E-commerce by very nature generates big data which has mine of information hidden in it. As customers search products, shortlist them, buy them and pay for them, they leave a trace of their preferences, behavior and habits. E-commerce companies are using machine learning to decipher customer’s buying behavior, their price elasticity and brand preferences. With this information, e-commerce players are using intelligent automation to prompt customers with ‘next best action’ in terms of campaigns, new launches or recommendations. Such interventions are known to improves sales by 10-30% at minimal extra investment. MAcine learning is further helping these players in intelligently routing their packages to cutting down delivery time and reducing cost.
As buyers get used to self-help based online buying intelligent systems will play a big role in success of online players.
It is possible to train ML based models to identify the ‘topic’ on which customers have commented and additionally assess sentiment of every comment. To assess the topic, NLP technique can be used. For example, in case of sale of shoes, customers would generally comment about a few fixed set of topics like shoe’s fitting, style, comfort, price, color, brand authenticity, etc. A trained NLP model can peruse through the thousands of comments in the feedback section and highlight which topics customer have generally commented about. Further sentiment of feedback comments can be assed using ML modeling. Historical feedback review comments can be first categorized as positive, negative and neutral. The ML model can then be trained on such historical (training) data and then tested using fresh comments for the model’s accuracy in assessing a sentiment of the comments. Once the model offers good accuracy, ML-based model can offer great efficiency by perusing through thousands of comments, assessing their sentiments and providing a summary of percent positive, negative and neutral comments.
ML and NLP can thus offer great insights on the topic (subject) on which customers are commenting and related sentiment. This can enable e-commerce players to undertake improvements on product, distribution or servicing areas as needed.
Intelligent automation which includes RPA, OCR and NLP technologies can be used to collate information coming from new sellers through various sources like scanned images, PDF, MS Word, MS Excel and validate those and enter into the IT applications. This can speed up the onboarding process and enable quick follow-up with sellers for missing information. The extent of data quality check can improve with pre-defined business rules in RPA and OCR. Further, with intelligent automation the overall cost of the onboarding process can be reduced drastically and scaling up business can be easily achieved without the need to build larger teams.
ML models can be trained to recognize patterns of search, surfing and purchase behavior of the millions of online buyers using unsupervised learning techniques. Once, the model recognizes dozens or hundreds of such user patterns, the websites can monitor behavior of buyers online and match them with past patterns to identify what could be the best product, brand, pricing that the buyer would prefer. This can greatly assist the buyers in easily coming across what they had like to purchase and e-commerce players can improve their sales prospects. ML-model based Product recommendations can enable business growth by 10-25% in a very cost effective manner.
A leading travel company
The company carried out manual reconciliation of issued tickets and billings between the airline company and its customers. This was tedious, error prone and time consuming. We helped the company deploy intelligent automation which provided seamless reconciliation from ticket requested till the final billing to the company’s customers.Read More