How to execute commercial analytics strategies that deliver value Integrate data Scale analysis and and insight insight development To drive market performance, companies must be able to synthesize new data sources, apply advanced analytics and use Firms that use commercial analytics Effective commercial analytics requires those analytics when and where they make effectively can integrate different kinds of continuous insight across the organization. decisions. Yet many struggle with how to data from multiple different sources. Classic A one-off analysis or an ad hoc project can unlock their commercial performance. As a point-of-sale data is fine for reporting be useful, of course. But for real value, result, they cling to historical practices and market share, but its relevance to decision- it’s critical that you create a consistent dated approaches. making is limited and shrinking fast. approach and a capability you can grow. It’s time to move on. Our work with clients Those that buy syndicated data are realizing Leveraging a scale analytics platform has identified five areas that will be essential it might be better to fuel their own or third- wrapped in a solid data governance in helping you transform the way your firm party decision platforms with disaggregated operating model will drive value creation. uses commercial analytics and data. Get data feeds, combining structured and Analytics will continue to be more real-time these five right and you will position your unstructured data at a fraction of the cost. and self-service. An analytics platform firm to compete effectively on analytics Those savings can then be used to build will allow for the industrialization of the today and in the next several years: new execution capabilities, driving data- analytical science while providing the investment ROI. flexibility, speed and cost advantage to We have seen companies integrate direct-to- power commercial decisions at the speed consumer, i.e., eCommerce, data and retail of business. store data along social sentiment insight An important early step is to develop a with various econometric factors to unlock common analytics “language” in your a better demand forecast. Not only did organization. When people are talking about forecasting error get reduced, it also helped a key measure, for example, there needs to shape their channel strategies to be more be clear agreement about what they mean. responsive to dynamic consumer behavior. Without that clarity, it becomes harder to drive adoption or to hold people accountable. We’ve seen one company that had six The human side of analytics different methodologies for calculating A recent EY and Forbes Insights survey approach to data and analytics. price elasticity and developing a price found that one-third of executives Example: How might the organization’s architecture — each created by a division worldwide — from a range of industries — culture need to change? or region working in isolation. were trying to build a strong analytics • Micro issues — the individual human In today’s global consumer products culture. But, just as many said, their behavioral dynamics to consider when company, delivering a scale analytics managers and staff felt threatened designing the application of analytics capability that has a consistent methodology by analytics. Example: Do the “consumers” of and a common language unites and raises This suggests that many organizations analytics feel sufficiently empowered the capability of the organization, while still are neglecting the “softer” capabilities to act and are their incentives aligned maintaining local decision-making. needed to use analytics effectively, appropriately? notably those relating to behavior. Almost all automated processes require We separate this challenge into two a human to make a business decision broad areas: or change a business process, as a • Macro issues — the higher-level result of analytics. An organization that organizational and leadership works to remove behavioral barriers is requirements to address when more likely to achieve full value from its conceptualizing the organization’s analytics investments. 4