Rise.global is an awesome tool to run an Employee Personal Analytics program – giving each employee their own relevant, data dashboard so they can optimise their working day, get better at their jobs and make better business decisions. This post explains employee personal analytics and outlines the key work packages you’ll need to make it happen in your organisation.
Since we know that data driven decisions tend to be better decisions (HBR2012) there is now a move, within organisations seeking to build a data-driven culture, to provide relevant data analytics dashboards, traditionally given only to managers, right down to employees themselves.
Gartner calls this trend “Personal Analytics” and recently added it to the very beginning of it’s popular “hype cycle” of emerging technologies.
The very best Personal Analytics tools go beyond simply personalising existing business insight reports to individuals – they are apps in themselves. As with so much enterprise technology, the user experience of these tools has already been trailblazed in the consumer marketplace by activity tracking apps like MyRunKeeper, Endomondo, Strava, Nike+ and Fitbit.
Activity tracking apps offer more than just an analytics dashboard – they offer social comparison, achievements and automated coaching. These features lead to intrinsically motivated consumers: people who opt in to sharing their data with these personal analytics apps in return for accurate feedback and an engaging experience.
For the brand sponsoring the activity tracker, the return comes from brand engagement, loyalty and real activity data from customers. In return the consumer gets a powerful, relevant self-improvement tool. This model appears to work. In running Nike+ alone has 28 million users while challenger brand EndoMondo spent $500m on MyFitnessPal and Endomondo to try and keep up. Fitbit leads the way with step tracking for over 29 million users, including 1000 corporate customers using Fitbit as part of their employee wellness program.
Bringing personal analytics technology into an organisation seems like a no-brainer. If this technology can motivate and train runners to run faster, surely it can help sales people fill their pipeline or customer service reps close more happy customers?
The answer to this is of course yes – these programs can and do deliver significant value. Most importantly, in a world where the medium is part of the message, they can be used to signal an overall shift towards individual employee empowerment and digital transformation.
Rise has been running personal analytics programs like this for enterprise clients for the past 4 years. During that time we’ve identified that successful programs don’t just happen on their own, they are more than software and data, they take planning and effort.
In this post I want to outline some of the work packages that you’ll need to consider when embarking on your own program. Whether you’re focused on just your immediate team or have a broader, perhaps global, remit, you’ll need to consider each area carefully.
1. Program Sponsorship
All good programs need a sponsor, someone in or near the C-suite, that wants to see personal analytics made available to some or all of the employees. The sponsor is ultimately responsible for setting the business goals of the whole program – such as driving adoption of digital tools, achieving sales via social media and so on.
Business goals will vary – is the objective to improve an OCB (Organisational Citizenship Behaviour) such as social media use or sustainability at work? Or is it to improve a role based skillset – such as adopting new Customer Relationship Management software and processes?
On the outside Personal Analytics programs can look very similar to traditional measurement or business insight programs. Ensuring everyone on the project understands the difference is key to a successful program. Making the program opt-in (or at minimum opt-out) is a clear signal of a personal analytics program that has employee needs at its heart.
2. Score Algorithms
Good personal analytics programs don’t bewilder their users with a potentially competing array of statistics – composite indices are the order of the day – single balanced scores that go up or down. These have many benefits:
- accessible for busy professionals
- simple to understand good and bad
- weightings can be used to embed business priorities
- allows easy comparison with peers
Defining and managing a single score algorithm is not so straightforward. What matters to marketing isn’t necessarily what matters to sales. The current priorities of North America are not necessarily the same as those in Europe. How do you decide what metrics count? While usually the score algorithm is defined by an expert to begin with, in mature programs this becomes the job of a scoring committee.
Some organisations like to put all their eggs in one basket and focus on a single score for all roles and all behaviours while others prefer a separate algorithm for each role / behaviour.
The flip side to the positive benefits of a score algorithm are the unwanted behaviours caused as players try to “game the system”. This is an inherent problem in any personal analytics program and one I’m very interested in personally. On my GamificationOfWork blog I’ve listed 16 classic design fails that should prove sobering reading. They aren’t insurmountable though, so do drop me an email if you’d like a copy of my white paper on 12 anti-gaming mechanisms you can use in your next program.
Considering anti-gaming issues at scale can take time. The more sophisticated your program, the more important this can be to maintain the trust and credibility of your user community.
Framing your program right takes time. Prospective user interviews are a great way to understand personal analytics from their point of view. Without good positioning your program may come across as “just another measurement tool” or “something from management”.
I’m a firm believer that personal analytics should be rolled out as an opt-in solution for employees, or at worst “highly recommended” – this drives higher ownership of the program among employees and empowers them to suggest those key improvements that will make the program really effective in the long term. To avoid the “empty bar” problem I find there is rarely a shortage of senior executives willing to be the guinea pigs on the initial leaderboard.
5. Player Experience Design
Planning the player experience means looking at the program and imagining your users as “players”. This has been widely described as “gamification” though is also termed behaviour engineering and motivational design.
A good player experience has obvious score systems, achievements (aka badges) and social features such as leaderboards that add colour to an otherwise dry, analytics experience. This absolutely doesn’t mean making it all into a game with swords & dragons etc. and also doesn’t mean littering it with incentives like ipads and cash payments! What it does mean is structuring the experience for people who want to progress at their own pace, who want to see both the inchstones and milestones on their journey and be able to see their status against peers, whether that’s in teams or as individuals.
Benchmarking versus peers on a leaderboard is one of the most powerful and engaging elements however it must be done with care. For example, not all countries even allow employee leaderboards (e.g. Germany) and a badly rolled out leaderboard (a global top 10,000 for example) can actually demotivate the staff languishing in the lower echelons. A good player experience design takes into account social comparison theory – the idea that we are more motivated by being a big fish in a small pond than being a small fish in a very large one…
6. Visual Design
Once you’ve got your positioning and player experience design nailed down it’s time to provide the right copy and design that ensures your program resonates with its target audience. After all personal analytics is a tool for individuals, offering a relevant score, the design and copy should reflect their priorities.
7. Communications Planning
Hot on the heals of the design and positioning conversation are the actual planned communications around the program. This will start with launch announcements but can be expanded to include collective commentary (who are the movers and shakers this week – how are we doing as an organisation) and inter-player sharing (how I managed to increase my score 5 weeks in a row…). A good communications strategy can bring your personal analytics program to life by encouraging players whether veterans or newbies to mentor each other.
8. Data Management
At the back end of any analytics program is a chunk of big data. That data needs attentive management and the occasional sanity check to ensure that released scores are correct and still relevant. Nowhere is this more true than in the world of online media where data availability can change at a moment’s notice.
9. Data Sourcing
Often you’ll also need to source the data for your program from a different system. Systems integration can help you connect the dots and ensure your personal analytics tool includes key metrics from both internal and externally managed systems.
10. Success Tracking
Finally, you’ll need to practice what you preach. Tracking the success of your program means reporting back on your own key success metrics. In theory this should be easy since you’ll probably already have all the data you need – there may still be some work to do in converting that into convenient aggregate statistics though.
So clearly, there is more to a personal analytics project than perhaps meets the eye but with good planning you should find that your program delights its target customers and helps build the right positive habits and behaviours within your organisation.