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!

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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.

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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 »

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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 »

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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 »

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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 »

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Why Pricing Must Be a Continuous Process (Part 1)

At some point, every homeowner learns an important lesson about how to save money on air conditioning during the hottest part of the summer. Generally speaking, it costs less to keep your house at a relatively even, tolerable temperature, then to turn off the unit entirely during the day and blast the A/C in the evening when you are home. The process of re-cooling the entire house each time wastes a lot of energy to get to a comfortable temperature again.

Multiple optimal prices can exist for a product, even in transparent markets. Note that all of the prices in this image apply to the exact same HP printer.

The lessons of efficiently cooling a home can be applied to many scenarios. In business, having a system in place for tweaking procedures continuously is easier to manage over time than are prolonged periods of stasis followed by dramatic transformations. Transformations are complicated. They are often expensive. If too much time passes between transformations, the organization’s inertia coefficient (a 100% made-up term) passes a critical threshold. After that point, two outcomes are the most likely, with a few shades of gray in between: (1) transformation projects mushroom from merely “expensive” to “expensive and painful”, or (2) the company is too lethargic to change, effectively dooming the business to eventual defeat or absorption by more innovative rivals. For the sake of comprehensiveness, I have to acknowledge that for a fortunate few, “federal bailout” must now be added to this list as a third possible outcome. However, in a few years we will see if my suspicion that outcome three eventually finds its way back to outcome two turns out to be correct. Read the rest of this entry »

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How Major League Baseball Can Steal Profits Back From Ticket Scalpers Using the Right Pricing Solution

The National Baseball Hall of Fame recently inducted Ricky Henderson, one of baseball’s most prolific base stealers with a record 1,406 bases stolen in his career – yet, Major League Baseball has failed to deal with scalpers who steal millions in profits from their franchises every year. Scalpers have seized the lost opportunity where Baseball franchises lock in their ticket prices months before the season starts and choose not to adjust prices throughout the season. A more efficient secondary market thrives due to the scalpers’ ability to factor in several game variables (e.g. strength of opponent, seat type, starting lineup, weather conditions, etc.), as well as buyer-specific factors (e.g. age, attitude, clothing, jewelry, etc.) to determine the maximum (and therefore optimal from the seller’s perspective) price that each person is willing to pay. Another advantage for scalpers is their ability to immediately negotiate if the buyer doesn’t accept the first price, carefully moving the price down until both the buyer and seller agree upon a satisfactory price. To help reclaim these lost profits, the San Francisco Giants are now testing dynamic pricing software to help adjust ticket prices based on the expected consumer demand for each game. So what exactly is dynamic pricing, and is it powerful enough to replace the individualized pricing, negotiation, and sales effectiveness of ticket scalpers? Read the rest of this entry »

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Missing the Ocean for the Stream: What We Can and Cannot Learn from IBM’s New Breakthrough

As part of its perpetual quest to reinvent and perfect its business model, IBM has made an aggressive push into the analytics market in the last half-dozen or so years. The company’s slick, though occasionally confusing ad campaigns (remember those ads with the mysterious red box being unveiled?) often announce its new initiatives, though it is not always clear that a new announcement is indeed a major one. In the analytics space, however, Big Blue does mean business. The announcement of its sizable new business analytics and optimization division is clearly intended to prove as much. Shortly after its announcement, IBM also unveiled a new stream computing platform called “System S” to much fanfare. The breathless enthusiasm of business journalists, technology bloggers and investment analysts has been palpable. But what exactly does this technological advancement do, and what does it mean for your business?

To answer this question, let’s begin briefly by dissecting what IBM has introduced. Imagine that you are receiving a continuous stream of data, such as stock prices on the Nasdaq. These figures must be quickly analyzed so that the proper buy and sell orders can be placed. Suppose that you also need to base your decisions not just on the Nasdaq prices but also the numbers figures coming in from dozens of other exchanges. Read the rest of this entry »

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Why Credit Doesn’t Matter to Maintain Competitive Advantage

Realizing I would be without a wireless connection on my train ride to NYC, I stopped to grab some light reading material at a kiosk in Union Station, where I found a plethora of headlines devoted to capital spending. I know that the loss of $50 Trillion in wealth in the last 18 months led to a severe credit crunch, but wasn’t that old news? Aren’t businesses starting to rebound with the distribution of the $700 Billion in TARP funds that helped prop up banks and car companies, along with another $2.5 Trillion spent to support the struggling financial system? I take a quick look through the daily business headlines, and they continue to reflect a particularly bleak outlook for businesses that are still struggling with low expectations for growth and profits, costly and scarce credit, weak consumer demand and a glut of production capacity. To compound matters, the current administration and Treasury Department will implement extensive financial regulations to curb future financial crises, and banks continue tightening their lending standards for all types of business loans. I hope these measures reduce the risk of another bubble market, but at what cost will these measures reduce the opportunity for many businesses to effectively compete in this economy? One thing is obvious: credit will no longer be a cheap commodity for businesses in the near future, period. But then again, is credit really necessary for businesses to stay competitive? Read the rest of this entry »

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