Empowering Retail with AI - Make Smarter Decisions with Predictive Analytics
Predictive Analytics and AI in Retail:
Predictive analytics combined with AI can proactively detect trends in consumer behavior and purchasing patterns to improve customer experience and ensure efficient use of resources for the business. The use of AI and ML can help retailers stay ahead of their competition and gain considerable market share by predicting customer demand at the most granular level using past data and leading indicators, adopt real-time dynamic pricing of the products to ensure minimal wastage and optimal profits and overall deliver a superior customer experience.
There is early adoption of predictive analytics, and AI in almost 40% of the fast-paced organizations around the world. This trend is expected to grow at an accelerated pace to around 80 per cent by 2021. These numbers show confidence in the technology and show the urgency to adopt these techniques to stay ahead.
Barriers to Adopting AI for Business:
Even though there is a considerable need to draw deeper insights from the customer and business using AI, there are some barriers which are hindering the smooth adoption of analytics in the industry. One of the most common barriers to adoption is older generation executives and leaders who are also the decision-makers being hesitant with the complexity or unfamiliarity of the concept. Another reason is high implementation costs and difficulty in collecting the correct data which is the backbone of any analytics application. Thanks to the recent advancements in big data and cloud technologies, implementation costs and gathering information is getting cheaper by the day. There are challenges associated with converting info into actionable plans because of lack of experience in the same and lack of data-deciphering talent, so such companies can start their journey to become a data-driven organization with the help of partners like Convergytics who have nearly a decade of experience in delivering actionable AI for its clients across the globe.
Various Solutions/Use Cases employed in Predictive Analytics and AI for Retail
Retail thrives on AI and typical use cases as implemented by companies around the world. According to a paper on State-of-the-art and adoption of artificial intelligence in retailing by Felix Weber and Reinhard Schütte majority of the use cases are around Goods Management and Ordering (Demand Forecasting and merchandising, Inventory Analytics), Customer Service (Customer Insights and Personalization) and Smart Logistics.
Demand forecasting and merchandising
The more you understand the customer by studying their behavior, trends and drivers the better you are equipped to meet their demands and ensure that the right fit of product is done based on Locations and Partners with it being available at the optimum price ensuring maximized returns. To optimize the product quantity and placement the business needs ability to identify demand accurately with Product and store wise accuracy which has shown to improve MAPE by 3.5 percentage points in short term forecasts using the past sales data along with leading indicators of the market.
Price determination can be done based on the many factors like staleness in case of perishable products, substitutes and market technology demand in case of technology of products etc. This can ensure optimum sales and minimum wastage leading to increased top and bottomline.
Inventory Analytics
A significant challenge for retailers is maintaining accurate inventory which is evident by the $1.1 trillion cash trapped in inventory. Using IOT sensors and data from cameras for video analytics which gives a technological advantage ensuring a proper flow of information in the supply chain by tracking the inventory and connecting more parts of Operations providing the ability to have a comprehensive view of stores, warehouse and in-transit inventory. The current inventory status and location when cross referenced with Planning and Forecasting systems can enable the algorithms to automatically order optimal inventory.
Customer Insights
To understand the customer is of utmost importance, and it was straightforward when the sales reps interacted with the customer and get the sense of what type of products the customer needs. This can be done centrally and help the business understand the customer and gain better insights into customer behavior for an omnichannel strategy.
Stores can utilize the camera feed to understand which products customers are interacting and which products are finally bought. Along with face recognition along with loyalty cards used in stores, the offline experience of the customer can be stitched together with online data to deliver a seamless omni-channel experience.
Personalization of Customer Experience
According to HBR, 73% of the consumers use multiple channels to shop and personalized experience over all those channels appeal to 80% of the customers according to Edelman. Big data can help in driving these personalized experiences for the customers by using features like engagement experience and rapid fulfilment of products and services at a price which resonates with that specific customer. The quality of the data can be improved with digital channels, customer profiling and in-store sensors. This context-rich customer data along with multiple checkpoints like multiple pos, mobile application, website and social handles can capture the personalized data from all the different checkpoints which can be utilized for better customer experience and product fulfilment through the preferred channel.
Conclusion:
The Retail Industry can and should benefit from the use of AI and predictive analytics to derive insights about customer behavior and trends in the supply chain which can be implemented by automation like automatic order placement or Personalized Chatbots which use the insights captured from various touch points.
There are a lot of proactive methods of harnessing new and extensive data sources in unique ways, be it video Analytics or IoT. The granularity and insights from the structured as well as unstructured data, can help deliver value by uncovering hidden patterns for actionable insight like better AI driven Inventory Reviews.
About Convergytics:
Convergytics is a Microsoft Gold Partner for Data and Analytics. We focus on four core areas:
(1) Data Management and Data Engineering: data ingestion, validation, transformation, warehousing and OLAP
(2) BI and Advanced Dashboards: reporting KPIs/metrics, real-time BI, automated monitoring and alerts, ETL, executive/operational dashboards
(3) Digital and CRM Analytics: site optimization and A/B testing, digital audits and implementations, optimizing digital media spends, real time personalization and predictive CRM, and automated reporting
(4) AI and Machine Learning: forecasting, safety and risk, defect prevention and preventive maintenance use cases