Early adopters are taking the approach of building a ‘playbook’ which will equip any large-scale roll-out initiative. This approach gets significantly bolstered when data-driven changes produce the expected results, and success breeds success.

It has been amazing and hugely rewarding to see retail customers get excited about the transformative potential of AI-based video analytics. The prospect of transforming things that happen in the physical space into data that can be manipulated, compared, contrasted, and interpolated is exciting for any tech junkie. But the most common path to success has been the early adopters choosing to experiment or dabble in tests and trials, to quickly find out that nothing was what they thought it was, and to discover that the patterns of behavior that they took for granted didn’t even exist at all!

At first, they were skeptical of the data, and one would think it was a bad thing, but it was not; it made them dig deeper into their business with the goal of challenging the results, which led to a stronger appreciation of the technology and the impact it could have on their business and strategic goals.

The moment a new technology comes along that challenges assumptions and sheds a bright light on areas of the business that were: a) the way things have always been done, b) black magic, c) smoke and mirrors, d) based on decades of experience… (you get the picture), it is a game changer.

At this stage, customers realize that they now have the power to REALLY make data-driven decisions in a manner that is scalable and incredibly flexible. This has opened the door to agile deployments of new concepts where the first experiences provide not just pass/fail, but why we passed and why we failed.

Across the board, we have seen the early adopters taking the approach of building a ‘playbook’ which will equip any large-scale roll-out initiative with objective and accurate data collection systems. Contextual data collection and analysis is becoming a mandatory part of any new customer-facing technology (i.e. self-checkout) or broad-based marketing initiatives (i.e. ads running on DOH screens). When the new thing is working well or isn’t working well, there is data available to explain why assumptions were right, or why assumptions were wrong. This approach gets significantly bolstered when data-driven changes produce the expected results, and success breeds success. Monumental and daunting roll-outs are now planned and executed in an agile manner, even across thousands of locations, in a shorter time frame and without falling back on trial and error. It has become all about bringing the digital A/B testing mentality to the physical world: doing more of what works and less of what doesn’t.