Retention marketing gets a modern treatment

Every marketer knows the adages and has heard the numbers: acquiring customers is more expensive than retaining them.

Yet many marketers still don't prioritize retention over acquisition.

There is a certain rationale behind that. For starters, there's really no such thing as 100% retention. (If nothing else, all of today's customers will be dead within 100 years!) So, without a rate of acquisition that beats the rate of attrition, your customer base will shrink to nothing at some point in time.

And then there's the competition: the need to win a sustainable market share. If you're not acquiring the new customers in your market, someone else is. That someone else will get the economies of scale, and you will lose power. After that, your future acquisition efforts become more expensive as you have to take a greater amount from competitors instead of acquiring the prospects that no one has yet.

You have to keep acquiring.

Still, this rationale doesn't diminish the need to retain customers in order to capture more of their life-time value potential. In fact, it highlights the need for more, or better, retention efforts.

Of course, not everyone is totally ignoring retention. But even among those who do retention activities, these will often be quite unoptimized. Studies show that even those who do a great job segmenting and targeting for acquisition tend not to apply similar analysis, profiling and personalization to their retention marketing. Or if they do, it is only a coarse bucketing of offers—wisdom of the crowds stuff.

One company is highly focused on the discipline of retention marketing. Retention Science are applying the modern techniques of data science to the task of retention. This is important because, while the tools and techniques are similar at a high level, the levers behind optimizing retention are different than those of acquisition. For marketers, and for the tools they use, a focus on the science, strategies, and tactics of retention makes a difference.

Executing Retention Marketing is playing both offense and defense. Offense, in that you can grow usage among your naturally higher ROI existing customers. Defense, in that you can keep them coming back before they seek out the competition.

If you're firing on all your acquisition cylinders, but still looking for growth—and better ROI—turn to the science and art of retention marketing.

The challenger sale and marketing

I see The Challenger Sale on a lot of office book shelves these days. If you're not familiar, the Challenger Sale is the next step in the evolution from transactional sales to solution selling and now beyond. 

Setting out to try and identify what "star" performers are doing differently—with the goal of training the middle 60% to be better—the authors undertook a large scale study of thousands of sales people, looking at 40+ dimensions.

Doing factor analysis, they found that reps, across industries, clustered into five groups. Four of the groups, the Hard Worker, the Relationship Builder, the Lone Wolf, the Reactive Problem Solver are likely recognizable to anyone who works with sales people. The fifth was the one they dubbed the Challenger. And it was this group that consistently performed better.

Each group brings a number of good qualities to the table. What the Challengers bring is a, well, challenging approach. They do what the authors call Teach, Tailor and Take Control.  They teach prospects, tailor a solution, then take control of the relationship.

I'll leave it to you to read the book and explore the details. What motivated me to post this was what became clear as soon as I saw the "Teach, Tailor, Take control" framework for selling.

That is, Challenger sales reps succeed because they do exactly what marketing is supposed to do. When its done right, Teach, Tailor, Take control is marketing. We educate, frame the solution, and nurture the prospect along their buying cycle. In other words the best selling approach is not selling, it's marketing.

Whatever the benefits of bringing a Challenger framework to your organization's sales team are, one of the biggest opportunities is to help align sales and marketing.

Marketing Innovation and Inefficiency

Imagine you are offered the job of Head Chef at a very unique food catering business: it has a wide-open menu.

Your clients--event planners, restaurant owners, and other caterers--can order any food in any quantity at any time.  Sometimes they pre-order for a large event far in the future. Often, you find out on Tuesday they need it for Thursday. They can walk up and order a single plate of food to go. (Make that Now, please!) They can order any cuisine in any combination from appetizers to desert. They always want the latest fad food. (Make that Paleo diet! Now, please!)  Your clients are very fickle; they make last minute changes, without regard to how long it takes to make a proper sauce. They don't listen to your advice or care for your gastronomical experience (Yes, Hollandaise sauce on the sherbet. Now, please!) But they do hold you accountable for their success. You have some really sophisticated kitchen gadgets, but no time to set them up properly or train your cooking staff. So your crew whisk by hand, aren't trained on the new ovens, and yet have to cobble together a mind-numbing array of techniques to cover the breadth of cuisines ordered. Speaking of which, order-tracking happens across several systems that aren't integrated--so you print out at each step and re-enter by hand. On top of all this, you are only as good as your last meal: because your clients are all food critics! A couple of bad reviews and you and your crew are in hot water, defending your lives with no proof that you are "adding value." 

