How machine learning is enabling data driven retail transformation?
Customer segmentations can be created with associative data of one group of buyers with other groups who have no connection whatsoever with the former.BY Guest author | Oct 26, 2018 | comments ( 0 ) |
Retailers have been struggling to predict and analyse shopper behaviour forever. No amount of data is enough. Data is the heartbeat of any retailer. Retailers are bombarded with data from digital channels, offline channels, social media and more. There is always the conundrum where there is enough data about what to do with it and how actionable insights can be derived. There are several tools available in the market but then retailers are trying to find the silver bullet.
The quantity of data is not a problem anymore, but the accuracy and relevancy are. Artificial intelligence and machine learning have provided the light at the end of the tunnel for retailers to build models with available data and to predict shopper behaviour. For example, if you look at transactional data, models can be built to segment customers, increase sales and predict inventory. Machine learning models can help retailers predict the early adopter customers who buy the product as soon as it comes to the store. The same way we can predict the followers and the late adopters. With this data, ML can help build associative relationships between all the categories i.e. early adopters to late adopters which can also predict which product will take off and which will not. By building relationships with the influencers and the followers, customer segmentation can be achieved just by building models using historical data. Imagine this is just for one product. If we assume retailers who have around 100- 200 SKUs, the combinations are mind boggling. This is where Machine learning comes to the rescue. The above is just one of the use cases.
By analysing social media data, correlations can be built by studying buying patterns of customers who buy early in the cycle and how they play a role in the lifecycle of that product. Customer segmentations can be created with associative data of one group of buyers with other groups who have no connection whatsoever with the former. All these segmentations, analysis of data can be executed through machine learning. We have just scraped the surface of what machine learning models can do.
Retailers are at a great advantage of benefiting from the intersection of machine learning and big data. The pressure to handle substantial amounts of data across various product categories and geographies and to track consumer shopping habits makes machine learning a compelling technology to adopt. If applied correctly, Retailers will now be able to:
- Target shoppers with more accuracy and relevancy to increase conversion
- Gain ultra- personalized recommendations
- Have better usage of shopper data than before
- Know more about buying habits
- Provide superior ultra-personalized experience
- Build a scientific model behind all marketing initiatives
- Have better supply chain planning
- Build price optimization and promotions
According to Mckinsey, Data driven organizations are 23 times more likely to get new customers, 6 times as likely to have loyal customers and 19 times as likely to be a profit-making enterprise. 58% of the products sold by Amazon are attributed to their recommendation engine. The algorithms used for recommendation not only looks at shopper behaviour but also the purchasing profile of other consumers who are similar in nature.
This helps Amazon to create customer profiles and provide targeted product recommendations. Some of the common customer segmentation models are Collaborative models wherein it analyses relationship between customers’ social interactions. Content based segmentation looks at the historical behaviour of content consumption i.e. similarity in purchases. Segmentation through Categorization is another way of forming customer groups based on product category. When recommendations are rightly done, shoppers keep coming back and which in-turn offers superior customer experience. This is possible only when existing data is used the right way. Better use of data will help retailers to shift towards optimization and efficiency and move away from the excess. ML data models can help retailers in reducing cost, better decision making and process automation.
Supply chain and inventory management are getting radically transformed once retailers figure out what to sell to consumers. Machine learning models can predict real time the inventory that is needed and can give vendors notice to keep stocks ready and the location of the store or warehouse where the product needs to go.
Machine Learning and AI usage by retailers are on the rise. It can boost efficiency, productivity and profitability while at the same time give customers an ultra-personalized customer experience. By efficiently using the existing data, retailers can optimize their operations and marketing initiatives. The onus is on retailers to deep dive into data analytics and to connect with shoppers to provide intuitive and complete shopping experiences through better decision making.
The article has been penned down by Raghunath Vijayaraghavan, Director- Global Marketing, Aspire Systems
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