5 Problems with Marketing Analysis

What do we often forget to mention?

I missed an important birthday last month; the banner ad turned 40.  We're certainly not going to see the 40% CTR that this ATT ad achieved back then.  In fact we're not likely to see much above a single digit or even decimal place.

At the time I don't think anyone had an idea what the click thru rate would be, but in today's data-driven world we should have some clues.  We have all kinds of systems and analytic tools that predict what a CTR might be. But each of those come with one or more constraints that we often forget to convey.

1. Time, data and resources will narrow the options we can use to analyze a problem; need an answer today? or next week?
2. Explanations always rest on assumptions as we reduce the problem to one that is manageable; did we remember to tell anyone?
3. Our mental models are biased to begin with, and we may not realize it.
4. Data is left behind due to data collection procedures or application code; design choices made before we started the analysis limit what we can and can not do/say.
5. Our use of data may be different than how it was intended; this is a common question when creating mashups particularly with public data.

So, say out loud "this is what we don't know"....

The Need to Link Data to Marketing

How do we come to understand things?

In the era of "big data" this and "big data" that, we have a problem. Our minds are wired for language not data. Rarely do we have direct intuition about data, what it means, and what we ought to do with it.  We need to invent the Rosetta Stone that bridges the hieroglyphics of machine learning with the ancient language of marketing.

Consider the following examples:

• What is the cost function of loyalty?
• What does unsupervised learning tell us about branding?
• Where does gradient descent take us in terms of understanding engagement?

On the left side are data terms; on the right are marketing terms.   Each takes a common, yet ill-defined marketing idea (loyalty, branding and engagement) and maps it to an aspect/method of the big data world.

• Loyalty has a cost (or a profit) that comes at a price. What is the allowable marketing cost of achieving it?
• Branding is often described as being in the mind of the beholder; thus we should not label things as A or B but rather be surprised by how consumers cluster things.
• Engagement is a cumulative set of interactions that may (or may not) move us toward conversion; we need to understand the marginal contribution of each step.

Only if both audiences, data folk and marketers alike, can ask and answer these and a myriad of similar questions will we be able to translate from one world to another.

Digital Differentiation and The Chore of Choice

How does differentiation apply in a digital world?

This post was inspired after reading "Digital Brand Differentiation - 3 Frameworks to Follow and asking the question above.

Differentiation is required when we are faced with the chore of choosing.  It works because it provides a focused benefit that we can internalize and compartmentalize as "that does this".  So when we have "this" need we lean toward buying "that" product.  It is a step toward branding.

The qualifier 'digital' raises some questions to consider - some apply to digital products others apply to digital tactics for analog products.

1. What new dimensions are available upon which to differentiate 'digitally'?  (Which old ones don't matter any more?)  
2. How does digital information access and transparency change the relative importance of 'being different' in the decision process?
3. Are there categories where it is more/less important to be different because of digital?  Why so?
4. With the digital consideration set now being defined by the amount of time we invest, is it more important to be the first to be perceived as satisfying the basic need rather than doing it differently?

Jack Trout's "Differentiate or Die" is a good place to start on the first question since it highlights 20+ dimensions upon which create that distinction in people's mind.  Written in 2001 it probably doesn't cover the full implications of digital so could use an update from a digital perspective.  For example, I don't recall it using communal content (social networks or reviews) as the basis of differentiation.

A counter argument to the differentiation argument was offered by Killian Branding in a post entitled ""Differentiate or Die" is dead. RIP, USP".  That post concludes with the statement "Visibility is now more important than differentiation. Mission One: Get into the selection set."   To paraphrase the logic, if you're not at the top of the list (Yelp, TripAdvisor, Google, etc.) it doesn't matter how you are different.

What makes digital different is that the consideration set emerges/contracts/grows over the decision process - it is no longer an all or nothing proposition of being in front of the toothpaste shelf or looking at Thai restaurants in the yellow pages.  This would suggest that the emphasis on differentiation may rise/fall across the journey based on inferred intent.

The 5 Persona of Big Data

How do we decide WHICH Big Data is right?

The stories about Big Data talk about it as if it is one thing; it isn't. Big Data actually encompasses several different universes based to some extent on the fact that there are different underlying technology that can be deployed. And since technology is versatile but not omnipotent it makes some sense to first try to articulate what one is trying to achieve rather than just saying "let's do Big Data".

And any time there is hype like we're seeing today a gap emerges between what the business thinks it wants and what the technologists believes it can deliver.

To help bridge that gap it might help to describe the various approaches as people with skills and characteristics. To provide context, I'll start with the old guard and then introduce the new characters.

• Rachel - is the oldest with a very structured and ordered view of the world, her motto is "everything has a place, and everything is in its place" and put there immediately. She doesn't handle ambiguity very well and her rigid out look makes it difficult to change things as the world around her changes. She struggles when either too much complexity exists or the scale of the problem is too big. We all know Rachel as "relational databases" introduced in the '80s/'90s when computation, storage and memory were all expensive. She is showing her age. Think business intelligence and MySQL or Oracle.

• Chloe - views the world thru a different lens. Rather than looking at everything as well-structured events like Rachel, she is more interested in how groups of those things overlap on key attributes. She is less interested in "how many" than in "what are the commonalities/differences" in a set of things. In addition she clearly recognizes that every thing isn't known about everything so is quite comfortable with uncertainty. These holes in the data create a sparse matrix that is often best handled by "column-oriented databases". Think highly variable data elements and GoogleBigTable or Hbase.

• Kayla - looks at the onslaught of data and says "I can manage this if I spread it around across as many buckets as possible." Her goal is to preserve things as quickly as possible and to make sure they are retrieved upon request. She defers understanding and definition to others. In the purest sense "key-value pairs" make the data opaque by design, trading insight and thus latency for scale and availability. While excellent at writing information out her challenge is trying to organize and integrate data from several processes that all use the same term to mean different things. Think extreme write and access of user-supplied data and Apache Cassandra or Amazon Dynamo.

• Danielle - has probably the most unstructured view of the world "everything is a chunk of stuff and tags". Success then is dependent on the completeness and accuracy of the descriptors assigned to a chunk since searching its entire contents can be costly. The unstructured view applies to connectedness as well as content. In the "document store" world there are few relationships between the chunks beyond the tags. Consider posts tagged with #BigData - the hashtag is the only commonality. There are no implicit relationships beyond that fact. Think content management with mongoDB or CouchDB.

• Grace - is the most social and believes relationships are everything. She thrives in scenarios where the existence or nature of a connection matters more than what is connected. This focus makes it very easy to extend the universe in new ways as new things happen or new actors emerge. Grace exists as "graph databases" that have been around for a while, but most noticeably in social networking and social sciences. Think recommendations, communities of interest and Horton or Neo4J.

