The CPG market is overcrowded with thousands of brands fighting for attention and wallet share. In a tough, highly segmented market, large FMCG companies must continuously study consumers to understand the intricacies and the co-relation between product consumption and consumer behavior patterns. The most effective consumer research studies are observational in nature. Video is becoming the medium of choice to record and analyze consumer behavior. Product design changes are incorporated post-analysis of consumer behavior by R&D teams.
Consumer behavior research conducted by our customer thus far, has followed the traditional established methodology:
- Conduct focus groups with a sample size of consumers
- Deploy surveys and questionnaires with close and open-ended questions
- Study hours of video footage on behavior patterns
But this approach to consumer research is fraught with challenges.
Challenge 1: Time consuming. Manual study and analysis of recorded videos take 2x-3x of the recorded video duration, to review, analyse and generate insights for behavior and deliver the final report. Moreover, insights generated from such studies may not be easily searchable or referenced for future analysis.
Challenge 2: Manpower intensive. A large team of analysts is required to study hours of video footage. The process of review is tedious and vulnerable to human error, which could affect the quality of the analysis.
Challenge 3: Inconsistency of analysis. Analysis of video footage studying video footage of consumer behavior and insights thus generated are vulnerable to human perception.
This results in inconsistency of insights generated, negatively impacting ROI.
Challenge 4: Timely decision-making. Since the conventional methods are time and effort-intensive, product engineering teams are frequently handicapped in taking timely decisions, which might have a negative impact on marketing and therefore, revenue growth.
Challenge 5: Resistance to technology driven analysis. Given that research into consumer behavior has primarily been the domain of human experts, accepting a technology solution to improve analysis efficiency is not easy to accomplish.
The task: To overcome the challenges outlined above, we had to develop technology that achieved the following business objectives:
- Shrinking the time taken for video analysis
- Matching the accuracy of human analysis and eventually surpassing the same
- Significantly reducing costs with faster time of completion and minimizing errors