectual property and even votes.

As a (distributed) database that is as open, borderless and secure, blockchain continues to find new uses, and has been adopted by every major technology company. IBM, for example, made an early investment in blockchain technology and IBM Blockchain.

“Blockchain technology also securely moves and stores host money, titles, deeds, music, art, scientific discoveries, intellectual property and even votes.”

Blockchain 2.0 — Triggered, Programmatic Transactions

Blockchain 2.0 is the rapid evolution of blockchain, and where blockchain offers the potential for transformation of the way we engage in commerce and business at an Internet scale.

Remember, blockchain is a distributed, cryptographically secured database. It makes sense that an evolution would allow programming code, or chain code, to manipulate the transactions in a blockchain — and that’s exactly what has happened.

In one example, SAP is using blockchain software to let patients share electronic medical records with doctors or drug makers for a specific time period, such as during medical care or a study.

In another example, they designed a system for farmers’ weather insurance. It pulls rainfall data from sensors in the field, then automatically informs insurers if there’s a drought that would trigger a payout.

Smart Attribution Modeling

Depending on the size and scope of your advertising and marketing spend, you may have spent time and effort thinking about attribution modeling. Different organizations have very different approaches to attribution.

analyticsDepending on the size and scope of your advertising and marketing spend, you may have spent time and effort thinking about attribution modeling. Different organizations have very different approaches to attribution.

To this end, developing a valuable attribution model that serves your goals and your business can take many forms. Herein, I’ve put together some criteria that’ve been used effectively by a number of organizations we’ve worked with to inform decision-making and use of attribution methods and models.

First Things First: Determine Your End

The most important questions senior marketers need to ask going into an attribution initiative, at any level of investment, include:

  • “What is the purpose for attributing (estimating) media value?” You may be surprised how often that answer is ill-defined. Make sure you can answer, in simple business outcome terms, what the purpose of your attribution is. All else fails if this step is missed.
  • “How logical, defensible and credible is a potential attribution methodology?” While attribution, by its nature, is rarely deterministic, it is requisite that a methodology is credible and has robust basis, or a raison d’etre, if you will, if it is to add value. The understanding individuals often develop is an appreciation that the assumptions underpinning any attribution strategy are tenants of the strategy itself.

The right answers for any brand depend on keeping the end in mind and knowing the expected outcome. So the logical starting point is defining your purpose for attributing media value, as described in that context. For example, “to get the best ROI from our advertising investments.”

3 Strategic Attribution Model Levers

In the spirit of keeping it simple, we think in terms of three strategic attribution levers that an organization can benefit from. These strategic levers are used to inform both the attribution model selection and the weighting of channels. They are as follows:

  • Engagement: Measures a customer’s depth of interaction and potentially, the relationship with the brand.
  • Recency: The amount of time lapsed since the last touch. For example, all other things being equal, a touch yesterday is more valuable than a touch 45 days prior.
  • Intent: Identifies a need the user has or information the user is seeking. Intent is specifically valuable in search, and sometimes in social media. Lead generation programs demonstrate intent, as well. The point of considering “intent” is that it prequalifies traffic in a meaningful way. If the consumer exhibits intent-driven behavior — that should be weighted heavier in your attribution thought process.

While the decision to “attribute” always means judgment is incorporated, the credibility of the attribution is higher when media touches are evaluated within the three strategic levers and should always be based on the nature of the interaction — or lack thereof. If a user did not engage with an ad, then the amount of interaction is lower or even zero.

The following chart breaks out major channels and how you might evaluate each of the strategic levers discussed above.

Ferranti display ad chart

Ferranti display ad chart part two

The ‘Bonus’ Lever: Measurability

Measurability is the “fourth” strategic lever, and can be considered optional for very large brands utilizing traditional non-digital channels extensively. A channel that has evidence associated with its performance is one that can be weighted accordingly. When a channel is measurable, the weighting in the attribution model can be scaled to leverage the predictability of that channel; thereby, improving the efficacy of the attribution. It is a reality that some channels however, will have hard measures, while others require more assumptions and inferences.

Brands should give thoughtful consideration to not inadvertently “reward” a channel, simply because it is hard to measure — and, by the same token, not unnecessarily punish them, either.

