Cross-selling, re-marketing, packaged services, customised offers, or personalised products, the additions brought by Data Science to the e-commerce industry are endless. Customer data is being hounded like never before, and products and offerings are hurled on them from all direction possible, depending upon their buying patterns, search histories, and behavioural analysis.
Personalized User Experience
The research says that the maximum impact an e-retailer can make on sales is through personalization i.e. the process of tailoring content to individual users’ characteristics or preferences. It makes interaction faster and easier and, consequently, increasing customer satisfaction and retention. Our state-of-the-art models and algorithms can help achieve this in no time.
Dynamic Pricing and discounts
This is a technique to identify the patterns of different price sensitivities based on the purchase behaviour, transactional behaviour and demographic information of customers. Dynamic pricing techniques help to bring the right price to the right customer and focused customer acquisition by better understanding of the customer and predict the price sensitivity score of the customer.
Abandonment of online carts
All online retailers experience shopping cart abandonment. In order to combat shopping cart abandonment, deep learning is utilized by analysing the shopping experience from the moment a shopper adds a product to their cart through purchase completion in order to determine what post-abandonment marketing tactics can be leveraged to eventually gain conversion. Covering every fork in the road that could cause a customer to abandon their purchase and automating post-abandonment messages will lead to increased revenue without requiring a major effort.
We can help companies in utilizing machine learning for short term fixes to marketing campaign mix along with more crucial long term insights into maximizing the lifetime value of a customer over time. The campaign analysis and optimization process can be divided into two major categories, namely; one, implementation in areas that need quick improvement to identify and provide quick and effective results and two, the process of continual optimization over time and includes improving the customer’s overall lifetime value.
With increase in the consumers’ sophistication and empowerment for expecting businesses to understand when and how they wish to be engaged, we can help the e-commerce companies to transform their data using into actionable insights so that they can anticipate what customers want and discover the most effective ways to improve customer acquisition. Companies can also improve the customer acquisition through segmentation and clustering techniques, reduce costs by targeting prospects that are mostly likely to respond and, anticipate which products, services and or features customers care about the most.