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Online retailers possess a lot of information about their clients’ preferences and habits that, if properly exploited, may be used to enhance their overall performance. However, finding the right takeaways involves the analysis of several millions of registers, rendering this task significantly complex. One of the leading international online retailers commissioned Axon with the objective of providing analytic results and conclusions on the behaviour of customers in relation to the various campaigns launched by our client and building a statistical model that predicted customer behaviour and recommended marketing actions based on a pre-defined set of objectives (e.g. maximise profitability, increase sales, attract new customers).
Firstly, we performed an analysis of all the orders placed in our client’s in-store and online channels over a two years period. This analysis allowed us to provide valuable insights to our client in terms of:
Following our initial backwards assessment, we built a predictive model, fit with machine-learning algorithms, that allowed our client to design its marketing campaigns according to the specific objectives pursued at any given time. The model was continuously fed with new and live data, thus improving the accuracy of its estimates (e.g. impact on sales, acquisition of new customers, ROI) over time.
The predictive model we implemented for our client became the “right-hand” of its marketing department over the coming months. In the 12 months following its deployment, our customer’s sales increased by over 20% and the number of unsuccessful marketing campaigns was slashed by over 80%.