Identifying customer behavior, finding the relationship between products and content delivery by the retailer inside the store or on online has been a big hassle for many store operators.
Tools and Technologies: Python, Machine Learning, Association rules
We determined the accurate point of sale to increase profits. Also, identified the products/items associations that have a good buying history to sell them together. Based on the customer demographics and their buying patterns helped to reach the right target market. Ultimately, this enables in predicting sales at the right time at the right place for the right customer.
Approach: Implementing AI and ML
Analyzing various prospects of customer behavior inside the store. With multiple sets of data available to analyze customer behavior of retail stores, businesses can classify data for defining the right product association, trip types, point of sale and marketing.
Effective implementation of ML algorithms using association rules and Apriori algorithm, Analytics on buying trends, we used different mathematical formulas to identify the association between products/items, in creating appropriate segments.
To evaluate the outcome, the metric values such as lift, confidence, and support values are considered for the performance of the model.
Enhancing your business
Introducing combo offers depending on products often purchased together.
Organizing and placing associated products or categories in the nearby aisles inside a store.
Managing inventory upon product demands and the products which sell the most together.
Developing more appealing product sets and forecasting sales seasons and items.
Implement customer segmentation and create customer profiling based on their buying pattern
IT consulting is a critical business where spending exorbitantly on hiring recruiters to manually associate job applicants with open positions. Although having sophisticated automated infrastructure, yet it is inevitable to hire recruiters to delegate work. By automating this whole process without any human interaction would drive the firm into profitability and also enable recruiters to be out selling and increase revenue.
Tools and Technologies: Python, Flask API, NLP
Utilizing Artificial Intelligence and Machine Learning techniques, we had developed a search product to entirely automate the job matching process for both the job applicants and the companies.
The job applicants can log in to the consulting website and search for a job that is suitable for their skill set, and upload their resume as they always would. However, with the AI on the backend, instantaneously accumulates the data from respective fields such as experience, skillset, previous job responsibilities, and other variables. This data are compared and incorporated and build on other pre-trained data points to improve job matching performance and deliver more accurate job options to the job seeker.
The existing information that the AI is building on are:
Filed information from this applicants resume
Data acquired with previous resumes of job seekers for several job roles.
Statistics obtained from past searches and successful matches
Enables the organization to select the candidates swiftly and less human resources
The recruiters can thrive on new business endeavors and generate more revenue by focusing on client acquisition.
Allows potential job applicants to secure significant job roles.
Increased job fit and satisfaction