“If I would have asked people what they wanted, they would have said faster horses” is a famous quote by Henry Ford, the inventor of the automobile.

When it comes to innovation, not only is it hard for people to look beyond what they already know but oftentimes, there’s also resistance when it comes to introducing new concepts or technologies.

When we started INCRMNTAL, there were already ways to “measure” marketing: GeoLift experiments, A/B testing users, last-touch attribution, and yet – advertisers kept wasting budgets with redundant ad spend and fraud was still having a rave.

Fast forward a few years, and not much has changed. Many advertisers realize that almost half their advertising budget is still being wasted, the trouble is that they don’t know which half.

Here we saw a gap in the market. The old ways were horses. We wanted to invent a car.

It was obvious to us that the current measurement methodologies weren’t working and we needed a new approach. Unlike other methods, INCRMNTAL’s approach to measurement utilizes a disruptive new technology called Causal AI.

Where conversion lift and Geo Lift experiments are grappling to find their way in a privacy-first world, our platform is privacy-safe by nature. INCRMNTAL is able to use Causal AI to untangle the statistical relationship between variables into causal relationships and provide insights into why a certain marketing tactic proved a success or failure based on all related variables such as the channel, the creative and external factors such as the weather or news agenda.

Our solution is focused around time series interruptions (ITS) and takes inspiration from computational frameworks such as causal discovery, extending into various related domains to manage multiple activities happening at the same time frames and incorporating vital covariates such as external factors and seasonality.

What distinguishes INCRMNTAL from other solutions is the following; our commitment to unbiased models (unlike MMM), our independence from A/B testing and the need to shut down marketing activities (the latter being a fundamental part of GeoLift).

What’s more, our distinctive platform empowers marketers to measure both digital and offline advertising, while providing a revolutionary alternative to the conventional attribution models currently in use.

Causal AI is still in its infancy but according to the Gartner hype cycle, it is expected to mature within 2 to 5 years. 

(source: Gartner Hype Cycle for AI, 2023)
 

Here we see the five phases of the Innovation Adoption Lifecycle. Right now, Causal AI is in the first stage and as such, is only being used by the innovators, visionaries, and early adopters.

In order to get to the Slope of enlightenment – technologies need to be simplified, almost commoditized, and standardized, which is why, we’ve been focusing on creating more accessible modes of the platform, and cooperating with academia to write further research on the causal AI methodology for in use of marketing measurement.

If you’ve never seen a hype cycle, and you may wonder about the stages - the ones you really need to care about are these:

• Innovation Trigger is around the time when a new technology is invented. 
• Peak of inflated expectations is usually the “hype” phase, while people realize that the innovation has been in the market for a couple of years already. 
• Slope of enlightenment is when you stop thinking about a technology as being “special”, but just start taking it for granted.  

We’ve also been working with our customers to publish case studies on how brands are using INCRMNTAL and have validated our methodology using real experiments, or even using fake data (Gameloft case study).

We know Causal AI is ripe for the hype. Our aim is to bring this technology to the marketing masses quickly, so even the laggards get onboard with this privacy-safe, highly effective gold standard for measurement sooner rather than later, and unlock the true value of their marketing budgets.