Following lean methodology, set up a number of experiments to reach product-market fit. brought its award-winning big-data driven anti-fraud solution into the boiling space of online advertisers. According to the IAB (International Advertisement Bureau) more than a third of the global online advertisement traffic is either non-human - meaning bot-generated - or fraud. This is the problem that solves. Online advertisers hence spend significantly more than what they should. Advertisers prefer commissioning agencies to buy traffic for them. The upper-end of the agencies are working hard to deliver premium quality traffic and go as transparent as possible, though even best providers do not account for clicks/per page nor where those clicks come from. With a few exceptions, however advertisers are not aware of the problem, so selling to them require long-term market education effort.

Potentially best clients for the service are ad networks - demand and supply aggregators that sell traffic to advertisers/ agencies. They do understand the problem and can apply different pricing options for audited/non-audited traffic. Ad networks, however are giants with traffic starting around 1 billion impressions per month and like any industry huge players they are unwilling to talk to early stage startups, only with scale-ups after solution has been proven.

Advertisement agencies were next choice. Finally, the product offer met enthusiastic customers, willing to pay and engage with the product. Furthermore, European ad agencies are interested in more than just preventing fraudulent traffic - they are looking for an end-to end solution that helps them better protect their clients’ brands. So not only got first evangelists (product-market fit), but was also able to get valuable customer input to further develop the solution. Advertisement agencies, however are significantly smaller in volume (150 million impressions per month) and they apply anti-fraud solution per advertisement campaign (repeated, but not recurrent revenue).

Further experiment was concluded buying, cleaning and reselling traffic. It is not the main business focus, but helped market understanding and product development. The lean experiments’ learnings have been continuously implemented in’s business and financial plan developed by Invendor. Each type of client differs from the others in sales cycle length, CAC, pricing, as it depends on volume, product cost hence profitability. Invendor developed reporting towards investors combining leading market (lean analytic indicators) with traditional financial performance indicators. Invendor supported management to breakdown business targets on individual levels creating an actionable controlling framework to support growth and innovation.