The use of mathematics in marketing seems to be one of the hot topics today.  But is it that new?  Not really.  Here’s a little piece I wrote recently on the history of mathematical marketing.  I split in into four main era’s.

First Era : The Early Days of Direct Response

It’s probably fair to assume that the 1st applications of mathematics in marketing started soon after the invention of the first direct response campaigns. The first mail-order catalog was invented by Aaron Montgomery Ward in 1872, and it was copied by Richard Sears and Alvah Roebuck in 1886.  While there is no real evidence of how the early catalog pioneers measured their success and optimized their operations, they had the ability to do so, and the fact that catalogs are both around today suggests they probably did a good job at it! 

Claude Hopkins’ Scientific Advertising (1923) was one of the first books on the topic.  It opens with the following words: “The time has come when advertising has in some hands reached the status of a science. It is based on fixed principles and is reasonably exact. The causes and effects have been analyzed until they are well understood. The correct methods of procedure have been proved and established. We know what is most effective, and we act on basic laws.”  Hopkins and, later, John Caples — with his Tested Advertising Methods (1932) — wrote mainly about mail-order and other direct response vehicles. 5 They measured what was easy to measure and thereby focused mainly on short term effects. Unfortunately, from a measurability POV, the primary focus of marketing efforts would soon be directed toward mass media, and hence new techniques would be required to maintain the same levels of marketing accountability.

Second Era : Mass Marketing Effectiveness

The first applications of more advanced mathematical techniques in marketing can be traced back to the 1950s, when operations research and management science models in production and manufacturing that had become popular during and just after WW II were being applied to marketing for the first time.  In those days, of course, marketing relied nearly exclusively on mass media such as print and radio, and later, TV. Data on effectiveness of marketing in these mass media was scarce, which meant that the application of scientific methods in marketing had its limitations.  Data was either gathered through tracking sales and investments over time or through polls, which had been around ever since Raymond Rubicam hired George Gallup in 1932.  Panels were another popular source of data. But econometric modeling became the technique of choice in this era.  It helped marketers better understand the impact of various elements of the marketing and media mix on outcomes such as brand awareness, consideration and, ultimately, sales and profit.  Early work from the likes of Timothy Joyce, Colin McDonald, Simon Broadbent in the UK and John Little in the US helped shape scientific marketing in this era.

Today, independent companies — such as Market Share Partners, MMA and the Hudson River Group — specialize in econometric modeling and still use pretty much the same techniques to make recommendations regarding the effectiveness of mass media.  This includes determining the impact of different marketing investment levels, the contribution of individual elements of the marketing mix, and the timing of the effects.  The insights lead to recommendations as to how much of any budget should be allocated to TV, radio, print and OOH, and what the timing and geographic dispersion of the investment, should be.

Econometric modeling has been around for a while now, and its power in helping marketers understand what works and what doesn’t has been demonstrated over time.  Today, however, its use is still relatively limited. To with, the UK’s IPA awards set the international gold standard for advertising case material, but focus, on proof of effectiveness, only 15% of the case studies submitted for the awards use econometric modeling to identify the effects of campaigns. It seems that after all these years, econometric modeling still hasn’t been adopted in day-to-day marketing decision making.

Third Era : 1990 CRM Effectiveness

The third era happened during the 1990s, when customer relationship management (CRM) became an obsession for many marketers.  During this period, the possibilities offered by new, powerful database solutions really transformed direct marketing — and scientific marketing with it.  The customer relationship management (CRM) revolution in the 1990s forced companies to think in a customer- centric way.   Frederick Reichheld published The Loyalty Effect in 1996, in which he demonstrated that a 5% improvement in customer retention rates usually yields a 25% to 100% increase in profit.  That same year Garth Hallberg’s All Consumers Are Not Created Equal appeared, in which he demonstrated that a small proportion of the average company’s customer base usually represents a disproportionate share of company revenue.

Companies became determined to get to know their most valuable customers and focused on keeping them by treating them differentially. Loyalty cards were introduced that allowed companies to capture transactional data, and they invested heavily in data warehousing technology that stored all customer information in one database.  These “single customer views” allowed companies to analyze their customers’ transactions, value, responses to communications and even demographics.  RFM models classified customers according to recency, frequency and monetary value of purchases.  Lifetime value models predicted what a customer would be worth over their entire lifetime.  Anti-attrition models were built to predict an individual’s likelihood to cease being an active consumer.  In this same period, a plethora of other analytical tools and frameworks were born that allows companies to better understand who their most valuable customers are, what their next move would be, and how they could be influenced through direct, one-to-one communications.

