Machine learning is one of the most searched keywords on any search engine at this point in time. The reason is quite clear; the benefits of utilizing it in any industry are beyond imagination. Machine-learning, a subset of artificial intelligence, is making computers learn from data to find patterns & generate business insights.
Machine learning in e commerce 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 online business decisions. With advancements in technology, machine learning is very much accessible for any small to medium enterprise in the retail industry.
However, thousands of companies are not capitalizing on the value generated from machine learning. We will briefly discuss in this blog the most useful cases of machine learning in e commerce.
With the smart tech advancement, machine learning is very much accessible for any small to medium enterprise.
Use Cases of Machine Learning in E Commerce
- User churn prediction: By using customer transactional historic data and other behavioral 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. This will in turn improve the retention of customers.
- Recommendation engine: Up-selling & cross-selling based on machine learning basket analytics can boost revenue. Everyone knows about Amazon product recommendations. It has been surfaced in one of the reports that 27% of Amazon revenue comes from recommendations only. The pricing of the products can be tweaked according to the insights. The power of the recommender engine in the personalization of customer’s experiences can be estimated from these numbers themselves.
- 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 segmentation, users can be defined in the specific type of users to best understand 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 optimization: Every marketing campaign has its cost. To better manage the marketing budget, one needs to analyze which campaign is doing well on the online store 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.
- Product inventory optimization: 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.
Those were the key areas where e-commerce retailers can make better business decisions using machine learning and in turn, see impressive results. In addition, fraud detection, customer service, voice analytics, web page & content selection analytics, image recognition, and a lot more can make managers better at business decisions.
We here at DataToBiz with a team of machine learning experts can support your business to create an affordable machine learning platform for your business and get you closer to your goals. Contact us for more info on Machine Learning Consulting Services.