How Srikrishna Swaminathan Built Factors.AI After Creating InMobi's $100M Business Unit
The InMobi VP who processed a billion clicks daily saw what others missed: B2B marketers needed AI-powered attribution to survive the privacy-first era
In 2019, Srikrishna Swaminathan stood at a crossroads that would have paralyzed most executives. As Vice President at InMobi, he had built the Wadogo affiliate business from zero to over $100 million in annual revenue with 40% margins, managing partnerships with thousands of publishers and processing more than a billion clicks daily. The rational choice was to keep climbing. Instead, he walked away.
The decision crystallized during conversations with Arvind, his college friend from RV College who had just sold Chattymeet to Freshworks. Even at $100 million in revenue, Freshworks struggled with a deceptively simple question: which marketing dollars actually drive sales? Despite sophisticated engineering talent and substantial investment in analytics infrastructure, they couldn’t connect the dots between their ad spend and revenue outcomes.
“Freshworks is a huge SaaS company. They were doing more than 100 million revenue, spending close to 25 to 30% of their revenue on marketing, digital marketing alone. All this data is spread in multiple places. And that problem was how do I get predictable pipeline?”
Check out the video of the conversation here or read on for insights.
The Unusual Education of a Systems Thinker
Srikrishna’s path to founding Factors.AI reflects an unusual pattern of domain accumulation. After graduating from RV College with an electrical engineering degree in 2005, he spent two years at Cognizant as a programmer analyst, earning a top 5% rating. But rather than deepening his technical expertise, he pivoted into sales at Dabur India, managing ₹750 million in annual revenue across Orissa state with a 23-member team serving over 21,000 retail outlets monthly.
An MBA from IIM Calcutta provided strategic framework, followed by two years in investment banking at Allegro Capital, where he learned to value businesses and understand market dynamics in India’s emerging e-commerce sector. Each role built a different lens: the engineer understood system architecture, the salesman understood customer pain, the strategist understood market timing, the banker understood capital efficiency.
When InMobi recruited him in 2013, this multidisciplinary foundation would prove essential. He built an affiliate business that processed attribution across multiple hops, connecting advertisers to publishers through intermediary networks while maintaining accuracy. Tracking URLs captured device data, timestamps, geographic information, and behavioral signals. Fraud detection algorithms screened for click spam and bot traffic across billions of daily events.
“At peak, it was more closer to 40% plus in margins. And it was a $100 million plus business. It was one of the most profitable units within InMobi as well.”
The business economics were sophisticated. Initially operating at zero gross margin to build volume, Srikrishna gradually optimized bid-ask spreads between advertisers and publishers. Working capital management became crucial, as payment delays could cripple smaller publishers. At peak, the business ran 600-700 campaigns daily across multiple countries.
The technical lessons would prove invaluable. Data stitching across fragmented systems wasn’t just an operational challenge, it was the core problem that would define his next venture.
Three Converging Forces and a Market Opportunity
By 2020, Srikrishna identified three trends converging to create what he saw as a generational infrastructure opportunity in B2B marketing.
First, the B2B SaaS market was growing 30-40% annually. Companies typically spent 25-30% of revenue on marketing, creating strong incentive to optimize that investment. Second, marketing technology stacks were fragmenting rapidly. Companies that once relied on Google Ads and Salesforce now deployed LinkedIn campaigns, marketing automation platforms, webinar tools, content management systems, and email nurturing sequences. Each tool generated proprietary data, but none could show the complete customer journey.
Third, privacy regulations were dismantling traditional attribution infrastructure. GDPR in Europe, California’s privacy laws, and Apple’s iOS changes meant third-party tracking was becoming unreliable or impossible. Companies needed first-party data solutions that could operate within these new constraints.
“We are in the golden age of data, data everywhere, but how do you process the data? I’m a CEO of water, but you don’t have the right desalination plants. You’re not going to be able to drink anything.”
