BI and analytical tools are more powerful and more useful than ever before. So why is it such a struggle to get business users to adopt them?
Despite the buzz around Business Intelligence (BI), it’s not easy to get business users to adopt analytics software and tools as part of their day-to-day work. Why not? Most organizations agree that data-backed decisions fare better than those based on instinct or routine. Access to data is faster and more reliable. So what’s the problem?
Why Business Users Are Slow to Adopt Analytics Tools
To understand what’s going on, we first have to approach things from the users’ point of view. The G2 Crowd’s The State of Software Happiness Report 2019 looked at business software as a whole and revealed that:
- 51% of employees surveyed had been unhappy at work because of the software provided
- 24% considered leaving their jobs because of company software
- 12% actually did quit over software
- 95% would be happier and more productive with better software
We’ve seen most of these dynamics mirrored in the pain points of new clients, where we’ve been tasked with translating their increasingly valuable (and prolific) data into something executives and their teams can actually use. Here are some reasons given for low employee usage rates:
- “There’s a substantial learning curve.”
- “The new tools are hard to use.”
- “These programs don’t meet my/our business needs.”
- “We need more training!”
- “I don’t like change.”
Five Lessons Learned to Deliver an App Decision Makers WANT to Use
Even if you’ll be outsourcing the design and development of an analytics tool, company leadership will still play an essential role in the process. To get business users on board with the finished product, you must:
1. Provide Clear Recommendations, Not Just Insights
Even the slickest interface isn’t going to win any hearts (or minds) if it gives vague insights or questionable predictions. So, keep in mind that:
- Users want a specific action to take to achieve a specified business goal. A general insight, with no direction on how to apply it to achieve the goal at hand, doesn’t add value. The decision-maker is still left to routine or instinct to move forward. Deliver clear recommendations of what can be done.
- Accuracy matters. To generate relevant recommendations, your tool absolutely must be built on sound analytical frameworks, predictive models, and the technology to stitch it all together. The tools must also take into account business context unique to your company and industry.
- Recommendation vs Prescriptive: To clarify, the much-used prescriptive analytics refers to the predictive models mentioned in the point above on accuracy. It is one critical component of four, to ensure relevant recommendations. To make quality recommendations, you join predictive models with analytical frameworks, technology and context.
2. Provide Comprehensive Information Behind the Recommendations
Your teams know your business well—most have helped grow your company into what it is today. Blind recommendations won’t—and shouldn’t—fly. To gain adoption, tools need to include information and rationale for recommendations. Show the relevant information in one place for instant, easy access.
For example, a sales tool might include a prioritized list of activities per account, which products/services they’ll buy next, recommendations on the best action for individual prospects, and reasons why that prospect is likely to make that purchase soon. It’s important to let users know why each recommendation is made.
3. Put End Users in Control
Give end users as much power as you can within the app.
- Run What if Scenarios: Allow users to independently adjust parameters and business constraints to see how recommendations and expected results would be impacted.
- Test Different Business Goals: By testing varying business goals, users can make more informed strategic business decisions. For example, if my goal is to grow revenue vs grow market share, what actions are recommended and what results can be expected?
- KPIs and Results: Ensure what-if scenarios assess the impact on KPIs and results. For example, offering a deeper discount might actually erode perceived value and result in decreased unit and dollar sales among your target audience.
- Allow users to provide feedback on recommendations. BI tools are underpinned by sophisticated algorithms that can self-learn and improve their performance. If users provide feedback on the tools recommendations – good and bad – the recommendations will get better as the system matures.
4. Design an Interface Business Users Understand
The Lazy User Principle is alive and strong. Essentially, this principle states that people will choose things that take less effort over more complicated offerings.
- KISS (keep it simple sweetie). Your tool should have a clean, intuitive interface with your own familiar business language. No IT speak. No superfluous inputs, elements, choices, etc.
- Give users a voice in the design process. Allow plenty of time for user testing, and make sure you’re testing it out on actual users. Incorporate their feedback. Don’t get caught in the idea that designers and developers, with their extensive knowledge and skills, will automatically know better than business users. Remember who will be using this tool in daily decision-making. Letting them help guide the design will not only increase adoption, but can create internal advocates.
- Consider user workflow. Integrate your new tool into employees’ existing workflows. It has to make sense in their day-to-day demands. The simpler it is to access and incorporate, the happier your users will be.
- No Technical Support Needed: Users should be able to perform these actions quickly, without involving the IT or the analytics team.
- Keep Training Simple: If you find that extensive detailed training is required, then you need to be more diligent on these first four points. We’re happy to help if you still are struggling.
5. Build Trust Before the Rollout
A recurring issue with user adoption of business analytics is that the tool provided doesn’t really meet the business’ or users’ needs. You can avoid this by capturing all the business context and business rules before you start building your analytics tool. As you build, keep ensuring that the tool will meet these needs. This means doing extensive user testing.
Soft launching the tool, perhaps to one or two groups or to a single department, is also effective. This can find flaws that sneak through the development iterations and all those rounds of user testing. It can also build internal buzz around the tool. At this stage and throughout the tool’s lifecycle, it’s important to include active user feedback, i.e. whether to accept or reject recommendations.
Accelerating Business Analytics Adoption in Your Organization
As we’ve seen, getting business users to accept analytics tools can be done, especially when the tools are designed to meet business and user needs. You can accelerate analytics adoption by thoroughly incorporating user feedback before, during, and after the design and rollout process. And finally, it’s important to set users up for success by giving them the training they need and incorporating the tool into their existing workflows.