Monday, January 26, 2015

Step 1 in Generating Insights: Grab a Marshmallow

How should we approach developing insights?

The typical path to insights is lined with roadblocks of making sure the anticipated results are scaleable, generalizable and solid enough to base a business on. We typically don't go looking until we're sure that what we find is usable and workable. To be honest, we're handcuffing ourselves; maybe there is another path.

In The Marshmallow Challenge kindergartners routinely trounce MBA's at building the tallest structure possible out of spaghetti, tape and string because they do rather than plan.

From: source
Because they aren't riddled with fear of failure, they just try lots of approaches. In 18 minutes they cycle through ideas, learning along the way. In the meantime MBA students, and executives, are planning, allocating resources, and probably doing a bit of posturing to be seen as successful. By the time 'the experts' finally get to the objective, they've run out of time and have to bet the ranch on one tact. The results are predictable.

The rapid iteration of 'test and learn' needs to be applied more often in insights development..this can be in terms of thought exercises "How might it be different?" or allocating budget to learn rather than sell.

Insights are things you don't know you need to know, so how can they be judged a priori?

Sunday, January 25, 2015

Touch Points and the Illusionary Path-to-Purchase

How do consumers end up where they do?

We try to understand the path-to-purchase by stitching together data from various touch points and appending them to an artificial identifier meant to represent a consumer.

I'm not sure if it is a love relationship or a hate relationship with technology, but marketers use a variety of data-related tools to connect with consumers and figure out how they came to buy. According to recent Winterberry research on "Marketing Data Technology" (registration required) firms use 12+ different tools on average in the course of marketing. And with that many tools attempting to measure the influence of marketing on behavior something is likely to be amiss.

At a recent conference on data and analytics John Pestana of ObservePoint illustrated the complexity of all this by listing the tags or the scripts on a single web sites that log consumer behavior.

This major media/news site had:
  • 18 applications
  • 29 tags
  • 124 variables
His question: What are the odds that something is broken? Like finding correlations in Big Data, it is pretty much guaranteed. Breaks or discontinuities in data capture mean we have to guess about what happens in between what we can observe. It is worse if different definitions are used for the same event. We've all been in meetings where the sole purpose was to agree on who had the right numbers, when we should be talking about how to help consumers choose our products and services.

The biggest issue is not the quality of the data we do collect, but rather the fact that we're missing all manner of influences and behaviors. Integrating our touch points does not come close to providing a 360-degree view of the consumer.

Maybe we should ask a different question: What if we captured the path first?

Since we're likely to sleep with smartphones no reason we can't imagine passively capturing our path and then overlay marketing on that. Sure there are challenges - permission, revolving MAC address, proximity, etc. But nothing a panel couldn't solve.

I'd rather have gaps in marketing than blind spots on the path.

Wednesday, January 21, 2015

A Great Combination of Brand, Positioning and Packaging

What happens when it all comes together?

Some times you just smile.

http://www.boxedwaterisbetter.com/


The Millennial Skills Gap

Why don't the best and brightest work here?

This story is based on conversations around a panel discussion at the Utah State of Data, an event discussing the impact of data on education, finance, employment, government, and technology. While localized to Salt Lake City there are some general ideas about positioning and differentiation of a specific market.

Salt Lake City's biggest differentiator is the outdoors. "Life Elevated" is often aligned to what one can do within 20 minutes of downtown. We also have infrastructure - the NSA data center is here so the pipes got to be big, a hospital system that services 10 states and thus unique service delivery needs, and a work force with a decent ethos of helping rather than competing.

But it appears we cannot attract and retain the best and brightest young technologists. The logic woven from several conversations goes like this:
  • A career is nothing more than a series of interesting projects and changing jobs to do something new different is common in strong tech markets (a year at Facebook, two at Twitter, a startup and then a new product launch and sabbatical at Google). Interns brought here don't convert because there aren't enough big cool things going on like the Bay, Seattle, Boston, Austin, etc.
  • Job hopping creates a rapid rise in salaries for high-demand skills, and not only in technology, as companies compete for scarce resources with great credentials. Within a market it is business-as-normal, but across cities it makes "market-competitive compensation" surveys from your HR team irrelevant. If I'm willing to move, then the market is the country not your locale.
  • SLC is not a "Friends" city where millennials conduct life 24*7 in coffee shops and other urban venues. We'll be lucky if we get to 18*6.  So, even if we get the best there isn't a lifestyle typically associated with high energy folks.
  • We are good for families so we can attract and retain those wanting to settle down a bit, i.e., those in their 30's or 40's. They have experience.
  • At that stage of our lives job hopping isn't viewed as a means of defining oneself. And without movement, salaries don't rise based on intense competition.
  • If salaries aren't competitive we can't attract the talent we desire.  
And the circle begins again.

It would seem we're caught between our aspirations "Silicon Slopes is next Silicon Valley" - but based on deal flow funding so can Saint Louis, Munich, Seoul and Cincinnati (Beijing tops the list in terms of number of deals.)

So what kinds of companies should we create when the workforce is a) experienced and b) content? Rather than core game changing technologies, are there other classes of things we should focus on rather than the "next Google" (and good luck with that anywhere.) 
  • Can we rent company founders and focus on later stages of the evolution requiring experienced management?
  • Can we blend technology, the willingness to help and experience to solve human needs?
  • Can we take what is unique and different and position ourselves better?


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.