True Business Intelligence
There's no shortage of survey companies offering similar services. They'll tell you that from 1,000 respondents, the mean satisfaction score is 5 out of 10. Useful, but that's merely scratching the surface. At Digital Research, we delve deeper, examining who these respondents are, where they come from, and what motivates their responses.
We recognise that people who've had negative experiences are typically more motivated to complete surveys than those with positive ones. This response bias requires statistical normalisation, but that's just the beginning. The true picture emerges when we segment responses by interaction patterns.
Consider how satisfaction scores vary across:
- No recorded interaction
- Single recorded interaction
- Single recorded interaction with return or complaint
- Multiple recorded interactions
- Multiple recorded interactions with a return or complaint
- And so on...
This deeper analysis reveals insights that would otherwise remain hidden. A low rating from someone with a single interaction and a complaint is expected—clearly something went wrong. However, a low rating from someone with multiple interactions but no formal complaints is far more telling. This is the nuanced territory that most research firms avoid.
Demographics, socioeconomic factors and environmental context are all crucial when analysing human survey data. For instance, if we observe a pattern of poor ratings without complaints from lower socioeconomic groups, whilst other demographics report satisfactory experiences, this suggests price sensitivity rather than service failure. Conversely, complaints predominantly from more affluent respondents might indicate quality or service issues. These valuable insights simply aren't possible without proper respondent analysis and segmentation.