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article imagePredicting consumer behavior is key to business success

By Tim Sandle     Jan 21, 2018 in Business
Customer serving businesses can grow and remain competitive if they can predict what consumers are most interested in and how consumers might react to certain offers or incentives.
If it’s a rainy day consumers are more likely to head to the cinema or they might be tempted to order in pizza and play video games at home. Understanding what consumers might or might not do under a given set of circumstances is key for marketing firms in targeting the latest offers to the right demographic group.
This means predictive analytics is giving brands the capability to automate marketing responses in any given customer scenario.
To achieve this requires the right type of technology that is capable of targeting the right type of consumers at the right time and in the right place. Examples include a couple out shopping in a mall receiving a smartphone message about a 2-for-1 cinema offer.
Cinema and data analytics
Movie-goers purchase tickets at an AMC cinema in California in August. The giant cinema chain has si...
Movie-goers purchase tickets at an AMC cinema in California in August. The giant cinema chain has signed a deal to build and operate movie theatres in Saudi Arabia after the conservative kingdom lifted a decades-old ban
FREDERIC J. BROWN, AFP
An example is with the Turkish scheme Sinemia, founded by Rıfat Oğuz. The scheme offers a cinema card project to users in Turkey (the scheme has also been expanded in to the U.S. $11 billion cinema market). The scheme is a rival to MoviePass, offering competitive features like the ability to purchase tickets in advance using a preferred method (such as Fandango or MovieTickets.com).
Retailers using big data
HK CWB Times Square Shop Marks & Spencer
HK CWB Times Square Shop Marks & Spencer
TShops
A different take on the weather comes from U.K. retail chain Marks and Spencer. Here Mike Whitelegge, head of big data solutions at the company, says the introduction of Marks and Spencer’s recent Sparks loyalty scheme (introduced in 2016) has been helpful in terms of unlocking the power of predictive analytics. Speaking with Marketing Week the analyst notes that the “triangulation” of data sets is particularly interesting as it enables marketers to link behavior with psychology around factors such as the weather. This means if the weather is hot, card holders receive offers for t-shirts or beachwear. If the weather is rainy, the raincoat or knee-length boots offers might come through.
“Every day our shopping experiences are subconsciously filtered,” he explains. “I find working in retail interesting for this very reason. We’re all consumers and [in this industry] it’s easy to humanise it.”
Fast food outlets using predictive analytics
A pizza box for Domino s
A pizza box for Domino's
miskan
A different example is with pizza. A company like Dominos Pizza is using location-based services to activate consumer loyalty offers, based on artificial intelligence following and analyzing the habits of customers. The company is much a technology company that happens distribute pizza as it is a pizza delivery company.
Dominos Pizza, The Daily Sabah reports, also use data to analyze different outlets, reviewing factors like average service time, comparative turnover, monthly sales of branches and whether customers order from the website or mobile applications.
Predictive analytics also work for charities as well or those who specialize in environmental products. Research by Michele Laroche and colleagues has found, using various statistical analyses, the demographic, psychological and behavioral profiles of consumers who are willing to pay more for environmentally friendly products. For instance this research found that the segment of consumers more likely to purchase products marketed as ‘green’ or ‘environmentally friendly’ were be females, married and with at least one child living at home. This information is used by marketers to undertake targeted advertising.
More about Customer service, Consumer, big data, data analytics