Mobile Walla

Nindia Dada founded Mobile Walla in 2013 after raising $4 million in Series A venture funding. The company positioned itself as an audience platform for mobile marketers—similar to Oracle, BlueRai, and DataLogics—but with a specialized focus on mobile consumer behavior data. Dada brought significant credibility to the venture: he previously founded Chitandy Technologies (backed by Kleiner Perkins, acquired by Cisco) and had taught at Georgia Tech, University of Arizona, and National University of Singapore.

The path to product-market fit wasn't straightforward. In 2014, Mobile Walla's first revenue year, the company did $1 million in revenue—but entirely from media buying, not data. In 2015, this grew to $4 million, still 100% from media. This wasn't a sustainable long-term strategy; it was a tactical move to generate revenue and keep investors happy while the actual data platform was being built underneath. "I just didn't want to be spending money," Dada explained. "So I said, can I make some revenue out of the media we are buying?" The company convinced clients that they could add significant value by layering data insights on top of the media spend.

Mobile Walla's data customers emerged through creative early partnerships. When they "had nothing to offer," the team went to ad exchanges and begged for access: "We'll buy this information from you." Over time, as their data corpus grew, they shifted from being a buyer to a trading partner. By late 2015, they started collecting data through two primary channels: (1) bid requests and ad requests flowing through ad exchanges in return for valuable insights they provided back, and (2) pixel data from ads served through DSPs and ad networks. As their customer base grew, they convinced these platforms to embed a Mobile Walla pixel in every ad, creating a flywheel of data collection.

Their first real data revenue came in May 2016: just $12,000. But the traction was unmistakable. By June 2017, they hit $250,000 in a single month—a 20x increase in 13 months. "Our first month of always-on data revenues were in May of 2016," Dada said. "That's when we had like audience revenues, real audience revenues, we had small SaaS, and we did $12,000. In the month of June this year, which is 13 months from then, we just hit $250,000 in data revenues."

The pivot away from media buying in mid-2016 was crucial but counterintuitive. In the first half of 2016, they did $3.25 million from media; in the second half, they stopped almost entirely to focus on building the data business. Full-year 2016 revenue came to just over $4 million ($750k from data, the rest legacy media), appearing flat versus 2015 to outside observers. This created a significant challenge with investors.

However, Dada had insight that vindicated the decision: by June 2017, their SaaS business was generating $150,000/month from 9 enterprise customers paying $8,500-$41,000 monthly subscriptions, plus another ~$100,000/month from audience segment sales through partners like Oracle. Zero churn (only 2 customer losses in their lifetime, out of 11 acquired). Zero paid marketing. Zero SEO. Three salespeople (two dedicated, plus Dada himself) closing deals in ~45 days.

Their biggest customer was Oracle, to which they became one of the largest contributors to the Oracle BlueKey marketplace. Other major partners included WPP, Unilever, and Procter & Gamble. They also played a notable (though undisclosed) role in the 2016 U.S. presidential election, creating precise segments (e.g., evangelical Christians identified by weekly church attendance over 6 months) and providing real-time polling location tracking for a major party's get-out-the-vote efforts—generating significant revenue in the process.

By June 2017, Mobile Walla was projecting $5.1 million in annual data revenue with a lean 38-person team distributed globally: 10 in the U.S. (split between New York and Atlanta), 12 in Singapore, and 16 in India (Calcutta). Notably, their India team wasn't engineers—they handled manual data acquisition work like mapping every point of interest and Fortune 500 office location. Gross margins on the SaaS business were strong; the main cost was people, not customer acquisition. Despite the impressive traction, Dada hadn't yet raised a Series B, waiting for "four or five quarters of data revenues to show growth" before approaching new investors. His existing backers remained supportive throughout the transition.