The client was facing many challenges, including scattered customer and sales data, a lack of real-time data insights and no proactive inventory management. Furthermore, they could not target customers with personalized marketing campaigns due to the lack of information about trends and preferences. The client faced difficulty in expanding online business while missing sales opportunities.
About Client
Our client is a leading eCommerce business owner with a growing customer base and an extensive range of consumer electronic products. They offered excellent customer service. The online store couldn't keep up with evolving customer needs and had issues in managing inventory. The client was looking for a tailored web-based solution that would enable them to make informed decisions based on future trends and the varying demands of several products like smartphones, televisions, computers, and wearables.
Objectives
The main objective was to build a customized eCommerce software powered by AI that could focus on customer preferences and requirement predictions. Other objectives include effective inventory management and sales forecasting. The client wanted to collect real-time data from various sources and improve target marketing based on the customer’s behavior.
Problem Statement
The client was experiencing difficulties in managing inventory effectively. They also struggled with running marketing campaigns due to a lack of insight into changing customer needs and trends. Existing systems of the client had several loopholes including the lack of data analysis and inventory management functionality. It resulted in overstocking and missed sales opportunities.
Challenges for the Client
Challenges for the Client
Our Solutions
Our experienced in-house AI development team developed a bespoke AI-powered web solution after working closely with the client. It has several built-in features, including
Predictive Analytics Model
We made a machine learning-based model that could analyze sales patterns, customer behavior, and seasonal trends using algorithms like AutoRegressive Integrated Moving Average and Long Short-Term Memory. Based on this, the model predicts future demands.
Customer Categories
Using advanced learning techniques, we made a granular segmentation framework. In the framework, we included behavioral analytics, purchase style, and demographics. With the help of Python libraries, we performed data preprocessing and feature engineering.
Robust Pipeline
We structured a strong ETL (Extract, Transform, Load) pipeline that collected data from various sources, including transactional databases, web analytics platforms, and third-party APIs. These data were channelized into a centralized platform built on the cloud.
Cloud Infrastructure
We installed solutions on AWS with auto-scaling capabilities. The AWS infrastructure can automatically adjust based on workload demand. It gives top performance even during peak hours. Moreover, AWS Cloud provides high availability, fault tolerance, and scalability.
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Technology Stack
Outcome
Within three months of deployment of the AI-powered web platform, there was a 20% improvement in inventory turnover, 35% reduction in stock issues, and a 25% boost in campaign ROI. The client gets a future-ready web solution that provides real-time insights for faster decision-making. This helps them to gain a competitive edge in the thriving eCommerce sector.
