Artificial intelligence (AI) tools, models, and processes are essential building blocks to create a competitive intelligence framework to support strategic decision-making.
According to an article in VentureBeat, a successful competitive intelligence strategy includes four critical phases: planning and defining the research objectives, gathering relevant data, processing and analyzing the data, and ultimately acting on the insights gained. Michael Fagan, chief data scientist, Mesmerise Group, and CEO and co-founder of Resonance Labs, believes that the most crucial ingredient for any competitive analysis is its data sources, as a single point-of-view dataset can often lead to misinterpreting the output. To overcome this, he suggests utilizing multiple data sources but warned that each comes with its own biases.
“We first needed to align the datasets by understanding the natural distributions and applying weights. This data enabled us to predict the search share pretty accurately on a weekly basis. It also showed our share of the market, what terms and topics were standard, and what was up and coming. Having this information initially can be sobering, but this is a baseline,” he said.
“Adding machine learning to the mix further enables you to interpret the recorded patterns and create automated processes so that the intelligence gained is timely enough to take action and positively impact your business over your competitors,” Fagan told VentureBeat. “To stay ahead of the curve, you need to focus on your base data and ensure you have a solid governance structure in place and standard techniques to compensate for biases. Once you have this, you can always be confident that the intelligence layer will add value.”
Read the full article: Touched by AI: Competitive intelligence culls new data insights | VentureBeat