Which persona we adopt depends a lot on what we want to accomplish. And it is quite possible we'll end deploying more than one solution in our quest to "do Big Data."

{re}thinking with Data

How do we go about finding insights?

Let's start with what we mean by 'insights' -- here's my working definition in the context of marketing:

the identification of a previously unknown connection between marketing activities and consumer behavior that changes how we align our solutions we human needs.

Or, simply the finding of something "I didn't know I needed to know."

The typical story line for doing analysis consists of the following chapters:

1. State your objective
2. Define your strategy and tactics for achieving them
3. Create a measurement framework of KPI's that defines success
4. Articulate the dimensions of the business/behavior you need to understand
5. Set targets or goals that you need to compare to in order to track profess

And along the path you're defining a technical implementation and data capture plan to get from #1 to #5 and then refine.

If we're not careful, there are several risks in that plan.

First, we don't do all the steps...there is a tendency to jump in and build something; we forget to ask "why are we doing this in the first place?" enough times.    Think about the difference between Cliff Notes and the real thing.  A good essay requires reflection.

Second, we prematurely narrow the list of potentially valuable options.  This "focusing illusion" creates a bias because we tend to look no further than the first idea.  This is akin to judging a book by its cover. 

Third, we often view the problem in terms of outcomes related to our current business model not what might have caused them. While it is imperative to have metrics to track, they are simply links between behavior and performance.    Conversion rate is not a behavior.

The remedy to these risks is to spend more time thinking and that requires deep domain knowledge as well as the ability and willingness to explore.

For a good read try "Thinking with Data" by Max Shron

Using Your Own Customers to Crowd-Source Analysis

How can we leverage the fact that we're creatures of habit?

We often talk and read about benchmarks by tactic.  For instance 'email open rate' is tracked because it is the gate keeper to engagement and involvement.   As an example Silverpop reported the median open rate in APAC in 2012 as 27.2%.

I chose a two-year old number from 5,000 miles away in order to focus on the fact that these metrics are generated thru the lens of the campaign, not the consumer.

The health of a continuity email program relies on involvement over time and leads to the important question: How many more emails will you open?

The nice thing about 27.2% is that it is derived from nothing but 0s and 1s - consumers did or did not open the email.   If we look at the campaign or program level we now have a series of events from our intended audience that help answer that bigger question.  It turns out that there are two truths about consumer marketing metrics:

1. Even if your numbers are flat, it is highly unlikely that it is the same consumers each week.
2. The number of times consumers do something very often follows a predictable pattern.

In the case of email, consumers act just the way they do with buying products - that is there is a decay in the number people who do sequentially more things. 

The number of additional emails opened looks like this for one program.

Yes, the campaign had a problem with relevancy - something originally hidden by the rate of acquiring the email list.  Message: fix the communication strategy.

This is a very simple example of crowd-sourced analytics, there are lots of behaviors that can be treated in a similar fashion.  In fact there is a whole class of work being done in anomaly detection that takes advantage of habits.

How to Make Sharing Work

Why do people share? And with whom?

A recent post on LinkedIn about the phenomenon of sharing made the point:  we all ask for a share, but virtually no one offers a reason as to why we should bother.   Making it clear what we're offering and what action we want should be basic marketing.

But what should our expectations be about sharing?

The following is some of what Google learned in the development of circles.

First, circles exist indicating that people categorize others according to some meta-association.  There are likely some standard classes of association - think function like work or school and strength of the relationship like fraternity or building association.  It is easy enough to prove this, just compare one's FB friends and LinkedIn connections - some overlap, some exclusivity for defined reasons.

This means sharing must be selective - if we have circles then we don't intend to send everything to everyone.  Therefore we need to consider the why and who questions of context and audience.

Why do people share? the reason or context of sharing can be for one of three reasons.  It may be...

• personal - stories or opinions about oneself
• conversation - contribute to a discussion
• evangelism - spreading the good word (or funny video)

Who is the recipient? the audience is often based on a choice as to whether the content/context combination is...

• private and appropriate for only a select few, 
• relevant to a community of interest, or
• interesting to the masses.

So, before throwing the share button on every thing possible give some thought to how context and audience relate to your offer and call to action.

There is the obvious questions about what is shared...but I'll leave the content discussion for another time.


Source: extracted from David Huffaker's discussion of extracting meaning from data in "Doing Data Science".

3 Implications of Implementing Analytics

Are there {un}intended consequences of being data-driven?

As analytics moves closer to what Bill Franks of Teradata recently described in a post on operational analtyics there are organizational changes looming on the horizon.   A couple of things come to mind:

• Predicting the Future Creates the Future: If analytic output is implemented by the business then the creators need to share responsibility for success or failure.  This changes the "analysis as a service" model quite a bit.
• Opportunities Will Be In-Market Before the Business Case is Written:  The emerging trend in all of science is to analyze the data to uncover new theories, whereas in the past we started with a hypothesis and then collected the data to test it. Analytic-driven discovery inverts the venerable command and control approval process.  Which leads to...
• Results Trump Responsibilities:  This central tenet about rewarding value creation over resource control comes from accenture's blog discussing the twelve self-evident truths of being digital.  Proving an impact has more weight than claiming one.

This suggests that future line managers will come from the ranks of analysts.  

5 Questions To Ask After Each A/B Test

Why do we test?

Very often the discussion about testing is framed in the measurement side of things.  "To improve ROI" is a fairly typical the answer to the question above.  Yet, from an analytic or data science perspective that actually misses the point.

The reason we test is to gain knowledge.

To be sure testing green buttons vs. red buttons should be framed in terms of conversion metrics.  But more importantly we need to be asking ourselves the following questions:

1. Why did it work?
2. What are the segment characteristics of the winning option?
3. Among which segment did it NOT work?
4. What do the results tell us about the decision making process?
5. Where else in the pipeline or funnel can we apply this learning?

Understanding the answers to these questions will likely have a bigger impact on the business then testing purple in the next round.

Big Data vs. Data Science

What is the difference?

A lot of conversations I'm in having these days ask about these two phrases:  Have I done it? Can I lead a team doing it?   To answer I've had to put some stakes in the ground and define them from my point of view.

• Big Data:  a state in which current systems and capacities are simply overwhelmed. One cannot use traditional thinking or tools because the data doesn't fit in memory on a single machine.
• Data Science: the process of interrogating data in hopes of improving the human condition.

While Big Data is a state of being it is by no means static.  Like the rapids on the Inga river it can be a massive torrent of moving droplets.  The bigger the wave, the more a Data Scientist {team} needs computer science skills to navigate from point to point.  And unlike its predecessors "Data Science" as a discipline starts from a different place: given data, what questions could be answered?   Empirical, theoretical and computational sciences start with a question and don't actually have much data - they tackle different problems through observation, logic/proof and Big Hardware.

