With an increase in customer base, there is an enormous increase in the amount of data within companies. Companies try to retain/collect their user's data to improve their customer experiences and offer best possible solutions for their clients using this data. The data also help these companies understand who are their privileged clients. They can use this data in multiple ways for the benefit of both clients as well as companies. Using this data they can understand the need of the customers, and further design and improve their service in a particular direction to provide best possible solutions for their clients which lead to more satisfied consumer base and high-end service quality delivered, ultimately helps in achieving brand value for the company. There are plenty of more use cases where, analysis of this data leads to client retainment, attracting more customers, benefiting regular customers, increasing employees productivity etc.
Though this data provide us with enormous use cases and could benefit companies in various ways, the problem comes in while we want to process this data. Processing of this data and making sense out of this data is quite complex, as this being a multi-dimension data. To process such data we at Hureka make use of various Machine Learning / Deep Learning algorithms to benefit our customers.
Some of the algorithms we have worked on are listed below:
- Sentiment Analysis
- Text Classification
- Chat bots
- Predictive Modeling
- Market Basket Analysis
- Recommendation engine
- Entity Detection
- Document Clustering
This is a technique with which one can make analysis over the review / survey filled in by the customer regarding company/their products / services. Such analysis provides our clients with deeper insights regarding company's product/service they are offering and what reviews do their consumers have regarding the products or services they are using. These insights could be used to further improve company's product and service, ultimately benefiting both the company and their clients.
Companies have lots of text to be processed these days. This text comes to the companies in the form of email or other sources through feedback/suggestions etc. Companies need to respond to these communications on regular basis to maintain customer relationship. Many of such communications could be responded automatically by using Machine learning/Deep learning techniques, which could ultimately help companies reduce their time of response and cost of people required to be hired for responding to these communications.
Chatbots are machines which talk to companies client and respond to their basic queries based on the products/services being offered by the companies. Bots understand the main intent of the query and understand the context of the queries being asked by their clients in the previous queries and respond to them on that basis. This helps a company to reduce their response time for their customer's query and even improve customers engagement leading to happy customers base.
There could be scenarios where customers have a lot of data which could be used as a learning to predict the pattern of the sale of certain products/service in future which could help in the planning of inventory or put up some offer to cause an increase in sale or increase in overall productivity of the company. The data could also be used in understanding the increase in cost/decrease in sales or any such behaviors etc. being observed within the company by doing detailed analysis on the data provided by the company. Such exercises help companies increase their revenues/improve efficiency by making an understanding of the data and taking an appropriate decision based on the analysis.
Market Basket Analysis:
Retail store need to understand their customer's buying pattern, what all item are bought together, when do customer visit their shops etc. Having such an understanding provide details regarding how item in their store could be placed so that the customer who looks for some set of products could be kept near by, which brings them an ease of access while purchasing them. Such information could also be used in analyzing, when to run offers in the store and what kind of offers could increase their revenues or help them maintain their inventory easily.
Such engine helps in recommending products to the clients. These recommendations are done by initially profiling a customer and then proposing the customer with the best-suited products based on their profile and similar product that are competitive with the product they have been buying in the past. Good recommendations lead to increase in sales for the companies and time saving for their clients.
This is a process where we try to extract important information from the text which is of prime importance for the customers. Going through a huge set of documents usually requires a lot of manual effort. Using entity detection we could automate the processes and come up with an approach which could solve their problems easily.
There are lots of documents with companies these days, which need to be processed on regular basis. Document clustering helps in aligning documents on the basis of domains/topics which could be acted upon easily. We could also make a recommendation on the new documents coming up into the system to cluster them appropriately. Once clustered these documents could easily be acted upon to perform required operations.