Over- or under-weighting channels that have weak evidence of conversion value can actually reduce the performance of the overall media mix.

Viewability and Display-Weighting

While reach, frequency and targeting are hallmarks of display advertising, it has the widely known challenge of “viewability.” Viewability is when an ad is served (and paid for) but a consumer does not see it.

When the objective is to improve the ROI of the media mix, ads that are never seen (un-viewable) should be accounted for in the attributed value of the channel.

One way marketers simplify account viewability concerns is by deducting the percentage of ads that can never be seen on a percentage basis when weighting online display in the model. Bear in mind, “viewable” generally means that only part of the ad was viewable for 1 second. Specific viewability metrics should be discussed and negotiated with media outlets or networks you work with.

How Much Is Viewable or Unviewable?

A recent study done by Google identifies that many display ads are never viewed; therefore, the weighting of display ads should consider this reality (opens as a PDF).

Here are some of the issues with viewability that should influence the weighting of display.

  • 1 percent of all impressions measured are not seen, but the average publisher viewability is 50.2 percent.
  • The most viewable ad sizes are vertical units. Above the fold is not always viewable … Worth considering when weighting display.
  • Page position isn’t always the best indicator of viewability.
  • Viewability varies across industries. While it ranges across content verticals and industries, content that holds a user’s attention has the highest viewability.
  • The most important thing is to give viewability consideration and weight based on your own experience.

Frequently Used Attribution Models

Let’s summarize the most popular attribution models in order of frequency of use, and as based on field experience. There are many more models you may consider, and this list is not intended to be exhaustive.

  • Last Click: 100 percent of the sale is credited to the last click, given its immediacy in driving the sale.
  • Linear Attribution: Equal weighting is given to all touchpoints, regardless of when they occurred. Its strength and weakness is in its simplicity. Not every touch is equal and for good reasons that we’ll describe in some detail below.
  • Time-Decay Models: The media touchpoint closest to conversion gets most of the credit, and the touchpoint prior to that will get less credit. This is the best of the simple approaches. It does not, however, account for brand discovery.
  • Position Model: Position model utilizes intuition and assumption to spread the weights of touches over time, heavying up the first and last touches, and considering the middle touches to spread the difference evenly across them. To be clear, this model presupposes “zero” brand awareness — and, therefore, that every customer “discovered” the brand from a (display/banner) ad impression, for example. Blanketing an audience in advertisements can provide great reach and frequency. It also sets a lot of cookies, which can be used to set the first “position.”

Pointers for Getting Started

The closer you can get to individualized attribution vs. broadcast attribution, the stronger the returns. For example, attribution by segment can provide insights you miss when measuring the aggregate.

Channel measurability should be weighted accordingly. Non-measurable channels should be measured by depth of observable engagement.

The Time-Decay model is widely considered a good place for brands to use when getting started in media attribution. Brands can simply insert logical and evidence-based assumptions and customize the half-life of decay based on the Three Strategic Levers described above.

Follow-up discussion and analysis can refine your thinking and allow you to provide a rationale that helps achieve the most credible, logical and valuable attribution capability.


Machine Learning and AI — What’s ‘Real,’ What’s Required

Big data has gone full-cycle. Quite a while ago, big data had its beginning within the realm of academic research. Recognizing its usefulness, niche businesses then began implementing big data. Massive companies, such as Google, began commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big. This all makes for a lot of noise in the marketplace.

Data graphicBig data has gone full-cycle.

Quite a while ago, big data had its beginning within the realm of academic research. Niche businesses then began implementing big data after recognizing its usefulness. Next, massive companies (like Google) started commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big.

This all makes for a lot of noise in the marketplace.

Today, we hear folks without applied mathematics or computer science backgrounds talking big data, algorithms and artificial intelligence (AI) at cocktail parties. The fluency has grown rather quickly: A CMO I’ve known for years used to wince when we talked analytics, but now she enthusiastically discusses her firm’s AI initiatives. She’s not running marketing at Google or IBM Watson, either — she sells clothing online.