Because we're looking at the world passing by as a torrential stream of bits we need to have a goal, an objective or a problem to solve. One simply doesn't just jump in, there needs to be a plan and a lot of preparation (did I mention a LOT of preparation) grounded in experience, math and statistics.

Big is in the eye of the beholder.

Having worked with US and Canadian clients there is a line in the sand where things seem big.   For example a reasonably sized loyalty program for a national US retailer is considered big by Canadian standards since it is larger than the total population.  Frame of reference matters.

Science is a pursuit, a line of reasoning not an algorithm.

Along the path we need to visualize, explain and communicate what we've learned to date. Sometimes it is enough to know that a tactical change improves conversion because of correlation; other times we need to explain why and address causality.

Big Data is not Data Science and Data Science is not Big Data although it is quite clear the two overlap and the most frequently mentioned stories come out of that intersection.

Congo: The Grand Inga Project

The story of Steve Fisher and friends running those rapids was released in a documentary in 2012.

Dear Account Manager: Pleases don't ask me #2

Can I have some insights with that report?

Unlike a side of bacon, insights can't be ordered up on demand.  The best insights don't come from a short-order analyst, they come from those with a deep understanding of the business problem.  And like good recipes, they take a while to develop.

Nueske's Bacon

A good analytic team should be able to respond quickly to questions like "where are we up/down? and what are the likely drivers?"  But coming up with a new view on why consumers behave the way they do that changes how we market isn't suitable for "order up."

Please give us time...

Dear Account Manager: Please Don't Ask Me

What is the average {fill in your favorite event here}?

Average is the most dangerous word in marketing for three reasons.

• First, our goal is to satisfy a need in a differentiated manner such that consumers make a connection with us.  There is no individual who believes she truly is average, so why should we think that way?  
• Second, our job is to change history - new products, new markets, and better growth all succeed when we focus our attention away from the mushy middle.  
• Third, as a measure of central tendency, it is either technically inappropriate because the underlying data doesn't behave normally and / or a single number masks too much useful information.

Consider the following plot of a typical event - there are some who do it once, a bunch who lump together at some low level and then a long tail out to super-consumers who do whatever this represents a lot.   Consumer behavior often looks this way - product trial, application usage and email opens all take this form.

The bars are the data, the lines are two different ways of smoothing the data so that we can draw conclusions or possibly make predictions.

• Red is what we were all taught in class and produces an average of 42, which is almost on top of a big dip in the event count - as well as the meaning of life. Are we missing something important? Notice that it assumes we do less than zero things, an impossibility.
• Blue is a better overall fit and shifts the curve to the left where it appears more logical, but like the normal curve it still misses the post-fifty dip.

Statistics, even as simple as average, work on a set of assumptions.  The above picture suggests that the red ones aren't quite right and the blue ones are probably much better.   There are other ways to describe those events, so I need more information to help you.

Instead of asking what the average is, ask me the following questions:

• What does the distribution of {event} tell us about our customers?
• Are there gaps or lumps that present opportunities to adjust our marketing?
• What is different about consumers on one end versus the other?
• How many events should we expect over what time frame?

And I promise I won't answer gamma, Pareto-NBD, or Weibull....

Post inspired by "Doing Data Science" by Cathy O'Neil and Rachel Shutt as well as  Eric Cai - the chemical statistician - and his series on R-bloggers.

Programmatic Creative

Is that title an oxymoron?

This morning a post in iMedia entitled "Programmatic Creative: The bridge between beauty and data" the authors make the case for linking the data and creative teams.   The story line is in the context of display advertising and real-time-bidding although the prime example is the virtual car buying process created by Jaguar Land Rover.

The concept is that one can optimize creative, not unlike what we've already proven with A/B testing on web sites, to improve conversion.  This is part of the larger trend around what the Winterberry Group calls programmatic marketing (registration required for white paper) where there is a method to the madness that some call marketing.

Having worked across the marketing spectrum of agencies, marketing services firms and media/publishers here's my thoughts on the analyst's role in this.

1. Creative directors work with account planning to distill a client's request into a manifesto which is a concise description of the need to be satisfied.   In a somewhat ironic twist, the best creative solutions emerge when the situation is described in a very specific and precise way.  Thus, the role of analyst is to help sharpen that single paragraph to the point where the creative team can do their thing. 
2. Marketing services firms often help with consumer segmentation, campaign strategy, and tactical execution.  They are likely to be on the hook for measurement and performance analyses.  Here the role of the analyst is a little like burning both end of the candle - input to the creative process as well as confirmation of its impact.  
3. Media/publishers have yet a different point of view.  Their success relies on identifying and providing audiences that advertisers are interested in reaching.  In this scenario the analyst is looking across campaigns, web site signals and other data sources to identify and create the appropriate segments.  Providing counts of visitors is no longer enough and content interaction is becoming key.

These areas will probably converge faster in direct response advertising, e.g. driving sales or using promotional content, than in awareness scenarios due to fewer measurement challenges.  But as we better understand how people decide we can expect the learning to be applied to brand campaigns as well.

All this suggests that analyst or data scientist needs to have conviction and step up to line decision making.  No longer are we just a staff function providing options and opinions.

To answer the question: 'nope; programmatic creative- in the larger sense - will likely be the norm.'

Using Language to Build Communities of Interest

What can words tell us about interests?

Imagine playing the word association game and have to identify a community of interest from a single word: "Drift"

It could relate to communities focused on:

• fly fishing: a boat used on rivers or a cast that is free of any pull on the line
• racing: the act of oversteering and letting the rear wheels go wide
• film: two Australian brothers create a surf company

In a LinkedIn post I updated some thoughts on Leveraging Communities of Interest and suggested that the language of a community is likely to be distinctive.

This idea implies we develop a thesaurus (or possibly even an ontology) for a COI that captures the concepts, their synonyms and the relationships between the words used. Taking a body of content, extracting the terms and building in the relationships is the role of a new type of marketing analyst.   This underused marketing technology (taxonomy) allows us to analyze:

• Terms used: both unique and unusual frequency counts are the first hint of the existence of community
• Relationships: phrases, as opposed singletons, highlight how terms go together.  Related, broader and narrower concepts clearly separate the three examples above
• Synonyms: alternative labels for a concept are another source of clues

This approach is a little different than text mining or sentiment analysis, although the underlying technical tools are often similar, i.e. some form of natural language processing (NLP), because the end goal is the management of a vocabulary. To take full advantage of such analysis it should be deployed at the source of tagging since too often meta tags are whatever comes to mind at the time of creation. If you've ever gone back and looked at tags across a large number of articles, you probably know what I mean.

