The Curve: When to thank, encourage, and reward

What can we learn from transaction history?

The advantage of working on the 'agency' side is that we can see across several different industries.   When comparing how frequently a customer shops across categories one can get a sense of the range of possibilities.

The following chart plots the 'depth of repeat' curve for several different industries - from fast moving retail to 'once in a blue moon' service organizations.   To make comparative statements, each was normalized to the first visit, i.e. 100%.

There are several points to be gleaned from the chart based on looking at the odds of coming back for one more visit.

• The drop from 1st to 2nd purchase is a steep fall off for all five examples.   This suggests a strong "Thank You" program to help consumers decide to come back.
• There is a range of transactions where consumers need to be "Encouraged" to return.  The group of customers with 2-5 transactions may need to be treated to a different set of offers.
• Loyalty can be operationally defined at the point at which the odds of coming back are greater than some point on the curve, e.g. 50%.  These few, rare individuals need to be "Rewarded"

To a large degree the shape of the curve is driven by purchase cycle.  For instance, the average interval for the retail company is much shorter than the Service and Auto 2 companies.

But can this information be predicted based on knowledge of past behavior?   In a word: "Yes"

Here is the customer profile for one of the auto/service companies; it shows the number of consumers by number of repeat transactions over a two-year period against the estimated count of customers.   The estimated number of repeat transactions are based on a combination of Recency and Frequency type information alone.

While possibly not as valuable as a predictive model due to the lack of explanatory variables; this does provide a benchmark method of defining what the expected number of purchases a person will have.  Against this backdrop we can see if direct-to-consumer programs move the needle.

Organizing Digital Marketing

Since consumers don't care about channels, why should we organize that way?

In an era when everything is interactive and chunks of content are consumed on any of four screens as appropriate; what does a marketing organization need to look like to be successful? A couple of scenarios come to mind:

• Direct-to-consumer: the delivery of messages aimed at facilitating a decision by an individual or a group.   This covers targeted media whether traditional direct marketing, consumer promotions, pure Internet/Mobile plays, or custom content served up based on behavior. 
• Engagement: the provision of applications, leisure content, or information with the desire of establishing rapport by helping or entertaining people.  This covers aspects of marketing where the focus is typically on brand and relationship building, e.g. consumer experience. 
• Insights:  the identification of the "Ah Ha" or "Oh No" moments that come from connecting all the dots that digital self-expression leaves behind. Plus consumer expectations for real time response means we're entering the era of Track>Execute>Adapt where the data must define the plan.  

This structure rises above the challenges that arise with the proliferation of channels, technologies and tools. If fact, tools like social technology can be used for both objectives - influence and support. As an example twitter can deliver offers direct-to-consumer or engage them with links to interesting content.

It also provides a better platform for career growth since it is highly likely that a large number of CMOs in the future will come from this part of the business. 

Notes:
For some comments on digital structure and issues eConsultancy's produced a video summary of their conference on the topic. 
For a look at how the total communication spend is broken out, Veronis, Stuhler, Stevenson (VSS) released their updated spending forecast.

Digital Behavior vs. Digital Identity

What do we see when we only have bits?

Due to the nature of technology, identifying actual people based on their digital footprints is extremely difficult.  In fact it often presents a fleeting, alternative reality. 

Cookies, IP addresses and other technical devices are at best a proxy for a real person; at worst they are an inaccurate puzzle with missing pieces.   To make matters worse these methods are limited in terms of both time and device.   Just try to identify me across four screens and six months.   Add to the mix the response time that digital marketing allows and we must focus on the here and now.

The result is that we have to balance our focus of what a person does over time with what is the behavior being exhibited on a device right now.   The rise of retargeting technology seems to be a good example of this new duality.

AhHa or UhOh Moments of Analysis

Just what is an insight?

On LinkedIn there is a long running discussion on defining 'insight' in one word.   My favorite pair is 'AhHa!' and 'UhOh'.    These two words sum up the essence of insights - tell me something I didn't know I need to know.  

Both reactions suggest that a flash connection happened that allows the analysis to turn into action.  In one case it is opportunistic; in the other corrective.  

• Opportunistic: finding new markets, product uses or ways to connect with consumers
• Corrective: adjusting plans to better reflect reality 

In both cases analysts have done their job - help marketing change the future.

Estimating Customer Profitability

How can one predict the future?

Given that customers are fickle and firms are inconsistent the idea of predicting future behavior, much less profitability, is fraught with perils.   In fact, it is often the answer to 'what have you done recently?' that is the best predictor of future profits.   While current or average profit is a naive assumption it often out performs much more complex approaches.   Recent research took a stab at addressing the question again focusing on a series of questions.

1. What marketing activity is the customer likely to encounter in the future?
2. What is the likelihood of purchasing given the contact plan?
3. What is the probable profit given that a future purchase is likely?

Each question results in a series of estimates for every customer across the planning horizon of 12 quarters.  To assess whether it works, the summary looks at customers at two different points in time and plots the migration of people across profitability segments. 

Since the answers are all estimates and subject to error the authors added a nice twist; they simulated a 1,000 different futures for each customer based on the uncertainty in answering the above questions and then pooled the results.   This approach of creating a myriad of potential outcomes worked better than the benchmark models.

Like so many marketing problems, the future is all about best guesses based on alternative reality rather than the certainty of a point estimate.   

Content is Not A Device nor a Channel

What contributes to conversion?

