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Customer analytics denotes the processes and technologies that unlock insights hidden in customer data. Insights help businesses engage better with customers and get them to buy more. Sandeep Mittal, managing director of Cartesian Consulting, a marketing analytics firm, outlines Domino's methodical approach to customer analytics, which has yielded coupon redemption rates as high as 30 percent and substantial incremental sales.
1. Put a team in place
Domino's had a “precision marketing” team in place even before we entered the picture. This team must have great belief in data and what it can do. It makes a huge difference if the team doesn’t see analytics as an external practice fighting for its attention but an internal practice that will bring great success.
Working together, we’ve switched Domino's marketing strategy from being one focused on Customer Relationship Management to one emphasising Customer Lifecycle Management. Correspondingly, we have gone from a campaign every 40 days to 60 a day. We had quick wins; Domino's has used every victory to drive a culture change so that the team gets better co-operation from all concerned. To help spread organisational awareness about the contribution of analytics, we’ve gone wide on its business impact.
2. Collate customer data
Customer information is the starting point for diving into the deep end of analytics. Good quality time spent here forms the bedrock for successful outcomes. Behavioural (transactional) data rules if you want to predict behaviour. Information like what people buy, when they buy, full price or discounted, how often they buy, when they last bought and what offers they buy with can help build a statistical model to predict who will respond to sales campaigns.
Domino's knows who buys pizza weekend dinner times, who has moved from regular to thin crust, who has tried their new product, who has never ever had choco-lava cake, who usually orders one pizza but sometimes orders five and so on.
Next up is feedback and satisfaction data followed by profile information. We’ve found that of the top 20 variables that predict future customer behaviour, profile variables contribute less than 5 percent. So we tell businesses: you have access to bills, that’s enough for a start.
3. Derive insights from data
At the outset, Domino's spent a lot of time understanding customers, clustering and segmenting, building predictive models, testing offers and communication combinations. They predicted (and continue to do so) who will redeem coupons, who will transact, even who they should not send offers to. They matched offers to customers. They tested communication mediums and timing, they even tested the negative impact of over-communication! They are very aligned to contact-optimisation. Other important analytics exercises are pricing analysis to determine which products cannot bear a price hike, store analysis to examine the location advantages of individual stores, adoption analysis for new product launches to know which customers to target, etc.
4. Let the action begin
Preparedness paves the way for hectic action to get serious bottom line impact. Today, Domino's runs around 60 daily campaigns on millions of customers, taking into account everything they have learned about the customer even as recently as the previous night.
A thorough attribution and tracking mechanism ensures Domino's knows exactly what it is getting out of the analytics and precision marketing efforts. Over to Domino's for the last word:
Our Customer Lifecycle Management activities are yielding 30 to 45 percent incremental EBIDTA across different customer segments. Domino’s has registered Return on Marketing Investment (ROMI) over 1000 percent because the cost of outreach activities (sending SMSes) is very low,” says Harneet Rajpal, Senior Vice President, Domino's.
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