4-Cs Series: Complexity and Campaign Marketing (it’s harder than a Rubik’s Cube)

Veteran marketing managers can tell war stories of battles fought to secure marketing budgets – the pitches and cajoling to focus C-suite attention on the strategic and the tactical importance of effective marketing campaigns.  Getting something close to the budget you want may be just cause for heaving a big sigh of relief, but these days few marketing managers will be found clinking glasses of Veuve Clicquot in celebration.  Once the budget is in hand the real work begins.  The economic downturn has put constraints on the total number of dollars you have to spread among competing projects, but it has done nothing to constrain the nearly limitless ways those dollars can be allocated.   “Do more with less” is the mantra of the day.  To make those scarcer dollars go further means relying on more than traditional finger-in-the-wind gut instincts to tell you what campaigns will work and what campaigns won’t work.  Campaign marketing – the art of pulling together targeted messages for specific geographic markets, consumer segments and product types – is in need of a healthy dose of scientific rigor.

A mere three dimensions of complexity

Remember the Rubik’s Cube?  That delightfully maddening cultural relic of the 1980s was challenging because you had to configure the right sequence of moves in three dimensions.  One small misstep – one rotation along the wrong axis – and the whole strategy would fall apart.  Now, think of trying to solve a Rubik’s Cube-like puzzle, not in three dimensions but in at least five!  Our visual cortex regions boggle trying to imagine what this hypercube would even look like.  Yet that is the gauntlet thrown down to campaign marketing managers: configure the (1) right customer with the (2) right product and the (3) right promotional offer using the (4) message via the (5) right channel.  A typical challenge of this nature presented itself to one of our clients recently: configure eight potential messages to 50 customer segments in 70 regional markets concerning 50 product categories and four distribution channels:

8 x 50 x 70 x 50 x 4 = 5.6 million unique campaigns   for budget consideration!

That is obviously a larger number of alternative spending choices than the unaided human brain can reasonably analyze.  But the complexity doesn’t end there.  With the old Rubik’s Cube there was only one objective: get each face of the cube to be one single color.  Not so with the Marketing Hypercube (pictured in the diagram).  There are in fact multiple potential target outcomes of any given campaign.  Is the objective to build initial awareness of the company or the product?  Or is it to instill preference among an audience already familiar with the product?  Or, alternatively, is it to maximize actual purchases through targeted prices, promotional incentives, penetration opportunities, and/or purchase timing strategies?  In effect, the targeted sales & marketing outcomes themselves represent yet another dimension of complexity.

Multi-dimensional campaign marketing challenges

So how do we solve a problem of this magnitude of complexity?

Perhaps it is somewhat counterintuitive, given that we have called for a strong dose of scientific rigor, but the first order of business is to put aside the mathematics, take a step back and employ some good old-fashioned human judgment (don’t worry, we’ll shortly come back to the mathematics when we start to build predictive models around customers, messages and objectives).  Let’s start by remembering what we are trying to accomplish: to configure a campaign that will most effectively resonate with the target customer segments and accomplish our specified performance objectives.   We want to be able to predict the effect of the campaign before it is even launched.  This requires making some basic assumptions – but before your analysts integrate these assumptions into predictive models they need to obtain bottom-up business insights. These insights come from experience gained by your sales associates through interaction with their customers.  For example, they can be gleaned from short 30-45 second surveys and similar diagnostic tools built around particular initiatives (e.g. price, penetration, wallet share, loyalty, and general awareness-familiarity-preference survey templates).

The next challenge is to align these insights with the right segments.  Don’t think of this as a “once-and-done” event.  You have hundreds of thousands of customers and there are near-limitless ways to segment them.  The segments around which you build your first campaign iteration may not be the segments you employ in the end – or perhaps you will learn that those segments require different campaign strategies.  This is an iterative process – sampling, inputting new insights into existing predictive models, aligning campaigns to segments, resampling, revising segment strategies, updating model assumptions and constraints, and repeating.