While we’re likely in one of the most amazing periods in history to be in business, it does not come without its challenges. These days, you have to sift through all of the clutter when it comes to innovations in the marketing space.

Let’s see if we can simplify what the data pundits are tweeting and discern where the value really is.

Machine Learning

Machine learning (ML) occurs through networks of algorithms.

First, the good news: ML really works.

As we’ve discussed in “Marketing Machines — Possible or Pipedream?” ML is used to ingest large amounts of data and identify patterns in that data. The machine “learns” by ingesting, transforming and then conditioning a learning algorithm with your dataset.

ML will find the statistical relationships (models) between your various data points to articulate how efficiently your business is running. By calculating the best potential models, it can also show you what improvements you can make. ML can deduce your most profitable business targets. It can tell you who is likely to buy shoes priced over $800, or which production line is most likely to break down in the wintertime.

But ML Isn’t Foolproof

Machine Learning can surely help us find structure and patterns in data through statistics and the power of cloud computing. Amazon’s ML cloud computing capability, for example, isn’t specific to any domain and arguably works with any inputs. It will consistently output a result or target. Yet that very flexibility is where ML can prove risky:

“If you can dump anything into an ML process, and have it come up with an answer, you’d be wise to be wary of that answer.”

ML techniques all require you provide it with a “universe”. This universe consists of all the likely permutations representative of your purpose. If your conditioning data is skewed heavily to sneakers under $75, it will prove very hard to predict what customers are likely to buy $800 shoes.

This may sound like an unfair example, but consider the marketers who are out to break into the higher-end sales but only have data from their pre-existing customers. If skewed interpretations were applied to new-customer marketing (and they can be), your returns could be even worse than without any ML interference. The fact is, there are far more experiments where ML doesn’t produce a valuable outcome than those that do. But as technology and big data are refined over time, better results will be achieved across the board.

Analytics and model-building are highly iterative processes. If an ML process is focused on only a particular niche, the likelihood of getting better results sooner is higher — but still iterative. Despite its current limits, AI offers a deeper and more layered method of applying iterative math to break down large data questions than raw manpower.

Google’s AlphaGo AI beat champion Lee Sedol in a tournament of Go by exponentiating component questions, covering as many bases as it could. While AlphaGo works similarly in many ways to the human mind in this way, it did also have the advantage of iteratively playing against itself thousands of times.

Humans can’t do that.

The Bottom Line: Good Data In, Good Comes Out

Whether Google’s AlphaGo, Amazon’s ML tools or your home-grown mashup, the quality of the data that goes into ML is the largest factor you can control in creating value with systems-driven optimization.

In an age where many organizations have siloed data or cumbersome messes, along with marketing organizations that don’t even have a reliable marketing operations database, this is no small challenge. Getting your data centralized, organized and accessible is a requisite first step. Get that right, and there may be opportunities ahead to drive value up.

Customer Value: Narrowcasting vs. Broadcasting

Virtually every brand I’ve met with during the last few months is hungry for new customers. Many organizations are hooked on customer acquisition. That is, in order to hit sales plans for the organization, new customers will be required in large numbers.

Winning Over Consumers: The 4 essential content considerations that drive prospects to choose you over your competitorsVirtually every brand I’ve met with during the last few months is hungry for new customers. The war for the customer is on. For more on growing your customer base, consider reading “Bigger Is Better, How to Scale Up Customer Acquisition Smarter,” an article about how to grow your customer base.

Many organizations are hooked on customer acquisition. That is, in order to hit sales plans for the organization, new customers will be required in large numbers. It’s about as easy to kick “acquisition addiction” as it is to kick any other addiction for most brands. Try going without coffee suddenly, and see how your head feels. It’s not very different from reducing a business’s dependence on customer acquisition as a means of achieving revenue and profit targets.

Organizations that need ever larger numbers of new customers to achieve growth goals eventually will find the cost of acquiring incremental net new customers can become prohibitive. [Editor’s note: See “Wells Fargo Fiasco.”]

