CASE STUDIES
What We Can Do For You
Client
Challenge
Demand forecasting is a resource intensive and a very manual process and Unilever wanted to see if Machine Learning can automate this process with increased accuracy & bias or not.
Solution
Output
Forecast accuracies were improved significantly and the model building process is now getting automated as well. Currently these models are in parallel run for assessing live performances.
Client
Challenge
Similar to Unilever, Avon Products had issues with their demand forecasting process. The forecasts were generated manually using business understanding and perception rather than any data driven statistical approach. The demand planning exercise involved various iterations of adjusting price and then re-running the forecasts, then assessing the impact and then again re-adjusting. This entire process of planning took 3 months.
Solution
Output
Forecast accuracies were improved significantly and the simulation tool helped the clients plan for their campaign effectively. Although we are not involved in the process any more, but currently Avon is rolling out the modelling approach to many other countries with the help of their internal data science team.
If you have any questions about demand forecasting and data analytics in the UK and worldwide, please contact our team.
Client
Challenge
Due to increasing risk of account hacking and phishing, the DWP wanted to identify the anomalous emails coming in and out of their network that could threaten their infrastructure.
Solution
Output
Client
Challenge
Mix conducted a global survey on a popular ‘shoe’ brand with the aim of understanding their brand perception and brand position vs. competition in UK, France, Germany, Hungary, USA, China and Japan.
The challenge was to interpret key perceptions and customer sentiment from the survey responses, and in a scalable way.
Solution
Output
Client
Challenge
Lotus is one of the biggest retailers in China but their customer retention numbers were decreasing month on month in the preceding year. They wanted to know why their customers were leaving and to also predict which customers were likely to churn, so that they could run targeted campaigns to prevent this.
Solution
Output
If you would like further information about machine learning and data analytics in the UK and worldwide, please contact our team to hear how we can help your business.
Client
Challenge
Lotus wanted to build an automated personalised recommendation system for their instore promotion. They had large screens set up in different areas of the store and upon scanning the loyalty card, six of the current promotions were to be displayed which was based on an algorithm that is real time and relevant to that customer.
Solution
Output