The problem for retail is at the root: our research through the 2013 Economist Intelligence Report shows that "one in ten retailers have no plans of collecting social media data while another 23% are still putting collection plans in place. While one-quarter do collect and analyze such information, this is still well below the rate that both financial services firms and technologies companies do." Clearly, retail has a long way to go. But make no mistake, if it doesn’t begin the journey soon, the consequences can be disastrous.
Investments in data and analytics must see immediate improvement. Luckily, several retailers have recognized the need. Our experie?nce in the industry shows that savvy retailers are upping the game with new investments in data and analytics. These investments are being made with the intent of improving performance on several fronts. The expected improvements range from enhancing customer experience, improving financial decision-making, managing store replenishment, uncovering store requirements, improving ROI on promotion campaigns, and fine tuning and creating visibility into supply chains. For high performance retailers the fundamental focus of investments in data and analytics is on creating real-time insights into customers and the business.
The areas in which data and analytics can have a major strategic impact include:
- Improved margins
- Improved customer satisfaction
- Increased market share
- Improved comparable store sales
On a tactical level, data and analytics are helping retailers improve forecasting and planning accuracy, retention/frequency of loyal customers and conversions for ad/promotion spending.
The key to success with data is to shift the focus of analytics from 'what happened' to 'what is happening now' and 'what will happen next'. To enable this, retail must begin by gathering more granular data from online stores, mobile interactions, CRM, credit card usage, in store video data, social interactions etc and marry it with traditional customer profiles, buying history and demographic data. The challenge is to manage this structured and unstructured data before leveraging it with automated decision-enabling algorithms. Where should your data journey begin? What are the challenges you are likely to face? Where does this journey end?