By understanding concepts, relationships and synonyms used by a community we could devise ways to assign a consumer to one or more of the communities. It would also provide the means to rate content in terms of effectiveness within and across communities.

The more content you create, the more important vocabulary is - particularly if you're a publisher.

Top 10 Algorithms Affecting Marketing

How can we relate math and marketing?

Earlier this year io9 listed the "10 Algorithms that Dominate Our World."  These complex math functions are:

• Google Search - at 67% of search traffic, 'nuff said.
• Facebook's News Feed - they pick what you see
• OKCupid Date Matching - more successful than pickup lines because of weighted data
• NSA Data Collection - the method has been redacted
• "You may also enjoy" - from Amazon to Zappos guiding the next choice is rampant
• Google Adwords - figuring out if you'll be satisfied with the results, and then charging more
• High Frequency Stock Trading - leaves humans out of the equation
• MP3 Compression - the pipe is only so big, everything should be compacted
• CRUSH (Criminal Reduction Utilizing Statistical History) - public sector success story
• Auto-Tune - pitch blending for fun and profit, just ask Cher

As a consumer I don't care how they work, I'm just glad that they do.  However, as a marketer I do care since these algorithms sit between my campaigns and my results.   These algorithms are complex, proprietary and changing so fast that understanding the specifics across the board is out of the question.  That said, here are some ideas on how they relate to a marketing point of view:

• Organic search is a popularity contest with one judge and a hidden score card.  Since our content is judged against that from everyone else we need to constantly be looking at the world from the perspective of 'how do we help consumers find what they need'.
• Social news feeds take into account the wisdom of the crowd in determining what to run past you. From this we should be thinking about what content archetypes and forms creates interest and engagement.
• Matching algorithms often work with layers of weighted information across many dimensions. In many respects this follows the same process as branding - reduce the reasons to believe to a promise and ultimately to a single essence that aligns a solution with a need.
• Algorithmic adjudication or determination poses some ethical questions about permitted use. Something anyone dealing with privacy already knows all to well.  Data is not neutral, observation is biased, and all models are based on assumptions and decisions.  
• Recommendation engines are at the heart of personalization and dynamic content.  However, there is a risk of over filtering and missing that fact that decisions are based on emotions rather than facts. "I didn't know I wanted to have...." is tough to program if you've never had....
• Paid search is a combination of two sets of results - theirs and ours.  This is a perfect place to think about and work on the problems of resource allocation and attribution in part because it is down at the intent level of the consumer journey.
• Programmatic marketing, at least in the narrow sense of real time bidding (RTB), evolved from the ability to arbitrage at speed.  With RTB we're still left with two key questions:  What should we pay to reach an audience? and What should we tell them?  Creative optimization is next.
• Compression is an analytic process that removes redundancy and noise in a defined manner.  The best parallel I can think of is the creation of consumer segments - we abstract and reduce the most important details and hope for a lossless solution.
• Data mashups, particularly physical location + digital activity, are a breeding ground for unique insights.  They are also a good way to start to break down traditional channel silos because they look at the world in a new light where everyone can contribute. Adding algorithms makes it better.
• No marketing activity has perfect pitch so comparing data to match a known standard sounds a bit like forecasting.  Since forecasts are always wrong, the interesting bits are "why?" and "what did we learn?"

I'm sure there are numerous other algorithms and parallels - please add yours.

Two Uses of Visualization

What are the use cases for visualizing data?

Visualization has become the new buzz word.   First there was 'analysis' (came to the fore back when we first started doing pivot tables), then we had 'insights' (because it sounds cooler and technology companies wanted to be thought leaders). These concepts have morphed into machine learning (because it all seems too complex for mere mortals) and or visualization.

Visualization is used for two very different purposes:

• exploration: looking for that insight that changes how we market
• explanation: illustrating what we want to communicate after we've found it 

The skills and tools we use for those two functions are very different.

Exploration visualization needs to focus on patterns and relationships  - the first image uses the sample data sets accompanying "Doing Data Science" and is produced in R which allows for a train of thought to be followed quickly. It shows the distribution of click thru rate by age of consumer.  We already know that CTR is low (big bump to the left.) What is important to takeaway is that the relationship between click thru rate and age category is consistent, i.e. the lines pretty much stack on top of each other. This leads to another question: do we need to rethink the idea that age-based cohorts are different.

You may notice that there is a group of consumers who appear to not have been born yet, i.e. their age is given as (-Inf,0].  Knowing the data helps here since one has to be logged in order to link to age implying that this group consists of anonymous visitors.  So, this plot actually uncovers another point - CTR among subscribers doesn't vary from non-subscribers raising new questions about the value of engagement.  The data team should be versed in this type of visualization and questioning.

In contrast explanation needs to look like Consumer Barometer where a lot of design time went into creating an interactive display of how/where people buy stuff.  Like any dashboard, it provides a series of predefined navigation paths.  In this case the user can traverse along geographic, demographic, product dimensions. This specific image captures variance in online research by product category (color) and penetration (size) for 40+ year old consumers in Hong Kong.  Instantly we see that "they are more active in computer/electronic categories than any other."  This is where we partner with the creative or UX teams to highlight the differences and communicate key points.

Two uses, two technology stacks, two (actually more) teams.

Creating Segments from Signals Using a DMP

Just what does c_product=="apple" mean?

Previously I outlined some thoughts on developing segments that focused on more strategic uses.  The end result of the top down process is 5-to-8 key personae that should be tracked as part of any management reporting process.

However, when working at the campaign level the process of defining segments is a bit different because we're now more interested in getting the most bang for the buck as quickly as possible.  Optimization and bidding decisions are the epitome of this tactical thinking and the more variations we can test the better.

Think blocks...

Nowhere is this distinction more evident than in the process of developing segments for digital targeting using a data management platform (DMP).  In this case we start with the signals generated by traffic, convert them to traits that are appended to each visitor and then create the segments based on those attributes. This bottom up process takes a lot of structure and quite a bit of restraint not to end up with a bazillion one-off segments.  

The organization of traits and segments is the most critical aspect of a successful implementation even more so than the script that generates the signals .  These folder structures (I'm using Adobe Audience Manager as a mental model) are "priceless", a term used by a colleague with more experience than I at the time.

Here are lessons learned from a recent experience:

• Use tag management to standardize or at least rationalize different tagging schemes before the signals arrive.  This becomes more important as the number of sources or sites increases.  Imagine trolling thru an unused signal report where each source has its own taxonomy.   Since these signals get converted to simple values, e.g. "apple", there is no other context provided than what you see.  The band aid is having to add traits just to define what you mean...this gets ugly as the "or" and "and" conditions get strung out.

