YipitData tests a $2.5B-$3B sale, and the price is on the data, not the code

YipitData is exploring a sale that could value the New York data provider at $2.5 billion to $3 billion, Reuters reported on August 20th, with Goldman Sachs advising and early discussions involving both strategic buyers and private equity firms ^[1][2]. The process may not produce a transaction ^[3], but the range itself is a usable data point for anyone deciding whether their defensibility sits in the product surface or in the dataset underneath it ^[4].

Start with the arithmetic, because that is where the argument lives. People familiar with the process told Reuters that YipitData is targeting approximately $280 million in annual recurring revenue in 2026, with revenue growing by more than 30% ^[5]. That puts the asking range at roughly nine to 11 times the ARR target ^[6]. Those figures are sourced estimates, not company disclosures; YipitData has not published them ^[7]. Working backwards from the growth figure, a $280 million 2026 target implies a 2025 base of about $215 million or less ^[1].

The last marked price was lower. Carlyle led a Series E of up to $475 million in December 2021 that took YipitData above $1 billion, with Norwest, an investor since 2019, remaining a shareholder ^[8][9]. The proposed range would more than double that 2021 mark ^[10], though not necessarily for every holder: ownership, debt and the primary-secondary split in earlier rounds are not detailed in the announcements ^[11]. The nearest private comparison is AlphaSense, which said in June it raised $350 million at a $7.5 billion valuation after exceeding $600 million in ARR, about 12.5 times ^[12]. AlphaSense sells a broader AI-driven market-intelligence platform and disclosed its own numbers, so the comparison is imperfect ^[13]. On the reported figures, YipitData is testing a multiple roughly 1.5 to 3.5 turns below it ^[2].

What is being bought is an accumulation, not a feature set. YipitData combines card transactions, receipts, web data and app-usage information with analyst research ^[14], and says its coverage spans more than 500,000 companies and $1.8 trillion in business-to-business spending ^[15]. It says it serves more than 650 investors, brands and retailers and employs more than 750 people ^[16]; Reuters named Walmart, Lowe's and Ulta Beauty among its customers ^[17]. Those corporate accounts widen the buyer's story beyond hedge funds and into enterprise research budgets ^[18]. At the reported target, revenue per employee works out to roughly $373,000 ^[3].

The origin story is the part operators should sit with. Yipit launched in 2010 as a daily-deals aggregator during Groupon's ascent, and the founders launched YipitData in 2013 after recognising their systems could turn web activity into research for institutional investors, according to Norwest ^[19]. Reuters traces the company to 2010 while YipitData itself lists 2013 as the founding year of the current operation ^[20]. Vinicius Vacanti has written that the first version was built in three days and that deals were initially entered and categorised by hand ^[21]; he taught himself Python and Django after an outsourced prototype failed ^[22]. The consumer product faded with its market. The collection machinery did not ^[23].

Watch whether a transaction closes at all, and at what multiple relative to AlphaSense's 12.5 times ^[12]. Watch whether the $280 million target is ever substantiated outside the sale process ^[7]. And watch the corporate accounts: hedge-fund research budgets are cyclical, and retailer contracts are the part of this asset a buyer is most likely to be underwriting ^[18].