What are the latest trends in customer research, and how can you stay ahead of them?

Online communities help in gathering relatively unbiased opinions of the customers. One of the important consideration is the ability to motivate participants in the online community to contribute. Motivation in the form of badges, reward points, leader boards and community voice badge are some of the commonly known motivators that have worked effectively. Customer research using online community is also relatively cost effective compared to other forms of research. The cost of acquiring customer to the online community is likely the only cost. Online community does not entail costs related printed stationery, creative design or physical mailing. The key challenge is attracting and involving the right customers, to be planned carefully.

Artificial Intelligence is effective due to the involvement of large volume, variety and velocity of customer data. Traditional data collection, cleaning and enriching, analysis and visualization techniques could be enhanced with AI in a few definitive ways. Firstly, data collection from complex data formats such as images or videos needs AI (e.g item details from Point of Sale receipts). Next, data cleaning and enriching the data with Machine Learning provides better quality of data (e.g predict the item in Point of Sale receipt using abbreviated description). Thirdly, data analysis with AI provides predictive capabilities (e.g recommender system). Finally, visualizing data with AI empowers decision making (e.g Network analysis)

Agile methodology is ideal for customer research. Customer research projects could be very demanding, time consuming and complex. Breaking them down into smaller 'sprints' with defined goals helps to manage the dimensions of complexity. Customer research is like navigating our way through a forest with many uncertainties. Adapting each subsequent iteration based on the current step taken is highly recommended due to the high cost, time and effort involved in the process. Customer research needs simplicity in approach, even if sophisticated tools are used. Simplicity should be reflected in the simple and clear research questions, data collection points, easy to interpret instruments for data collection and easy to understand visualizations

Ethical customer research should focus on the tenets of the ethical AI. Factors such as accountability, privacy, fairness, security, explainability, robustness, reliability, safety and inclusiveness should be part of the research process. Accountability talks about who could be approached if something goes awry in the research. Privacy ensures the data that is collected do not encroach personal freedom. Security provides all-round protection to the data and actors involved. Fairness makes sure there is no bias in the process. Explainability is needed to back-up the outcomes with the source. Robustness, reliability and safety ensures stability in an ever changing environment. These aspects should be considered in ethical customer research

Recently I was a subject of a research study conducted in a customer experience innovation lab at the University of South Florida. A simulated environment was created to study the neuro-signals generated during an exercise that involves a set of decision making process. The study captured my decisions and the signals in an anonymized research. It was an interesting study to participate in. The purpose of the study was clear, the technologies used were simple and effective to capture the behavior responses and the outcomes were recorded in a computer system for aggregated analysis. I participated voluntarily as it was quite interesting. Such studies are quite helpful in customer research.