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.
90% of people in Asia want brands to do something about the issues they care about. But what do they care about? And what exactly do they want brands to do? Let us tell you more.
90% of people in Asia want brands to do something about the issues they care about. But what do they care about? And what exactly do they want brands to do? Let us tell you more.
Never before as visible as today. In Peru, there are more than 1.7 million adults who recognize themselves as LGTBIQ+. However, day by day they face a world that judges and discriminates them for being and expressing how they feel. This research approach this phenomenon, not only to understand it, but also to change it. In three stages (exploration, ideation and co-creation) the population of Metropolitan Lima was segmented to design solutions with multidisciplinary experts and validate them with citizens in virtual communities.
This paper is about the fusion of quantitative with qualitative research. It showcases how quantitative segmentation (and the resultant prime target market identification) and qualitative prime target market motivators can be undertaken in a single source study. The fusion of Stage 1 Quantitative Segmentation with Stage 2 Qualitative Motivators enables costs to be reduced by approximately 25% compared to traditional two staged research. In essence, the result is quantitative segmentation, qualitative motivator, single source data and qualitative insight with quantitative numeracy.
Innovation of qualitative/quantitative fusion, evidenced through a case study. Envisage a segmentation study that also identifies psychological motivators: deep qual insight with quant numeracy. But wait, there's more, costs down 25% and time down 50%!
Thus the proactive research was able to effectively utilise technology, passive techniques and leverage internal and secondary data available to deliver quality and on the go insights leading to improved engagement and increased revenue. There was also no compromise on the quality in spite of the end to end quick research TAT of less than two months.
The Talent Contest: ESOMAR Research Effectiveness Award Finalist.
The Talent Contest: ESOMAR Research Effectiveness Award Finalist.