Uncovering Insights and Building Efficiencies

Reach out to us for:

  • Research Design
  • Questionnaire
  • Project Management
  • Analytics
  • Reporting
  • Research Advice

We offer a plethora of services ranging from customized primary research solutions to secondary data synthesis to uncover insights. We have expertise in building efficiencies in the research ecosystem through Centralisation and Hub solutions. Normative databasing is an area which has proved to be very useful in uncovering insights as well as build efficiencies for studies carried out on a regular basis (particularly in the product testing domain).

Our primary research solutions range from addressing specific parts of the marketing mix (e.g. pack test) to strategic solutions (e.g. segmentation). Occasionally, clients wish to avoid utilizing primary research solutions. In such cases, we ensure that we gain an improved understanding of the data already present internally in their organisation using our advanced analytics and tools. Additionally, we comb through the external secondary sources of data in order to lend clarity to current learnings or offer our perspective to the client’s marketing problem.

We also assist clients in creating a harmonious ecosystem in order to achieve optimum process efficiency through our Global Hub Model. It identifies a focal point wherein a multitude of processes are performed in sync. In order to achieve both cost and process oriented benefits, this model is based out of a centralized location for performing all local and global activities effectively. Our experiences of running similar programmes for clients has shown that this model is the most effective solution for improving efficiency through Process Optimization. Currently, we are running global hubs from multiple locations for a number of Fortune 500 companies.

We aid in the creation of a normative database in order to leverage existing data so as to create benchmarks, as well as generate insights, using advanced analytics. Essentially, a ‘norm’ is a benchmark or standard that is robust. For example, in product testing, norm benchmarking involves comparing a product survey’s score with a single benchmark OR norm on a range of attributes. This norm is arrived at after analysing the test scores of a large number of similar products (belonging to the same category, in most cases) tested in prior researches on comparable attributes.

The test scores are compiled to form the normative database, which helps:

  • provide robust test results - since a product may be superior to the benchmark but still not the best product in the market. Hence, a mediocre product may emerge as the best, if the benchmark, itself, is incorrect.
  • improve project efficiency - reduce sample size, time, and cost since there is no need to conduct benchmark tests every time
  • enable data-mining in order to generate product insights