marketing analytics

Want to reduce churn?  Don’t model churn.

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At least, don’t model churn as your only step.

A very common approach to churn reduction is to build predictive models, which are intended to produce a score for the likelihood a customer will churn within a given time period.  The approach is so common there are libraries of code available online for this purpose.  ChatGPT can provide Python code for the task.

But is it the right thing to do? 

We have been helping marketers reduce churn for over 20 years.  In our experience, predicting churn is a good first step, but you cannot stop there.

You actually want to predict not just churn risk, but the probability you can save the relationship, given your history of communication and offers used for this purpose. 

The highest churn risk scores may be pointing you to customers that are more than halfway out the door anyway.  Not only will your communications and offers not dissuade them…it’s too late…it might actually make things worse.  There is a “wake up” effect we have seen where at-risk customers realize their contract is up for renewal, for example, and it’s time to shop around, not jump at your first offer.

Think recovery, not just risk.

Next, think about the value of the relationship.  What is the predicted lifetime value in the coming years, after your planned intervention?  How long will they stay with you, what revenues would you predict over that period, and at what profit margins?  Knowing this number gives you the starting point for a key question: what are you prepared to spend to save this relationship? 

Extrapolate that question across all risk-save scores and you get a sense of the envelope of budget you need to reduce churn, as well as a sense of how much of a dent you can make in the churn rate.

Another way to think about churn reduction is as a problem in optimizing individual contacts….what offer, message, channel and timing.  This is where a product like our 1:1 Optimization system can help direct traffic and make both planning and execution easier.

Finally, you probably don’t have a churn problem (what??).  You probably have several.  Customers don’t all leave for the same reason, and a tactic designed to keep them for one irritant may be completely irrelevant, even counterproductive for another. 

Consider an example from wireless services.  What if a customer is irritated about billing problems, not resolved with your customer service team, repeatedly?  Consider another customer who is irritated about dropped calls.  What can you do to counter these two problems?  Would the same tactic work for both?  Would a tactic designed to work well for one be as effective for the other?

You should think about a modelling solution that provides risk scores, save scores, and what we call Action Codes.  Action codes combine an evaluation of why a customer is at risk with an action you choose to suppress that risk.  All three scores can be funnelled to the customer-facing systems you will use to deliver messages, whether that is an all purpose CRM system, a channel focused system such as for email, or other.

Put it all together and you have the answers you need to plan effective churn programs and translate strategies into individual action plans.

Best of luck and if we can help, don’t hesitate to contact us.

There’s a better way to evaluate campaigns (including digital) than traditional MMM

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This post appeared originally on the Crater Lake blog, here

Marketers have yearned for the data to help them decide how much to spend, and where for many years, data that would give them answers to questions such as:

How much should I spend on advertising?

How do I best allocate this budget to channels, content, markets and over time? What is my real ROI and lift in sales volumes?

More than half a century ago, statistical modeling methods began to be applied to answer questions like these. Over the years, as marketing began to broaden its remit beyond media advertising, these methods began to incorporate other elements of the marketing mix. As a result, the term MMM evolved from Media Mix Modeling to Marketing Mix Modeling. Today, MMM is often, and fairly, criticized for leaving marketers grappling for answers that the techniques themselves are just not suited to deliver.

  • How does MMM help with the details of planning and buying online media?
  • Can MMM measure the long term effect of advertising?
  • Can MMM help us understand the effects of creative?
  • Can MMM incorporate the impact of customer experience?

In short, traditional MMM is too limited in its capabilities to be useful for today’s marketer. To try to tackle these new requirements, Crater Lake has chosen to use a wheel as a metaphor. Each spoke on the wheel represents a channel. In the hub, all channels come together.

If we want to build a model that measures the impact of all channels we are operating in the hub. In the hub, we need to describe each channel using variables that ALL channels have in common.

But this inevitably means some of the data available to an analyst in a spoke is not used.

In the Crater Lake approach, our omnichannel model (hub) uses a unit of analysis that is a combination of geography (small areas) and time (usually day).

