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.

8 things marketers can do to become more accountable

The recession has brought an unprecedented drive for accountability in the marketing world.  Here are 8 things marketers can do to become more accountable :

1. Start with the basics

If you want to understand the impact of your marketing efforts you need to start with measurement, then move on to analysis of the results and finally use the insights from the analysis to optimize your marketing efforts.  Now marketing budgets are under increased scrutiny many companies are looking for instant optimization by skipping measurement and analysis.  This never works.  You need the foundation of metrics and insights before you can optimize.

2. Align metrics to objectives

Measure what you need to measure, not what you can measure.  Many marketers get over-excited about modern measurement tools that allow them to track everything.  This often means they end up drowning in metrics.  Starting from the business, marketing and communications objectives and aligning your metrics to them will ensure you focus on the metrics that matter.  Mapping metrics to objectives also often improves the quality of the objectives themselves.

3. Use more than 1 metric

The last couple of years have seen a number of efforts by marketing professionals and academics to summarize marketing’s performance in one metric.  Reicheld’s Net Promoter Score, Peppers and Rogers’ Return On Customer and Doyle’s Increase in Shareholder Value are examples of such efforts.  While some of these metrics can be useful performance indicators, they will never be able to explain the workings of marketing in its entirety. 

4. Use a mix of financial and non-financial metrics

Finance departments everywhere are forcing marketing departments to adopt financial metrics.  This is a good thing.  Focusing on financial metrics only however is not.  It does not improve the understanding of how marketing works.  Counting the money is not enough. Marketers (and finance people) also need to understand where the money is coming from.  London School of Economics professor and marketing accountability guru Tim Ambler once compared the exclusive focus on financial metrics to the effects of cannabis – it distorts the reality and thereby creates the illusion of control.

5. Build a results repository

A results repository which stores all marketing related metrics in on place is probably the most important asset a marketer can have in the quest for accountability.  Though this sounds very obvious, it is surprising how few companies have one.  This doesn’t have to be a sophisticated database.  A simple excel sheet or access table can often be a great improvement.  It’s not about the technology, it’s about having the discipline of consistently capturing marketing performance metrics over time. 

6. Track marketing cost data

It is interesting how few companies adequately track metrics related to the costs of marketing.  Cost metrics tend to be stored in financial systems and kept at a very aggregate level.  This often makes them useless for performance analysis.  Keeping track of your marketing costs by marketing objective, program, channel, geography, segment and medium can greatly improve your ability to understand the effectiveness of your marketing investment.

7. Use statistical modeling to identify drivers of business performance

Econometric modeling has been around since the 1950’s and it’s power in helping understand marketing effectiveness is well documented and understood.  A recent study of the IPA awards however revealed that only 15% of the submissions used econometric modeling to measure performance.  In my experience, even companies who do econometric modeling often struggle with integrating them in marketing decision making.  This is a shame as econometric modeling can often identify the key marketing drivers that are correlated with business outcomes and help marketers focus on them.

8. Build a marketing dashboard

Marketing dashboards are a very visible and tangible output of your measurement efforts.  This not only makes them popular, it also means dashboards can become the catalyst for doing the 7 things mentioned above.  If you build a dashboard you start with measurement.  The design of a dashboard usually leads to a conversation about what metrics are important.  You will need a set of metrics to populate the dashboard, which usually includes both financial and non-financial metrics.  The financial metrics will require the discipline of tracking marketing cost metrics.  And all metrics need to be stored in results repository that powers the dashboard.  A typical results repository also usually holds 80-90% of the data required to do econometric modeling.