It may sound tedious.  But over time this iterative process will help you greatly improve the accuracy of your predictive campaign models.  You will be in the position to pinpoint the effects that a specific campaign had on improving the value of certain customers’ transaction baskets through penetration initiatives, for example, or to measure the contribution of a customer loyalty campaign to actual revenue saved through decreased contract defections.  Those 5.6 million alternative budget allocations will start to look less daunting, and you will have a higher degree of confidence in making spending decisions closely aligned at a very granular level with your demand environment (for example, our client was able to more than triple its customer conversion rate through applying science to its campaign marketing process).  In short, you will be able to do more with less – even if you are one of the many people who never did get the hang of the Rubik’s Cube!

Subscribe   |  

Density, Sparsity and the 4-Cs

Solving the micromarketing challenges of the Information Age

We live in the Age of Information, so we are told.  Never before has so much raw data existed bearing testament to every  pulsebeat of human commerce, every touchpoint between a customer and a good or service.  The problem for decision-makers, according to the conventional wisdom, is Information Overload – volumes more data to analyze than the human brain can easily digest.  But it is not that simple – there are deeper challenges below the surface.

Information is not always where you need it

While the conventional wisdom is right in the aggregate, the lush and dense information rainforest starts to turn remarkably arid and sparse as you drill down into the nuanced segments of your demand environment.  At the micromarket level, infrequent transactional activity in the long tail of customers and SKUs yields little insight to inform decision making.  Managers thus face challenges that go well beyond the simplistic construct of TMI (too much information).  They need tools for managing the real information problems in their micromarkets.  These tools need to address head-on the challenges posed by what we call the 4-Cs:

• Complexity: With near-limitless combinations – of customers, products, locations, messages and channels – managers need the ability to first aggregate and then disentangle how variables related to price, assortment, advertising & promotions, and sales mechanisms affect customer demand and thus impact firmwide performance metrics like market share or profit margin.  Not knowing what impacts sales or profits raises the risk of suboptimal performance. Advanced scientific methods can help fill in the gaps where data sparsity exists and extend the vision of key decision-makers far into the details of what moves their markets.

• Coordination: Marketing involves a series of decisions, all of which have an impact on each other – yet each decision often gets made in an organizational silo isolated from other decisions.  This can produce persistently suboptimal outcomes unless managers can overcome the limitations of organizational silos.  Holistic optimization tools that provide visibility across silos and facilitate “what-if” experimentation can help achieve a clear, coordinated understanding of each single decision in a more integrated context.

• Connection: Managers need to connect to what the market is telling them in real time.  Historical transaction data can only help so much in an environment of constant flux: customer tastes change and competitive threats emerge in a Petri dish of constantly evolving activity.  It is not enough for decision-makers to learn from their quantitative systems: the systems have to learn from them as well.  This is what it means to be market aware: intimately connecting human experience and judgment with machine-based algorithms for optimal decision guidance.

• Customization: Insightful managers know that there is no such thing as an “average” customer.  Marketing and sales messages that play to a perceived average will wind up being average themselves – in other words falling short of truly connecting in the best way with target customers.  The fact is that every enterprise’s customer base is unique – defined by a distinctive combination of tastes, wants, needs and propensity to spend.  This is true even if the product line is what most observers would view as commodities.  Customizing a value proposition down to the most granular level possible can unlock the power of micromarket monopoly and defend against the margin-eroding practices of cutthroat price competition.

If the Information Age were really all about combing through volumes of aggregate data to develop key marketing decisions for your average customers then the 4-C framework would not matter so much – you could price, advertise and sell based on their perceived wants, needs and spending propensities.  But that average customer doesn’t exist.  The more precisely you can gain the necessary insights into micromarket uniqueness, the more you can calibrate marketing and sales decisions to optimal advantage.

