Developments in machine learning has a great influence on the e-commerce sector. Online purchase is steadily on the rise, allowing companies to capture all kinds of data on the customer experience, including
- what are customers interested in
- geographical location of visitors
- what time it the sales most or least
- when does traffic peak on the site
- for how long they are on any given product page
- what is the preference of the items
- what are the items sold most on any particular given day of the week
- what other products are they looking at
- what do they buy
- what are the triggers of the decision from the last window browsed before the purchase
- how are they affected by rates, reviews and coupon
- what is the optimal discount rate that triggers the purchase
Data received thus helps to make informed decisions based on the knowledge acquired through analysis. It makes it easier to collect and process the data, helps to see factors that went previously unforeseen.
What is machine learning?
Machine learning is defined as the science of getting computers to act without being explicitly programmed1. Based on pattern recognition and computational learning theories in artificial intelligence, machine learning uses the study and construction of algorithms to learn from and make predictions on data2.
Data driven predictions or decisions can be made using algorithms and building the right model for data inputs. Machine learning is used for computing from a variety of data sets where designing and programming with least error and highest accuracy is not possible.
Applications of machine learning in the e-commerce industry
Cross selling/ Recommender Systems
'You may also like ' Section: Using a customer's past browsing and purchase history, what will they likely want to purchase in the future? This refers to making predictions based on the previously available data i.e what kind of product can I recommend for you?
While the customer types in on search, this feature allows autocompletion suggestions using the most likely search terms. The algorithms takes into account frequencies of specific search terms as well as the particular customer profile (e.g. previous product views, age range, previous search terms).
Using prediction, you can try and figure out the optimal discount at which will a customer succumb. Minimize the number and rate of discount you give while maximizing the number of conversions you can get.
Sentiment and trend analysis
Evaluate the public perception of a product based on sources like social media.
Prediction can be made on when customers’ interest for a particular product or feature will cease to be profitable allowing for countermeasures like cross selling. Deciphering the characteristics of churners allows a company to reach out to churners, update the product or even discontinue a particular product line.
Inbound and outbound logistics optimization
How many of what thing do you need and where will we need them?
Match the data received against different data points and extract fraud scenarios. One instance can be matching the shipping address against the address on the credit card: it can raise a red flag if it does not match.
Products can be sorted automatically to speed up inventory management and improve customer navigation.
Wallet share estimation
Information emerging from the data analysis can give the proportion of a customer's usual spending in a category allowing the business to identify upsell and cross-sell opportunities.
Automatically adapt price as a function of supply and demand. Uber is a classic example of usage of dynamic pricing. The surcharge applies when demand surpasses supply like during rush hour. It is a major way to optimize revenue.
Supply and demand analysis and forecast/ Inventory Forecast
Make production and distribution more efficient by predicting market demands. Demand forecasting is all about understanding consumer demand for goods or services. Knowing how much stock to keep means avoiding unnecessary spending on overstocking or surplus on the one hand and avoiding the other extreme i.e sales lost due to lack of inventory or supply of goods. This enables lean inventory and prevents out of stock situations.
Chatbots and automatic answering of phone calls
This feature in e-commerce has been developed through speech recognition/synthesis and natural language processing via deep learning.
Understanding the target helps you determine exactly what your products or services will be, and what kind of customer service tactics work best.
Merchandizing and Inventory Management
When to start stocking and how many. This is particularly applicable in the case of perishable goods.
From the data, prediction of customers most likely to buy can be targeted for product launches.
Optimal channels can be targeted based on behavioral characteristics.
What is the probability of inducing the desired behavior with a discount?
Market Basket Analysis
Market basket analysis helps in determining what products customers purchase together. For instance, market basket analysis might tell a retailer that customers often purchase bacon and egg together or shampoo and conditioner together, or beachwear and suntan lotion so putting both items on promotion at the same time would not create a significant increase in revenue, while a promotion involving just one of the items would likely drive sales of the other.
The right algorithms in machine learning help in the above processes.
Major Challenges of Machine Learning are
- Acquisition of relevant data
- Interpretation of data analysis results
- Building the most accurate Machine Learning model without too much bias
With the advances in machine learning, more and more applications are emerging in the fields of e-commerce and healthcare.
1. Machine Learning: Stanford
2. Machine Learning: Wikipedia
3. Influencing Customer Through Infinite Personalization
4. Top 5 Machine Learning Applications for E-commerce
5. Data Science Use Cases in Kaggle.com
6. What are the applications of machine learning in the e-commerce industry?