Data Traps: Why Users Don't Trust Apps and How This Distorts Data

Why Users Don't Trust Apps

Most drivers know the locations of local speed traps. It seems obvious why the data that radar guns collect can’t be trusted: drivers slow down to avoid getting a ticket. In other words, they change their behavior to avoid the downside of being tracked. In the same way, users see enterprise apps as data traps. Like drivers, they change their behavior when reporting online. The result: highly distorted data that, at best, are ignored or, at worst, guide decisions to poor results.

Increasing Trust and Return-On-Use

If radar guns distort data, then their counterparts, “Your Speed” signs, may provide clues to solving the problem. Using the same tracking technologies, signs that show drivers their actual speeds encourage behavior change with no downside. For enterprise systems, this means creating an app that helps users see how they are performing free from the downside. To meet this need, OnCorps built a platform that lets organizations configure real-time decision diagnostics for their employees and customers. Once configured, users see real-time benchmarks of their decisions compared to peers and correlated to outcomes. The app can protect individual answers from being seen by any administrator or executive. Each time the app is used, decisions are stored in a decision diary similar to a Netflix record. With this diary, users can track their decisions, see the relationship of decisions to outcomes, be nudged when performance changes, and be matched with peers. Our early pilots have shown this has significantly boosted user engagement – frequently four- and fivefold.

Case Study Involving CTOs

For example, we built a digital innovation benchmark for the CTO of a major services firm. The tool let executives anonymously compare, in real-time, their digital priorities. He sent invites through our app to 60 global CIOs and CTOs and received a 50 percent response rate within 8 hours and a 98 percent response rate overall. When their meeting was over, the firm sent a standard meeting survey using a different tool and received less than a 10 percent response rate. This underscores the difference between providing users a high return-on-use experience versus simply asking for data.

Odds Tables for Sales

To highlight the issue of data distortion, OnCorps is helping a global technology firm improve sales and margins by offering sales people real-time mobile pricing apps. One feature of the app is an estimator of the odds of winning deals. To offer this feature, we linked our app with their CRM (customer relationship management) system to create odds tables. We were amazed to find their overall win rate was 90%. Why so high? The firm was losing market share and had a history of swift terminations for poor performance. Sales people may have been withholding bad information to avoid being fired. The illusion of success may prevent the organization from taking swifter action. By contrast, we created odds tables for a rapidly growing software company gaining market share. Their overall win rate was 35%. With no looming threat of the axe, sales people were apparently freer to share data. In both scenarios, OnCorps has created mobile diagnostic apps that will collect fresh data with each use. To remain synchronized, we are integrating the apps to and SAP. With these apps, users can confidently diagnose pricing and win/loss odds while building a confidential decision diary to guide future decisions.

Changing Decisions

Our mission is to change behavior in a way that improves outcomes. To do this, we first change what was once a reporting task to a visual self-diagnostic. Then, we make the answers confidential. Finally, we use a combination of behavioral economics, machine learning and sabermetrics to provide deeper insights to each decision maker. We still have a lot to learn, but we are hopeful that by using the app, organizations can rebuild trust, capture more accurate data, and show meaningful results. Data in itself is worthless without behavior change. Providing trusted, personalized decision analytics is one key way to make that change happen.