Brands can now track online and in-store consumer behavior in a way that will help them target their marketing and predict monetary trends.
Punchh’s new machine learning initiative, Predictive Customer Lifetime Value (PCLV), is connecting the gaps between the in-store and online experiences. Using a new advanced algorithm, companies can track the expected CLV of any individual customer, even from the first transaction. A statement issued said “PCLV uses machine learning to calculate the CLV of each individual customer using projected estimates of the customer’s monetary worth of the business over time.” Punchh said retailers can run campaigns that take into account the predictive lifetime value of any given customer, as well as use the information to predict what consumers will be purchasing and what sells best online and in-store.
In-store retailers are able to collect this data from email addresses and phone numbers given at the register, coupons used and issued, previous credit card transactions and online purchases on a site after visiting a store in person. Punchh helps accelerate the potential of the brands, and then the brands can tailor their campaigns to match the successful trends calculated from Punchh’s algorithm.
Punchh has generated PCLV scores for brands like Pizza Hut, TGI Fridays, Denny’s, Moe’s Southwest Grill and Smashburger, and while Punchh initially launched with the restaurant vertical, it has been expanding to retailers over the past year.
“What we do is we basically bring the power that makes the online experience so compelling down to the store level … by figuring out consumer identity and what the customer is buying at the moment of purchase in real time,” said Shyam Rao, the CEO of Punchh. “We are solving for the entirety of the consumer journey in store.”
Brands are working with Punchh in order to increase their knowledge of data on consumers. Rao said that Punchh is “powering these experiences on behalf of the brand. No consumer knows who Punchh is, but we have about 70 million consumers on the platform today.” That means that brands are using Punchh’s algorithm to collect data on spending trends without drawing unnecessary public knowledge.
Punchh targets two types of consumers: It helps brands acquire customers who are new to the brand, converting them from anonymous shoppers to being somewhat known. Brands have less information about these anonymous consumers, which includes consumers who pay in cash, and what they’re buying. Brands can then convert this first group of consumers into loyal consumers by gaining personal information like email addresses and phone numbers in exchange for notifications of sales, discounts or other perks of a loyalty program. For these loyalty consumers, where the customers are known, Punchh provides a “seamless digital experiences that solves for convenience value and personalized experiences, these ubiquity experiences [using machine learning],” Rao said.
According to Rao, there are three fundamental expectations that consumers are interested in that brands must cater to: convenience and value, personalized experiences and ubiquity of experience. These three expectations must be balanced by combining the identity of the consumer with what they are buying. Rao used Amazon as an example of fulfilling the quintessential values of the customers while being one of the top retailers in online shopping. So Punchh is now trying to parallel the success of Amazon online to in-store retailers. Brands will be able to predict what products and marketing will do better in-store as well as online. By creating a more personalized experience between the brand and consumer, retailers can strengthen their business.