Broadcast vs. Narrowcast

The traditional model for advertising and customer acquisition has essentially been a broadcast approach, reaching a large audience that is generally descriptive of the customer a brand believes to be a fit. Contrast this with what is sometimes described as a “narrowcasting” strategy. Narrowcasting utilizes customer intelligence to understand a great number of discrete dimensions that a consumer possesses and can leverage statistical methods to validate the accuracy and predictiveness of targeting customers through these methods.

The chart below, depicting the value of customers acquired through traditional broadcast capabilities upfront and over time, helps illustrate why “broadcast” strategies for customer acquisition alone aren’t enough.

Mike Ferranti chart

Broadcast Acquisition Strategies Lack Focus on Customer Value

Large numbers of customers have been acquired in a trailing 13-month window – lots of them. The challenge is this cohort of customers has been acquired without adequate consideration of the right target.

Consider the fact that the target customer value of average or better customers is around $500. In the example above, the marketer has acquired a large number of customers who are lagging in their economic contribution to the business. While the customer acquisition metrics may look good, this was a large campaign and produced several hundreds of thousands of customers over its duration – the average value of those customers is quite low, indeed.

Low Customer Value Manifests Itself, Even If Acquisition Volume Is High

When sales targets are rising, it becomes harder to justify the high cost of customer acquisition if the customers previously acquired are underperforming. This leads to a very common bind marketers are placed in. The only way to “make the number” is to acquire more and more customers.

The most competitive and high quality businesses steadily acquire and have a robust customer base whose economic contribution is materially higher. Consequently, profits are higher, and we have a fundamentally better business.

Oftentimes, “broadcast” advertising approaches define the target with a single criteria like age, income or geography. This can be effective, especially when the media is bought on a good value. However, “effective” is almost always defined as “number of customers acquired.” This, of course, is a reasonable way to judge the performance of the marketing – at least by traditional standards.

There is another way to measure the success of the campaign that is only just beginning to be understood by many traditional “broadcast” marketers: customer value. The chart above shows that this cohort of acquired customers had relatively low economic value.

Root Causes of Low Customer Value

What are the causes of low value? It would be fair to start with the ongoing marketing and relationship with the customer. Bad service could keep customers from returning. Poor quality could lead to excessive returns. Over-promotion could drive down value. Getting the message and frequency wrong could lead to underperformance of the cohort. All viable reasons for lower value that need to be rationally and methodically ruled out prior to looking elsewhere.

Therefore, if operational issues are not clear – either through organizational KPI tracking, or simply by monitoring Twitter – then a marketing professional needs to start looking at three things.

  • The Target (& Media)
  • The Offer (& Message)
  • The Creative

Given the target is historically responsible for up to 70 percent of the success of advertising, this is the first place a professional data-driven marketer would look.

Target Definition Defines the Customer You Acquire, and It Drives Customer Value.

A fact that is often overlooked is that target definition means not just focusing efforts and advertising spent on consumers who are most likely to convert and become customers, but it also defines what kind of customers they have the potential to become.

In conversations with CMOs, we often discuss “the target customer” or the “ideal customer” they wish to introduce their brand to. The descriptions, of course, vary by the brand and the product. Those target definitions are often more qualitative in nature. In fact, only about 30 percent of CMOs we engage with regularity are focused on using hard data to define their customer base. While these are helpful and create a vocabulary for discussing and defining who the customer is, those primarily qualitative descriptors are often sculpted to align with media descriptors that make targeting “big and simple.”

“While simplifying is good business, when simplicity masks underlying business model challenges, a deeper look will ultimately be required, if not forced on the organization.”

While we would not refute a place for those descriptors of a valued consumer, they do fall short of true target definition. Ideally, the process of defining the customer who a brand wishes to pursue must begin with a thorough inventory of the customers it already has, and a substantial enhancement of those customer records – which provides vibrant metrics on affluence, age, ethnography, urbanicity, purchasing behaviors, credit history, geo- and demographics, net worth, income, online purchasing, offline purchasing and potentially a great deal more.

Keeping It Simple: Target the Customers Who Have Greater Potential Value

It’s not enough to have a great story about the “broadcast market” we wish to identify customers from. Today, marketers would do well to more narrowly (thus “narrowcasting”) define not only the customer, but the most valuable customer. In our experience, the minority of marketers have a shared definition of the customer and of the most valuable customer across the organization.