No thanks, you say! But if you run a mid-to-large marketing department, you already have this job.

The rapid innovation in everything from web engagement to marketing automation, analytics, big data, SEO/search, business intelligence, social media, video, content marketing, and whatever was released while you read this list ... is creating inefficiencies faster than you can snuff them out.

Marketing is a labor intensive activity.

Errors and delays are costly, both directly and as missed market opportunities. Inefficiencies mean higher costs and lost revenues. And they can't be solved solely by adding more, smarter people. Even the best professionals need an organized environment, infrastructure, and process to do the cooperative work of marketing.

Organizational capability is equally important for talented people.

So what's the result without it?

• Marketing inefficiency--marketing is a game of critical masses and tipping points. There is a point below which you are better off doing nothing, because you are not getting the reach and lift you need to make a difference. This is where inefficiencies kill even great concepts. 
• Under leveraged systems--with no time or plan to properly integrate systems, make them user friendly, and train users on them, the end result is Underutilization. If you pay for a marketing automation platform, but don't actually run nurturing programs, you may be an Underutilizer. 
• Insufficient data visibility--manual processes and poorly integrated systems mean there is no end-to-end visibility of data. Those two classic aphorisms should be ringing in your head now: "but you don't know which half" and "you can't improve what you can't measure."
• Inefficient change management--rather than being handled by a rapid response, curve balls like a new marketing channels or integrating an acquisition merely add to the inefficiencies and further slow everything down.

Underpinning all this inefficiency is technology. 

Well, that's not fair. Technology doesn't kill inefficiency. People do. Two elements are lacking as we absorb all this technology. First, we don't add greater technology management capabilities into marketing organization. Second, we don't educate marketers on how to use new channels and tactics within a strategy.

In other words, our People, Processes, and Technology are out of balance and can't handle our marketing aspirations. How to balance them? The answer lies in moving marketing technology from ad-hoc to strategic management. This doesn't mean losing agility. (You probably only think you are agile now anyway). Just the opposite, it means deliberately creating agility.

Next up, we'll cover topics on doing this in the real world.

For now, check out resources around MarTech and Marketing Operations & Technology Summit.

Marketing: Organize for Productivity

People.  Processes.  Product.

PPP.

When I was a software developer, a good practice for staffing teams was to balance PPP. If your people were very familiar with the product (its technology and its user stories) you needed a lot less process. Conversely, if the people were not familiar with the product, or if there were not a lot of people, you needed more process.

The state of PPP subsequently drove the time and cost of the project. So, if you have good processes and people who know them well, you can make a better product in relatively less time.

The PPP relationship comes into play when you organize your marketing team as well. You can apply PPP when staffing a project or when reorganizing the entire department.

Lets face it, marketing is getting more responsibility without the chance (not to mention the funding) to manage the people, process and technology required to do a good job. For the people in PPP, that leads to what Gary Katz, Chair, Marketing Operations & Technology Summit recently summed up as, "a career in marketing that feels like repeat episodes of Survivor – fun and exciting at first; overwhelming and exhausting after that"

So what are our options. One thing that shouldn't be up for debate is the need for a layer of technology, data, and measurement. We've reached the point where all but the smallest marketing department NEEDS it's own IS and Program Office. Only a very small shop should bake this directly into the marketer's roles. For one you need consistency in handling data, web engagement and CRM. Same is true for measurement. You want the latter to be both comprehensive in scope and independent of marketers.

Working above the Ops layer, we have some options.

Here's where we can PPP! What type of people do you have or can you recruit? Should you organize horizontally or vertically? That depends on your people, process, products.