• Separate traits into independent dimensions that represent specific elements of behavior, e.g. location, site structure, content elements, and events/actions.  You can deconstruct past campaigns into its component parts, but don't think too much about those segments at this point.  The goal is to resist the injection of complex logic too soon in the process which ends up decreasing the value of the traits over the long term because they are too specific.

• Think of segments at the DMP level as building blocks, not the final target group.  In this way we can easily mix and match at will at the destination or ad server side of things when we know what the campaign objective is.   We want to encourage reuse from both and immediate efficiency perspective, but also for analysis purposes later on.  By having building blocks we can look across campaigns for insights.

As a really simple example, consider building segments that a retailer can use for targeting it's customers.   There are at least two definitions here - site visitors (i.e., "h_referer") as well as those within a defined trade area (i.e., "d_postal").   These should be passed as two segments rather than one to allow different campaign rules to apply of various combinations at various points in time in the future.

• Visitor In-Area:  retain, defend, reward
• Visitor Out of Area:  understand
• Non-Visitor In-Area: acquire, make aware

Now imagine a dozen or more classes of building blocks...

As analysts we should be in the business of creating the building blocks, not a specific audience segment.

3 Steps for Market Segmentation

What should we be thinking when defining segments?

The path from consumer interactions to valuable segmentation requires taking a series of steps rather than making one declarative statement about who our market is.

• Step 1: Define why we need segments.   The technical definition of a segment - a group of consumers with a common need that are expected to respond similarly to our messages - doesn't actually help us much. For instance, "web site visitor" is a characteristic, not a segment per se since we have all types of visitors who are there for different reasons.  So I start with the business objective we are trying to improve.

• Step 2: Given a metric that matters, identify what characteristics, attributes or behavioral traits could help identify members of a segment.  Since segments are a mental model for grouping consumers and rarely something people actually call themselves we're left with the challenge of identifying a collection of proxies that indicate membership.   I work through a list of attributes as if they were software feature requests: what traits 'could, should, and do' make a difference.

• Step 3: Report by segment.  We've gone thru the steps necessary to create and target segments, it would be a shame not to report and analyze that way (happens all too often.)  The biggest obstacle is that the things we can track (digital activity) isn't always what we want to report (change in sales or marketing impact.)  And outside of pure-play ecommerce I'm going to be modeling that impact. 

The implications of these steps are:

1. There is more than one segmentation scheme and our first ideas coming out of a conference room probably aren't the best
2. This reality leads to a Test & Learn approach to the definition not just the creative/offer side of things
3. Segments need to be stable over the marketing planning horizon in order assess change and affect budgeting

Analyst Job Defined

What is the role of an analyst?

Ran into this graphic from gapingvoid...Hugh MacLeod's site about using art to transform business... that sums it up quite nicely.

We're not paid to report the numbers but rather as Marc Cendella explained our job is "to share and explain how you can make the connections between those bits of information."  The goal of analysis is to improve things...be it a campaign, marketing in general, or the human condition.

Master Marketing Profile: New from Adobe

Just how they do that?

At the Adobe Summit this week several core services (much better construct than 'shared services') were introduced.  While not terribly sexy, they do go to the continuing evolution of an enterprise platform.   One of the new elements is "Master Marketing Profile" described as a single identifier or a 'ring to rule them all.'

Conceptually I'm intrigued since we have a need to look at audiences from the perspective of a publisher as well as a marketing services firm.  And as with any new service the gap between the idea described in the opening session on a 50' screen and the explanation of it on a monitor at the booth was quite noticeable.  We had questions and it felt like the answer was "I know a guy who knows a guy" who wasn't around at the moment.

So I thought I'd post my questions.   And to provide context, let's start with the following scenario.  I want to access an audience based on the following:

• Activity on our owned and operated sites using only content about a category of interest
• Traffic and events on the networks we represent that fit an 'audience extension model'
• Email and print subscription information for that category from a related line of business
• Loyalty and transaction based information from a retailer who promotes items in the category
• Social graph and interests from an advertiser's customer base that has expressed interest in a brand in the category

While going from site activity to testing on the same site seemed to be the emphasis in the beta testing, the above real-life problem generates a series of questions about the Master Marketing Profile.   I've tried to frame then in general terms, but they are biased to specific Adobe products/constructs.

1. Is the identifier built bottom up, e.g. a cookie, or from top down, e.g. total business?
2. Is this the commercialization of the 'universal ID' concept that existed in professional services?
3. If it is the master, is it safe to assume the existing identifiers are slaves and still remain active?
4. Is there one or minimum set of IDs required to build the master? 
5. Must all child identifiers be an existing Adobe ID?
6. As a core service, can I use it without analytics?
7. Can I define the master to be the data management platform ID or even a client or advertiser's identifier?
8. How are conflicts of information handled when dealing with multiple touch points?
9. What is the logic sequence for aligning the above sources into a master?
10. How will we know whether the process works or not?  
11. What percent of a given source can we expect to be mapped?
12. How do we ensure orphans remain viable entities over time if they are not in the 'audience library'?
13. How do we know which sources were used in a given profile or segment?
14. Is there a probability of belonging associated with each source of contribution?
15. How does the mapping process impact the creation of downstream business rules and event triggers?
16. If a map is built with incomplete information, how do we avoid garbage in gospel out?
17. Is membership in a segment based on this identifier a "Yes" "No" proposition, or "Maybe"?  (What is the likelihood that this identifier really does represent what we think we want?)
18. Is this a precursor to another consumer pool and compete with Axciom, KBM and Liveramp for matching services?
19. What information is in the payload when shipping segments?    
20. Does the process provide an audit trail of the mapping such that it can be defended?
21. Where is PII used and then masked in the process?
22. How are cookies, digital fingerprints, and device ID's utilized in the process?
23. How confident should we be we won't piss off consumers because of a technical black box?

If I get answers, I'll post them...

Impact of Store Brands on Store Loyalty

Why and when would I be likely to choose a store brand?

A recent article in the Journal of Marketing analyzed the relationship between the amount of money a consumer spends on private label brands and her loyalty to the retailer.  Store brands now represent 15% of global retail revenue according to AC Nielsen.  The idea that as I add more store brands to my basket (larger share of wallet) the higher my store loyalty will be is not a new one.

But as with all aspects of marketing, different segments and different categories work differently.  The relationship between private labels and loyalty is a bit more complex than 'the more the better.'

