Analytics leader Sid Raisoni speaks with Savio Rodrigues, VP Client Partnership at Trianz – USA, in a video interview about the metrics of measuring the success of demand sensing, blending real-time data with AI and ML to forecast demand, and bridging the gap between long and short term forecasts.
Raisoni discusses the metrics of measuring the success of demand sensing at the beginning of the conversation. The first thing that a company should do is establish a baseline of how good are the cross-selling, upselling, and conversion rates, he says.
Among other specific metrics, he mentions forecast accuracy, suggested order conversions, and forecasted missed orders. Citing an instance, he shares how an increase in suggested order conversion by 85% resulted in a 10% increase in sales, for a certain company.
Explaining further, Raisoni states that order conversion is an arm of cross-selling and upselling. It is AI-driven and can be delivered at the point of action through phone directly on the sales/customer representatives' conversations with customers, he adds.
Next, Raisoni sheds light on how blending real-time data with AI and ML is helping organizations forecast future demand accurately. Emphasizing how demand sensing works, he notes that there is a component dimension of analyzing enterprise data like sales history, pricing, promotions, and web traffic.
In continuation, Raisoni maintains that although transactional data is required for such analysis, due to technology advancements, organizations can use real-time data on weather, events, holiday seasons, locations, and more. The real-time data from these microeconomic factors are married to data sets to create models that sense demand at a heightened level, he asserts.
This in turn provides optimized discounts and meaningful cross-selling suggestions which can increase sales, says Raisoni. Therefore, real-time data gives companies the edge that can help in increasing sales, he affirms.
Moving forward, Raisoni keeps his stance on bridging the gap between long-term and short-term demand sensing. According to him, long-term forecasts are likely to be more inaccurate than short-term ones.
However, he states that the long-term forecasts should not be ignored as they do signal what a company will be doing in six to eight months. Raisoni mentions that as the time horizon collapses, the short-term forecast will be more accurate as it would rely on the signals provided by long-term ones.
Citing another instance, he says that if a company wants to avoid losing certain orders due to past supply chain issues, it can leverage demand sensing. Further, the company can deliver on business accelerators such as cross-selling, smart suggested ordering, and price optimization.
In conclusion, Raisoni states that putting all of these together leads to optimized outcomes and AI enables the value to be delivered at scale in a calculated and meaningful way, which cannot be done by a sales representative alone.
CDO Magazine appreciates Sid Raisoni for sharing his insights with our global community.