For example, is your product a highly technical, B2B offering that requires rocket scientists to understand the product, its uses, and the marketplace? Probably these product marketers won't have the depth, or the inclination, to also execute a campaign. You'll thus need a layer of marketing execution specialists--web, search, automation, content.  This creates a more horizontal structure where a layer of planners who don't execute work with a layer of execution specialists who do.

On the other hand, if you have a more general use product, you could build a team of marketers who work the messaging and targeting strategies and then execute the tactics to deliver.  The extreme example of this is the marketing-department-of-one at a startup.

The People and Process you need in these scenarios are different.

Note too that the slices, vertical or horizontal, can be teams as well as individuals. And within these the same rules can apply, recursively. In the example above, maybe Campaign A has Planners and Executors in a horizontal relationship. Campaign B though has several vertical people, one for social, one for search, one for events.

If you're marketing function feels a lot like Gary's Survivor mode, you may need to step back and balance your PPP!

My Conversion to Evernote

I've been a OneNote user for some time. For taking notes, especially during meetings and calls, and for capturing ideas quickly, it really does the trick. The integration with Office and the ability to access online notebooks from multiple devices--especially mobile phones--means I'm never far from my notes.

Happy with OneNote, I never understood the enthusiasm around Evernote. But count me among the converted.

It began, innocently enough, when I was looking for a better way to track projects and tasks.

The Getting Things Done (GTD) techniques kept appearing in searches. Eventually, I ended up on Lifehacker with articles galore about GTD and Evernote. Finally, I came across TSW--the secret weapon.  Reading through, I realized it was a good system for managing projects, and even though it could be done in a number of software (or "hardware" notebooks), the example used Evernote.

That's where it started to click. Evernote has a number of features and an ecosystem that makes it so much more than a note taking tool or web clipping utility. Notebook Stacks and tag hierarchies, along with search, index, OCR and a host of other functions turn it from utility software to the way to manage your own, unstructured, "big data."

(One curious fact, as I draft this in Evernote, the word Evernote is flagged as a spelling error.)

Which in many ways makes it even better than specific task and project solutions like Asana and Wrike. And even my favorite, Trello. These tools try, but don't really succeed, at letting you filter your views and lists as quickly as Evernote. Both the functionality and the UI of Evernote lets you get to what you want quickly. And it is a intrinsically a workspace for file and content storage. Whereas that always feels bolted on in task software.

At this point, I've only managed my own task and project list with Evernote. I still have to find out how it would work for a team of people to share projects, tasks, and workspaces. If anyone has used it in a collaboration environment, let us know how it works.

The Marketing of Data Blending: Is It as Simple as All That?

The automatic data blending features in a number of new or updated BI and visualization tools offer to bring complex analytics to the ubiquitous “Non-technical Business User” without the need for data scientists. Is it as simple as all that?

One thing that is telling is that the examples and demos by the vendors employ fairly simple data--clearly already cleaned up to Join easily. With the examples, one often could have made the conclusions simply looking at the data in a table.

Still, some people simply work better with visuals than with tables of numbers. So there is potential for big value in end user tools.

But there is also a big “Only” in vendor statements like “only requires user intervention to resolve conflicts.” Pulling together data from disparate systems, even when they are modeling the same entities, is rife with conflict.

Having “manually blended” data myself many times over the years, I know the drill. Export from a variety of sources; upload into Access or SQL Server; change up data types to get them consistent; use SQL queries to join, sort, filter and explore; finally generate charts. I can tell you it is easy to get the wrong results when you are not careful with your joins. I believe that if the source data is clean and obvious enough, then automated blending tools can do a good job with this. But there’s the rub, the data isn't always clean and obvious. And the non-technical business user isn’t in a position to know it.

To be clear, I’m not saying there isn't a place for these tools. I just believe that they still take a more “data aware” user to do the blending. In other words, they are great for analysts to do ad-hoc research projects and to figure out ETL jobs.

In fact that’s the way we've ended up deploying. Analysts use Tableau and Datameer to build dashboards for business users without having to make a data mart first. This is a great benefit because users get the visualizations in hand and can make change requests. Over time, this iterative period winds down and we have a very good model encoded in the blending setups should we choose to implement a structured data warehouse or mart.