To achieve this, we can determine customer data attributes that are predictive of customer value. Under this strategy, we go beyond trial (conversion) but begin with the end in mind … acquiring customers who have the greatest potential to become more frequent and more loyal customers in the first place.

This is entirely feasible with the right data set and a modeling exercise against the highest value segment of the current customer base.

Once this “narrowcast” target is formally defined by attributes that are most predictive of their future buying behavior and spending proclivity, we can begin to define the advertising and marketing that would have the greatest effect on the brand.

Saturation Marketing to the Highest Value Targets

While many brands are still struggling to implement and execute true “narrowcast” advertising, there is ample opportunity this approach affords them. Consider the impact of acquiring a base of customers statistically more likely to spend more, and purchase more often. Not only because they possess the means or discretionary income, but possess other necessary factors to actually spend in the category, and do so more vigorously.

Profit per customer goes up dramatically when you weed out those more likely to buy once and then stop at the trial stage of a customer relationship – and instead acquire more customers with all of the necessary attributes to become “best customers.”

A Direct Approach to Brand Investment: Narrowcasting

Given that a narrowcast approach can help a brand be selective in cultivating the customer base that transforms the value of the business and enables marketers to produce more predictable sales, the challenge in some organizations is finding the budget to do this – as it’s not a traditional budget item. Remember at one point, search marketing wasn’t either.

For brands looking to grow smarter and more reliably, one solution that can work is to allocate a portion of the “branding” budget to delivering awareness-generating messages – with a reasonable call to action, to stimulate trial, not in a broadly targeted group (“banded income” is a typical criteria) but on a predictive basis. Models can help in selecting the target and matching back to postal, email addresses and display targeting cookies. It can produce a rich, immersive campaign, focused only on the individuals who a brand in your category really must reach and convert to perform at a high level.

Consider the impact it can have on customer value.

Mike Ferranti chart two

Note, in the above, the customer count is a LOT lower than the broadcast approach. This new approach does not replace, for example, national television in terms of reach. However, we note the rather overt shift between customer value from the first chart. This is what may be expected of narrowcast campaigns that have been intelligently constructed and tested.

This is a different view and brings some of the techniques from programmatic advertising, database marketing and predictive analytics to bear on the customer acquisition challenge. But the results can be impressive, and marketers will ultimately make the majority of their advertising decisions through a more “narrow” or focused lens as the technology grinds forward in efficiency.
To be sure, many firms are already executing these strategies today. These firms are building competitive advantages through a more robust, valuable and loyal customer base who will endure for many years into the future.

Full-Price Customers: How to Get, Keep Them

The refrain from retail CMOs has been consistent and almost deafening. They say: “We don’t just need more customers, but the right customers.”

Full-price customers, Mike FerrantiThe refrain from retail CMOs has been consistent and almost deafening. They say:

“We don’t just need more customers, but the right customers.”

“We need to grow margins.”

“We need to reduce our dependency on discounts.”

Even during this year of economic recovery, luxury brands in particular have been seeking to improve margins and sales by selling more full-price purchases — all while retail, at large, has been crushed.

But the high-end isn’t the only one that desires full-price sales. Remember not too long ago, JCPenney infamously tried to eliminate discounting and offer a fair, low price every day? We know how that worked out.

Starting shortly after the Great Recession in 2008, as both business and consumer spending dried up, marketers were forced to adopt traditional strategies for creating incremental revenue in a difficult environment. The range of tactics deployed was extensive; yet, pricing became more important and varied than ever before.

This phenomenon, you remember, was so widespread, that an entire category was born — “flash sale” websites like Totsy, Groupon, Gilt, Rue La La and Zulily. eCommerce juggernaut Amazon came out its their “Golden Box” and more recently, ushered in PrimeDay.

The Customer Is in Control

While price cadences, markdowns and closeouts are not new, something more fundamental began happening among consumers. It was the confluence of accelerating globalization, mass adoption of the Web and the deep scarring from the recession — that began driving up savings rates and reducing debt, which appears to have infused a new ethos among consumers and their perception of bargains.

