Data can make shopping delightful, convenient and personal for customers. Retailers can do a lot with the current data to optimize their business through AI and Advanced Analytics.
Loyalty has a lot of value in a society which is getting increasingly untrustworthy. Customers want to stick to the brands which are consistently providing them comfortable experiences. A majority of consumers will stick to the experiences which are personal and delightful.
However, with the onslaught of e-commerce, it has become increasingly difficult from brick-and-mortar retailers to deliver those experiences and yet match the personalization and price-points. E-commerce has a huge advantage in gathering individual customer information. Web trackers can create complete customer journeys in no time about what was purchased, when it was purchased and how the customer end up purchasing the products. Not only that, the trackers can go beyond the current website to follow the customers before and after the shopping.
Data can make shopping delightful, convenient and personal for customers. This is the reason why retailers are sticking to digital loyalty programs to track customer movements. Even though it is tough to match the pool of data that the websites have, retailers can do a lot with the current data to optimize their business through AI and Advanced Analytics:
1. Simplifying Data Collection
While E-commerce websites collect a lot of data, not all data is equal. In fact, a lot of information can be extracted through very basic data points like Products, Frequency, Order Value and Time of Purchase, etc.
Shoppers are very algorithmic in nature and having a simplified data lake which focuses on quality rather than quantity can go a long way to make data-driven optimizations. Some of the very important metrics like Lifetime Value and Cross-Sell Recommendations can be derived very easily with these simplified datasets.
Other optimizations like Churn and Lifestages are harder to calculate. However, since most attributes are correlated, a lot of missing information can be imputed using the basic variables. Coupled with proven behavioural models like Pareto/NBD and Survival Analysis, additional variables can be created which can boost the customer predictions towards high accuracy.
When it comes to customizing creative experiences, a dashboard with basic metrics and predictions can be much handier than Big Data.
2. Going Omni-Channel
It all about learning and adaptation. While E-commerce has cannibalized market share from retailers, an opposite strategy can be highly effective. In fact, a substantial amount of retailers are moving towards a hybrid model.
Combining offline and online behaviours can be a highly effective strategy. We have seen that creating additional variables like search terms from the websites can boost the prediction accuracy of CLTV models by 15-20%. Not all customers are the same and knowing your customers can make a lot of difference. Accurate CLTV Predictions can help you prioritize the best customers who are likely to generate the most value for your business. It is all about protecting the core of the retail business.
CDPs have gained a lot of popularity recently because of their ability to quickly combine different datasets together and giving a comprehensive picture of customers. Getting the 360-degree profiles of the customers can boost ROI for marketing, service and supply chain departments.
3. Identifying Customer Behaviours
Understanding what drives your customers can help you create leverage in running your operations. How do market trends affect demand? Can weather play a role in deciding which products to buy? Can a friend's recommendation penetrate a mind riddled by online reviews?
Creating correlations between events and customer decisions is the holy grail of data science. Having behavioural models which can decipher the codes of actions can have a long-lasting impact of sales strategy. Here are some fun facts:
- 78% of customers still prefer to make in-store purchases over e-commerce websites if the experiences are enjoyable
- Generation Z is more likely to interact with in-store associates than the Millenials
- Most Millennials prefer to have on the spot recommendations available
- 79% of the customers research the products online before making a purchase
- More than 50% of the consumers have made a complaint on social media
- $1Trillion in sales is lost because retailers do not have the right product at the right time at the right location
Needless to say that Customer Data Lake, Behavioral Analytics, Forecasting engines, and Social Media listening tools are not something that retailers can compete without in the modern era.
4. Adoption of New Technologies
Facial Recognition checkouts? Inventory management through a camera? Beacons and Sensors? They are not just cool new technologies that retailers are adopting. They are ways for retailers to collect more information about the customers.
While Retailers are awe-struck at Amazon Go and Alibaba Herma, the point that most people are missing out at is Data. These technologies have the potential to highlight the parts of consumer behaviour which were impossible to know sometime back.
Adopting these technologies and changing the business models will be key for Retail to thwart the E-commerce onslaught. No matter how much digitalization has penetrated our lives, we still want to go out and experience physical reality. This battle is not just between Retail and E-commerce, it is to preserve an important part of human desire by finding the right balance and creating sustainable business models.
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