I really like these tools in the right hands. I simply believe the technology hasn’t caught up with the marketing yet.

The Trouble With Agile Marketing

Okay, that title was click bait. Because the fault with agile marketing is not with Agile but with the people who don't get it.

Garçon, fetch me my soapbox, s'il vous plaît ...

You can recognize these types because they are the ones saying, "we just need to be more Agile" when you have more work than the capacity to do it.

To these types, "Agile" means being ready to drop everything and switch on a dime to the new "highest priority" project that comes along. Oh, and that previous "highest priority" project still has to get done. "Be more Agile, will you?"

And you can recognize them because to them Agile means atomic tasks can be done faster. "Laying out that landing page used to take 4 hours. But we're Agile now, it should take one hour, right?"

Perhaps the worst, though, is that Agile somehow equals "no need to plan or prioritize." Think of something, just do it. "Of course, we're Agile."

"Writing great email copy, researching keywords, or coding an FFT routine aren't ipso facto faster because you're doing these tasks within some overall Agile process.

These ingredients feed a recipe called Chaos. As others have pointed out, Agile is good for Complex environments, but not for Chaotic ones.

Okay, I proved that I can complain. But what do I think Agile Marketing is?  Fair question.

Let's start with the agile part. The agile in Agile is innately about the output. Relative to the pre-Agile universe, the output--be it a software program or a marketing campaign--is flexible & adaptable relative to output in waterfall type planning and execution cycles.

What the agile in Agile is not about is the process speed or the amount of planning and work required to generate any piece of output. It's not really less effort to do any atomic task. Sure, task productivity can be affected by better tools, better languages, and other advances. But writing great email copy, researching keywords, or coding an FFT routine aren't ipso facto faster because you're doing these tasks within some overall Agile process.

And let's not kid ourselves. Agile requires at least as much communication, process and project management. At least. It's not some magical ordered chaos that simply gets work done faster because you say 'agile' three times while you click your heels.

What Agile brings to marketing is the ability to learn and respond quicker than with monolithic planning and execution cycles. Before the recent evolution of tools like marketing automation and channels like search and social media, marketing required big bets that weren't easy to change. Now it's easier to experiment. Easier to kill something that's not working. Agile marketing is using processes that take advantage of these developments. Through these processes, Agile makes it easier to focus on what the end user needs rather than what the marketer wants.

It is still hard work, though.

Content Marketing, Again

Content Marketing isn't new.

Creating useful content with the goal of selling product has a rich tradition.

What’s different in the digital age is

1. buyers are much more empowered to drive their information gathering. And,
2. marketers now have much more data on who the buyer is and her place in the sales cycle.

The key to success is content created around buyers’ priorities--useful for them to do their jobs!

If you are useful to buyers, you will get the chance to change minds and to drive action.

Remember: it’s not about you; it’s about the the buyer.

Start by mapping your prospects buying process--from exploring to deciding to buy to selecting vendors, negotiating, installing and support.

What information needs do they have at each stage--by persona?

Fill those needs with expert answers and useful tools.

But be strategic for yourself too. Make differentiated content by persona and by buying stage.

This way, when visitors download, you know about their role: are they the tech buyer, the bean counter, the business manager? Don't wrap it all in one piece of content. You won't know who's reading it, because it could be any of your target personas.

When a business manager downloads your ROI calculator, she’s likely moved down the funnel far enough to qualify. And if you build your content right, you’ll know when she is.

That’s it from 50,000 feet: create content that is useful for your prospects AND helps you know who they are and where they are in their buying decision.

Bayesian Analysis of an A/B Test

Last month I noted that I was looking into Bayesian data analysis to aid better decision-making with marketing data. Well, it's time to start using some software (R) and getting to work.

Links to articles on the advantage of Bayesian data analysis over traditional null-hypothesis significance testing (NHST) were provided in that first post. More good articles can be found on John K. Kruschke's blog.

In a nutshell, on the plus side with Bayesian analysis you can generate richer information about your parameters. And you don't have the  problems of the investigator's intent and the hidden priors of NHST.