Millennials today refer to saving money as a sort of “hack.” The Internet is filled with “life hacks” and more relevantly: “savings hacks.” Even if they are spending that savings on going out at night — that, too, has spawned what may be a generational interest in getting more for their money. It’s a badge among them to find the cleverest ways to pay less and get more.

While walking to a meeting, I realized I had not put collar stays in my shirt collar. I was going to stop at a retail store on the way, and instead of Googling where to buy them as I did (I’m a Gen Xer, and that’s what I would do) a Millennial did a different search, and we stopped at Starbucks. He came out with a wooden stirrer, and snapped off a piece to fit each side. Then he started on where I could buy custom dress shirts for 30 percent less than what I was likely paying. (He was right.)

This anecdote isn’t intended to communicate how clever this was. It’s intended to illustrate a new consumer behavior, born of the intersection of rising influences of “digital natives,” mobile tech, cloud computing, and the impossible rate of change that comes with it.

If you don’t sell to Millennials today, odds are you will be targeting them soon. Today, the oldest Millennials are entering the accessible- and luxury-buying brackets, and they will take their toll on them. I’ve already worked with clients who are offering products designed to entice new buyers of their brands into trial, they are not discounted products, but new products designed to appeal to the more price-conscious Millennial. These new offerings are changing the way brands market and sell.

Existing Full-Price Buyers

Our recent internal study looked at the impact of eliminating sale items for a brand that typically sells to more affluent customers. A few things happened almost immediately:

  • With sale items gone, on-site searches skyrocketed, as the price-conscious consumer hunted for the sale
  • The conversion rate plummeted for consumers who looked for and could not find a sale
  • Full-price purchases went up as a percentage of sales — yet revenue declined

The short-term effect was that revenue dropped materially at the outset. It was essentially the same behavior of discount buyers at other organizations that abruptly “eliminated” sale pricing. The longer-term impact is still unfolding, but surely not every customer is a full-price buyer, and some will never return — unless the sale returns.

Further analysis illustrates there are at least three types of buyers when it comes to price:

  • Full-Price Buyers (price inelastic)
  • Discount Buyers (price elastic)
  • Both (buyers who consume incentives and buy at full price)

Strategies for these different types of customers range from simple to exhaustive. In short, our goal is to understand the elasticity of demand associated with each customer and the goods she purchases. The best place to start is with a simple segmentation of each of the types of buyers based on their consumption of discount promotions.

The ‘Privacy Shield’ Is Here — How It Affects You

There’s a new framework for creating greater data privacy between the United States and the European Union. While it’s taken two years of work, some would argue little has changed and that it’s likely to get struck down — others laud the progress. Let’s get clarity on what that means for businesses leveraging data “across the pond.”

Privacy ShieldThere’s a new framework for creating greater data privacy between the United States and the European Union. While it’s taken two years of work, some would argue little has changed and that it’s likely to get struck down — others laud the progress. Let’s get clarity on what that means for businesses leveraging data “across the pond.”

Transatlantic Data Privacy Is Dead. Long Live Transatlantic Data Privacy.

First there was SafeHarbor, the European Union-United States agreement to protect data privacy of users in Europe as that data pulsed across the Internet and into the United States. It was arguably a historic step, but it ultimately was struck down and eliminated. Many questioned its value beyond being “a step.” It has now been replaced by the “EU-U.S. Privacy Shield,” which imposes greater obligations on U.S. businesses to protect Europeans’ personal data.

The Privacy Shield Agreement establishes a whole new set of legal requirements by the E.C.J. the European Court of Justice, which also ruled the previous Safe Harbour framework invalid.

What Is the Privacy Shield?

First and foremost, the Privacy Shield is opt-in. If your business doesn’t opt-in, you don’t have to abide by it. The downside, you won’t be published on the “list” of Privacy Shield Compliant companies. European consumers could refuse to do business with you, and it could become a media problem — though, the average consumer probably doesn’t know the ins and outs of data privacy. So its success will, in part, rely on its adoption. If it is not adopted widely, we can expect additional regulations to compel organizations exporting data from the E.U. to meet the objectives of the Privacy Shield.

