Is Big Data a marketer’s dream come true?

Is Big Data a marketer’s dream come true?

By Laure Claire & Benoit Reillier 

The concept of “big-data” is on everybody’s lips these days. As every self-respecting new buzzword, “big-data” may be a bit over hyped but this doesn’t mean that real changes are not underway… especially for marketers. So what is Big Data and what does it mean? 

Big Data, which is after all simply the perfect alignment of technologies enabling the cost effective collection, storage and analysis of large quantities of data, will result in step changes in terms of marketing capabilities. While the concept of gathering and analysing lots of data is, in itself, far from new we have now reached a “tipping point” enabled by cheaper collection, storage and processing of digital information allowing organization to gain additional marketing insights and capabilities. The shift to on-line models[1] is a further catalyst for the deployment of “Big Data” enabled marketing strategies.

This in turn has a significant implication for organizations in general and for the marketing function in particular.

Here are some examples of marketing ‘dreams’ that may soon come true:

  • Segments of one: a century ago firms commenced their marketing journey with no segmentation at all (e.g. monopoly providers or utilities, first cars[1], etc.). Then firms started to apply broad segmentation techniques (business vs. residential for example) and as time went by new smart ways were developed to identify increasingly narrow segments of the market (e.g. clusters). It was especially effective in markets characterized by fragmentation of demand (e.g. non homogeneous tastes) and allowed for the emergence of highly targeted niche brands. The nirvana however is a “segment of one”… and with the amount of individual information now available thanks to Big Data companies should soon be able to target customers with uniquely suited propositions.
  • Predicting behaviour: aren’t you amazed at the way Amazon is able to recommend relevant things based on past purchases and browsing history? This is about to get much scarier (for customers) and much more interesting (for marketers) or maybe scarily interesting if you are both. Credit card companies can now predict a divorce (with a 95%+ accuracy) two years before it occurs[2]. Some supermarket chains can now predict pregnancies with a high degree of accuracy and even realised that they had to make their targeted advertising look random in order not to spook the recipients about how much they really knew[3]. How likely you are to switch service providers, the amount you are prepared to gamble, or the likely brand of your next car are now variables that can be quite accurately inferred from previous behaviours. No doubt that more sophisticated models using additional information about you will provide increasingly accurate predictions about life events and consumption patterns.
  • ‘Perfect’ pricing discrimination: incidentally this is also an economist’s dream (often called ‘level one’ pricing discrimination). Big Data offers the ability to charge each customer exactly what they are prepared to pay for a given good (no more/no less), which would allow the firm to capture all the income available (i.e. clients prepared to pay a lot do while those who can only afford less, pay less but still contribute to total revenues). This “optimal” pricing strategy has so far eluded many firms that have therefore been forced to over-price (where firms forgo revenues of priced out demand) or under-price (where individuals with high propensity to pay… don’t). Big Data is however a necessary but not sufficient condition to be able to price in this way. Other conditions need to be met (e.g. no possibility to arbitrage between consumers, etc.) but some online platform models, such as eBay’s ‘auction’ and ‘make an offer’ formats, are now getting close to this optimal state.
  • Customer Lifetime Value (or CLV): CLV is a powerful concept treating each client as a small ‘investment project’. There is an acquisition cost, a stream of net cash flows (made up of income less cost to serve) all discounted at an appropriate rate over the ‘lifetime’ of the customer in order to derive its current value to the firm. Up to now, this powerful framework has been difficult to implement at the customer level. This was because the information was not available and firms had to use less accurate proxies[4] instead. These approximations often resulted in averages or product led cost and revenue allocations that defeated the purpose of the exercise in the first place. This is however about to change as clever CLV metrics are now enabled by Big Data and will no doubt lead to advanced profit based analysis and strategies.
  • Cross Selling:up or cross selling products didn’t wait for Big Data (remember this enticing ‘offer’ of a glass of champagne/aperitif last time you visited a posh restaurant?) but this is now taking a new dimension based on personal ‘recommendations’ that are generated thanks to Big Data techniques. Amazon’s ability to suggest new products based on past purchases and browsing history is about to become pervasive through targeted ads, as well as clever implementations of up and down selling strategies based on Big Data analysis. It is going to become even more tempting to fill your virtual cart with this extra impulse buy product judiciously suggested…
  • Location: where you are is an increasingly valuable (and available) piece of information. Credit card companies know where you transact at a point of sale (or ATM) and mobile operators know where you are 24/7 of course (as they need that information in order to allow you to receive and make calls). It is therefore not surprising that retailers are keen to add this information to their “Big Data” arsenal to leverage geo-localisation data to offer targeted time bound local offers to customers in their neighbourhood.
  • Real time PR & customer service: new applications are now scanning the various social media channels to see what customers are saying about your products. Innovative brands have started to harness these capabilities to quickly react to opinion formers’ complaints and fix issues that these early adopters are experiencing. Given the negative impact of disgruntled customers writing negative comments on blogs or social networks such strategies are likely to become part of the marketing arsenal of many brands. Mondelez [5], recently signed a global deal with Twitter, who set up dedicated teams in Mondelez’ biggest markets (US, UK, Bazil and India) to help with real time marketing activities[6].

As we have seen, armed with Big Data, future marketers will know (or have a pretty good idea about) who you are, what you are going to do, how much you are prepared to pay, how much you are worth to them, what else to sell you, and where and when to sell it to you. They will also use this intelligence to communicate with you through appropriate channels and support you after the sale.

These are only some of the new tools becoming available to marketers, but it is clear much deeper organizational changes will be required for the value of Big Data to be fully exploited and captured by companies. Some online pioneers, often operating digital platform businesses such as Amazon, eBay or Google, are literally powered by Big Data and couldn’t exist without these analytical capabilities. Some other traditional businesses have a longer way to go since doing nothing on the Big Data front will leave them exposed to more nimble and more customer focused competitors.

More fundamentally, the logical next step to leverage Big Data capabilities is tooffer a unique product for each customer.Clearly this is easier said than done (and more relevant in product/markets where individual product features are highly valued) but this is where the potential for Big Data really resides… when it shapes not only the marketing function but permeates the strategy of the company, its supply chain and its delivery model.

One thing is sure; irrespective of the shelf life of the buzzword “Big Data” the use of analytics to better understand and serve customers is here to stay. Firms ignoring this will do so at their peril.

Laure Claire and Benoit Reillier are co-founders of Launchworks & Co (,, a boutique consulting firm based in London, UK. Launchworks Ventures provides strategic advice to digital, telecoms and tech companies with a particular focus on platform businesses.

Useful links:

MSc in Marketing & Creativity
Creative London Summer Course

[1] Henry Ford famously stated in 1909 that the Ford T was available in any colour customers wanted as long as it was black.

[2] See for example: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by former Columbia professor Eric Siegel or the following online article:

[4] Such as profitability of tariff plans instead of profitability of customers for telecoms operators

[5] Formerly known as Krafts and owner of a large portfolio of global food brands including Cadburry, Toblerone, Cote d’Or, Carte Noire, Hollywood gums, etc.