1. Trying to boil the ocean – The potential data sets a retailer can use are virtually endless. From customer behaviors and internal performance metrics to third-party industry statistics/trends from companies like Nielsen to open data available on economies worldwide – companies that try to gain meaningful insights from ALL of the data available are destined to fail. Instead, focus solely on the key metrics that matter most to your business.
2. Ignoring leading indicators – Speaking of the metrics that matter to your business, those that tell you what happened in the past can certainly help you add context to previous performance. However, they do nothing to help you plan for the future. Instead, prioritize the data sets that help predict your future performance.
3. Creating single use, point-in-time data models – Many current statistical modeling methods are out of date as soon as they are developed. Creating data models that paint a picture of a specific point in time can’t help you plan. Instead, create models that are automatically updated as leading indicators and key performance metrics change. Read a detailed conversation on leading indicators here.
4. Predictive analytics for CPG and RetailNot aligning with the company’s data analytics strategy – At this point, retailers know that they should be using data. However, incorporating data analytics into business processes requires more than just collecting and reviewing data. The time spent on these efforts is wasted if business units don’t understand the methodology or do feel that insights from the data team are not timely enough.
5. Ignoring what the data says – Because nearly half of executives are not aligned with their own data strategy, too often, executives rely on gut feel. Research is showing that this statistically leads to decisions with poor outcomes. With consumer preferences changing rapidly in response to e-commerce, wearables, and potentially soon, VR, there is no better time to incorporate analytics to prepare for these changing dynamics.
#business #data analytic
