A.I, Machine Learning, Deep Learning, Yada Yada Yada…Should Retailers Really Care?
Retail analytics has come a long way since the initial days of affinity analysis (remember the famous ‘beer and diaper’ parable). In fact, retailers have already been leveraging big data for making strategic decisions in the areas of business operations as well as forecasting, merchandising, and hyper personalization. While data may be the oxygen that fuels analytics, humungous volumes of data churned by digital overwhelm even sophisticated analytical platforms to parse data on a scale that allows retailers take decisions in real-time. So, how do we overcome the limitations of current systems to handle the huge data influx? By converting the existing challenge (big data) into a solution (feed more and more data to the system). While this may seem counter intuitive, Artificial Intelligence (read A.I.) is now at the very peak of inflated expectations, according to the Gartner’s Hype Cycle for Emerging Technologies, dominating key discussions in top technology and economic forums.
A 360⁰ view driven personalization based on demographic and transactional data will no longer be enough if businesses aspire to stay differentiated. To create lasting customer value as well as improvise business operations, retailers need to cross over from relying on predictive analytics to leveraging artificial or predictive intelligence in real time.
A.I is to the retail customer what Jeeves was to Bertie Wooster
Move over faceted search. Retailers like North Face and 1800-Flowers.com are using Artificial Intelligence powered recommendations, helping customers find the right apparel, gifts, etc. with considerable success.
A.I in customer experience is just the tip of the iceberg. A.I is all poised to be a game changer in key retail domain areas such as merchandising, order management, and customer engagement empowering retailers to realize disproportionate gains.
The “intelligence” in artificial intelligence is exactly what it seems to be: the ability to think independently, to grow more knowledgeable from being exposed to more information and to adapt and adjust when things change. So, instead of systems that rely on hard coded programming, we will have systems that work on sophisticated algorithms that will allow them to ingest vast volumes of data and adapt feedback to changing environments.
The low costs of computing and storage as well as sophisticated algorithms superseding pre-programmed instructions make it possible to rapidly adopt artificial intelligence — marked by deep learning, machine learning and robotics — to not just beef up personalization initiatives but improvise business operations and bolster supply chain value.
In Retail St., relying on traditional analytics is tantamount to crossing a busy junction based on yesterday’s traffic signals.
While the above analogy may be exaggerated and hypothetical, it sure raises the need for sophisticated solutions (read AI) that can not only offer insights empowering businesses to take just-in-time decisions but also create opportunities to serve customers in ‘magical’ ways.