Tuesday, February 24, 2015

Marketing Challenges: Updated

What's changing in marketing?

I stumbled upon a piece in McKinsey's insights section entitled "The Changing Face of Marketing". The author outlines six factors impacting marketing.

  1. Customer - "the end users of almost every company’s products are shifting in makeup, location, and number at an ever-increasing rate."
  2. Insights - "If knowledge about future customers is essential, and if the quality of the marketing output is materially affected by the caliber of the informational input" then we need more.
  3. Technology - "the computerization of many areas of marketing is only a matter of time"
  4. Testing - "more controlled experimentation to narrow the odds of an error in making marketing changes"
  5. Sales as marketing - [the] "job is becoming less and less the presentation of the company’s product line, more and more the marketing of integrated systems."
  6. Global - the problem is "determining how to provide most efficiently the marketing services needed—services that in many companies today are directed, if not executed outright, by a central corporate staff."
This summarizes what we often see in the marketing services world everyday.

It seems that the only thing new is history we haven't learned yet. The article was written in 1966. 

Tuesday, February 17, 2015

Predicting Music Popularity and Marketing Analytics

What do Taylor Swift's videos have to tell us about marketing?

There's a machine learning competition running here in Salt Lake City that deals with predicting the popularity of Taylor Swift videos from snippets of music. And by snippets I mean 1/10th of a second of a song. The goal is to classify whether a song is a 'success' or 'not' (if you can take 30m views as not being successful and 300m as success.)


In marketing we try to understand what makes a consumer tick, but in this case an audio track has been converted to a series of numbers that look something like the following. Now imagine 3,300 columns of numbers for each sample...

  Success     V1        V2      V3        V4       V5      V6      V7     V8        V9
       0       -5787    9566    -511    -2274 18589   1170    2232   5073   -2578
       1        1067   -  521    1524     -209    -957    -777  -2666     716   -3273
       1          397     -314   -1701   -2568    -463   1123    1041     916      789
       1          784   -1017    2038     4134   1453    3731   3644    2759  -2033
       0     -19632 -13344 -14428 -13195  -3940  -1306  -1206   -1303  -1256

This is where you have to trust the machine and the team. There will be no conference room debate as to what impacts success - the math just works. And that raises an important question: how do we now improve the consumer experience and deliver on the brand promise?

Keeping with the music theme, a musicologist can deconstruct the signals into themes and styles much in the same way that Pandora was created from a music genome. As a result the experts won't be just the ones who can apply a Restricted-Boltzmann-Machine to a classification problem, but those who can translate the results. Interpreters will be in as much demand as the coders.

Keeping my Rosetta Stone polished.

Tuesday, February 10, 2015

Rainfall is a Path to Purchase

How does rainfall relate to sales?

The weather service now measure things with such precision that we can determine flat (rain) vs. elongated (ice) droplets, etc. A new project on data science competition site Kaggle focuses on predicting how much rain actually hits the ground from a series observations on the way down. The objective of the contest is to estimate the likely outcomes from an hour's worth of data. Such models have implications for agriculture, highway safety, and other resource allocation projects.

Weather now measured in two-dimensions
This sounds like marketing mix and the path to purchase.

Marketing is often faced with a similar problem: how to align out of store activity, digital or otherwise, with in-store sales. Like rain, sales vary with time and space so the analogy is apt.

Recently Yicheng Song and colleagues at Boston University have been working on identifying common path-to-purchase analyses that combine multiple touch points and discrete sales outcomes. Their approach can be summarized as follows:
  1. There are patterns to the path to purchase (clusters of consumers emerge along common lines)
  2. The path and outcome varies by initial stimulus (catalog, email > online, offline)
  3. Paths traverse on and offline steps  (variance is a matter of degree, not one or the other)
What I find interesting is that the approach works with segments and plugs missing data (all too common in path to purchase work) to get a handle on what is likely to happen. Like most marketing analyses these days we don't know anything with certainty but can only talk in directional terms. This means we might be able nudge the pendulum one way or another with a change in the marketing mix but stuff still happens on the way to the store.

Maybe there's something more to be learned from the rain.

Thursday, February 05, 2015

Image Recognition - a first look

Does auto-classification of images work?

There is a trend toward "Analtyics-as-a-Service" where companies can leverage specialized functions on demand. There are predictive modeling options like IBM's Watson and MonkeyLearn for text modeling. There are even image recognition options emerging that offer auto-classification.

And as part of a personal interest on figuring out how content works I'm experimenting with various ways of tagging content - images, descriptions, and reviews - to see what might emerge.

So as a test I submitted the following picture to a service I found: imagga.

Red Duvet
So, here's what came back as suggested tags.

  • Shopping cart
  • 3d
  • People
  • Man
  • ...
  • Jukebox

Not exactly what I would have picked...so I did it again.

This seemed to produce more useful tags (and had higher confidence scores).

  • Sofa
  • Furniture
  • Bed
  • Interior
  • ...
  • Relaxation
The tags come with confidence scores - not sure what they mean yet - but the scores for the second picture were higher. Definitely an interesting learning exercise. More to follow...