The one hitch to Bayesian analysis is that you may have to compute a difficult integral. There are a few techniques to make it doable. First, make the math easier--use conjugate priors and known distributions. Second, numerically approximate the integral; you'll need this especially if your priors can't be adequately expressed with a convenient distribution. This grid approximation won't work, though, if your parameter space gets too large. Then you are on to Markov chain Monte Carlo (MCMC) methods that extend to large parameter spaces.

Over several posts, we'll look at all 3 techniques, even though we can often get away with conjugate priors and convenient math.

For more detail, we're inspired by and playing along with Chapter 8 of Doing Bayesian Data Analysis by John K. Kruschke. We'll just be using a marketing case instead of the ubiquitous coin flips. (The base R scripts used for the below come from the book.)

A lot of digital marketing cases involve two binomial proportions--open rates, click rates, conversion rates. Luckily, these cases can be easy to solve via exact formal analysis. Because the Bernoulli likelihood function has a ready conjugate function for our prior--the beta distribution.

When we're doing an A/B test of two treatments, we're trying to make inferences about two independent proportions. We are talking about the space of the two combined parameters--every combination of Click-through rate A (θ_A) and Click-through rate B (θ_B).

So what are we testing, anyway? Well, earlier this year some of us wanted to test a marketer's practice of sending out self-promotional, announcement emails. Buried in these announcement emails were offers for a white paper--an actually valuable offer. In other words, in order to promote a useful white paper, the marketer is sending a "Come See How Wonderful We Are at XYZ Expo" email. (This can be considered a classic case of going through the marketing motions.) Our hypothesis was that an email directly promoting the white paper would convert better.

So let's get to it.

We need to determine our priors, calculate the likelihood from the data, use Bayes formula to derive the posterior distribution of our parameters (the click-through rates) and then we can compare the underlying rates from A and B. We can do this with an exact analysis using beta distribution priors and Bernoulli likelihoods.

Priors and Likelihoods and Posteriors, Oh My

We're examining a parameter space over θ_A and θ_B. That is the probability p(θ_A, θ_B) over all combinations of θ_A and ϑ_B. In our case, since our channels are independent, p(θ_A, θ_B) = p(θ_A) * p(θ_B). Similarly, our likelihood is a product of two Bernoulli functions. We combine these priors and likelihood functions and, through the magic of beta distros being conjugate to Bernoulli likelihoods, badda bing, badda boom, our posterior is a product of independent beta distributions too.

We have our recipe. Just add some constants, some data, and stir.

Priors

Our historical data shows around a 20% conversion rate for offer emails to this target, so we'll use that in choosing both priors, setting them the same. A beta distribution with a = 2 and b = 8 works nicely.

The prior for B is the same. 

A perspective plot over both parameters looks as follows.

Likelihood

Our likelihood functions are:

θ_A^z_A * (1 - θ_A)^{(N_A - z_A)}

and

θ_B^z_B * (1 - θ_B)^{(N_B - z_B)}

where the z's and N's are the number of hits and sample size, respectively, of each channel, A and B. Here is where our sample data entered the mix.

In our test, A is the group that received the self-promotional email. B is the offer email. Our actual data is:

z_A = 85 ; N_A = 637
z_B = 219 ; N_B = 722

Posterior

Through our "badda-bing" math, we have the product of independent beta distributions as our posterior:

beta(θ_A | z_A + a, N_A - z_A + b), and
beta(θ_B | z_B + a, N_B - z_B + b).

Now we can examine the posterior distribution by evaluating it across the θ_A, θ_B space. Here is the contour plot.

What have we learned?

Looking at this plot we can see that θ_B is very likely greater than θ_A. 

How much greater?

We can sample from the two independent beta distributions of the posterior and compare them. Here we can see the mean of the difference and the 95% HDI (Highest Density Interval) of the difference. The mean and HDI are above zero, leading us to believe that the difference in B over A is real.

That is, the offer email performs better than the self-promotional one. 

Much better.

Conclusion: make your content and your emails for your reader, not about yourself.