Marketing analytics lessons from the KGB

“Half the money I spend on advertising is wasted, the trouble is I don't know which half.” ~ John Wanamaker, U.S. department store magnate and merchandising / advertising pioneer.

I’m not going to claim that I can pinpoint exactly which half of your marketing dollars are wasted in the space of this post, but I am going to illustrate that basic analytic techniques are available that can considerably narrow down the range of uncertainty and provide actionable insights for your marketing efforts.

KGBOur story begins with a fascinating article that surfaced last week by Jonathan Haslam, professor of the history of international relations at Cambridge University, the subject of which was how, during the Cold War, was the KGB able to so easily and readily identify undercover CIA agents?

The Soviet efforts were so successful that the head of the KGB counterintelligence group, Yuri Totrov, was known within CIA circles as the “Shadow Director of Personnel” on account of how much he seemed to know about the foreign posting of CIA agents. How he was able to unmask and compromise entire intelligence networks was the subject of much handwringing, debate and speculation, the leading candidate being a highly placed mole within the Agency. What other explanation could there possibly be, right?

Wrong. Read More »

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What is it like to be a customer?

bat2To paraphrase Thomas Nagel’s famous 1974 paper on consciousness, “What is it like to be a bat?”, I want to instead ask the question, “What is it like to be a customer?” Nagel’s argument was geared at refuting reductionism - the philosophical position that a complex system is nothing more than the sum of its parts. Such a materialist approach omits the essential components of consciousness ("emergent properties" we would say today): an actor with motives and feelings and a personality. We typically approach the customer in the same fashion – our hypothetical target consumer is typically nothing more than the sum of our data and demographics combined with our own products and services.

I want to digress for a moment to illustrate and highlight this important point about “being like something”. What is it like to be you? Read More »

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Big Model: The necessary complement to big data

With all the hype over big data we often overlook the importance of modeling as its necessary counterpart. There are two independent limiting factors when it comes to decision support: the quality of the data, and the quality of the model. Most of the big data hype assumes that the data is always the limiting factor, and while that may be for a majority of projects, I’d venture that bad or inadequate models share more of the blame than we care to admit.

stone-balanceIt’s a balancing act, between the quantity and quality of our data, and the quality and fit-for-purposeness of our models, a relationship that can frequently get significantly out of balance. Or more likely, complete mismatches between data and modeling can crop up all over our organization. In one instance we may have remarkable models starved for good data, and on the other hand, volumes of sensor or customer data sit idle with no established approach to exploration, analysis and action.

This imperative to balance the data with the model reminds me of an espionage story from WWII. Read More »

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Visualization – Worth a thousand words

Why visualization? Several reasons, actually, the most compelling being that sometimes visualization literally solves the problem for you.

I remember an exercise in eighth grade English class where we were asked to describe, in words only, an object set in front of us with sufficient clarity such that our classmates, sequestered outside the room, could accurately draw the object from our written description. The object was a bowtie shaped set-top UHF antenna.

bike2The exercise was a disaster. Which of course was the objective, at least from the teacher's perspective, who was attempting to demonstrate how difficult clear, comprehensible writing can be. From our perspective, however, all we could focus on was what idiots the recipients of our written descriptions must have been. “Why did you draw the loops at right angles to each other when I clearly indicated they were in the same plane as the base?” Looking back, had I been clever enough, I realize now that I should have “cheated” and used a typographical approach to illustrate the object diagrammatically with my otherwise 'descriptive' words.

Live and learn. But what did I learn? Read More »

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Why build models?

why2We are all modelers.  Whenever you plan, you are building a model.  Whenever you imagine, you are building a model. When you create, write, paint or speak, you first build in your head a model of what you want to accomplish, and then fill in the details with words, movements or other actions in order to realize that model.

Models work via a three-part structure:  Input, Mechanism, and Output.  When we use models, we are generally confident in only two of the three stages, and we use the process to determine the unknown stage.