Srikrishna recruited two co-founders with complementary expertise. Arvind brought AI and machine learning capabilities from Google and Chattymeet. Praveen Das, who had worked alongside Srikrishna at InMobi leading data products, brought deep understanding of customer data platforms and audience segmentation.
The Technical Innovation: AI-Powered Pattern Recognition
The core technical challenge involves exponential complexity. In B2B, a single deal might involve multiple stakeholders from the same company, each interacting with content across different channels over weeks or months. A CFO might see a LinkedIn ad, an operations director might attend a webinar, and a CEO might read a whitepaper. Connecting these touchpoints to eventual revenue requires sophisticated identity resolution and attribution modeling.
Traditional solutions required hiring data engineers, writing SQL queries, and configuring dashboards, a process taking months and requiring ongoing maintenance. Factors.AI automates this through OAuth integrations that connect major marketing platforms within 15 minutes. JavaScript SDK deployment on company websites captures behavioral data through first-party cookies. The platform then stitches user journeys using email matching, cookie tracking, and behavioral fingerprinting.
The differentiating innovation is the “Explain” feature, which applies information gain algorithms to automatically identify performance patterns. Rather than forcing marketers to manually test hundreds of hypotheses, the platform builds tree structures of user attributes and event sequences, then ranks insights by statistical significance and sample size.
“You see so many paths coming in there. What the algorithm does is it makes a tree-based design. People did this, then this, then this. Each user would have user attributes, each event would have event attributes. Then he looks for information gain, wherever there is higher information gain, those things get ranked higher.”
The system calculates deviations from baseline conversion rates across all possible attribute combinations and surfaces the most significant patterns. A 2% average conversion rate might jump to 10% for specific cities or demographics, or drop to near zero for others. Speed is equally critical: real-time processing allows marketers to make decisions during weekly planning meetings rather than waiting days for custom queries.
Growth Strategy: Customers Before Revenue
Rather than optimizing for revenue, Factors.AI prioritizes customer acquisition and product usage metrics. The company currently serves 30 customers and targets 100 by the end of 2024, with plans to reach 200 customers before focusing intensively on revenue optimization.
Pricing is deliberately simple: $99 to $3,000 monthly based on tracked users (ranging from 10,000 to 4 million), with no seat limits or feature restrictions. The company targets $1.5 million in annual recurring revenue by year-end.
“What we are optimizing on currently is increasing the number of customers and improving the product usage metrics. Eventually, we’ll come back to the pricing, but currently we are pricing only on monthly tracked users.”
The onboarding experience reflects this philosophy. Standard templates showcase data immediately upon integration. Pre-built dashboards display common B2B metrics: channel performance, content attribution, funnel conversion rates. Most customers generate actionable insights within a week, compared to months for traditional implementations.
The Industry Shift and Future Vision
The market Factors.AI addresses is undergoing fundamental transformation. Marketing professionals who once focused on brand building and creative storytelling now face intense pressure to demonstrate measurable ROI and pipeline contribution.
“Marketers 10 years back used to be far more brand, storytelling, positioning. Now they are fundamentally data-driven. They are quizzed very clearly within their own teams and companies saying, show me the data, this works, use the data to make decisions.”
The regulatory environment accelerates this transition. Privacy laws create competitive advantages for companies with sophisticated first-party data capabilities. Organizations that can effectively measure and optimize marketing spend within privacy constraints will outperform competitors relying on increasingly unreliable third-party attribution.
The next evolution involves moving beyond analytics to automated workflows. Srikrishna envisions automatic audience creation for ad retargeting, account scoring for sales teams using intent signals, and email campaign triggers based on multi-touch attribution insights. This progression mirrors how CRM systems evolved from contact databases to comprehensive sales automation platforms.
Srikrishna’s journey from processing billion-click ad networks to building B2B marketing attribution reveals a consistent thread: recognizing when data complexity creates infrastructure opportunities. His multidisciplinary background enabled pattern recognition across domains that specialists might miss. The lesson for founders centers on how domain expertise in technically complex fields creates defensible advantages, and how recognizing convergence of trends, rather than individual innovations, defines successful market timing.
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