Many of the mathematical techniques behind these models were very old.  Statistical techniques such as logistic regressions and discriminant analysis became powerful tools, once applied to customer level data.  These more traditional techniques were supplemented by new data-mining techniques mad possible by ever-increasing computing power that collected vast quantities of data, as well as by developments in machine learning and artificial intelligence.  Data mining gurus such as Michael J. A. Berry and Gordon S. Linoff made new techniques such as neural networks, genetic algorithms and decision trees popular and added them to the mathematical toolkit.  To this day, companies such as Dunnhumby, Epsilon and Acxiom still thrive in what is now a mature, very scientific, data-rich CRM industry.  The CRM revolution expanded marketing effectiveness tools and techniques considerably, and the toolkit’s ability to analyze vast quantities of data was soon tested on customer-centric data derived from digital media.

Fourth Era : Digital Effectiveness

One of the main promises of the digital communications era is that everything is measurable.  In digital, everything generates data — and the volumes are enormous.  Google’s digital database is probably the largest, capturing almost 10 billion searches per month.  These huge quantities of data can give companies unprecedented visibility into how our customers engage with brands and how that engagement ultimately leads to revenue. 

E-commerce environments provide us with a closed-loop system, which in marketing effectiveness terms gets us close to nirvana.  Digital media data can show us exactly which media individuals have been exposed to.  Website data can show us where individuals came from (or, in the case of search, what terms they typed in to arrive at a site).  We can then observe these individuals’ entire shopping behavior, all the way to their actual conversion to a sale.  With more and more media becoming digital, we could easily imagine a scenario where most, if not all, media exposures can be traced to an individual sale.

Digital data is also available in real time.  We no longer have to wait weeks or months before we can observe the impact of our marketing activities.  We can get a read almost instantaneously, allowing for real-time optimization.

This abundance of data, the promise of a closed loop and the speed with which we can react to insights have given birth to a wide range of analytical services in digital. 

Web analytics vendors such as Omniture, Coremetrics and Google Analytics specialize in gathering the vast amounts of data generated by websites and transforming this data into insights as to how many people come to a site and how they behave.  This very powerful information can help streamline online processes such as online registrations, downloads and purchases, and it  can play a vital role in  site redesign and optimization.

Ad servers such as Google’s DoubleClick can provide data on online media exposures and click-throughs (and beyond the click data) that enable us to optimize real-time frequency of exposure and automatically drive creative rotation decisions.  Companies such as Memetrics (now part of Accenture), Offermatica (now part of Omniture) and Tumri have automated multivariate testing.

Tacoda (now part of AOL) and Audience Science (former Revenue Science), among others, are applying the mathematical targeting techniques first pioneered in the CRM era to digital data in a way that has made behavioral targeting almost a commodity.

Vendors such as TNS Cymfony, Nielsen BuzzMetrics and Radian6 specialize in analyzing what people write on blogs and message boards and in forums.  Other vendors such as 33Across are popping up who will analyze the connections between people on social networks to optimize social media communications.

Soon all media will be digital.  Today, Google TV enables you to use TV set-top-box data to analyze advertising tune-out rates, allowing us to optimize their TV commercials by using a number of the digital optimization techniques mentioned above.

It seems that, almost every day, new companies find ways to apply mathematics to the vast amounts of digital data currently available in order to optimize marketing efforts.  Digital really has put the math revolution in marketing on steroids.

Two weeks ago I wrote a post about setting the marketing budget.  It described the often very basic rules marketers use to make multi million dollar investment decisions in marketing.  It also had data from a recent McKinsey survey showing that although 45% of the companies were planning to reduce their budgets, 27% had no changes planned, 20% of companies were planning to increase their budget and 9% didn’t know.  What can we learn from past recessions that can help us spend our marketing dollars better during this one?

Over the last months professional marketing literature has been dominated by material advising marketers on what to do during a recession.  A lot of that material covered rules and recommendations about how marketing budgets should be set or adjusted during recessions.  Reading through it I noticed very little new thinking.  Most of the articles were either reruns of material that was written after past recessions or they are articles that use the same methodologies used in those articles on more recent data. 

The most popular methodology used to demonstrate what happens when advertising budgets get cut during a recession compares companies that cut their advertising to those that held their spend stable or even increased it.  Performance of these companies is then compared during the recession and in a period after that. 

This methodology was 1^st tried out in the 1920’s by Roland S Vaile.  He published his results in the Harvard Business Review issue of April 1927.  Vaile compared companies that maintained their advertising spend during the 1923 recession to those that cut their budgets.  He found that the biggest sales increases were recorded by companies that advertised the most.

