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Qual and quant partying together
The document repetitively lists the terms 'shopaholic,' 'scout,' 'gamer,' 'squirrel,' 'good sort,' and 'trainspotter.' These terms appear multiple times, emphasizing their significance. The lack of context makes it unclear how these concepts are related.
Qual and quant partying together
- 32. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 34. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 36. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 37. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 39. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 41. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 42. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 44. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 46. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
- 48. Shopaholic Scout GamerSquirrel Good Sort Trainspotter
Editor's Notes
- #5 All too often, people focusing on qualitative data, and those focusing on quantitative, don’t share insights. They’re trying to solve the same problems, but they’re not sharing insights. Often they don’t know each other’s names. In some cases, they don’t even know each other exists. Yet there is so much to be learnt and so much to be gained by sharing insights and learning from each other. Bring the qual and the quant together and make some magic! Here’s some ideas about how….
- #8 E.g. what if you have been asked to look into how to stop customers from churning/ (leaving your organisation)? Build up a picture using your quant data – who is leaving? Are there any patterns of behaviour leading up to that? Learn as much as you can, so that you can pinpoint where to research further.
- #11 Prove the ROI of qual! Everything we do does (or should!) have an objective. A goal for the business. And that means there is an outcome we are trying to get. And outcomes will always have things you can measure to determine whether or not you were successful in achieving that goal. So figure out what those are before you start, and figure out how to measure and track them using quant data.
- #12 Some examples: Net Promoter Score (NPS) Customer Satisfaction Time to complete tasks Drop-out rates Churn numbers Campaign success measures Word-of-mouth acquisition Contact centre call volumes Staff happiness levels
- #27 Simple questions that are easy to answer. Two options are good, or small scales. The questions should go to the heart of personas – values and beliefs, priorities and influences. Motivations and preferences to certain behaviours.
- #28 You’ll need to add a field to your customer database, so you can run queries with your quantitative insights and reference against Personas. Be prepared for this to take much longer than it should – go get friendly with the devs!
- #34 Issue identified: far too many of the Good Sort people don’t know there is a mobile app, despite the effort put in to educate during the onboarding process. Opportunity for improvement!
- #36 Problem: heaps of Scouts aren’t downloading the mobile app. We know through Persona research that when Scouts are fully engaged with our company, mobile app is their channel of choice and highly valued. Opportunity to understand what’s driving this evidenced behaviour and improve things!
- #39 Gamers are very confident they’re saving money.
- #41 Scouts aren’t confident they’re saving. Opportunity: what’s going on here? How can we improve their confidence? Overlaying qual with quant pin points an area of opportunity for further research!
- #44 We know this Persona type are not loyal customers – they chase deals and special offers and novelty. So even though their consideration of switching is high, this fits what we expect. Which means – no issue here. Yay!
- #46 Shopaholics love us. This is great news! Again, no issue here, but we’ll keep tracking to make sure we stay this high for them.
- #48 Scouts aren’t as confident. They are naturally sticky customers. Opportunity to learn and improve!
- #50 Overlaying the quant with the qual Personas gives us a clear picture of what’s actually going on for these customers. We can compare evidenced behaviour with the behaviour we would expect due to Persona traits and look for the differences, as they highlight areas of opportunity to learn more and improve what we are doing. Quant data also helps us continuously refine our Personas. Are they still using technology the services in the same way that they were two years ago? Or do we need to update some of the finer details, such as mobile phone use? Cycle the knowledge in both directions.