So, if you are a marketing manager in a highly competitive industry  like foodservices, consumer packaged goods or retail, what should you be looking for in business intelligence & analytical solutions to take on the 4-C challenges?  Ask yourself three questions:

1. Can the solution really cope with the complexity of my demand environment in a way that is commercially viable, i.e. that keeps up with the fast pace of my daily decision-making?
2. Is the solution seamlessly compatible with my company’s existing technology platform including existing ERP and other critically important business intelligence?
3. Can my sales reps continue to do their jobs effectively and impart their experience and judgment without compromising the integrity of the system’s recommendations?

We’re going to come back and explore each of these questions in subsequent postings.

Subscribe   |  

Crunch the Numbers that Really Matter (hint:they’re the ones that relate to downstream demand)

A New Approach to Trade Spend for Foodservice Manufacturers

There is no shortage of quantitative analysis in the trade spend practices of foodservice manufacturers.  Unfortunately, very little of this analysis helps give decision-makers insights about the effectiveness of their trade spend programs.  The numbers being crunched do not relate to signals about actual downstream demand, but rather to the formidable mountain of claims from their distributors.  These claims come in all manner of data formats and accounting entries and it typically takes armies of brokers, salespeople and financial staff to figure them out.  After all the cumbersome and error-prone line-by-line calculations to validate claims are said and done, you are no more informed about the profitability or the potential risks associated with any given program.  No wonder there is widespread dissatisfaction with the effectiveness of these programs.  Over 75% of manufacturers in this sector consider their trade spend initiatives to be inefficient, according to the 2010 Market Intelligence Foodservice Trade Survey. Read the rest of this entry »

Subscribe   |  

Brand Loyalty: The Uphill (but Winnable) Battle for Heartshare

The other day I conducted a little thought exercise, and it brought me back to a question that often comes up in my line of work: the fleetingness of brand loyalty in the age of marketing message saturation and the daunting challenge for brand managers and other decision-makers whose livelihoods depend on the existence of such loyalty among their customers.  Happily for those who walk the brand beat, there is a ray of hope in this otherwise cautionary tale.

Olay, Nivea, Neutrogena and L’Oreal are all established beauty products brands with a broad array of medium-priced product lines and multiple product offerings in each.  More to the point, for purposes of this thought exercise of mine, is that each of them offers a range of good quality facial cleansers, a product I buy on average about once every two months.  The exercise was to determine what, if any, brand loyalty existed in my facial cleanser purchases over the last 2 years.  The answer appeared to be: none.  Nada.  At some point over those past 24 months and (give or take) 12 purchases, my domestic shelf space has been occupied by at least one representative facial cleanser SKU from each of those brands.  I wondered why this was the case.  And then I remembered that it was not always thus.  Long ago (more years than I care to disclose) there was a rather splendid product by Neutrogena called the Facial Cleansing Bar. Read the rest of this entry »

Subscribe   |  

Physics Envy: Pervasive, But Not Incurable

Everywhere you look, it seems, people are talking about “physics envy”.  This derisive term mocks the attempt of economists and other social sciences practitioners to imbue their disciplines with the equations and mathematical rigor of physics – a rigor that many believe fails when applied to the messy environments of disciplines like sociology or economics.  It’s not a new term – economist Philip Mirowski contributed to the Finnish Economic Papers series way back in 1992 with a piece entitled “Do Economists Suffer from Physics Envy?”

kinetic energy, not supply & demand

Eighteen years later the answer from many observation posts along the byways of public discourse appears to be: yes, they most certainly do, and so do their fellow travelers, business and financial markets experts.  After all, we just barely survived the most devastating economic event of our times, deeper and more far-reaching than any downturn since the Great Depression, and all the high priests of the field can do is shake their heads and say “wow, I sure didn’t see that coming.”  Distrust of fancy math is rampant in all walks of business life.  That presents a real problem for enterprise decision-makers at a time when they need smart quantitative tools – yes, fancy math and all – more than ever.  Markets are more complex than at any time in human history.  Giant waves of transactional data inundate marketing managers with new information every day.  Managers need science to help them gain valuable insights into the markets for their products and services – but how do they know that the growing number and variety of scientific marketing tools out there aren’t infected with the nasty symptoms of physics envy? Read the rest of this entry »