Buchen Advertising Inc. applied the same methodology to 4 recessions in the 40’s, 50’s and 60’s.  They were able to show that companies who cut their budgets during the recession not only performed worse during those recession but also in the subsequent years of recovery.

In 1982 Cahners Publishing Company published the results of an analysis of the Profit Impact of Marketing Studies (PIMS) database which then held information for almost 2000 companies.  They showed that companies who increased their budgets during a recession gained on average 1.5% in market share during those periods.

Similar research was done by Biel (1991), Hillier and Baxter (2001), Buck (2001).  The most recent example was presented at an IPA conference in 2008.  Malik consulting repeated the analysis Hillier and Baxter did in 2001 on more recent data.  The conclusion was very similar to all of the studies mentioned so far.  Companies who increase their budgets during a recession show increases in market share and return on capital employed (ROCE) in the years after the recession. 

While the observations in all these studies are accurate, I often feel nervous about the recommendations derived from them.  Most people claim these studies make a case for increasing investments during a recession to outmuscle weaker competitors and gain market share that can lead to a sustained advantage during the recovery and beyond.  This implies causality and none of the studies above delivers proof of this.  Do companies who spend more during a recession perform better in the long run because they increased their spend?  Or are companies who perform well before, during and after a recession better placed to maintain or increase their spend during a recession?  Some of the authors acknowledge this issue of potential reverse causality.  Few offer an alternative approach. 

I believe a more thorough approach is required to make the case for sustained marketing investment during tough economic times, especially if we want the arguments to stand up to the scrutiny of already skeptical CFO’s and CEO’s.  Three things can help here :

1. Increase the use of econometric modeling

2. Take a holistic view of the marketing plan

3. Determine the long term effects of marketing 

Increase the use of econometric modeling : Econometric modeling has been around for a long and it’s power in helping marketers understand what works and what doesn’t has been demonstrated over time.  Today however it’s use is still fairly limited.  Only 15% of the case studies submitted for the IPA awards use econometric modeling to identify the effects of campaigns.  The most often quoted paper that uses econometric modeling to make a case for sustained investments throughout a recession is probably Cutting Adspend in  Recession Delays Recovery by Dyson in 2008.  The article uses econometric modeling to prove exactly what it’s title says.  Dyson showed that “the increased spend required during the recovery just to get back to pre-recession sales levels within a year will have to be around 60 per cent higher than the amount saved by cutting the ad budget in the first place”. 

Take a holistic view of the marketing plan : Let’s say you have build econometric models that give you a clear sight into what would happen if budgets are cut.  One would still need to take into account media costs and consumption.  Media costs often fall during a recession.  Simon Broadbent (1999) gave a very interesting example which demonstrated the impact of cost savings due to falling media costs.  This in one example leads him to recommend increasing spend during the recession of the early 90’s.  There is also evidence that media consumption can change during a recession.  People stay home and consume more media, which could increase the efficiency of media spend during tough times.  Clearly an approach like this which uses modeling and takes into account the specific context of the brand, the category and the media landscape is much more thorough and easier to defend than the one size fits all golden rules described earlier.

Determine the long term effects of marketing : Budget cuts during a recession are often the result of a short term view.  Advertising efforts rarely pay themselves back in the short term.  As described in a previous post on the long term effects of advertising, 5 recent studies showed that every 1$ spent on advertising on average only generates 50c in payback in the short term (Dyson 2008).  However, there is growing evidence that the long term effects could be 2-6 times the size of the short term effects.  Broadbent (1999) called quantifying the long term effects of advertising the most important task facing advertising researchers.  He said that a lot of work still needs to be done in the area and that this work shouldn’t just be done in tough times.  The understanding of long term effects is incredibly important as it can impact shareholder value.  According to Barwise (1999) it is a myth that financial markets only care about short term financial results.  They tend to maximize long term shareholder value.  It is therefore up to the marketing community to demonstrate that long term shareholder value is not only a function of short term financial performance but also of sustained longer term investment in the brand.

References

P Barwise, Advertising for Long term Shareholder Value, Admap Magazine October 1999

A Biel, The cost of cutbacks, Admap Magazine 1991

S Broadbent, Tough Times, Admap Magazine April 1999

S Buck, The true cost of cutting adspend, WARC, January 2001

P Dyson, Cutting adspend in a recession delays recovery, WARC Online, March 2008

P Field, Marketing in a downturn: Lessons from the past, Market Leader, Issue 42 2008

T Hillier & M Baxter, How to prevent a hangover, Market Leader, Issue 14, 2001

B Ryan, Advertising in a Recession, AAAA, Value of Advertising Committee, 1991