Until recently, all customers have been tricky. Predictions based around their current and future behaviours, likes and dislikes, have been relatively sketchy and unscientific, making a hard job of forecasting product demand and securing brand loyalty. Businesses knew the customers were there, but what would they do next and why? How could you track and attempt to manage and promote purchases? Customer activities were just marks in the sand.

The digital world and the fundamental role of smartphones, tablets, TVs and games consoles in everyday lives and relationships has been a godsend to the marketeer. Here, instead, is a permanent whiteboard recording trails of each choice, every passing interest and response to a promotion we make, and all within the context of posted material on our routines and lifestyles. This ever-growing mountain of data, of course, has no value in itself. Instead, Marketing Analytics has allowed all kinds of data, the structured (clicks on websites) and unstructured (reviews, free text and postings), to be mined and analysed in meaningful ways to both allow for instant digital tailoring of offerings, and near real time decision-making from marketers. Analytics has become an essential part of marketing activity for many businesses, particularly those in retail, rapidly growing as a proportion of overall marketing budget expenditure. The Gartner research group predicted that spending on marketing technologies would exceed those of an organisation’s Chief Information Officer in 2017.

But that’s at one end of the scale of organizational ambition, among FMCG businesses with loyalty cards or those set up to capture data from their consumer markets. The reality is that most businesses don’t even collect the most straightforward data from their customers, don’t know what the actual response was to the most recent promotion activity or what the basis is for future promotions. Firms aren’t necessarily aware of what data they have or could capture and what its value is, either to themselves or to other organisations looking for intelligence into their customer base.

Working down in the mine

New consumer insights, gaining deeper intelligence, are important principles. But what can then be done to capture business advantage? Marketing Analytics is much more than just the dashboard of web analytics; it is a smart mix of measurement and data generation, statistical modelling, and information technology, to support human decision-making. The result is improved organisational effectiveness.

Sharpening the tools

Marketing Analytics can’t be bolted on to an organisation, it needs to be an integrated element of a marketing strategy and business decision-making. A particular challenge being faced by organisations is balancing the skills mix.

What had been largely a creative discipline is now increasingly dependent on its inter-relationship with the hard numbers and people who can manage high-level analytics. All the outputs from algorithms need to be converted effectively into marketing campaigns, part-IT led, part creative. A survey of Chief Marketing Officers in the US in 2014 suggested only 3.4% thought they had the staff needed to make full use of Marketing Analytics. This is an alarming number for a business function that, in the words of Management-guru Peter Drucker, is the only function that brings in money while all other activities are costs.

A key challenge organisations face is finding staff with the necessary skills: in statistics, in data analysis, market research, IT and programming. With this need in mind, Lancaster university Management School has developed a Masters in Marketing Analytics, a flexible degree programme which runs each year, including a company based project with clients that have included Lego, Tesco, BT and Orange.

Businesses need to consider the potential of Marketing Analytics for improving decision-making across an operation, not keep its role to a single team and limited remit. And that will mean a culture change, based on expert staff and enhanced data. Support needs to be established at the top of an organisation, with all senior managers acting as champions sold on the value of the new insights and how they can be used to refine and target business activities.

1. Revenue Management - Airlines and hotels are prime examples of operations that have used big data from customer choices to provide differentiated pricing strategies - a different price depending on when they book - and so enable revenues to be maximised in the busiest periods and help to spread demand to quieter periods. This approach also helps retailers to tailor their promotions - focusing on the potential for limited cost effective deals. At a more basic level, capturing customer data provides the foundation for forecasting what products will sell over time at what price and where the biggest markets are and will be.

2. Customer Engagement - Data can be used to suggest the ‘Next Best Action’, supporting both marketing objectives of an organisation as well as providing genuinely useful information linked to a customer’s needs and interests. Customer information and transaction data is used in real-time by a website algorithm to work out what products are most likely to be of interest, what’s available and what offers can be made. This will be familiar from sites such as Amazon and Spotify, presented in a simple, friendly form as “if you like this, then you’ll like that” suggestions on your purchase page. In this way, the traditional approach to marketing is reversed. Not a cycle of products created and then attempts to target likely customers; but making use of existing relationships to then make the best offers in terms of what’s available. The approach encourages cross-selling but also greater customer loyalty - giving a sense of interacting with a business that knows and anticipates your needs, in the style of an old-fashioned shopkeeper.

3. Personlising Shopping - Moving on from the Next Best Action tactic is the overall tailoring of an online experience. Businesses with a webbased shopfront are making use of A/B analysis as a means of maximising the revenue from customer interactions with the site. Essentially this works by continually refining the content, calls to action, look and feel of pages to find the combinations that attract the greatest levels of engagement and conversion. Site users are taking part in an experiment, some shown page A, some page B, to see which is the most effective for them, or their specific customer group. The results accumulated over time form solid evidence of what works and for who. Site experiences can be further personalised through data on user browsing histories and purchases as well as the time of year and customer location to create a picture of interests and affinities, and to predict current and future behaviour. The picture forms the basis of personalising what’s displayed on the page, what’s prioritised and when. There are issues with data privacy laws that need to be resolved as this activity evolves. In some countries, like Switzerland and Germany, many consumers are very sceptical about their data being used for personalised ads or geo location data. The more companies use this data without thinking carefully the more likely it will become restricted or banned.

4. Monetising Your Data - New sources of revenue are being created from the existence of customer data in itself. Mobile phone companies, for example, have access to constantly updated real-time data on customer geographic location and actions, which can be of great value to businesses looking to target people at specific times and in specific places (you’re out for the evening would these restaurant deals and taxi offers be of interest?). It’s important in this information age to think again about what data is being collected, what else would be possible - and reasonable in terms of balancing collection with privacy - and what insights it would bring. And in turn, it’s important to be aware of what data might be collected by other organisations and how that could benefit your business. Entering new markets can be high risk. But what if you could access detailed and relevant customer data from a specific region first?

Takeaways: LUMS is an established player in the area of Marketing Analytics and is expanding its operations with new staff and, building on the long-established Centre for Forecasting, establishing a new Centre for Marketing Analytics and Forecasting. Updates on the work can be found here: www.lancaster.ac.uk/lums/cmaf The Masters in Marketing Analytics, a flexible degree programme which runs each year. Visit the School’s website for more information.

Distinguished professor robert Fildes is Joint Director of the Centre for Marketing Analytics and Forecasting. He was co-founder of the International Institute of Forecasters and International Journal of Forecasting. His research has encompassed business forecasting, operational research and marketing analytics across retail and consumer products firms. r.fildes@lancaster.ac.uk