Using Analytics to Differentiate Your Customer Service
Differentiated service is about making your company easier to do business with, anticipating the needs of your customers, and having products available when needed.
For example, when I travel last minute, I expect my preferred rental car provider to have a vehicle available for me when I arrive. I don’t think about their current demand, nor do I think about the individual profiles of other renters. As a good customer, I simply desire that they have a car available. Not having a car available because they lacked the proper insight into my demand means they lost an opportunity.
The same holds true for the customer service operations of process industry manufacturers. Their customers all expect the material they need to be where it’s needed, when it’s needed, and that the transportation assets are lined up accordingly. During instances when normal demand signals are absent, companies that can anticipate, plan, and meet customer expectations, despite less-than-ideal circumstances, become the winners through differentiation. Companies that lack the ability to effectively see and efficiently fulfill expectations when usual demand signs are missing risk watching their business go to a competitor.
Descriptive, predictive, and prescriptive analytics are necessary to contribute to an organization’s goal of improved and more informed decision-making, and should co-exist. One is not better than the others; they are just different.
Descriptive analytics paint a picture of what is currently happening, while predictive and prescriptive analytics unlock differentiated capabilities. This enables companies to shift perspective beyond the horizon and look into the future to notice demand signals, market volatility, or transportation issues before they happen.
Predictive analytics give supply chains the ability to respond to actual market conditions, predict consumer behavior, and identify possible delivery constraints. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors so companies can assess risk or opportunity associated with a particular set of conditions to guide decision-making.
For example, companies can use weather data to predict buying behavior or potential supply disruptions, forecast future demand, predict the price of fuel, and sense transportation hub congestion to dynamically re-route to less congested ports.
Prescriptive analytics envision many different scenarios using intelligence gathered in real time, and present an optimal solution. Prescriptive analytics help companies decide the best course of action to take given certain business objectives, requirements, and constraints. It seeks to find the optimal solution given a variety of choices, alternatives, and influences that might affect the outcome.
With descriptive and predictive analytics helping to understand the drivers behind customer buying patterns, combined with prescriptive analytics that can determine the optimal schedule, production, inventory, and supply chain network design, businesses can cost-effectively anticipate customer needs and thus provide high levels of service.