The most familiar construction is where we know, or have confidence in, our inputs and our mechanism (the mechanism being the rules or algorithm that generates output from inputs), which we call “prediction”.  We use this structure to predict, or forecast, a wide variety of outputs, from tomorrow’s weather to next month’s sales to next year’s election.

But prediction is not the only available model structure.  Read More »

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Big data demands Big quality

data qualityUnlike data acquisition, which can accumulate exponentially, we generally address data error correction on an exception basis, using manual, linearly-scaled resources.  We cannot possibly scale manual data correction to keep up with our increased data volumes, which means we must automate our data quality processes with tools at least as robust as our data collection and storage resources.  We cannot afford to scale up the human resources that today correct perhaps 100 customer name, address, part number or shipment date errors per week, to handle thousands or tens of thousands of such errors.  Our only alternative is to automate and catch / fix those errors up front.

In a previous post (“Big Silos”) I posited three primary sources of big data:  visually dense (e.g. video, satellite), temporally dense (e.g. audio, sensor), and transactions (e.g. POS, SKUs).  I would like to amend that classification to now include a fourth primary source of big data that I had initially overlooked: unstructured data, including social media data.

80% of corporate data is unstructured, making it perhaps the single largest potential source of big data.  As we start to process, structure and store that unstructured data, through techniques such as text analytics and content categorization, we need to remember to apply the same data quality standards as we do to more traditional transactional data. Read More »

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Homo habilis to Homo sapiens – From tool user to data user

Are robots going to put us all out of work?  That seems to be the question on every click-bait economic article’s mind.   On this particular issue I’m taking a firm position:  NO! Emphatically, NO!  Robots are not going to take our jobs in any permanent sense that would entail a total reworking of basic human economics.

How can I be so certain?  Because we’ve been through this before on an even greater scale and not only survived but largely thrived.  I am of course talking about the industrial revolutions, both first and second, and whatever atomic / space / computer / information revolution we might be in right now.

tools1-olduvaiWhen speaking of the first and second Industrial Revolutions, we don’t refer to them as “robots” but instead call them machines and equipment.  Still, they are all kith and kin, part of the larger category we refer to as “tools”.  And when it comes to tools, we’ve been at this for 2.8 million years, so chill out everybody.  In fact, the first in line in the genus ‘homo’ was Homo habilis, “handy man/woman”, or ‘tool user’.

However, it’s not the Olduwan flaked stone tools that I’m entering into evidence here, but instead the machinery of every shape and kind that filled the first factories and furnaces of the industrializing West, displacing and disrupting the craft guilds and the farmers, but not resulting in the economic extinction of the species. Prior to the start of the Industrial revolution roughly 80% of Europe’s economy was agrarian.  By 1900 agriculture still accounted for 40-50% of the West’s GDP, and by WWII it was down to 15%.  Today those numbers hover around 2% for Europe and less than 1% for the U.S.

When it comes to the trade guilds, yes, the coopers, cobblers and colliers are long gone, and slightly more recently we’ve seen the demise of lamplighters, ice cutters, milkmen, bowling pin setters and switchboard operators.  And yet here we are. Read More »

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Is Data an Asset? The importance of the metaphors we use for data

The metaphors we choose to describe our data are important, for they can either open up the potential for understanding and insight, or they can limit our ability to effectively extract all the value our data may hold.  Consisting as it does of nothing but electric potentials, or variations in voltages and frequencies, when speaking about data we are forced out of necessity to speak in metaphor.  Even our cherished 1’s and 0’s are metaphors for the underlying electromagnetic reality, and in going from bits to bytes we’re already stacking metaphors on top of metaphors.

Let’s start with a metaphor I must see at least once a week in articles, blogs, white papers and webinars: data as an asset.  As a starting point, I’m okay with this, it seems an apt and accurate metaphor for data.  But let’s nail this “asset” terminology down a bit more.  If data is an asset, what sort of asset might that be?

big_data_linkedin3Is it a fixed asset like plant, property and equipment?  Meaning, is it like the other productive operational assets that can be leveraged as tools?  I suppose one could see it that way, but I find that particular metaphor hard to work with.

