The Value Proposition Canvas is a tool developed by Alexander Osterwalder to help businesses understand their customers' needs and design products and services that meet those needs. The canvas consists of two main components: the Customer Profile and the Value Proposition, which are further divided into sub-components.
We can use CustomerIQ to automatically organize our insights and build out our customer profile. Then you can work with your team to build out your value map, describing how your product lines up with the customer profile built by CustomerIQ.
Customer Jobs: Tag any feedback that provides insight into what tasks or goals your customers are trying to accomplish. This could involve functional jobs (performing specific tasks), social jobs (achieving specific social outcomes), or emotional jobs (achieving specific emotional outcomes).
Customer pains: Tag feedback that provides insight into the difficulties, frustrations, risks, or obstacles your customers face when trying to accomplish their jobs. This could involve anything that makes achieving a job harder or less satisfactory.
Customer desires: Tag feedback that gives insight into the outcomes and benefits your customers desire when accomplishing their jobs. This includes required outcomes (must-haves for the customer), expected outcomes (standard expectations), desired outcomes (benefits the customer would like to have), and unexpected outcomes (benefits the customer would be surprised to have).
Templates create helpful tags for you to organize your insights. Most templates use our classification view, which can tag all insights in view according to just a few examples. Here’s how to use this template:
And that’s it! CustomerIQ’s AI will go to work sorting your insights. This process typically takes only 20-30 seconds.
Pro tip: If you find that the newly formed groups have a large number of insights, you can drill down further and find themes within this new group by creating a new view, filtering by this classification tag, and using the AI to discover underlying themes. Learn more about classifying, then discovering.