How to democratise research data to empower teams
More and more teams are becoming advanced data-driven, integrating data in the decision making. Every department from product to commercial use data daily.
In fact, you can find a ton of tools to help with your product analytics, marketing analytics, support analytics and also sales analytics software too.
Along with the quantitative data are the qualitative data. The qualitative data are the user interviews, customer feedback, surveys, NPS scores and more.
🤷‍♂️ Why qualitative data are tricky to collect and analyse
The qualitative data despite the quantitative is more subjectively on how teams classify, store and retrieve them to answer complicated business questions.
Respect the quantitative data the qualitative data structure can vary massively from company to company.
More structured teams, for example, after the series A-B of the founding of the startup World, decide to have a dedicated team of researchers. This can vary massively from company to company, but this is a common trend that I noticed. Including, design research, user research, competitive research and more. This type of team conduct, summarise and dispatch research data across other teams from product and design teams.
Less structured teams don’t have a centralised way to collect qualitative data. This ends up that every function or team members do their own research, speak with their users and in many cases come up with a specific vision of the problem/solution. This can be an extreme case, but you would be surprised how many agile small startup teams work in silos, especially with the qualitative data.
📡 How qualitative data can become your competitive advantage
As the motto of Y Combinator, “build a product that your customers want”.
Indeed, I agree with that, but I also recognise that can be the founders’ nature to have a vivid image of the problem/solution in their head. In fact, many technical founders just start building the solution if they have the skills and time.
Also, if you are not technical, it is easier to be tided up with your product vision that you would like to build from zero to a fully-fledged product. In many cases, you will end up shopping for plenty of features like when you go to the supermarket to do groceries to build a proper 5 courses solution.
As this may sound familiar way to go. The hard part is to zoom out from your vision and your specific idea of the product/solution that you are building and speaking with users. Yes, you should do a ton of quantitative research before building anything to validate the problem/solution and the market you are entering, Steve Blank docet this here.
Many startups fail because they don’t have a product-market fit (PMF). They create something that customers don’t need and indeed are not willing to pay to solve their problem.
Here is the comparison between vitamins and pain killers, you should build a pain killer solution. When the users experience so bad a problem “pain” that they are willing to buy your solution ”pain killers”. Rather than vitamins that don’t have a strong “pain” as a problem and then cause a less acute demand.
Qualitative data are fundamental to getting PMF for your software but also vital to do new features. Improving over time your product with new functionalities and incremental changes are a must to delight your customers over time. It generates more and more value for them and remains one step ahead in your market respect competitors and incumbents.
🏗 5 Steps to succeed with your qualitative data
The user interview process is broken in many companies. Only a few people do user interviews and their findings are not classified with a common system across the company, shared and easy to retrieve for teams.
1. Create automatic flows inside your product to get a pipeline of customer interviews
Not all your customers would be available to speak with you. Also, the one that booked a customer interview with you still has a high chance of no show.
This underlines the importance to create automatic flows inside your product to have a constant volume of users to speak with and you should automate this process.
For example, you can identify the different steps on your products where to connect automatic email sequences to get users to book a call with your team.
2. Decide a common structure for your company to classify and store qualitative data
For example, a company like Notion use a tagging system. Where everyone that gather qualitative data can classify in a way that is univocally recognised across all the company.
An example, if you use AARRR pirate metrics for your funnel, you can have in place a dedicated tag for each step of the funnel. Similarly, you can have tags for specific subproblems or features you are trying to optimise.
The tagging system would depend on your team metrics and how work is structured and what is important for you and your team.
3. Retrieve your qualitative data easily
The importance to have a unique classification system for your qualitative data would help to retrieve your data.
For example, if you have a tagging system based on the pirate metrics funnel, you can retrieve all the activation qualitative data if you are trying to optimise a specific step.
In addition, the ability to retrieve historical qualitative data help to understand better the evolution of your users' behaviours, their problems along with the implementation of your product over a specific period.
Also, you can make a specific query qualitative data based on a specific time frame and compare different periods. Indeed, that would enhance also the use of your quantitative data, helping your team, which the quality of the product they are building for your users.
4. Create one shared place where your company stores the qualitative data
Every team normally has their folder where are grouped and stored their data. However, the advantage is to group in one place all your company qualitative data will enable each function to have access to an exponential quantity of data.
A comparison can be like the race to build a self-driving car. Having a unique depository for qualitative data in your company is like Tesla. They use all their fleets to harvest data and they are accessible to all teams inside Tesla. Rather than Google with Weymo, use their vehicles to gather data. Comparing the two Tesla has an exponential quantity of data that are increasing exponentially too over time. Rather than Google is like each function in your company that collect and use only the data they have collected.
It is easy to understand which system makes a competitive advantage. Also, this different system would increase the data gap between the two companies in the future.
5. Optimise your qualitative data with the software
Collecting, classifying and storing qualitative data are laborious and time-consuming.
There are a few semi-automatic solutions that you can build yourself using different software. For example, using Airtable to collect and classify data with a tagging system. Then you can use Zapier and other tools to semi-automatise the import of these data.
Other solutions, one time stop software that can help collect, classify, store and analyse qualitative data. This category of software a fairly new and I haven’t personally used any of them yet. However, it is on my to-do list to give them a go and if I would do, you can expect a reflection essay on that.
🥡🥢 Takeaways
The true competitive advantage is to shape a team culture that values qualitative data and embed them in their decision process along with quantitative data.
Take incremental steps to align all your teams and functions around qualitative data. Also, depending on which stage your company is you can go for one solution rather than another to handle quantitative data. Starting small is the simple secret to improving how you handle qualitative data in the short term. Don’t wait to align all your team on one perfect magnificent solution, because maybe that day could never arrive.
Building an automatic email sequence inside your product to have a pipeline of qualified users for the user interviews is a must. That would help to have more qualitative data at your disposal to answer business questions along with your quantitative data.
One thing interesting to be noticed is the power to build a tide community. Indeed, it will help to source a good volume of users for interviews over time and at any time, reducing the no show rate and strengthening the close loop between decision making and qualitative data.
Use tools to simplify the process and save time for the team because user interviews for some team members can be perceived as an extra task on their daily job like recruiting. Consider investing in software and tools as an investment rather than an expense for your team.