Building a customer insights repository
4 minutes read
Imagine if at the start of every new project, you had to forget everything that you know about customers within that domain. Everything you know about what’s important to them. The products and services they use. The pain they experience. The challenges they face. Rather than building on existing customer insights, you’d have to start from scratch every time. It would be a crazy way to work, and yet many teams do such a bad job of logging, consolidating and communicating pre-existing customer insights, that they may as well take this approach.
Trying to locate existing customer insights can all too often feel like hunting for fossils. Most of the time you’ll just find tiny fragments, which on their own really aren’t that insightful. Every so often, you’ll hit the jackpot in the form of some fossilised insights, a customer persona, or perhaps a long-lost customer research report. Like the fragments of a dinosaur skeleton there will be missing details and elements that you’ll have to piece together, but slowly over time a more fully formed picture will start to form. Whilst hunting for fossils might be fun (if you’re into that sort of thing), hunting for customer insights certainly isn’t.
To ensure that what is already known about customers doesn’t remain buried deep in the ground you should start building a customer insights repository. Read on to find out what a customer insights repository is, and how you can start building one using free tools such as airtable and reframer.
What is a customer insights repository?
A customer insights repository is somewhere to store, consolidate and mostly importantly surface and communicate customer insights. Think of it as the Natural History Museum of customer insights (for those unfamiliar with the Natural History Museum in London, it’s a very large and very famous museum of all things natural). A customer insights repository is a place where everyone can go to learn about customer; to discover and explore known customer insights and to access the vast collection of customer insight fossils available within.
Like the floors of a museum, a customer insights repository should have different levels of information. This allows the wood to be seen, along with the trees. The different levels should be:
- High-level insights
- Individual observations (a.k.a. nuggets)
- Raw research data
At the top are the high-level customer insights that have come out of research activities, such as research calls, user testing sessions, surveys and customer feedback. Insights are often in the form of documents, such as personas, empathy maps, job-to-be-done maps and value proposition statements.
2. Observations (a.k.a. nuggets)
In the middle are individual customer observations and pieces of feedback, such as observations from customer sessions, usability issues identified during usability testing and survey responses. Importantly these are date stamped and tagged to allow observations to be easily searched, filtered and grouped by topic. Tomer Sharon of WeWork refers to these atomic units of research as ‘nuggets’. I prefer the term ‘observations’ as I think that talking about nuggets just makes people think of KFC!
3. Research data
Forming the solid foundation of the customer insights repository is the raw customer research data. This might include notes from customer calls and visits, videos of user sessions, customer feedback and forum posts, and full unfiltered survey responses.
Building a customer insights repository
There are a growing number of platforms out there which promise to provide a quick and easy way to set-up a customer insights repository, such as the excellently named Nom Nom and Dovetail. However, I found that none of the platforms out there ticked all the boxes that I was looking for. I’ve therefore built a customer insights repository using a mixture of tools, most of which you’ll be glad to hear are free!
Confluence is used to present the high-level insights as an easy to navigate Wiki (at least that’s the idea) and to store some of the raw data, such as customer interview notes (OneDrive and Google Drive are also used to store raw customer research data). If you don’t have access to a Wiki such as Confluence or SharePoint, you could always build a simple website using a service such as WordPress.com.
Reframer is a splendid qualitative data analysis tool from Optimal Workshop. It’s currently free, although that will be changing in May 2019. Reframer allows research observation notes to be entered (or imported) and tagged with a pre-set taxonomy. Tagged notes can then be browsed within Reframer and exported as an Excel Spreadsheet.
I use Reframer to slice and dice the raw customer research data into individual observations and to add consistent tags which reflect the topics covered by the observations. This allows the observations to be easily searched, filtered and grouped within airtable because each observation has associated meta-data.
Airtable is like a cross between a spreadsheet and a database. It allows information to be easily cross-referenced, filtered, searched and grouped by topic. Airtable is not only very powerful and easy to use, it’s also free to use (until your tables get very large).
Observations are exported from Reframer and added to Airtable along with details of research activities and participants.
Populating the customer insights repository
Having a customer insights repository has meant that there is a little bit more work when it comes to recording customer research, but trust me, that extra work has been well worth it in the long run. The process for populating the customer insights repository is as follows:
1. Capture & upload customer insights
Raw customer research, in the form of usability testing sessions, research calls, survey results etc… are captured and uploaded to Confluence, OneDrive or Google Drive.
2. Record details of customer research
Details of customer research undertaken, such as research calls and usability testing sessions are recorded within airtable, along with a link to the raw customer research (e.g. research notes). The table within airtable provides a record of the research that has been undertaken, and the customers that have been involved.
3. Identify and tag individual observations
Individual insights and observations, such as usability issues, quotes, survey answers and research observations are tagged using Reframer. The tagged observations are then downloaded as an Excel spreadsheet and pasted into airtable.
4. Update high-level insights
High-level insights, such as jobs-to-be-done, personas and usability issues to be addressed are updated based on the observations. Where possible high-level insights are referenced back to the originating customer research (e.g. call notes).
Establishing a customer insights repository not only provides a home to customer insights, but believe me, the process of extracting and tagging individual observations will help to build all important high-level insights, such as personas, jobs-to-be-done and value propositions. Why not start building your own customer insights repository. It’s less work than you think and will help to ensure that insights are kept alive and are readily available, rather than simply gathering dust.