The Why and How of Making Enterprise Software That People Actually Use
Have you ever used your work applications — your CRM software, analytics applications, internal KM tools — and wondered, “Uggh. Why can’t my work applications be as friendly and intuitive as my personal applications?”
Well, you’re not the only one.
The gap between enterprise and personal world software is known as the ‘experience gap’. Enterprise software has always been engineered for security, authorization, and utility. Its inherent lack of intuitiveness has become increasingly jarring, especially in the face of the huge leaps that personal world software has made in the past few years. And that shows when you compare the adoption rates of enterprise and personal world applications.
In 2019, it is estimated that users spent 58 min on average daily on Facebook. According to another survey, Instagram can boast that 60% of its users log in daily.
And, unless you are a digital marketer or an influencer, these apps are virtually useless.
Compare that to analytics software. The numbers aren’t really easy to find publically (since they are likely terrible). But if we are to go by anecdotal insights from practitioners, services companies and client conversations we’ve had — the weekly active usage for their end-user install base is in the high 20s-low 30s. That is simply embarrassingly low for a breed of software that purports to help you make better decisions at your job — a place where you spend more than a third of your day and defines your quality of life (and what you will afford to post on your Instagram profile)
Why Enterprise Apps Pretty Much Suck
There is a reason why personal-world apps enjoy high adoption rates. Due to their relatively low utility — and the valuation numbers associated with a large, highly engaged user base — they had to turn stickiness into a perfect science. Enterprise world apps relied heavily on factors such as centralized buying centers (we use it because the IT team purchased it), an enterprise-wide mandate (we have to use it because my boss makes it compulsory for me to use it) and the utility they offer (I have no other tool that helps me analyze my data). These factors perhaps led to the complacency in enterprise software vendors — giving them zero incentive to focus on user adoption
However, the world is changing quickly. The new breed of SaaS-based, digital-native applications is fast arriving on the scene, threatening the hegemony of incumbent vendors. They talk about things like value-based pricing, the importance of user engagement, and low total cost of ownership (TCO). They know that their only chance of success is to hit incumbents where it will hit the hardest. The TCO of IT can no longer be rising 10% every year (oh, but its only 8% if you are sign up for a longer period!).
The conversation is fast shifting towards leaner, value-driven IT — lower costs for maintenance, lower cost for ownership, scaling flexibly and higher variable rewards for positive outcomes.
The same is true in the analytics world. Forward-thinking companies want products that can help their analysts defocus from the humdrum everyday stuff and focus on the longer strategic objectives. They want to get rid of the “reporting” and double-down on the “decisioning”. They want a lower cost of ownership and would happily pay top dollar for positive outcomes.
And one of those outcomes is higher adoption.
Three Key Principles Behind Driving Higher Adoption
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The three tenets of driving high adoption were very nicely summarized in a recent inquiry call I had with Gartner analyst James Richardson. We have been working assiduously towards the same goals. However, his recounting of what it takes to make high-adoption software was illuminating and provided a strong framework towards our continued effort. Here are his three defining points behind making sticky and high engagement products:
High Performance
The first non-negotiable criteria is a product that performs to a high standard. Google could have devised the best-looking search engine in the world and if it took too long to return search results, no one would use it. YouTube wouldn’t rake in USD 15bn in annual ad revenue (almost a Fortune 200 company in itself) if the videos took too long to load. Users wouldn’t be using Instagram for 58 min each day on average if it crashed all the time.
Engineering resiliency is the foundational, albeit unsexy, principle for products that drive adoption.
The enterprise world equivalent would be products that work fast and handle a ton of people, data and queries with relative ease. Under the hood, you need software that uses scalable, containerized components to power the front-end experience.
High Relevance
Why are print broadsheet newspapers losing ground to digital equivalents like Flipboard today (or indeed the digital variants of newspapers)? I’d argue that the key reason is relevancy. You don’t need all the information in the world. You want to know only what you need to know. That’s relevancy — knowing what people want and giving them the information they need.
There’s second key element to relevance, beyond what do I want to know. It is also — how do I prefer to consume what I want to know.
Today your phone is your primary screen and default gateway to access all your information — business or personal. Relevancy of information and relevancy to my preferred mode of consumption are both equally important. In the analytics world, it is the difference between a complex, one-size-fits-all dashboard that you need to creak open your laptop to see — and an almost a daily-briefing sort of functionality that brings the collapses all the data into small, simplified nuggets of information on your phone.
User Experience:
The final point — and one receiving increased attention it is due for is — the user experience. UX is not just how slick and in-tune it is with contemporary design standards. User experience goes further — delving into an understanding of how users interact with the software and getting to them to achieve their stated goal with minimal cognitive load.
The most successful applications get their users to achieve their intended goal intuitively — almost like using their muscle memory — with minimal training, onboarding and thinking required to get to it
The status quo in the enterprise software world has pretty much failed to deliver the data-driven utopia that executive leaders have desired. The status quo has led to data being used heavily by those who are so oriented but thought of as optional by those who find analytics tools too overwhelming, too unwieldy and over-engineered. The focus should be on value-realization — and the adoption that ultimately drives it.
