Meeting Retail’s Data Challenges
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To hear more on the impact of data-driven disruption across the retail landscape be sure to attend the Retail and Consumer Goods Analytics Summit. Mike Frazzini and executives for Hershey Company and Wakefern will explore the topic in a spirited c-suite roundtable discussion.
Leaders in retail technology are challenged as never before to simultaneously optimize the run, grow and transform (RGT) modes within their organizations. Data-driven disruption is both a causal factor in these challenges and an essential key to overcoming them to deliver successful outcomes. With advanced analytics, rapidly emerging data science roles and practices, and even artificial intelligence, data-driven disruption is the current wave transforming our industry. Doing more with more data is essential to meeting these challenges across all RGT modes.
Keeping a retail IT organization running at top capacity has never been more challenging, but data-driven disruptors can find ripe fruit laying on the ground. The run mode, with the goal of efficient and effective operations, is optimized by utilizing data science techniques like machine learning for more proactive anomaly detection in systems and application logging and monitoring systems. Quick wins in technology operations, coupled with additional investments in connected technology and data science, can pave the way to successful data disruption outcomes in business operations.
In the old days, time studies were performed to inform business operation improvement. Today, anything that was time studied in the past can be sensor adapted and connected for near real-time and ongoing studies. Examples abound, including everything from performance monitoring, policy and service-level compliance, and failure prediction.
Once a successful foundation and momentum is gained in the run mode, the focus can expand to optimize the grow and transformation modes. In grow mode, expanding existing business capabilities and leveraging data to understand customers and prospects to optimize touch points and experiences is vital.
An example of data-driven disruption for growth might be a rigorous approach to rapid A/B testing to grow web conversion and sales. Since there can be a myriad of opportunities to test, and not enough time and resources to test them all, this approach works best when prioritized by top-down strategic planning with well-defined critical success factors. Many advanced data-driven disruption cases can be envisioned from computer vision recommendation systems, automated merchandising systems, dynamic price optimization systems, and automated and predictive customer service through behavioral and NLP-based systems.
The transformation mode, with a goal of disruptive innovation and driving new business capabilities, can be the most difficult to achieve but lends itself well to data-driven disruption and can yield big payoffs. Mining customer data and feedback can drive transformative new product designs and category expansions. Transformative IoT products and services that are disruptive and driven by data can also be conceived. However, none of this comes without foresight and investment in a data supply chain, data platform, and data engineering.
Data-driven disruption can be the difference that propels a surviving retail enterprise to a thriving one. However, it is imperative that the retailer values data beyond the core functions of business intelligence, operational reporting, and base analytics. By building out data-driven initiatives progressively in the run mode, growth mode, and transformational modes, the retail enterprise can meet and exceed the ever-increasing challenges faced today and in the future.