Thursday, September 11, 2014

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...

Wednesday, September 10, 2014

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.

Tuesday, September 09, 2014

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.'

Monday, September 08, 2014

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.

Friday, September 05, 2014

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.