The research identified several factors that impact the relationship between the two consumer metrics: Private label share and Store loyalty.  In order of importance:

1. Low price seekers exhibit a stronger relationship between store brand share and store loyalty.  This is in part due to the tendency that I think of private label brands as less expensive to begin with.
2. When a retailer occupies a lower overall price positioning in my mind then as store brand share raises so does loyalty.  A collection of store brands from a low-price retailer makes it easier for me to decide.
3. Categories with high-involvement, or information seeking, are areas where the relationship between the two is also stronger. This suggests that unique content development should focus on categories where I need to find out things.
4. Highly commoditized categories, i.e., lots of acceptable alternatives available, mitigate the relationship between share and loyalty.  Basically, if all things are equal I might as well just pick one.

Thus, when it comes to distributing promotional content both the audience and the offer matter. 

5 Steps to Accessing Audiences

How do you find me on the Internet?

There is a lot of talk about audience extension, or the desire for a publisher to increase its value to advertisers by reaching site visitors elsewhere on the Internet.   However, the real business need is accessing an audience - not just reaching them off-site.

The idea of accessing an audience requires thinking differently about traditional content and visitor concepts.   Since we're no longer interested ONLY in what visitors do on our site, we have to rethink tagging, signal detection and the organization of traits/segments.

To illustrate, we (and yes I work for a publisher) have tags related to the existing content and its purpose for a given site.  But when looking across sites similar content may have very different contexts.  Add to the mix that similar looking tags may also mean very different things and you have a recipe for confusion.   And speaking of recipes, here's a set of tags that make perfect sense when the content is in fact a recipe:

meta property="article:tag" content="Kid-Friendly"
meta property="article:tag" content="Spring"
meta property="article:tag" content="Vegetarian"
meta property="article:tag" content="Bake"
meta property="article:tag" content="Pitas"
meta property="article:tag" content="Cheese"
meta property="article:tag" content="Tomatoes"
meta property="article:tag" content="Cabbage"
meta property="article:tag" content="Pineapple"

I can almost picture precisely what those tags mean.  Something like....

Rainbow Veggie Pizzas

But what would those tags mean if used elsewhere on the Internet?  Without more context the tags "Spring" and "Bake" are terribly ambiguous.

The challenge is ensuring content and tags are both used in a way that can be useful to reach audiences.  

From a text book perspective, the five-step approach for accessing audiences should be:

1. Develop the segmentation strategy – which audiences make the most sense to access across touch points.
2. Standardize the concepts – what ideas are core to the audience and how common are they across the entire spectrum? (dare I say, build a taxonomy)
3. Modify the tagging – allow the tagging of content to be both dynamic and link to the common concepts.
4. Capture the signals – actually, it is the classification of the signals that is more important.   We need to understand audience signals rather than site-specific signals.
5. Organize the traits – being able to build segments from pan-site signals requires a new way of thinking and organizing things.

For instance, imagine creating a 'fashionista' segment across food sites, style sites, and financial news….one can imagine linking 'formal dinner parties', 'walk-in-closets for shoes' and 'fashion week' as potential evidence of interest in fashion.   In a site-centric world the tagging strategy would be obvious and literal; in an audience-centric world the tagging is a little more about concepts and human judgement.

The reality is that we actually have a chicken and egg problem:  We need to collect data first in order to determine if we should/should not combine different traits into a valuable segment.  This is very much a test and learn environment.  And I'll save that discussion for a later post….

There are some good resources that explain more of the mechanics of audience extension; for instance AdMonster recently issued a of playbook on the topic.

Tim's Vermeer - Go See It

How does a painting come to be?

Last weekend in Toronto we went to see "Tim's Vermeer"...the story of Tim Jenison's quest to figure out how what could be the best painter - ever - did it.   What made his paintings stand out and stand the test of time (as in several centuries)?

The answer to 'how' is that he most likely had help in the form of mechanical aids - a simple lens and a simple mirror.   This combination allows the painter to ensure that the color he paints matches the color of the setting.   Now, there is no letter from one of his contemporaries like Pieter de Hooch saying,

"Dear Johannes...can I borrow your studio and lens/mirror contraption for the next 18 months to paint, can't seem to get the light quite right here across the river."

But as the scholars in the movie discuss, the painting itself gives clues on how it might have been done.  The light raking across the wall, the curve in the seahorse's tails most likely wouldn't have been painted the way they were - without help.  Using tools of the era, including grinding/polishing his own lens, Jenison takes us thru the five years it took to go from concept to final painting. 

The painting in question is "The Music Lesson" hanging in Buckingham Palace.

Is this the original, or the one created by a man who didn't paint?

ABCs of Successfully Distributing Promotional Content

How can we improve the odds of consumers paying attention to what we have to say?

In the development of anything dealing with the marketing of promotional content (disclosure: part of my day job), there are three challenges to overcome:

1. App Abandonment: with less than 25% of mobile apps being used more than once, there is obviously no silver bullet from a functional point of view.
2. Banner Blindness: recall, relevancy and results are often in the realm of single digits or lower making the shift from media tonnage to targeting an imperative.  We simply don't remember what we saw.
3. Coupon Clutter:  the impact of 329 billion coupons being distributed in the US alone has to drown some segments to the point of not even wanting to deal with coupons.

Since a PhD dissertation in 1999 first described the irony of consumers skipping the creative designed to help then find the information they were looking for there has been a lot of research into what makes for engaging experiences.  There is even an organization dedicated to banner blindness that quantifies the impact tuning out.   (Great infographic here.)

All this suggests consumers (or a least a sizable segment) don't want to deal with the hassle - they just want the benefit.  This point is confirmed in Inmar research (free registration required to download.)

There seem to be so many rules to coupons. I don't want to have to carry around the store coupon policies in order to get a deal.

So, entering the market are digital coupons of two types - Print-at-Home and Load-to-Card - to help remove the friction in the process of saving money.    The research ends with a consumer survey stating the obvious: Consumers want "easy" couponing.  More specifically, they don't want to do it -- they want somebody else to ensure they get the benefits.

• 65%  "I want coupons loaded to my store loyalty card for products that I normally buy."
• 65%  "I want stores to email me with coupons for products that I normally buy."
• 58%  "I want all available manufacturer's coupons to be loaded onto my store loyalty card."
• 63%  "I want all available store coupons to be loaded to my store loyalty card."

Q5.10: Now I want you to think specifically about coupons that you can access online using your computer, mobile phone, or tablet.  Please rate your agreement with the following statements (top 2 box -- Agree/Strongly Agree).

The answer to the opening question appears to be another question:  Should we identify a loyal segment where we can simply take coupons out of the equation?

Viral Marketing of Apps on Facebook

What form of sharing should I use?

A recent article in the Journal of Marketing analyzed the success (or failure) of 750 Facebook apps in an attempt to understand how various mechanisms of social sharing impact acceptance or reach.

