Retail and e-commerce

Market Outlook

Data Science can help retail and e-commerce businesses to interpret data and enables marketers and business owners to gain critical insights into their business performance, customer behavior and demographic, inventory, and competitors.

AI assists online retailers to deliver an optimized customer experience on and off their e-commerce websites by using collected business and customer data to make better business decisions and more accurately predict the future.


Personalized product recommendation:

Websites that recommend items you might like based on previous purchases use machine learning to analyze customer purchase history. Retailers rely on machine learning to capture data, analyze it, and use it to deliver a personalized experience, implement a marketing campaign, optimize pricing, and generate customer insights

Pricing Optimization:

AI-enabled dynamic pricing is a strategy for changing your product price based on supply and demand. With access to the right data, today’s tools can predict when and what to discount, dynamically calculating the minimum discount necessary for the sale.

Personalized Marketing Strategy:

Ecommerce businesses are always looking for novel ways to encourage existing customers to make more purchases or to find strategies to attract more customers. Data science and AI can contribute to it through ad retargeting optimization, channel mix optimization, and ad word buying optimization.

Trend Forecasting:

Artificial intelligence can e-commerce businesses predict the future so that they can adjust their offerings accordingly. AI and data science help to find out future sales and demand patterns according to customer behaviour to plan your inventory and prepare the delivery team accordingly.


Targeted Marketing and Advertising

Increased Customer Retention

Efficient Sales Process

Defined Product strategy

Case Study


Lulusar did not have any reporting for analyzing business performance. The business required both descriptive and predictive analytics for decision making.


The project started with developing dashboards for sales, products, and customer analytics to enable data-driven decision-making.

Phase 2’s scope was the optimization of marketing expenditure and consolidation of data from various sources(e-commerce sales, website, Facebook, Instagram and google ads) to analyse the effectiveness of each campaign on sales and see which marketing channel produces a better ROAS (Return on Ads Spend). Moreover, this involved building a machine learning model to predict marketing expenses across different channels which will help them achieve their target revenue. The spending was based on intuition before with no data backing. Phase 3 consists of building a design analytics dashboard which contains analytics of revenue and orders  

Moreover, we are also working on enhancing the marketing spend prediction model with some variables. In addition to that, the customer segmentation use-case is also being worked on to segment Lulusar users in different segments and send them targeted emails and SMS.


The team has a singular platform for analytics, which helps with data-driven decision-making across departments and increases visibility to the entire team. Lulusar can improve shipping or customer experience and even other operations through insights, for instance, how did the current month perform vs. last month in terms of lost revenue? How is the shipping and production team performing? Which designs or products are doing better in certain areas than others are? How to predict how much to spend on Paid Media on one channel vs another?