Every retail and e-commerce company wants to improve their customers’ experience on their platform or store. This has a direct impact on the company’s sales and therefore profit. Recommendation engines make use of customer data from their platform such as previous purchases, items added to cart or wish list, and so on, to recommend related products to the customers. 

Besides creating recommendation engines, several other things could be done with the data that retail and e-commerce companies have access to. Apart from trying to improve their customers’ experience, retail and e-commerce companies also aim to optimize their operations to make them more efficient, thereby reducing their operating costs. This can be achieved through:

1. Price optimization

‍Prices for goods at retail and e-commerce stores can be set in a much more calculated way with data science. Price optimization takes into consideration several things such as historical prices, competitor prices, season, and operating cost to come up with the optimal price for both the company and its customers. This is achievable using a machine learning model trained with the necessary data.

2. Inventory optimization

‍Inventory management is a critical aspect of every retail and e-commerce company. These companies need to answer the question of how much, when, and where to stock their various products. Answering these questions wrongly could lead to shortages, wastages, and other unnecessary costs. By analyzing data points such as supply, demand, customer behavior, costs(purchasing costs, transaction costs, holding costs, expiration costs, and shortage costs), the companies can significantly improve their inventory management.

Other ways data science can be used in the retail and e-commerce industry are in customer sentiment analysis, fraud detection, supply chain analytics, demand forecasting, and customer lifetime value prediction.

SynctacticAI is a data science as a service platform familiar with all these use cases for data science in the retail and e-commerce industry. It has a lot of features designed to make data analysis and data science easy and seamless for retail and e-commerce companies. 

They offer services such as Prediction and Forecasting(used in demand forecasting, customer lifetime value prediction, inventory, and price optimization, etc), Classification and Anomaly Detection (used in fraud detection), Natural Language Processing (used in customer sentiment analysis), and so on. 

Conclusion

By introducing data science into your retail or e-commerce business or augmenting your previous data science operations with some or all of the aforementioned techniques, your company is poised to be its most profitable, whilst still providing the best possible experience to its customers. SynctacticAI makes this process even easier for you with the various data science services it offers.

>> Book a meeting with SynctacticAI today to get your FREE Data Science Audit Today <<

References

• Data Science Strategy in Retail. Strategic Role of Data Science and AI… | by Kamal Mishra | Towards Data Science
• 11 Use Cases of Data Science in Retail - How tech giants are using it? - DataFlair (data-flair.training)
• A Framework for Inventory Optimization | by Nicolas Vandeput | Feb, 2021 | Towards Data Science

• Price Optimization: A Data Science Use Case | by Kayode Olatunji | Medium
• Natural language processing, Computer vision, Feature engineering - SynctacticAI