Consumer and retail
The customer is always right
In retail and consumer markets, the value of data is undeniable. Building a detailed profile of your customer gives you endless scope to attract, retain and increase the value of your customer base. A well-conceived and effectively implemented data strategy can give you the following benefits:
- Improved customer retention and reduced churn
- Pricing optimisation
- Upselling and cross-selling, product recommendations
- Better targeted digital marketing
- Improved and personalised user experience
- Customer segmentation
- Product/service personalisation
- Customer sentiment analysis.
Case study: Supply chain analytics (global retailer)
Challenge: We were tasked with creating bespoke algorithms for matching products in different markets based on product attributes. The client, a large global retailer, buys identical or very similar products at substantially different price points in different markets (e.g. USA and UK). Harmonising prices across markets can only be achieved after a detailed analysis of the price differential between markets has been completed.
Solution: We designed and built a bespoke platform for matching products across different markets based on a photograph, name and other metadata, and text description using natural language processing and image recognition techniques.
Technologies: Java, deep learning, natural language processing, unsupervised classification, computer vision/image processing, entity resolution, web scraping.
Result: Our work allowed the client to enter negotiations with their suppliers which could lead to an estimated annual saving of £17.5 million.
Case study: Digital transformation (energy / utilities)
Challenge: We were engaged by a large UK energy supplier with over 3 million customers and multi-billion dollar revenues to help start and grow their analytics capability.
Solution: We designed and built a production-grade data lake and constructed ELT pipelines to create data assets by integrating large multi-billion row datasets, from diverse sources and formats. We built advanced machine learning models on top of these data assets to deliver value via analytical insights, proofs of concept and visualisations across the business, from front line managers to C-level stakeholders. We also advised on appropriate database schemas, software, and technical implementation of analytical solutions for the data lake.
Technologies: Python, Java, supervised and unsupervised machine learning, deep learning, h2o.ai, Spark (EMR), AWS
Result: We built a cross-functional data science team from scratch to deliver enterprise-level data-driven solutions, enabling understanding of the customer journey and improvement of operational performance and resilience.