Predictive Marketing 101

I recently read an article by T.H. Davenport that dealt with the idea of predictive marketing. Lately it has been quite a buzzword and many businesses are keen to integrate it into their digital marketing strategies in the hope of seeing overnight results. But what exactly is predictive marketing?

Well, according to The 2015 State of Predictive Marketing Report, predictive marketing is the practice of extracting information from existing customer datasets to determine a pattern and predict future outcomes and trends.

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In order to know how to apply this process, I would recommend Davenports (2011) model describe in his article “Know what your customers want before they do”. It is an insightful article that deals with the topic of predictive marketing and the use of technology to “steer customers to the ‘right’ merchandise or services”.  In this Bitesize Solutions post, I will outline the main points of the article and give you a brief overview of related opportunities and threats.

It argues the importance of “next best offers” (NBOs) which is the ability to offer the customer a tailored product or service “at the right moment, at the right price, and in the right channel”. Of course, this is not a new concept, as big data has enabled businesses to maximize the likelihood of a sale. The significance of data-driven marketing was also highlighted as a top strategic priority in the Adobe Annual Digital Marketing Trends Report 2016.

Davenport explains that despite the importance of NBOs, many businesses are failing to leverage data to create competitive advantage or increase ROI. Digital marketing campaigns are deeply flawed with indiscriminate and ill-targeted pitches. The article presents a framework for crafting successful NBOs which involves the following four step process:

  1. Defining objectives
  2. Gathering data
  3. Using data analytics and business rules to devise and execute offers
  4. Learning from the performance of previous offers to enhance the strategy

The first step in the process involves defining the NBO objectives. The Big Data revolution has allowed businesses to know radically more about their customers and directly translate that knowledge into improved performance. Predictive models “will serve broad strategic goals” including increased sales, turnover and loyalty. Hence, management must define their objectives before implementing NBOs. It has been proven time and time again that data driven companies perform better on objective measures of financial and operational results than those who are not data driven.

This concept feeds into step two; gathering data. A vital part of the process is collecting and integrating customer data, product offerings, and the circumstances in which purchases are made. Davenport advocates Tesco’s method of understanding high relationship co-efficiencies between product offerings to anticipate customer wants. With advances in technology, and the introduction of the Hadoop framework, this has become relatively inexpensive and provides great tools for analysing the data, essential to the third step in the NBO model.

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Analysing customer behaviours data, cross purchase correlations and purchasing patterns allows the business to accurately predict customer wants. Progress in behavioural segmentation permits the identification of expected customer lifetime value and determines “whether its NBO to that customer should encourage or discourage defection”.

A carefully crafted NBO is dependent on delivery. The business must choose the appropriate medium of contact with the customer and humans are often the best channel for delivering upscale offers due to the importance of close relationships and complex customer’s needs. The NBO framework is an iterative process, facilitating constant improvement and delivering an offer that maximises potential of sales conversions. The increased speed of collection and analysis of data in recent years has made the NBO process quicker and of higher quality, more granular and more personalised.

Other factors must be taken into account when dealing with the introduction of data driven marketing, including “legal, ethical, and regulatory issues”. A business implementing an NBO strategy must be cautious of inadvertently crossing legal or ethical boundaries. Leveraging consumer data responsibly is key to sustaining trustworthy customer relationships. As regulation continues to evolve, through the EU-US Privacy Shield etc., companies must be careful not to unwittingly compromise consumer data through NBOs. The resulting backlash from both regulatory powers and the public could tarnish brand reputation.

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Overall, the use of the NBO framework results in high quality NBOs being produced. Successful implementation results in increased consumer awareness and achievement of strategic objectives. It is difficult to apply all four steps simultaneously, but progress in each area is essential to sustained competitive advantage in a data driven world. I would recommend working to improve each segment and always keeping the customer in mind.

There’s no time the present, why not give it a go and monitor the results of your predictive marketing campaign? I would love to hear how you get on so feel free to comment, like or share!Insta

 

 References

 

Davenport, T.H., Mule, L.D. and Lucker, J., (2011). Know what your customers want before they do. Harvard Business Review, 89(12), pp.84-92.

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