05 Dec 2025
Consumers leave patterns in everything that they do and through data science and behavioural insights you can anticipate what your consumers will want based on these. Through models such as predictive modelling and product adoption models you can predict future behaviour, for example whether your consumers will embrace the new product feature or not. And if the dataset has missing values? Data imputation models enable you to fill in the gaps in a statistically sound way and this not only tidies your dataset but also improves performance of your model. Read our article about some of the models used to truly understand consumer behaviour.
With the world getting more and more data-rich and digital, consumers leave patterns in everything that they do, from browsing on the web to how they react through times of changes in economy. Everything has a footprint. Think in simple terms, for instance about when you go to a shopping mall. You would very likely follow the same route, visit the same shops and have a coffee at your habitual cafeteria. You also spend roughly the same amount of hours, spend the same budget unless for a special occasion and park around the same two blocks, or at the same parking area.
Through proper data science methods you can recognise patterns and anticipate what customers need even before they explicitly ask for it. We see this constantly when we shop online, where if the system detected that I am looking for an evening dress for a special occasion, the site would also let me know of shoes and bags that would be perfect with the dress, it would also show me what those people whose preferences in clothes are similar to mine, have liked, viewed or bought.
Data science and behavioural insights can help businesses and teams understand where to place certain efforts by knowing the signals in complex datasets and the "why" for certain choices. Together they are useful tools in understanding consumer behaviours.
Predictive modelling and product adoption models can predict future behaviour and it is useful if someone wants to see the likelihood of customers embracing a new subscription or a digital feature, for example. And what if the dataset is incomplete? Data imputation techniques allow analysts to fill in missing values in a statistically sound way such as for demographic data. With imputation models you can reconstruct missing variables based on purchasing patterns, therefore such models tidy up the dataset as well as improve performance of every downstream model.
Now about understanding customer's sentiment. Many companies are all about collecting data, through surveys, questionnaires, polls...however customer feedback is not always given in a numerical form and so most feedback come in the shape of reviews, emails, support chats, telephone calls etc.. Natural Language Processing (NLP) enables businesses to extract meaning from these conversations and sentiment analysis can pinpoints areas of frustration and/or satisfaction amongst the customers, so that the Company can focus on those areas.