Most importantly however, the Privacy Shield includes, for the first time, written commitments and assurance regarding access to data by public authorities. For the first time, the United States has given written assurances that it will not conduct mass surveillance of data entering the U.S.

The new Privacy Shield agreement requires the U.S. to “monitor and enforce” more aggressively. Also, new and greater collaboration with the E.D.P.A., the European Data Protection Authorities, is required by the United States.

The goals of both the original Safe Harbor Agreement and the new Privacy Shield are quite similar. Businesses must treat data created in the E.U. in accordance with E.U. law, regardless of whether that data is physically stored on a server in New York or Paris.

So how do companies accomplish this? The answer is by basically stating “yes, we meet the E.U. standards.” So not much has changed between Safe Harbor and Privacy Shield here.

How Do the Safeguards in the Privacy Shield Work?

However new safeguards help enforce that both companies and governments abide by the Privacy Shield’s requirements:

  • The first difference is a real one … it now falls on the shoulders of the U.S. Department of Commerce to make sure that companies meet the more stringent data privacy requirements. The Department of Commerce will monitor whether companies publish their commitments, which makes them enforceable under U.S. law by the U.S. Federal Trade Commission. In addition, any company handling human resources data from Europe has to commit to comply with decisions by European DPAs.
  • Second, if your data originates from the European Union ― and you don’t have to be a European (the U.K. is still covered post “Brexit”) ― you can complain if you feel your privacy rights were violated. Those complaints will now be sent to the U.S. and must be addressed “expeditiously” and at “no cost to the individual.”
  • In the agreement, the United States “ruled out indiscriminate mass surveillance on personal data transferred to the U.S.” Furthermore, the U.S. promises in writing that mass collection of data originating from the E.U. will “only be used under specific pre-conditions and needs to be as targeted and focused as possible.”
  • An ombudsperson will now handle complaints about data that is accessed on “national security grounds” — they are tasked with working independently of all other federal security agencies, which is a significant commitment for the United States, given our recent history and experiences under the Patriot Act.

Implementation for U.S. Firms in Simple Terms

At this juncture there are still details being worked out in the Privacy Shield Framework, but the following are fairly clear steps:

  • Self-certify annually that they meet the requirements
  • Display privacy policy on their website.
  • Reply promptly to any complaints.

Some might call all this common sense, some may call it non-sense. But Data Privacy is an issue that we have to give credit for addressing, and leveraging the learnings from the judgement striking it down.

We recommend businesses leveraging personal data, whether exporting from the E.U. or solely using it domestically, exercise some simple, common-sense steps that are consistent with the Privacy Shield:

  • Publish a privacy policy.
  • Listen to your customer
  • Have a clear and simple statement on how you will use consumer data – and how you won’t.

These simple steps can get you started; and for sure, there will be more to come in regard to data privacy.

Relevancy, the Currency of Conversion

More. The marketer’s mandate will always be “more” — more traffic, more sales, more margins. Add to it that in order to get more, we’ll need to test more ideas, try new strategies, new media and mediums — not all of which will work.

Oliver Twist moreMore.

The marketer’s mandate will always be “more” — more traffic, more sales, more margins. Add to it that in order to get more, we’ll need to test more ideas, try new strategies, new media and mediums — not all of which will work.

More ultimately means sales conversion, and there’s a data-driven approach to getting more that leverages a new currency. Not Bitcoin, but relevancy, because relevancy is the “Currency of Conversion.” That conversion currency is based on the intelligent use of data.

Truly accomplishing data-driven success requires focus and simplifying — one of the few constants in business marketing.

Through advising dozens of organizations on the intelligent use of data to inform and improve performance, it is often helpful to come back to some of the fundamentals in thinking about the application of our data to business problems. And while often we focus on the “what” that has to do with data, let’s consider perhaps the most important question — “Why?”

Why Should I Inform Marketing With Data?

While it’s likely considered risky nowadays to lack a data strategy or better yet, a data-driven strategy, we do need to ask why. I’ve been surprised at the lack of fluency even experienced IT people and all kinds of marketers have when asked why they need to invest in data strategies. That’s despite the “reality” that everyone knows they “should.” Let’s deal with that.