While a lot of ink has been given to influence (Gladwell) and seeding-strategies (Watts) this research looked at what tools marketers can deploy to facilitate sharing.

Specifically, marketers can choose to use one or more of the following tactics

1. Unsolicited Messages vs. Solicited Message 
• A message appears in the inbox about a new app vs. see what apps appear on a members "About" page
2. Messages with incentives vs. those without
• "Try and get a month free" vs. just "Try it out"
3. Direct messages from friends
• Communication among 1st degree contacts (similar to forwarding an email to a specific individual)
4. Broadcast messages from strangers vs friends
• Timeline posts viewed by 2nd to n-degree contacts on others vs. own timeline

The analysis focused on how those choices impact the adoption rate of leisure vs. business apps (or in the language of academe - low utility vs. high utility). 

The key takeaway: the techniques that garnered 100m users for FarmVille in roughly 40 days would be counter productive (and possibly detrimental) to a business-oriented apps.    From the research:

The very mechanisms that made FarmVille so successful is a recipe for failure when used in a different product context.  Unsolicited and incentivized broadcast messages from friends are the least effective sharing mechanisms for primarily utilitarian [business] products.

You rarely see such strong language in journals.

And speaking of context, the results would theoretically be different on LinkedIN because it is by nature a business/high-utility social network.  The mechanisms of how people choose, and what information cues they use, differ depending if they're looking for a job or playing Candy Crush.

So, as we design campaigns with "Share This" functionality, we need to understand what the usefulness of the app/content is as well as the distribution platform and choose our tactics accordingly.

Shift to Digital: Part 5 - Operational Savings

Where does the money we want to shift come from?

The previous two posts in this series focused on Driving Growth and Building Leadership, but at some point we need to think about how to free up resources to dedicate to the new mix.   It is great to have a vision of two pie charts that split out the entire marketing budget into completely different approaches.   But we do need to find ways to allocate a reasonably fixed set of resources - moving 50% of a budget is vastly different than growing a budget by 50%.

So, here are a list of questions focused on operational and tactical topics.

1. Where is marketing needed vs. not needed?
2. Where is the ROMS (Return on Marketing Spend) strong/weak? 
3. What business and geo-demographic elements explain the variance in ROMS?
4. What is the value of each action leading a sale – by channel/source of traffic? 
5. What is the value of social sharing? (Interesting facts for e-commerce here.)
6. What is the value of an “Offer View” – by channel/source of traffic?  (single offer centric)
7. How do we align the digital footprint with terrestrial sales activity?
8. What value would creative optimization bring to improving conversions?
9. How can we leverage targeting, bidding and interaction history to reduce ‘wasted impressions’?
10. What is true cost of execution of a digital budget?
11. What is the financial contribution of individual touch points?
12. What are the reach, frequency optimums by channel and segment?
13. What is the difference in cost models of reaching consumers on-site and off-site?
14. To what extent does spend on loyalty programs offset ink and airwaves?
15. What is the most effective means of reaching a consumer/segment?
16. Where else can we amortize fixed costs of content production with the most bang-for-the-buck?
17. Which data sets should be integrated in which order to gain efficiency?
18. What is the dollar value of what we know to our suppliers, vendors and market in general?
19. Which loyalty or transactional segments work best for targeting?
20. How many digital impressions does it take to replace a TV ad or a page in a flyer?

I don't know if the answer to that last question is 100,000 or 1,000,000 or even 10,000,000 but it seems to be a worthy goal for an analytic program to focus on.

There are a variety of ways we can approach the shift to digital, but hopefully these 60 questions help provide some guidance on how the analytic program could be developed and managed.

Shift to Digital: Part 4 - Brand and Category Leadership

What position do we occupy in the mind?

This is the continuation of thinking about 'the shift to digital'.  The last post on the topic listed twenty questions that need answers when thinking about Driving Growth.  This set of questions focuses on what it might take to establish brand and category leadership.   As such, they focus a lot more on understanding consumers and customers because the mind is the most difficult thing to change.

1. What segments emerge from existing marketing activities?
2. What roles do existing product categories/segments play in driving store traffic?
3. What categories impact share of mind, share of wallet and cart size?
4. What segments exist based on how people shop?  (touch points, timing, content consumption)
5. How much and what type of information is needed to impact choice?
6. What digital metrics correlate with or predict sales?
7. How do consumers use shopping channels (store, catalog, ecommerce) differently?
8. What level of penetration is required for a loyalty program to impact store sales?
9. What type of cadence generates the desired behavior by segment by category by channel?
10. What consumer segments based on loyalty are a good proxy for the entire franchise?
11. What level of personalization moves the needle at an acceptable cost?
12. Does the pattern of consumer interactions mimic merchandising allocations?
13. What jobs need to be done by the consumer that have the most impact on transactions?
14. Do some categories do better in printed flyer, and others in the digital channels (flyer, email, mobile, display)
15. What is the impact of the gap between “Say – Do” on sales?   (eg add to list, but not buy)
16. How much of the journey do we need to see in order to impact choice?  
17. What is the lift associated with personalized offers based on transaction history?
18. What is the value of own-brand vs. manufacturer brand in driving behavior?
19. What is the contribution of owned, paid and earned media?
20. Where do out customers go to find information?

For each question there is a set of data than can be cobbled together to help answer it.  A key element is being able to reduce digital interactions to a point, i.e. geo-locate the activity.    And that space will be the subject of another post - How do we know where it happened?

Attribution: A Mixed Model

How do you attribute offline AND online marketing at the same time?

Giving credit where credit is due is hard, particularly in the the world of marketing.  To help me think thru this I mocked up the classic 2 by 2 matrix.

There are two broad approaches to attribution: Top-down and Bottom-up: as well as two different purposes: Tactical and Strategic.   This results in four ways to approach the problem; this doesn't mean there are four techniques - there are a myriad of them in each cell.  The example I'll use is the flyer or circular (disclosure: my day job focuses on this) in the context of reallocating budget from the printed version to the digital world.

Top-down approaches looking at the tactical level grew out of the Marketing Mix Modeling world.   Econometric models try to tease out and control for the effect/variability of marketing activities.   While some testing is often done, this approach won't support the needs of an organization looking at bold strategic moves (like re-purposing 50% of its budget) to something new.

In the digital realm, and in particular the e-commerce, bottom-up and path analysis of a specific consumer is often the basis for attribution.   Whether it is 'last click' or a more advanced form of attribution, it is still limited to a limited domain.

The 'shift to digital' series (posts before and after this one) focus on a big change where both styles of attribution are required.  So, the challenge is figuring out a way to link marketing mix models which can handle flyer distribution, OOH, and TV with their digital equivalents that work with anonymous, PII and segment data. 

It seems to me that the variance of one might be explained by the other....

