Consumer behavioral model optimizing conversion paths
Building a consumer behavioural model using ML tools to optimise conversion paths in the ecommerce industry
One of the areas where the proper use of data can significantly improve the results of companies is the e-commerce segment. 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:
- Who? (definition of a specific user),
- What? (offer, products, communication with the client, content),
- When? (time of contact with the client),
- Where? (selection of traffic sources and communication channels).
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.
Enzode is a Polish e-commerce and Big Data company.
The project concerns the creation of the world’s first system to automatically manage the path of user-to-customer conversion in the field of electronic advertising.
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.
Project in the testing phase. The algorithm already independently manages the division of paths for parts of the campaign. The results are as good as obtained by humans or even better.