Behavioural AnalyticsMarketing AnalyticsPredictive AnalyticsUser Churn

7 Ways to use machine learning in E-commerce​

Machine learning is one of the most searched keyword on any search engine at this point of time. The reason is quite clear; the benefits of utilising it in any industry is beyond imagination. Machine learning is making computers learn from data to find patterns & generate business insights. In e-commerce, machine learning is even far more relevant because of digitally generated user-specific data points. Daily, we read so much about big companies using machine learning in their business decisions. With the technological advancement, machine learning is very much accessible for any small to medium enterprise. However, still thousands of companies are not capitalising the value generated from machine learning. We will briefly discuss most useful cases of machine learning in e-commerce.

With the technological advancement, machine learning is very much accessible for any small to medium enterprise

machine learning in e-commerce

  • User churn prediction: By using customer transactional historic data and other behavioural traits, user churn probability can be predicted. Engaging a customer at right time can help reduce the churn if we know specific customers are about to churn, machine learning plays a pivotal role.
  • Recommendation engine: Up-selling & cross-selling based on machine learning basket analytics can boost revenue. Everyone know about amazon product recommendations. It has been surfaced in one of the report that 27% of Amazon revenue comes from recommendations only. The power of recommender engine can be estimated from these numbers itself.
  • Customer Life Time value v/s Customer acquisition cost : Understanding customer LTV can be very crucial for any business. Using RFM (recency, frequency & monetary), machine learning can figure out the customer LTV to make strategic decisions on acquisition channels & cost of acquisitions.
  • Customer segmentation: With statistical segmentations, users can be defined in the specific type of users to better understand of your customer base. Which type of users are more profitable, who buys more stuff. These types of answers will create a solid foundation for strategic business decisions.
  • Marketing Campaign optimisation: Every marketing campaign has its cost. To better manage marketing budget, one need to analyse which campaign doing well and why. Machine learning can work quite well in figuring this out.
  • Spatial analytics: Matching demand supply spatially & timely can be very productive in any business. Using machine learning, demand & supply can be predicted to take business actions to reduce this gap.spatial analysis
  • Product inventory optimisation: Another use case of machine learning is inventory management, with the demand prediction, a business can be lean enough to reduce storage & waiting for costs for various products.

The above mentioned key areas where any e-commerce firm can make better business decisions using machine learning. In addition, fraud detection, customer service, voice analytics, web page & content selection analytics, image recognition and lot more can make managers better at business decisions.

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