Virtual Fireside Chat: The Real Value of AI and Machine Learning in Retail Execution
Artificial Intelligence and machine learning are powerful additions to the modern consumer goods toolkit. The technologies are being embedded into software suites throughout the enterprise from CRM to retail execution.
While they are in widespread use across the industry, a level of confusion and uncertainty about their role in the enterprise and how to best develop, test and deploy AI/ML is still prevalent. To uncover some of the ways that AI and machine learning can have real value in retail execution, CGT hosted a virtual fireside chat with a pair of industry thought leaders.
Joining CGT managing editor Lisa Johnston were Phil Sweeney, VP strategic accounts, AFS Technologies by TELUS, and Andres Jejen, VP of solutions engineering, Exceedra by TELUS. Read on for an overview of the chat and watch the entire conversation, which includes a number of best practices for CGs.
Consumer goods companies still need to win at the shelf, which has translated into helping customers identify the right value proposition at the point of purchase. In order to succeed, this means creating “perfect store experiences” in which manufacturers and retailers have the right products available in the right locations, at the right time and at the right price.
If the perfect store model can be viewed as a snapshot in store compliance vs. how a CG is defining success, employing machine learning and AI can help isolate and analyze the factors having an impact. They can also determine — with reduced human intervention — the necessary actions to win.
What’s more, AI and ML can also help consumer goods companies refine their perfect store models over time in response to market variations.
As CGs look to build artificial intelligence into this journey, some companies remain reticent to invest at the necessary pace without having a clear ROI path.
To be sure, AI and machine learning aren’t new to consumer goods retail execution. What are new, however, are the technologies that can leverage more data sources with higher-performing algorithms.
Today’s CGs have the opportunity to receive a heightened level of data and insights — resulting in more valuable outcomes — by using some of the existing tools they already have thanks to pre-built AI models. And, ultimately, leveraging AI and ML can provide a level of analysis automation that only an army of people could execute manually.
Ultimately, one of the most significant ways artificial intelligence and machine learning drive real value in retail execution is through speed to outcome. Whether it’s compliance or out of stocks, these technologies can help CGs receive, share and react to the information, from supply chain planning to promotions.
To learn more about the value of AI and ML in retail execution — including what we might expect looking 10 years down the line — and to hear best practices about how CGs can implement these technologies and strategies, watch the full conversation with Jejen and Sweeney above.
