Accurate recruitment has been an age-old problem in the world of quantitative research. Think about the last seven days: How many hours did you spend watching Netflix? What is the total amount of money that you spent buying groceries, both offline and online? How many days ago did you last open an app that you do not open every day? The human mind falters in recalling the details of actions taken today, let alone in the last week or month. Nonetheless, traditional recruitment still relies heavily on the user's claim of her category and product usage, purchase history, etc., which leads to inaccurate targeting. An error in recruitment can lead to a much higher gap between the derived insights and the truth. This problem is accentuated even further for mobile-first brands where 'micro-segments' are based on not one but multiple parameters, like purchase history, wallet size, dormancy, product category, etc. In cases where the client's database is used to connect with users accurately (through email/SMS/call), the outcome ends up being inefficient in terms of scale, investments and/or timelines. Can technology really help in accurately recruiting users on the basis of all these parameters with zero margin of error, and complement the insights collected through claimed research at scale, as well as in a cost-efficient manner? This paper demonstrates how a mobile-first brand and its research partner came together to solve the micro-segment recruitment of its app users, in order to solve the core problem of user retention.
The 3 takeaways of the presentation are:- Hear why insights-driven experimentation is a powerful way to demonstrate the potential business impact of the insights organisation, beyond simply influencing decision making via desktop research, surveys, or qualitative research.- Experiments often fail. If that isn't true at your organisation, you are doing something wrong.- Know that there is value to be learned from failure, in both expected and unexpected ways.
This paper is an attempt to bring back Customer Satisfaction research to a precise task, and to present a straightforward method for accomplishing it. The task: point out areas where improvements are most likely to improve the customer retention rate. A data collection technique is presented which overcomes two major drawbacks of traditional methods. First, it deals with basic satisfaction correctly. Second, the swiftness of the interview allows for satisfaction and importance data to be collected single source. A case example illustrates how the method performs its task.
Expanding customer satisfaction measurement to the broader concept of Reference Group Management - to include customer satisfaction, employee orientation and image measurement, where appropriate - ensures that the correct decisions to improve customer satisfaction are taken, and that they can be implemented. Introducing the concept of Customer Retention enables the effect of customer satisfaction to be quantified in profit potential terms, providing a link between the 'soft' data of customer satisfaction measurement and the 'hard' data required for business management. The two together can have very substantial implications for the way a business manages the environment in which it operates, and consequently for the way it manages itself.