Moving higher up the balance sheet – is data like inventory?  As with inventory, we most certainly warehouse data, in, what else, data warehouses and data marts.  I’m even willing to take this inventory analogy one step further and say that we can subdivide our data-inventory into ‘raw material’, ‘work-in-process’ (WIP), and ‘finished goods’ data, adding value at each step as we integrate our data silos, sort, score and store it, or extract, transform, and load (ETL) the data into ever more accessible and valuable formats.  Yes, I think inventory might be a good one.

But why leave out the King of Assets – Cash; can data be like cash, like a currency?  Read More »

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Agile strategy, revisited

You know that feeling when all your ducks appear to be in a row, all the numbers add up, all the boxes have been checked, but you’ve still got a sneaking suspicion that something is wrong?  I’m not talking just gut feel here, more along the lines of, “The logic is impeccable, so one of the premises must be wrong, but who am I to question any of these accepted, time-worn, management truths?”   In small quantities, the exception(s) prove the rule, but when the exceptions start piling up sufficiently to become a recognizable category of their own, you are surely justified in calling the conventional wisdom into question.

That was precisely the feeling I had as I closed out this Value Alley post from early last year, “Agile Strategy”, which focused on Columbia Business School professor Rita Gunther McGrath’s article and interview in Strategy + Business: “It’s time for most companies to give up their quest to attain strategy’s holy grail: sustainable competitive advantage. The era of sustainable competitive advantage is being replaced by an age of flexibility”.

I concluded the post by hedging my bets: “I have not yet rendered judgment on the impending demise of competitive advantage.  My current bias is that firms will still have opportunities for competitive advantage through focus and development of core competencies in a particular Value Discipline, and that firms of the future will be successful by becoming agile experts within their chosen discipline.”

(see the full BCG article for the classification of industries within each style)

(see the full BCG article for the classification of industries within each style)

Coming to my attention, my rescue, and easing my angst, was this study from the Boston Consulting Group (BCG) – “Your Strategy needs a Strategy” – well worth the read and critical to my argument below.   In it, they expand the concept of strategy from a monolithic, one-size-fits-all approach to suggest that there are at least two dimensions across which strategy setting needs to be evaluated:  predictability and malleability.  In doing so, they provide a solid framework in which it is possible for both McGrath and her critics to be right, they provide the premises through which the exceptions can be properly understood not as exceptions, but simply as one of several available strategic options.

Read More »

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Transparency and the Information arms race

You are going to be spending proportionately more of your IT budget on security than you have previously spent or ever wanted to spend.  Why?  Because you and everyone else on this planet is engaged in the still early stages of an escalating information arms race, that, while you didn’t ask for it, neither can you avoid.  This escalation is being driven by two recent IT phenomena:  exploding connectivity and transparency.

An article in the March 2015 edition of Scientific American by Daniel Dennett, professor of cognitive science at Tufts, and Deb Roy, director of the Laboratory for Social Machines at MIT, expounds on this idea by linking it in a wonderful analogy to a hypothesis regarding the Cambrian explosion which occurred on this planet 542 million years ago.

fieldmuseumchicago_fullsize_story2The Cambrian explosion saw, in a mere geological instant, life on Earth move from single to multicellular organisms, and with it the emergence of all the varied body plans (phyla) we see today, from arthropods (lobsters) to mollusks (clams) to echinoderms (starfish) to chordates (sharks).  Prior to this, life had existed on Earth for over 3 billion years as nothing more complex than single-celled bacteria or algae, and then, in the blink of an eye over a span of only a few tens of millions of years, 34 of the 35 separate animal phyla arose from the relatively simple amoeba-like creatures of the day.  Why the sudden change?

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