A few years ago, an issue relating to generating traffic on an e-commerce website was analysed regardless of the actions taken by the user on the website. The integration of these has been noticeable for several years by activities of entities specialising in ‘Customer Journey Optimisation’. The new approach is holistic. It not only improves results by matching resources to each user more effectively but also allows for better and faster identification of changes taking place on the market (e.g. reasons for a drop in sales or the appearance of false traffic generated by bots). The process of optimising the customer conversion path at an individual level is one of the greatest challenges facing the e-commerce industry. By using advanced analytics based on personalised decisions, we answer four basic questions necessary to optimise the conversion path:
Despite the dynamic development of technology, many decisions in this process are still made by domain experts. Human intuition is naturally biased, so marketers often use the method of carrying out A/B tests to evaluate individual elements of the customer journey. However, the presented method has many disadvantages, including in a specific A/B test. And therefore, we check the impact of one specific (expertly-selected) feature, but we ignore the possibility of the interdependence of many features on each other (e.g. the common influence of the background and the words used in the message), and through a manual process, we limit ourselves to specifically selected features and segments. The project’s challenge is to address this problem.
Go ahead and learn what our client thinks about the project in our case study – Case Study Enzode
The system, apart from a high degree of automation, will be characterised by the fact that it will cover the entire conversion path, i.e. from advertising, through the user’s behaviour on the e-commerce website, to actions after leaving the website.
The applied approach will also allow for deep personalisation of the message depending on the user’s profile. The effects of algorithmically-made decisions will be better than decisions made by domain experts.
MIM Solutions was launched as a spin-off of the University of Warsaw’s Algorithms Group, directed by prof. Piotr Sankowski. The company has brought together experts passionate about solving practical algorithmic problems efficiently, and this has finally evolved towards machine learning. Although MIM Solutions is not a part of the university now, we are still in tight cooperation.
MIM Solutions specialises in difficult tasks. We are proficient in providing effective solutions, especially when standard methods have failed. However, for the most common problems we specialise in, we offer a set of generic services ready to swiftly deploy in any environment.
MIM Solutions is a company registered in the National Court Register kept by the District Court for the City of Warsaw, 13th Commercial Division of the National Court. Register. KRS: 0000581404, NIP: PL5213710082.
