Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
In 2015, we predicted the Rugby World Cup to great success, out predicting 99.68% of humans. In 2016, we predicted the results of the Oscars, accurately predicting DiCaprio’s first win. This year we'll be trying our hand at predicting the outcomes of the FIFA Football World Cup, and we're cautiously optimistic about our predictions.
According to this article by Frederick F. Reichheld in the Harvard Business Review, the average company loses about half its customers in a five-year period. When customers see a loss of value, they churn. The ultimate goal of a great loyalty strategy is to increase the perception of your solution’s value in the eyes of your customer, exactly when you need to. To achieve that, you need to be able to predict churn, many in the industry turn to leading (or lagging) indicators to indicate where efforts need to be focused.
With direct marketing, you likely have a benchmark success rate from previous similar campaigns that you base your goals on, and you try to optimise it through various strategies, whether by trying to offer the best deals or by using behavioural tactics.
Can you test a different approach? Is it better to use one or multiple channels? Is this approach really successful? My team and I frequently get asked these and other questions on a data-driven approach to acquisition lead selection. In this blog, I’ll answer some of the most frequent questions.
There are many benefits to using a data-driven approach to lead selection, including higher conversion rates and improved ROI. In this second blog of our mini-series on using advanced data analytics in your acquisition strategy, we’ll discuss the data that will form the basis of your approach.