Today, data is impacting your business more than you may realize. Before you go further into developing organizational strategies around big data, you need to understand the evolving influences of data and analytics on your business. You start this process by simulating the use of data by your organization in its current state, then understanding what your competitors are doing in this space, and finally understanding the correlation between different data points that impact your business.
You need to appreciate how the trajectory of your business has been influenced so far by the data elements you have used. You need to develop a historical perspective of this evolution process. For example, imagine you are a multi-purpose retailer such as a major grocery chain. Decades back, you would have used point-of-sale customer-buying data primarily to take an inventory and to make supply chain decisions. Over a period of time, you probably would have started using customer purchase data to understand affinities between items brought and how seasonality influences those decisions. Then, you most likely graduated to profiling customers on their social and economic background and understanding their buying behavior in those contexts. Today, you might even be using advanced customer analytics to proactively stock and push new or more items. You have graduated from using the same basic data to being more efficient and driving greater revenues and improving loyalty in a commoditized environment by giving a personalized experience to customers.
Next, you need to become familiar with how data-driven actions taken by others are influencing the course of your business and strategies. In the same continuing example, a start-up can use similar data obtained through cleverly designed surveys to become a recommendation channel and subsequently use this knowledge to drive collective bargains with you as a retailer, further challenging your current thin margins.
For this exercise, you need to scan competitors from your industry and all possible adjacencies to understand how they are using data to drive value in their business or even to change their business in terms of new ways of attracting revenue and customers, or possibly to make operations more profitable. The recommendation engine made extremely popular by Amazon is now replicated in many different industries.
Finally, to identify new possibilities, you need to gain new insights through the correlation of previously unpaired data elements. For example, as the large retailer being discussed, you have data on how much time your customers are spending in your store and how much they are spending in the snacks counter. Is there any correlation between the two? Assume that every customer who stays in your store for over two hours makes a purchase at the snacks counter. This creates additional business for you. If your snack counter is run by a franchisee, you can command higher rent by facilitating more traffic and purchases at the snack counter. You have found that 47 percent of your customers stay an average of 106 minutes per visit. Now that you know this, can you create incentives for your customers to stay for two hours or longer so that the likelihood of their visit to the snack counter increases? For example, you could engage your customers longer with a cooking show demonstration or a new product demonstration. With the right pairing of data and new insights derived from them, you can explore additional sources of revenue for your business.
