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Tomi.ai, an AI-powered platform that optimizes digital ads, today announced that it raised $1 million in seed funding contributed by Begin Capital and the Phystech Leadership Fund. Founder and CEO Konstantin Bayandin says that the proceeds will be put toward expanding the size of the Tomi’s and growing the company’s platform.
Companies with long and offline sales cycles in industries like real estate, automotive, and financial services sometimes struggle to optimize their digital ads for business outcomes. Due to low conversion rates and the offline nature of these outcomes, they tailor to leads and clicks, which can result in exorbitant customer acquisition costs.
Tomi aims to solve this by collecting online data from a tracking pixel on a company’s website and ad platform API integrations as well as transactions from customer relationship management systems. After recording 100 to 300 “positive outcomes” to train machine learning models, the service conducts a “dry run” on Facebook’s and Google’s ad platforms, comparing the results of controlled experiments to track performance uplift across platforms, channels, and campaigns.
Bayandin worked as senior director of digital marketing and technology at Compass and chief marketing officer at Ozon, where he focused on predictive modeling. While working at Compass, Bayandin says he witnessed how limited the opportunities were for mostly offline industries with long sales cycles, such as real estate, compared with ecommerce at Ozon.
“The vision is that Tomi becomes the gold standard solution for Facebook and Google ads targeting and optimization in traditional industry verticals with lead gen marketing for long offline sales cycles,” Bayandin told VentureBeat via email. “A number of tools advertise they do targeting and optimization of ad campaigns, a few tools do predictive targeting and optimization, but all of them use third-party data in one way or another, and it’s only us who rely on first-party data. Product-wise, we differ because of our laser focus on traditional industries that can’t leverage the power of ad system’s smart bidding owing to low conversions and a long sales cycle.”
With Tomi, customers, who pay only for incremental customer lifetime value and business outcomes, can use audiences optimized for expected lifetime value and target new visitors with high intent. The platform runs on a high-load, Google Cloud-powered instance that processes 30 million hits per day.
“Our machine learning algorithms have to learn from a few positive examples counting from 100 or more. The models also have to be stable in terms of little changes in user behavior, so that the variance for predictions is as little as possible. We are exploiting ‘bias variance tradeoff’ a lot by substituting rare actual transaction events by numerous synthetic conversions with non-discrete values,” Bayandin explained. “We also use a variant of transfer learning by training machine learning models on the overall website traffic and applying them to paid acquisition with the premise that user behavior depends on user intent rather than on the source of traffic. We use feature engineering for our models based on industry specific learning that we have learned with our customers.”
Bayandin characterizes the platform as a “natural fit” for large customers such as marketplaces, real estate, and financial companies — it currently has 10 mid-size and enterprise customers. “We’ve built the platform with only 8 people in the team now and plan to have 20 by the end of the year,” Bayandin said. “We launched the product just over a year ago and the revenue has grown 4 times since then … [We plan to use the seed funding] to focus on growth and product development to release a self-service platform later.”
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