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How to use big data, gamification for better rewards programs
Rewards programs are nearly as old as commerce, but whether the business is a restaurant with a punch card or customers earn redeemable points based on purchases, there are ways to improve the reward experience to build a more loyal customer base.
Over the past three years two trends have changed the way brands interact with consumers. First, consumers' interest is games and second, the data brands have collected from online consumers. According to one expert these two trends make it possible for brands to create immersive, relevant experiences - even in reward programs.
"Call it the Mary Poppins protocol: turning a task into a game always makes it more appealing. Loyalty programs have always had natural game-like attributes, encouraging participants to rack up points and redeem them for prizes, but modern gamification is about making the most of modern technology," said Christopher Barnard, Points President. "Its goal is to create a relationship of exchange where consumers are challenged and incentivized to share more of themselves: their time, their attention, their information, and ultimately their loyalty.
This can be done with an actual game or just by embedding gamification tactics into a brand's messaging."
By adding games or game-like infrastructures to loyalty programs brands create an app, social or mobile destination, bringing out the competitive edge in many customers as well as entertaining them.
Second, using big data
"Big data is a big buzzword, but massive volumes of information are meaningless unless companies understand which data points are relevant to their customers and strategies. Predictive analytics--using data to predict how customers will behave based on patterns of behavior among similar customers -- allow loyalty programs to exercise greater segment specificity and identify more accurate behavioral indicators," said Barnard. "If predictive analytics show that certain types of loyalty program members are likely to sign up for a credit card, for example, marketers can use that information to improve messaging that targets these customers for credit. Conversely, brands can segment customers who are not likely to sign up for a credit card (based on more data points than just their credit score) and target them with different messaging they are more likely to engage with."
Predictive analytics is invaluable because it can be used to predict how members will behave and what their Customer Lifetime Value (CLV) will be, and to adjust a program as necessary to affect future results.
Image via Shutterstock
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