Transforming raw data into actionable knowledge: Insight building


Transforming raw data into actionable knowledge: Insight building


Analysing data collected from sources such as user interviews, NPS surveys and user testing to extract meaningful insights.


Analysing data collected from sources such as user interviews, NPS surveys and user testing to extract meaningful insights.


The goal of insight building? Translate these insights into informed decisions and strategic actions that drive positive outcomes for our products.


The goal of insight building? Translate these insights into informed decisions and strategic actions that drive positive outcomes for our products.


Whats involved?

Systematic and methodical tagging

Utilising Dovetail's software, our research group created a database of "tags" tied to the feedback we acquired. This process entailed group think sessions for deciding tag names, their utilisation and allocation, and constant collaboration concerning the appropriate tags for various feedbacks.

Allocating and grouping tags

Once these tags had been refined and acquired, we analysed them and decided on specific groups for them. These included:

Client segmentation: this included tags like age groups, location, and the type of company.
Quality: this included the main four groups we used to base the majority of reports off of; usability, features, performance and suggestions. Alongside this we included product specific tags.
User and client metrics: this included skill level, number of users, team sizes and other general numerical and statistical tags.
User testing and journeys: purely related to any user tests conducted, how the user felt and personal feedback received from an individual.


Brainstorming

Analysing the target of our research

Prior to producing any analytical report, we, as researchers and designers, needed to confirm that we were thoroughly acquainted with what our product provided to our users.

This encompassed making use of our education platform, evaluating demos, and reviewing intrinsic help documents.

Discussions with product department

A constructive dialogue and continual exchange of ideas with our product department created an environment conducive to free inquisition.

Product managers maintained direct correspondence with our clientele, offering us crucial insights into what our customer feedback could potentially indicate in instances of uncertainty.

Figjam collaboration

As researchers we collaborated together on the information we had gathered from the previous steps mentioned above.

Utilising Figjam, we brought our knowledge together and created a structure to how the report should drive home the overall summary of our feedback.

Product specific tagging

To further enhance our repository of research, we also included a product specific tagging system. This involved separating certain features that may only be associated with one of our products, enabling us to target precise areas of our feedback.


Reporting
To achieve our goals for our reports, we classified them into three separate categories:

Clients combined

These reports would combine all our client's feedback specific to one of our products.

Products combined

These reports would combine feedback received on all of our products across all clients.

Target report

These reports would target a specific client and all of their feedback gathered.

Outcome

Drivers for change

The reports generated helped drive changes for our clients. The feedback we gathered built insights which targeted specific areas we can modify.

Results were presented to other departments and discussion were had to determine if these changes were feasible and where they were so, the changes were implemented.

Results

13 reports generated

Over 52 insights generated

Improved collaborative environment amongst departments

Repository of feedback for all departments to view and use for the future

Opened the door to some of the first user testing sessions conducted in the company

Increase in user satisfaction