Primary research involves getting original data directly about a product or a service and/or a market. There are two kinds of primary research:Qualitative and Quantitative. Data gathered using primary research is data that did not exist before. It is designed to answer specific questions of interest to the business or government. The following questions are examples:

• What proportion of customers believes the level of customer service provided by the business is rated good or excellent?
• What do customers think of a new version of a popular product?
• Who will be the next prime minister? 

Primary research – Data collection

• New Technologies:
  • iOmnibus® – using Web 2.0 facilities via FaceBook to survey with patients and carers and particularly useful for younger target patients in the 18 to 35 years age group.
  • iAdBoard® – virtual advisory board at a fraction of the typical advisory board cost and with very quick turnaround. It uses the internet to link KOLs for topic debates in their own time and then a remotely moderated forum is undertaken with a live webinar.
  • Web Panels – GPs, specialists, nurses, pharmacists, patients and carers.
• Traditional:
  • In person – IDIs, diad or triad IDIs, FGD, mini-FGD, ethnographic interview and CAPI

Quantitative research

Quantitative market research studies are designed to assess, predict, and estimate patient and doctor attitudes and behaviours, used for market sizing, market segmentation, and uncovering "drivers" for product preference.

Quantitative research techniques produce formal, statistically valid data to support business decisions. Most often quantitative models relate to market size, share, penetration, installed base and growth rates. They can also measure and track changes in customer attitudes, satisfaction, commitment and loyalty.

We have delivered quantitative research for:

• Assessing new markets
• Benchmarking
• Brand equity
• Designing end points of clinical trials 
• Designing winning product profiles
• Evaluating the impact of competitive products
• Forecasting product performance
• Identifying market opportunities
• Market segmentation
• Optimising product development
• Perceptual mapping of competing products and services
• Predicting new product uptake - peak share
• Product positioning
• Tracking brand performance

Our researchers employ sophisticated statistical analysis procedures and cutting edge data mining techniques using neural net and genetic algorithms.

• Bi-variate analysis
• Brand price trade-off (BPTO)
• CHAID or CART CHAID
• Cluster analysis
• Conjoint analysis – ACA, CBC, Discreet choice
• Data mining
• Factor analysis
• Health Belief Model (HBM)
• Hierarchical Bayes
• Latent Class Analysis
• MaxDiff Scaling or Maximum difference scaling
• Multi-dimensional scaling (MDS)
• Perceptual maps (correspondence or bi-plot)
• Structural Equation Modelling