Loyalty & Rewards

Cracking The Anonymity Code In Loyalty

Every brick-and-mortar retailer has three kinds of customers.

Anonymous guests may be regular, but the merchant hasn’t collected any data on them yet. Known consumers have provided some information such as a phone number, email address, or credit card number. Then there are the loyalists. These customers return again and again, and they don’t need to be incentivized to do so. They are loyal whether or not they get freebies out of the deal today.

The third type is what businesses aim to build out of all their customers. However, the anonymous and known customers may need a little extra push to get there – namely, that freebie that the already-loyalist customer didn’t need.

Incentivizing with special deals and offers can provide that push, but finding the right incentive will be different for every customer. Therefore, creating loyalists takes a lot more data than the average brick-and-mortar retailer collects about its consumers.

Shyam Rao, Punchh Co-founder and CEO, says that’s what the Punchh platform has endeavored to provide from day one, putting it on par with eCommerce merchants that already have that type of data.

Until now, though, Punchh’s focus has been on making loyalists out of known consumers. Getting customers out of the anonymous bucket has been more of a challenge, which he says will require further investments in machine learning and artificial intelligence (AI) to achieve.

From Anonymous To Known

That’s exactly what Punchh plans to do with the $20 million it raised in a recent Series B round led by Sapphire Ventures, announced today (April 12).

Rao said the company is coming out of beta with Punchh Acquire, a product intended to bridge the gap from anonymous to known. Say an anonymous guest hops on to the WiFi network at Pizza Hut. The restaurant can serve them with a 5 percent off coupon in exchange for sharing their email address.

The goal is to build from the moment the customer is unknown to the moment they decide to share their information, and this process must begin right when the customer enters the store. Based on the time of day and location, the merchant can already make assumptions about the guest and begin tailoring the experience to them.

From Responsive To Predictive

The time has come, Rao says, to move beyond a platform that is responsive to customer needs; the modern platform must predict needs, both for known and anonymous customers. That will require data not just about the person but about context, such as weather, local sports and politics calendars.

For instance, if it’s going to be a rainy weekend in Minneapolis but it’s the season opener for the Colts, there’s a prefabricated campaign that can help encourage people to visit and spend – the merchant just has to decide to run it.

Rao added that gathering and leveraging contextual data removes the guesswork around what deals to offer, when to offer them, and who to target. More data and better AI analytics can help businesses ensure that they are only offering discounts to people whose behavior it could change – again, loyalists are spending and earning rewards for that spend whether or not they get a coupon.

Meanwhile, hitting an anonymous guest with the right offer at the right time can bump them up to the known category, reinforcing their relationship with the brand. And relationships, Rao says, are what drive revenue.

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Latest Insights: 

The Which Apps Do They Want Study analyzes survey data collected from 1,045 American consumers to learn how they use merchant apps to enhance in-store shopping experiences, and their interest in downloading more in the future. Our research covered consumers’ usage of in-app features like loyalty and rewards offerings and in-store navigation, helping to assess how merchants can design apps to distinguish themselves from competitors.

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