Monday, November 03, 2014

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"....





Monday, October 20, 2014

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.

Wednesday, October 15, 2014

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.




Tuesday, October 14, 2014

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."

Thursday, September 25, 2014

{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