July 26, 2013,
Guru in a Bottle 
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Few would argue that the nature of social media, communication, information and the global economy have merged to transform the way we must now communicate and prospect for customers and clients.
In many respects, the strategies that are often employed to predict and manage customers’ or clients’ purchasing behaviour have been spurred on by the growth of online data, which in itself has its own challenges for the sales and marketing professional.
As I discuss in my new book, High Impact Marketing That Gets Results, what started life within the airline and hotel market segments has rapidly been adopted across other market segments that now database and online strategies proliferate sales pipeline management thinking.
Implementation of these techniques often requires a combination of sophisticated mix of tools with equally sophisticated statistical methods to help manage and track frequency and timing of purchase, repeat purchase behaviour, market share and other indicators of commercial success including cost of sales and profit data and return on equity.
Customers are more different and individual, more discerning and demanding than ever before. Whilst 100 years ago, a new car buyer would be more than happy to buy a Ford Model T, a model that hardly changed in decades, in ‘any colour as long as it’s black’, today customers are intelligent, expectant and pedantic. Their stated needs may well be true, but their unstated needs and wants often matter even more.
A recent survey in the UK showed that 70 per cent of the respondents found that online research and review to be extremely helpful in making a purchase decision and 97 per cent of them also trusted online reviews (both negative and positive) five times more than they trusted information in a TV commercial or newspaper advertisement.
Power to the people
A fundamental shift in power to the consumer, to the people has taken place and has transformed marketing thinking. It requires us to engage, to create and connect with consumers on a scale that we’ve never seen before. And this is regardless of whether they are in the home or at the office.
Every consumer today has either heard of or knows someone who’s been on the wrong side of a sales transaction that’s gone badly wrong. Today, its colleagues, friends, family and peers that your customers and clients will want to listen to and it’s this group that increasingly has a stronger influence on the ultimate purchase decision that’s being made.
Given these changes in consumer purchasing behaviour that have permeated all B2C and B2B market segments, the airline industry has emerged as a leader in the use of predictive modelling techniques to target desired customer segments.
This has resulted in better qualified leads as well as helping to reduce wasted sales and marketing efforts and resources – a useful lesson that can be applied by marketers in other business sectors.
For example, take the frequent flier customer segment. Customers in this segment generate the most profit for airlines and so it’s critical that an airline maximizes the retention of its best customers. In addition, it’s important to understand why these customers prefer to fly with the airline and leverage this knowledge in an effort to influence customers in lower performing segments in order to migrate them to frequent flier status as fast as possible.
In order to help them achieve this, airlines have developed predictive models to forecast customer’s retention probability as well as revenue or spend retention. A decline in either of these rates would weaken the profitability of the airline.
Customer behaviours that are likely be useful in predicting a potential decline in retention are an increase in the length of time between booking flights, purchasing fewer tickets from the airline or its affiliates or a combination of both.
Implementing a customer retention model gives the airline an efficient way to proactively detect any frequent fliers that may be a retention risk.
And armed with this insight, the airline can quickly communicate with these customers and attempt to offset this anticipated decline in ticket sales with targeted offers and loyalty incentives.
Response modelling
Response modelling is probably one of the frequently used predictive modelling techniques where customers are part of frequent flying programs. The technique attempts to leverage the airline’s knowledge on each individual customer to determine if they are a good candidate for a certain type of marketing program or promotion.
The initial development of these models often requires in-market testing to accumulate valuable customer response data and insights. Once collected, each customer can then be assigned a precise score representing their likelihood to respond to a specific program.
It is typical for customer populations to be separated into ten different sub-groups (deciles), with each decile containing a different probability of response.
Marketing professionals can then simulate response scenarios and perform return on investment (ROI) analyses to decide the appropriate number of customers or deciles to include in the program.
Forecasting customer behaviours
All airlines are keen to understand the future potential value of each of their customers. Predictive modelling techniques can be used to estimate the customer lifetime value as well as many other key profit impacting customer behaviours such as product purchase propensities (PPP), expected purchase cycles (EPC), aggregate spending levels (ASL), customer loyalty as well as customer service usage.
Behavioural forecasting models have also been developed that can support campaign targeting, financial and operational forecasting, customer investment allocation and inventory planning – particularly important when getting airplanes to fly at full capacity.
The behavioural predictions tend to be made at the individual customer level to support direct marketing activities that many airlines employ. However, the results can also be applied and reported at an aggregate level to effectively support business forecasting needs.
Marketing ROI Optimization
Predictive models also play an important role as airlines attempt to optimize the usage of some of their primary marketing levers such as the value proposition, price and media channel mix.
For example, predictive modelling and optimization techniques are often leveraged to help airlines get the greatest return on their marketing offer and promotional budgets.
In order to do this, the price elasticity curves of different customer groups are mapped so that the optimal offer can presented to each customer.
This is the offer that provides the greatest incremental lift in sales at the lowest cost to the airline and of course the optimal offer can vary by each customer group.
The more sophisticated model-driven, optimization tools are generally designed to allow airlines to forecast expected lift in sales under different types of scenarios. This granularity of insight is invaluable during the program and budget planning process.
Predicting the impact of marketing programs on customer behaviour
In today’s marketing environment, airlines increasingly use complex, multi-faceted programs to communicate with their customers.
Isolating the impact of individual marketing components on subsequent customer behaviour can be very tricky simply because individuals are exposed to a variety of messages and offers through many different channels and it’s not always possible to isolate these very easily.
However, given the scope that predictive modelling techniques can provide means that the ‘cause and effect’ of such marketing can be isolated and measured with more precision than was possible in the past, for example, predictive marketing mix models can help us better understand the impact of online and offline advertising so that media mix investments are informed by quantitative measures of expected incremental sales.
In summary, the starting point for marketing to customers, clients and prospects is to take a more enlightened and focused approach like the one adopted by the airline industry.
And in practical terms this means sitting in the passenger seat and not believing we can see everything that’s going on from the comfort of our own cabin.
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