January 8, 2024

Improving customer experience by making in-store purchases obsolete

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Improving customer experience by making in-store purchases obsolete

At a glance

Challenge

The COVID-19 pandemic accelerated e-commerce growth, prompting retailers to innovate for competitiveness. Our client, a large retail chain, sought to address two challenges: creating innovative services and improving the online customer experience. The solution involves weekly home deliveries with a suggested basket of products to enhance satisfaction and simplify online shopping.

Solution

The service aims to streamline the e-commerce basket-building process by predicting product families and suggesting items through a machine learning model. The model uses customer segmentation and transactional data, focusing on variables like behavior, product profiles, and seasonality. An interface allows customers to easily confirm or add products, simplifying the purchasing experience.

Results

The solution aligns with our client’s innovation goals. Through teamwork, it was deployed for feedback from a select group of customers. Recent results show that 55% of family-level recommendations were accepted, with 60% of products accepted at a 60% accuracy rate for quantity. The solution received positive feedback, with an NPS score of 7 to 8. Ongoing feedback will be crucial for enhancing predictive and prescriptive models.

Challenge

The COVID-19 pandemic accelerated e-commerce growth, prompting retailers to innovate for competitiveness. Our client, a large retail chain, sought to address two challenges: creating innovative services and improving the online customer experience. The solution involves weekly home deliveries with a suggested basket of products to enhance satisfaction and simplify online shopping.

Approach

Solution

The service aims to streamline the e-commerce basket-building process by predicting product families and suggesting items through a machine learning model. The model uses customer segmentation and transactional data, focusing on variables like behavior, product profiles, and seasonality. An interface allows customers to easily confirm or add products, simplifying the purchasing experience.

Results

The solution aligns with our client’s innovation goals. Through teamwork, it was deployed for feedback from a select group of customers. Recent results show that 55% of family-level recommendations were accepted, with 60% of products accepted at a 60% accuracy rate for quantity. The solution received positive feedback, with an NPS score of 7 to 8. Ongoing feedback will be crucial for enhancing predictive and prescriptive models.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

Covid pandemic scenario changed the retail status quo and the e-commerce channel witnessed an increase, in a short period, that was only expected to happen in the following years. Consequently, the online channel is, now more than ever, a challenging area where retailers are hard-pressed to strive for innovation and excellence.

Our client, a large retail chain, had long devoted considerable effort to create innovative services and improve the customer experience. Two key challenges needed to be addressed:

  • Which innovative service can be offered to the customer to face the pandemic?
  • How to improve customer online experience?

The proposed innovative service aims to improve customer experience by offering weekly home deliveries with a suggested basket of products.

This service’s main goal is to eliminate the need for customers going to the store to buy regular products and streamline the process of building the e-commerce basket.

The first step was to build a machine learning model that predicts the probability of a family of products being bought each week, complemented by a heuristic – a set of intelligent rules – that chooses the products inside each family.

The model was built on drivers gathered from customer segmentation and transactional data. This data allowed us to build variables related to customer consumption behavior, product consumption profile, purchase history for each group customer/product, and seasonality.

Our AI-generated summary

Our AI-generated summary

Subsequently, an interface was developed to communicate the suggested basket to the online store. The customer just needs to confirm the suggested products, or add new ones, and close the order. Customer life gets easier, right?

The designed solution meets our client’s ambition to be at the forefront of innovation. Thanks to the effort of teams from multiple areas, working together with the same goal, the solution was deployed to gather feedback from a selected range of customers.

The most recent results show that 55% of our recommendations at the family level were accepted by the customer.

Inside each family, 60% of the chosen products were accepted with a 60 % accuracy in the quantity suggested. The entire solution was well received by the initial customers, with a NPS score ranging from 7 to 8.

Going forward, there is still room for improvement and the increasing amount of feedback will be crucial to take the predictive and prescriptive models to the next level.

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