Shift to Digital: Part 3 - Driving Growth

What questions need answers?

In the continuation of a series of posts on shifting major portions of a marketing budget around, this post looks at the goal of "Driving Growth" and lists out a series of questions that could form projects.

Since growth focuses on results, the questions in this post tend to focus on monetary issues.  I'll save the consumer/customer questions to a post on building brand and category leadership.

The list:

1. How do sales change with respect to specific marketing activities?
2. How much of the effect of a tactic is offensive (lift/incremental) and how much is defensive (baseline/erosion)?
3. What is the sensitivity in sales to different marketing mix allocations?
4. What is the source of promotional sales? Brand, category, store, net-new?
5. What level of digital air cover is required to replace offline air cover?
6. To what extent to national decisions impact local sales?
7. What is the interaction between tactics?
8. Where is there headroom to actually grow the business?
9. What role does competitive presence play in our sales?
10. How much of a store's growth is controlled by the organization?
11. How can content be repurposed and distributed to impact sales?
12. What friction in the current business model prevents sales?
13. Which current non-digital steps could be (should be??) made digital?
14. How can decisions in-store be facilitated with mobile content and features?
15. What is the role of communal content (reviews, social) in making/speeding the choice process?
16. How should budgets be allocated geographically?
17. How do we forecast or extrapolate from a test to a broad roll out?
18. What digital/retail trends will work for us over 3-5 years?
19. What is the allowable marketing cost of each tactic in each area?
20. Do the puzzle pieces form more than one marketing plan?

All of the above require thinking about the holistic environment in which a store operates rather than looking at it from a channel perspective.   In the end, this is a problem of allocation where not all the information is at the same level or potentially not even capable of being integrated.  Our job is to reduce the risk of moving 50% of a budget away from one tactic to a collection of others by quantifying as many of the variables as possible.

Open Data and the Organization

What might open data do to the organization?

The trend of using accessible data from outside the organization continues.  McKinsey recently quantified the economic opportunities of using what is termed 'open data'.  They're big, but you'd expect that from a management consulting firm.   Tim O'Reilly describes open data this way:

There’s a pragmatic open and there’s an ideological open. And the pragmatic open is that [data is] available. It’s available in a timely way, in a nonpreferential way, so that some people don’t get better access than others.

Some implications:

1. The lack of control, and the potential for change, means those providing business requirements need to more like mentors and docents than hardliners and dictators.  Much more emphasis on thinking about 'why we will be successful' (strategy) rather than 'how we will accomplish it' (tactics.)
2. The value proposition may shift as organizations find they may have data that they want to contribute to the community.  These may be byproducts of processes that spin off data, e.g. geo-location and timing of distribution activity, that others may find beneficial or acting as a broker for a consortium of data used in benchmarking performance.
3. The skill set of marketing department will include a collection of hackers responsible for finding novel ways of identifying and satisfying market needs by combining internal core competencies and any/all external supporting data.

In short, open data will force organizations to clearly understand what its purpose is...others, will be using the same data to do similar things.

5 Analytic Steps in the Shift to Digital

How should marketing approach shuffling the budget?

Business objectives haven't changed a whole lot over time - drive growth, build brand/category leadership and create operational savings.    Broadly speaking these goals end up requiring shifting money around based on three steps.

1. Freeing resources from non-productive programs,
2. Identifying how marketing impacts key metrics among key segments, and
3. Re-purposing marketing dollars to more productive programs

Transferring money around at a tactical level, e.g. spot to national TV, display to search, print to tablet, and understanding the impact is fairly common and a tractable analytic problem.  But in a world of stagnant same store sales imagine that 33% or more of the largest budget item is focused on a single offline tactic, e.g television, flyer/circular, or out-of-home, and that money is put on the table as the foundation of 'doing things differently' and moving to digital.

We're not talking about our discretionary spend or bucket for experiments here, we're talking about betting the farm. 

In this case, there are numerous known unknowns, like 'what is the sales impact if we don't do that anymore', but also a very real potential for unknown unknowns - things we simply don't know to ask about yet. This begins to sound like a three-to-five year program, not a single campaign, that has as many cultural and process changes as it does tactical ones.

To help break down such a large problem from an analytic perspective, it is helpful to identify specific programs that shape our understanding and thus our planning.  Here's my initial categorization:

1. Document "Cost of Sales" attributable to each key tactic
2. Identify productivity of each tactic by geographic zones, e.g. store trading area.
3. Develop consumer segmentation model(s) based on how they choose and decide
4. Build a model for how different types of content work
5. Define how wholesale changes in the media mix impacts sales

Each of the above categories can then be further broken down into analytic projects, data-related activities as well as in-market pilots.   These will be the focus of the next set of posts that will list out 15-20 specific questions per goal marketers should be asking their data teams whose single biggest contribution to the transformation will be reducing risk by quantifying uncertainty.

(Disclosure - this problem is something we're facing at work and I'm sharing my thinking process of how we're helping clients get from A to B.)

This is the second part of a continuing series about the 'shift to digital'.  It started here.

The Shift to Digital

How do we get there from here?

Boston Consulting Group's 50th anniversary includes a survey on what troubles leaders the most.  (Can't find the source document, just the press release - so using the eMarketer blurb.) And for a 'consumer insights' professional it is good to see "Leveraging Customer Data" and "Digital Channels" in the top five, right behind "Open Innovation" and "Large-scale Transformation" and above "Distinctive Business Models."

There is nothing prescriptive about those items.  In the context of strategy development, they are broad constructs that shape or focus thinking - they are goals, not defined objectives or outcomes - those have to be company specific.

The eMarketer article highlights the challenges by industry and there are some differences.

• Consumer and Retail companies are most interested in growth in their current markets whereas Industrial firms are looking for new markets.
• Consumer and Retail firms also rank Leveraging Customer Data the highest.
• Digital Channels shows a wide swing (Technology is highest, Energy and Environment is very low).   C&R is above average.

So, weaving things together one can start to imagine a program-level plan to "Shift to Digital" for consumer/retail firms that is constrained by competitive activity, lack of customer insights, and a marketing budget that includes both on and off line tactics.  

In my mind, such a plan would have three parallel objectives:

1. Drive Growth: re-purpose marketing dollars to more productive programs.
2. Build Brand/Category Leadership: Increase penetration, share of wallet and loyalty among key segments for key categories.
3. Create Operational Savings: free resources from non-productive or sub-optimized activities (and not just marketing.)

Achieving these objectives based on what we know is hard enough, doing it while we are still learning what we don't know will prove to be interesting to say the least. 

So, this post is going to be the start of a series around the questions that need answers as firms make the shift.  It may even provide some ways to answer them along the way.

Stay tuned....and hope it helps.