Research - JPM Kübler
“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”
- Clive Humby -
As marketers we believe that to create value for your company, you have to first create value for your customers. In order to identify what consumers, potential customers, and current customers value, market research is essential.
Traditional marketing research relies on survey techniques to collect stated consumer information. Since more than 20 years we now enrich such stated preferences with observed consumer behavior data originating from customer tracking programs such as e.g. loyalty programs or panel scanner data tools.
With the rapid advancement of online shopping and the continuing digitalization of our world, we n access to more and more data. 90% of all data generated by mankind has been created within the last 2 years. Even though the rapid advancement of data generation and data collection tools has simultaneously brought up a lot of promises, little has been kept so far. We still see irrelevant, unpleasing, and non-appealing advertisements. Product recommendation systems have commonly a hard time predicting true preferences and learn only slowly what might be truly interesting for an individual consumer. Similarly, dynamic pricing and individual distribution remain behind the initial promises made.
Confronting today’s marketing managers with the question, why marketing is more and more perceived to be irrelevant and unpleasant, one main obstacle appears: too much data. Whereas companies did well in investing into large datawarehousing solutions, the analysis and creation of insights from the stored data seems still to be a key challenge.
As marketing scholars, we are interested in (1) understanding the utility of data in general to estimate and predict consumer behavior to identify potential value a company can offer to its customers. We therefore aim at developing, identifying and applying new methodologies, which may help marketers in the future with identifying valuable data sources and to estimate the value of specific data for the company.
Furthermore we see that classic and traditional (mostly) econometric tools are challenged by the size of the incoming data. Other disciplines such as e.g. computer sciences, linguistics, and bio-informatics have developed different tools, which allow machines to learn from data and to make predictions. Such machine learning and artificial intelligence tools may provide large opportunities for tomorrow’s marketers. Still we have yet little knowledge about how to use these tools in marketing and what these tools can do to develop value for consumers and companies. Therefore we are interested in (2) understanding which machine learning based techniques provide value to marketing.
Data and analytic tools are only one side of the coin. Insights gained from data and information need to be transferred into action. Insights need to be transformed into concrete marketing activities such as product development, pricing decisions, communication content and logistic decisions. Machine Learning and Big Data may offer rich opportunities to guide mangers taking these decisions. Nevertheless, so far it remains unclear which tools are especially helpful for which specific marketing tasks. Therefore we are interested in (3) understanding which marketing activities and instruments profit most from big data and artificial intelligence and which factors do moderate the suitability of these tools.