Subscribe   |  

Red Beads, Management Tools and the Elusive Quest for Strategic Advantage

Management tools do not automatically confer strategic advantage.  In principle any commercially available modern management tool from Total Quality Management to Lean Six Sigma, from Supply Chain Management to Price Optimization Models, is available to any and all paying customers on equal terms.  Two competitors in the same industry space may employ the exact same suite of management tools, but it is a good bet that their relative performance will vary considerably over time.  I don’t find this particularly surprising: generally speaking I subscribe to the view of competitive strategy vis a vis productivity enhancement tools eloquently expressed by Michael Porter in his 1996 Harvard Business Review article “What is Strategy?”  To wit: “Competitive strategy is about being different.  It means deliberately choosing a different set of activities to deliver a unique mix of value”.  That is to say, the act of hiring a Process Re-engineering implementation team or reinventing oneself overnight as a Learning Corporation will not automatically confer sustainable advantage.  Rather it is how (and if) those tools are integrated into a portfolio of aligned, mutually reinforcing organizational activities distinctive from those of competitors that will most likely make the advantage difference.

This makes sense to me.  Nonetheless I am often astonished by the frequent tendency among many corporate decision-makers to conflate the application of some management tool with a fabulous consultant-ese moniker into a “magic bullet” that will effortlessly change the organization overnight from a laggard to a market driving leader.  Then, as egregiously as they confer magic powers on the tools, after a few fiscal quarters the decision-makers realize they are not getting sustainable performance improvement, decide in their infinite wisdom that the inherent inadequacy of the tools is at fault, and consign them to the trash heap of unrealized expectations. Read the rest of this entry »

Subscribe   |  

A Beer on the Beach, and Other Mysteries of Fair Pricing

Businesses want us to view them as fair – there is arguably nothing more important than a reputation for fairness in the daily marketplace of commercial transactions. As business managers what can we do to ensure that decisions we make – about pricing or other actions that are clearly visible at the point of the customer-product interaction – will be seen as fair? Is fairness something absolute, immutable and precisely quantifiable?  Or is it situational, capricious and ever-changing?  The bad news, perhaps, is that ‘fairness’ is a very elusive notion to pin down with certainty – it’s hard to put fairness in a bottle and label it as such.  The good news is that fairness more than anything else is about perception and the relative judgments of your customers and potential customers in varying demand situations.  That’s good news because the better you understand the granular contours of your demand environment and the precise needs and propensities of your customers, the more likely you are to understand how to make decisions in that environment that are both fair to the customer and profit-optimizing to your business.

thirst-quenching - but is it fairly priced?

Here’s a test of fairness.  Imagine you are lying on the beach on a hot summer day and find yourself craving a cold, satisfying beer.  What price would you be willing to pay to quench your thirst?  Now imagine two alternative scenarios.  In one, the only place within walking distance to buy a beer is the poolside bar of a swanky five-star beachfront hotel.  In the other, there is a rather run-down beachfront grocery store that sells beer.  Imagine further that both the hotel and the grocery store sell the exact same brand and type of beer.  Does your maximum price point change depending on whether you think you are getting the beer from the hotel or the store?  Do you think it is fair for two different establishments to sell the same commodity for a different price? Read the rest of this entry »

Subscribe   |  

Fair Price, Optimal Price

Businesses seek to maximize the value they can obtain from their revenue models.  Price is the key lever decision-makers can operate to influence revenue, and in recent years a growing number of businesses have sought to implement strategies for actively managing the price lever – strategies such as demand management and revenue optimization.  However businesses are also highly sensitive to the perception by individual consumers and the society at large that their prices are fair, in other words that they do not violate widely held individual or societal norms.  Fair pricing matters – it matters to me, and to you, and perhaps ever more so in a climate characterized by economic uncertainty, downward pressure on demand and a perceptible decrease in the citizenry’s trust of public and private institutions.

