No one could foretell the pandemic, even with a crystal ball, let alone its impact on the global economy, consumer buying behaviors (online and instant home delivery), and the dramatic pressures placed on the supply chain (hello, toilet paper anyone?). Looking back, any predictions we made paled in comparison.
Looking forward Artificial Intelligence (AI) continues to make huge strides beyond the consumer homes and into the business world. We’ve witnessed first-hand how AI when applied to data analytics has a direct, positive impact on revenue, customer experience, and product development to name a few. We know AI is big, and its impact grows annually. But where is AI headed? What’s in store for 2022?
So, we asked our CTO, CEO, and our VP of Analytics what they see coming in 2022. Here are their predictions:
#1: AI takes a bigger role behind the scenes
More and more products are using AI tools to provide value to their customers, whether it is through search results, product recommendations, customer insights, etc. And the best AI tools will be the ones where the output is exactly what people care about and it’s so helpful that users don’t even realize there is AI behind it.
In 2022, we predict that every data analyst will be using at least one tool driven by AI to perform their daily work, whether they know it or not. This lack of realizing AI is in play is one of biggest compliments a data analyst can give an AI tool. It will become the norm that data analysts accept that AI is not competing with them, but instead that AI is complementary to and supportive of the work of human data analysts.
#2: The emergence of Activated Insights
Insights, until recently, have been information provided to a user who is then responsible for taking an action. With advances in automated analytics platforms, we predict the emergence of what we call “Activated Insights”. Activated Insights are mini-applications generated by AI-based analytics systems that combine the data, captured insights and custom tools into one package. As a result, each insight not only tells the user what is happening in their data, but bridges data by presenting the user with a variety of tools to immediately take specific actions on the insight.
For example, in the early days of March 2020 before the pandemic lockdowns began, a large retailer received an important insight about a rapid rise in the purchases of office furniture across their platform. Even though office furniture was only 0.4% of their sales, this single data insight (and the root causes underlying it) gave the retailer enough information to prepare ahead of the shift to the work-at-home economy and take the lead over competitors.
An insight such as this would fall well below the radar of most companies, but AI tools have the ability to look through the entirety of data to find these critical and strategically important insights. And with activated insights, the insight, underlying data, and the tools necessary to take action are all there in one package for the business to make the most of it.
#3: G4 will push the synthesis of Quantitative analytics + Qualitative analytics
As third-party cookies are phased out by Google and others, and G4 takes over the game, companies will need to look to other means in order to remain competitive and spot new trends and insights in customer behaviors. To keep their edge with these new quantitative blind spots, companies will start to bring more qualitative analytics (e.g., customer interviews, sentiment analysis, surveys, and focus groups) into their analysis mix, along with investing in first party cookies.
Companies will use all of these details to provide analysis that is more personalized for specific customer segments as it includes emotions and other human factors. The result will be business decisions that use both quantitative data analytics and qualitative analytics to create customized strategies that are unique to individual customer groups.