Solutions

Sports analytics aims to help management make informed decisions by using data related to any sport or game — for example, players' statistics, weather conditions, recent wins, and losses. One of the use cases is to predict the game results based on publicly available information.

See the Premier League match prediction dashboard here. Multi-layer neural network model, developed by Swords Consulting, is used to make the predictions in this use case.

Each customer response may carry multiple meanings. A scalable classification solution helps label each response by topics, subjects or self-defined classification like a priority. One of the common applications is text mining on customer's comments. This allows companies to process an enormous amount of customer responses effectively and quantitatively and lead to quick actions.

See the use case.

Customers only respond to highly relevant product recommendations. With the data available about the customers, including demographics, website behaviour, purchase history, and up-to-date search information, machine learning models predict which product recommendation is most likely to result in a sale for each individual customer.

The more customer data that becomes available, the more companies can understand which factors lead customers to desired actions. Machine learning models determine what type of contents the most appealing to each individual prospect, allowing customers to develop highly relevant content for website visitors, search networks or social media followers that in turn increase sales.

Determining the value of each customer, including likely future spending and the resulting profit margins, is a vital factor in deciding where to aim personalised marketing efforts. Lifetime customer value models determine the likely profit each individual customer will generate over the entire customer relationship, allowing marketers to focus their efforts on those likely to be most profitable.