Fortunately for business decision-makers, fair pricing and optimal pricing are not at odds with each other but can comfortably coexist.  Over the course of the coming weeks my colleagues at Sentrana and I will be approaching the rich topic of fair pricing in a series of exchanges on this blog.

debating the age-old question of fair price

What is a fair price?  This question has perplexed humanity throughout history.  Leading thought output of the ages, from Aristotle’s Nicomachean Ethics to the Summa Theologicae of  Thomas Aquinas, Pierre de Fermat’s probability proofs and Adam Smith’s classsical economics, have all weighed in with considered opinions on the fairness and justness of alternative ways to price economic goods and services, and the debate continues today.  A series of letters exchanged between Blaise Pascal and Pierre de Fermat in 1654 is often regarded as a primal cause of the development of modern probability theory: this exchange was actually an attempt to establish a scientific basis for the notion of fair price.  In his paper “The Unity and Diversity of Probability” Rutgers professor Glenn Shafer shows how these letters created hypothetical games of value that we today can recognize as the application of probability methods to defend a price as ‘fair’ under conditions of uncertainty. Read the rest of this entry »

Subscribe   |  

From 1.0 to 4.0 in 130,000 Years: Pricing’s Extraordinary Adventure from Haggling to Scientific Micromarketing

Pricing has evolved from the ancient art of haggling to the application of scientific methods to the micromarket.  In a sense we are going back to the unique knowledge of individual customers and products that existed in the old bazaars and town squares – but we’re armed with powerful technological tools of the 21st century.  The world of Pricing 4.0 is upon us.

But let’s start at the beginning.  In the beginning there was the trade, and the trade saved humanity.  Seriously.

Homo neanderthalensis – Neanderthal man – had been occupying the planet for about 200,000 years when our ancestral gene pool, Homo sapiens, showed up on the scene (both species evolved from a common ancestor Homo habilis that had begun to make and use basic tools about 2.5 Ma (million years ago), but their evolutionary paths diverged some 600,000 Ma).  Despite what would seem to be a solid first-mover advantage thriving in the harsh Ice Age climate of Europe and Western Asia, Neanderthal man vanished from the face of the earth sometime around 30,000 years ago while the progeny of H. sapiens went on to give the world the Hanging Gardens of Babylon, Magna Carta and How I Met Your Mother.  In 2005 academicians Richard Horan, Erwin Bulte and Jason Shogren presented a well-researched argument for why this happened: trade.  According to their paper “How Trade Saved Humanity from Biological Extinction: An Economic Theory of Neanderthal Extinction” it appears that our ancestors had particularly honed skills in organizing specialized activities such as tool-making, and trading their goods between different social organizations.  As the Ice Age melted and populations grew and migrated, the skills of free trade became an evolutionary competitive edge. Read the rest of this entry »

Subscribe   |  

Revenue Optimization: Coming Soon to a Big Drug Company Near You

Large brand-name drug companies – Big Pharma in the common vernacular – are not exactly known for competitive pricing or razor-thin margins.  For 2008 the industry was ranked third most profitable in the U.S. according to Fortune magazine, with average profit-to-sales margins of 19.3%.  That’s a pretty fat comfort zone compared to the scorched-earth landscape of many other industries…or is it?  Until recently Big Pharma was pretty consistent at the #1 spot in those rankings. A look under the microscope reveals some troubles bubbling up in the hitherto happy world of magic molecules and blockbuster brands.  These days the whole country seems transfixed by the subject of healthcare, and no matter what does or does not come out of the legislative sausage factory this year, some major trends are afoot that have potentially far-reaching consequences for Big Pharma and may influence the normally lackadaisical approach drug makers have exhibited to the prices they charge for their brand-name drugs – in particular when those drugs reach the end of their exclusivity protection period and go off patent. Read the rest of this entry »

Subscribe   |