What if consumer-packaged goods marketers had the technology to
identify and a database to keep track of purchases for a specific brand and product, and to identify the store where they were sold for any time and day -- and could take that data to build something
similar for lookalike audiences based on AI causal analysis?
Becausal, after extensive testing with more than 20 agencies, launched a platform using AI-driven causal analysis offering audience
segmentation and searchable CPG purchase datasets, helping brands understand why and when consumers buy. It also enables the creation of lookalike audiences using AI based on real shopping
behavior.
The platform offers audience data that identifies "total spend by retailer," "demographics," and "propensity to purchase." For marketers, it offers precise audience targeting,
personalized, insights into new product development, and the ability to send relevant messages to consumers.
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Early adopters -- ad agencies and CPG brands -- have asked for this type of
data.
Today the platform is in closed beta, but Becausal will open the platform to additional users in the new year.
Retailers can use the tool to understand the market and grow
their market share.
Ennis told MediaPost it is the only place to directly access Walmart purchase data outside of Walmart, which typically represents 40% of CPG brands’ active cash value
(ACV.)
Becausal, which rebranded from ScanBuy, spun
out the data business in April. Chuck Ennis, Becausal chief commercial officer and product manager, described the dashboard that displays the data as something similar to a retail media
dashboard.
The data helps marketers justify marketing and advertising budgets. It can tell brands things like why the frequent buyer of salty snacks waited a month to repurchase the Lay's
chips at Walmart, when they historically bought the product at Kroger.
In other words, the deterministic data combined from mobile shopping apps, direct feeds from retailers, and loyalty card
feeds, can help brands understand the number of consumers who recently purchased a specific product and from where.
It does this by allowing the platform to cross-map the data to the retailer
for a specific product. The data identifies when a brand or a retailer wins or loses business, and to what other brands or retailer, even down to the product, said Ennis.
“If they are a
Lay’s lapse buyer, the data can tell the brand why,” Ennis added. “If someone still buys in the category of snacks, yogurt, or breads, the data in the portal can identify the
audience.”
Marketers can target consumers who stopped buying products at one retailer, and began buying them at another through Becausal’s data, for example. CPG tend to license
the data to supplement their own first-party data, and agencies want it for audience building.
The database “CPG Data Store” supports about 20 agencies and brands and houses
about 8,000 audiences, with 16 million monthly active users (MAU). It helps marketers find brand-, product- and retail-level insights. Brands can search these databases for free.
Becausal has
deterministic, multi-sourced purchase data on three million consumers representing $300 million in purchases across 445,500 products. The data is updated weekly and allows marketers and retailers to
build and explore granular audience queries through a visual user interface.
“If you target consumers who like snacks, they might not like Lay’s, so you have wasted ad
budgets,” Ennis said.