The other day there was a good post on multichannel attribution on Occam's Razor that covered three different ways to think about the problem: Stimulus to Store, Screen Experience, and Channel Usage.  Each approach answers different questions and throws up unique sets of challenges.  Given that attribution analysis often results in the allocation of marketing spend to silos we continue to reinforce the bad habit of thinking in ways that consumers don't.  The examples in the post of changing channels and switching devices along the path to purchase reinforce this notion.  It also makes the case that the problem may actually be intractable in the long run because we simply don't know who the person is.

So, if that line of thinking is a potential red herring is there another question to consider?  Maybe we should think more about what consumers are consuming rather than how they are doing it since it is a given we should be creating content for multiple channels and devices. Shouldn't we be more interested in the fact that a person might be looking for aspiration, deals, feeds & speeds, or validation than what version of an operating system he uses? 

• Emotional content sparks interest and satisfies need and can be in the form of TV, print or branded content reached thru organic search.
• Promotional content satisfies the need to 'get a deal' and reduce the risk of trial and can be a direct response email, coupon code aggregator, or store flyer.
• Informational content provides the rational basis for defending a decision be it a feature-laden fact sheet or 3rd party comparison report.
• Communal content provides the validation of other people be it reviews on an e-commerce site or a quick response to a Tweet. 

A segmentation strategy based on content consumption habits is likely to be more useful to a marketer than delivery preferences. To be sure, this is not how most attribution discussions go but in reality both channels and devices are simply delivery vehicles for content. 

Analtyics Were at the Beginning of Cinema

Was it a creative idea or data that gave us motion pictures?

While associated with the creation of motion pictures, Eadweard Muybridge basically used data to settle a bet.  The question as to whether a horse's feet were all off the ground at one time while running was apparently the source of much discussion (and likely a wager or two).

To settle the bet for his patron Muybridge invented a system to trigger a series of pictures as a horse ran by - he also later created the zoopraxiscope to display images of what was to become stop-action or slow-motion photography to audiences.

Today Google honored him with a doodle....

Marketers Should Embrace Hadoop

Why is technology named for a stuffed elephant important?

As marketers we've been conditioned to do the following:

• To ask only really important questions because the half life of the others is much shorter than the time it takes to get answers.
• To view most data as having little value because we can't adequately see or filter it into anything useful.
• To accept answers to questions that technology can support based on yesterday's needs rather than what the business requires today.

We got into this situation for two very good (at the time) reasons.   First, the cost of data storage and processing was such that we had to be judicious about our requests - constantly making trade-offs between known costs and uncertain benefits.  Second, the need to report results upwards ended up with a belief that there is only one true number.   The result was a lot of time and effort spent on collecting and cleaning only the best data, putting it in a data warehouse, and judiciously handing out the keys to the kingdom.

An unintended consequence was that we tended to focus on structured data - things that could be represented in rows and columns like customer transactions - rather than the amorphous data of comments and images whose value was unclear.  Because the marginal cost of implementing changes to data and reports was high we were left with a situation where the addition of any new data source or question would be delayed by the process of getting funding. 

Well, that approach won't work anymore. 

Digital interaction spins off data in real time that we have to leverage in real time if we are to respond to consumer interests and actions.  Businesses need to consider that...

• Self-expression leaves a myriad of opinions and objects to be shared
• The infinite paths-to-purchase leave breadcrumbs at each and every step
• Human interaction influences opinion and choice more than the paid placement of messages

And a world in motion is very different than a static one in terms data volume, variety, and velocity.  This is the V3 world of Big Data where everything is simply more. 

In the past we could plan, execute and track; today we have to track, execute and plan.

It would probably be insane to try to implement a consolidated system that captured and stored all of those events with a customer loyalty program. So, if we can't build an uber data warehouse, what are we to do?  Well the folks at Google and Yahoo! solved that problem by distributing the question rather than centralizing the data in order to improve the indexing of web sites for search.  And out of that comes Hadoop a framework for data-intensive applications that leverages distributed processing designed to handle the type of data generated above. 

Consider the simple idea of presenting a customer with the next best product while she browses your web site. This requires he tight integration of real time events (what are you looking at), historic transaction data (what have you bought) and predictive analytics (what should we recommend).

Hadoop is not a replacement for existing infrastructure but represents a way to think about three key marketing needs.

1. How do we transform digital self expression in order to leverage it with historic transactions?
2. How do we do scale real time, event based analysis and deliver them where the consumer is right now? 
3. How can we overlay content consumption habits with our traditional segmentation schemes to deliver a better experience?  

The new schwag for both your CMO and CIO....

(source)

Building a Degree from the Data

What should we study?

It is not unusual for people to change colleges or even start-stop-start their education.   In certain segments it is common for applicants to come to the table with a substantial number of credits.   So, an interesting analytic problem would be to take those credits and come up with two to three alternative courses of study.  Maybe one that is of expressed interest and the others are simply the shortest distance between two points - now and the degree.   So, what would be required:

1. Courses taken: what credits have been earned to date.
2. Course map: articulation agreements align courses from one institution to another
3. Degree requirements: the core and elective courses for potential degrees

It becomes a database alignment and probability exercise to create several alternatives given a set of inputs.  To add some spice to the solution a simple way of flagging interests or using collaborative filtering or recommendations to make the solution more social.

With the proliferation of degree options it is no longer a simple choice of A vs. B.  It is more like A with some C and maybe a D thrown in because I don't like B.  And all these options actually make it hard for people to choose one university over another; a simple tool that provided some alternatives might just help conversion and win the race to rapport.