In a spoke, the unit of analysis may be different. Consider an email targeting and measurement model; that would use a unit of analysis that is an email address combined with day.

This means that while the “hub” model can certainly be used to measure cross-channel effects such as total lift, and it can also be used to allocate budget between channels, it’s application within a channel (in a spoke) is limited to the data available.

In our view, we compensate by building complementary models for each major spoke, and then solve for optimization across the entire system. When we want to integrate the hub learning with a spoke, we are pushing hub data back out into a spoke model. This means a spoke model (representing a single channel) can incorporate data about the presence and effect of other channels.

Our aim is threefold:

  1. to learn what we can from the totality of the effort.
  2. to push that learning into the data we have available on each individual channel thus allowing us to improve how we optimize effort within a channel.
  3. to optimize, across all channels.

There are many individual hurdles we need to overcome, given the huge quantity of data available on some channels and the comparative paucity out there on others. We believe that there really isn’t any point complaining about the lack of data, or its inconsistency. Over time the situation will improve but for marketers the time is now, and the need is immediate.

We work on the basis that 90% right and on time beats 100% right but too late every time.

We work with what we’ve got, and what we can get.

Making Sense of It All: The Clear Performance Narrative

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This post appeared originally on the Crater Lake website, here

How many times has the boardroom or hallway conversation at your company contained a variation on the phrase “we are just not on the same page”. It could be referring to a difference of opinion or insight between marketing teams, or between marketing and the C suite. Or it could be a deep debate about strategy or overcoming challenges. The problem is, as one exec puts it, most marketers now enjoy (endure?) a glut of information. What is missing is a narrative that pulls the pieces together and makes sense of it all.

That’s why we set out to create the Clear Performance Narrative, one of the key deliverables we provide to Crater Lake & Co clients.

What we quickly discovered is that, occasionally, the problem with creating a Clear Narrative is that some pieces of crucial data/insight are missing, but you only discover that as you knit the story together. The story needs more signal, and less noise.

To qualify as “Clear” the narrative must not only answer why we are getting the performance we are, but also, what we can do next to improve the outlook for the business.

We must sort out cause and effect, and quantify the impact and economic value of all marketing activities.

We must be able to validate our analytics, to provide evidence to all marketing disciplines and the C suite that the narrative we build around our insights is reliable.

We must be able to quantify incremental lift in a holistic way, measuring advertising alongside other drivers of the business, whether those drivers are controlled by marketing or not.

We must be able to balance short term and long term, which also means balancing risk and reward. We have the tools (or some of us have), but applying them is challenging for leadership without context. As one wag put it “facts tell, stories sell”.

A clear performance narrative is one readable by any marketing discipline; indeed, every discipline should see themselves in it, and find and endorse their place in the story.

We must be able to build a clear picture of our customers and prospects in ways that are not just analytical, but empathetic. The narrative is not just dry numbers but human touch and feel.

Think of the narrative as the story of your brand in motion. When done right, we can clearly see where we have been, where we need to go, and how to get there. The very best stories invite the listener and reader to dig in, get invested in the outcome and root for the heroes. Only in this narrative, the heroes are not mythological beings, but our team members, their ideas, and their work.

The story is one of progress towards a goal, one that all marketers endorse and believe in. There are obstacles to be overcome, opposition to be defeated or outmaneuvered, challenges emerging that were previously unseen. How will the story turn out? It very much depends on the characters that write it.

So why the emphasis on story, or narrative? Aren’t we just talking about yet another 70 page powerpoint deck?

No, because this is NOT another 70 page powerpoint deck. We have enough of those.

As Jonathan Haidt (social psychologist and Professor at NYU’s Stern School of Business) put it “The human mind is a story processor, not a logic processor.”

Not a surprise to advertisers, who of course do this every day in their creative work. So now we turn the techniques of story-telling to the task of synthesizing the work of advanced analytics, of research, and of marketing and business leadership.

Making sense of it all: it should be an exciting journey.

-The Partners of Crater Lake & Company