  • Reporting. Many organizations still desire better reporting, Key Performance Indicators (KPIs) being the most important. It’s a baseline use of data, and it’s important. So data serves a purpose and provides consistent, specific solutions to the questions “how are we doing?” It’s hard to operate without it, but it should become “table-stakes” in short order.
  • Analysis and Insights. Data, if organized and governed reasonably well, can yield insights. This requires you have an analyst with a big brain to pore through it. The analyst needs to know enough about your business to understand what is relevant and what is not. The analyst must also consider materiality and the difference between correlation and causality.

This last point being an all-too-common mistake. For example, “our customers are rich” so we need to target rich people. Being affluent may be correlated with buying your product, but it may not be causal! We’ve found this example many times when actually statistically testing to see what attributes have the most causal/predictive relationship. For a full study on causality vs. causation, see this piece from

  • Customer Intelligence. Customer Intelligence is the next-level beyond analytics. In CI, we now use purpose-specific algorithms to derive new data and to identify valuable patterns that arise in large amounts of data. It’s fair to call it “the union between marketing and data mining.” Customer Intelligence provides us the answers to questions we don’t ask because we don’t know how to answer them.

The Most Important Reason to Inform Marketing With Data

The low cost of communicating digitally has, in some cases, left relevancy underrated. This is no coincidence. When you spend real money to send a quality, brand-appropriate direct mail piece or even more money on television — you care a lot about relevancy. This message has to be right, it has to be on-brand, it has to resonate. Today, that mass-market TV ad isn’t a winner if it doesn’t “break the Internet.”

But when it’s an email that costs a fraction of a cent to deploy and just a few fixed dollars to create amortization over millions of recipients, we as marketers can get impressively lazy. Relevancy is trumped by low cost and high ROI. Who cares if the message is perceived as irrelevant? The email drop “worked.”

Let’s consider this further.

Let’s say the “less than relevant” drop had an out-sized 35 percent click rate. We know the sender names were likely those they anticipated email from, and the subject line was likely relevant. We can’t know the breakout of which send carried more weight without testing them. But if you subscribe to the school of thought that relevancy isn’t important, then testing probably is irrelevant to you, too. Before you decide “well, of course we think relevancy is important” — think about whether you’re really using it as a principle in your outbound marketing.

Customer ‘Loyalty’ — It’s Never a Sure Thing

As “Data,” “CRM” and the “Customer Database” continue the march toward the center of the marketing organization in tens of thousands of businesses, not surprisingly another “classic” marketing concept is rather “new” again — customer loyalty.

CRM keyAs “Data,” “CRM” and the “Customer Database” continue the march toward the center of the marketing organization in tens of thousands of businesses, not surprisingly another “classic” marketing concept is rather “new” again — customer loyalty.

Customer Loyalty: Where to Start

The right place to start in your consideration of customer loyalty and creating a loyalty program is simple, and yet relatively often, rather overlooked.

A simple question we ask when discussing the “loyalty” dimension of customer experiences and the customer data they produce goes something like this: What does success look like when you implement a customer loyalty program?

The answer to that varies — but the worst answer is “nothing.”

“Nothing” is never determining what the end in mind should be for your customer loyalty program in the first place. When organizations invest in defining the outcome they seek, they are roughly 20 percent of the way to success. As the axiom goes, “If you spend 90 percent of your time defining the problem, you’ll solve it in 10 percent of the time.”

What Can We Expect From Loyalty Initiatives?

So what should we expect from a customer loyalty program? Here’s a partial list we’ve heard from the brands where we’ve used customer data to inform and improve customer loyalty:

  • You keep those customers longer
  • You generate more social referrals
  • Those customers buy more often (frequency)
  • They are less sensitive to pricing and increases in particular
  • The cost of customer acquisition can actually be lower
  • Exchanges and returns decrease “naturally”
  • You recognize profit growth

These are all very real outcomes you can expect from a high-quality customer loyalty program built around your unique customer and your business.

How Does Loyalty Really Work?

This is the best question to ask. Most organizations I’ve worked with on managing a customer base (customer database) to drive business performance begin with a gold, or black card, a name for an elite club or a space they will convert to lavish “loyal” customers with attention.