Microsoft introduced Clippit—a character known infamously among its users as “Clippy”—in 1996, not long before the 100th birthday of the paper clip. This intelligent-user interface was the best-known iteration of Microsoft Office Assistant. Clippy hung in there until 2002 and finally vanished from the Office suite for good in 2007, but it remains immortalized in memes. Even Clippy’s designer admits to creating a character that “still annoys millions of people every day.”
Clippy’s failure suggests a misunderstanding of user needs. Instead of enhancing productivity, Clippy interrupted it, and gained a reputation as unhelpful, unwelcome, and, to many users, downright unpleasant.
Clippy had a worthy goal: to increase engagement with Microsoft Office users while they were using the product. So why did it fail so spectacularly?
Clippy didn’t have context. It didn’t fully understand user behavior and modify its interaction accordingly. For example, when you typed “dear,” Clippy wanted to help you write a letter. I just didn’t understand that you may not have wanted to write a letter in the first place.
Since then, data collection and analysis have made it possible to better understand end-user behavior and activity so that communication within a product can be optimized to drive and enhance user engagement. Compare Clippy with a more evolved form of data-driven in-application messaging, Amazon’s recommendation engine. Amazon’s algorithms pull together massive amounts of information on searches, purchases, and more. And because the information Amazon shares is often helpful, users continue to engage with Amazon and its recommendations. This results in more purchases, more data collection, and a greater ability for Amazon to further fine-tune its recommendations.
Today, software product managers can gain this insight and provide context through a powerful combination of software-usage analytics and in-application messaging. Successful user engagement is possible by driving dynamic messages that are contextually relevant and rooted in learnings from usage intelligence. By collecting data on feature and product usage, hardware and OS metrics, and more, usage analytics provide the insight needed to set the context for in-application messaging: who to communicate with, when to reach out, what to say, and how to listen and process information.
Improving User Experience and Product Adoption
Helping users solve problems and make their jobs easier is an enormous part of ensuring that they continue to use your products. In-application messaging, combined with usage analytics, can help you create specific sets of recommendations to guide and enhance user experiences. Context prevents the next Clippy.
Consider the hypothetical onboarding process for a new version of your software. Customers have upgraded, but usage analytics reveal that many aren’t leveraging the features your team envisioned as having the highest value. As a result, there’s a real possibility that loyal users will start to doubt the value of your upgrade.
When you can filter and correlate usage data by days since installation, time spent in the application, OS metrics, hardware architecture, and more, you gain insight into the onboarding challenges. By tracking how they are using (and not using) the application, you can understand why they have not accessed particular features or workflows. Armed with this information, you can drive deeper product engagement and satisfaction by leveraging in-app messaging to address why specific user segments aren’t using certain features.
For example, if analytics reveal that users of the latest release are using the export feature and then exiting the software, they may be having trouble using the reporting function (or they may not be aware that it exists). By leveraging this insight, your team can create deeply targeted messaging for this user segment that might include a relevant video to guide them through the process. It could be triggered when a user accesses the export feature X times in Y days and display an in-application message: “Need help using reporting? This quick video will show you how.” Increasing engagement at this stage can have ripple effects that include improved adoption and higher derived value from the software.
Improving and Accelerating Product Development
Usage analytics-driven, in-application messaging ensures that you understand the context and have the user’s attention when they are engaged with your product. As a result, it can complement and enhance your product development strategies.
With the constant demands for faster development cycles, beta testing has become central to accelerating product development and release. Usage analytics allow you to discover if users are struggling with a feature or workflow. This insight allows you to send contextually relevant messages at that exact point, creating a feedback loop to better understand positive and negative responses, and pinpoint behavior that led to the UX issues or broken flow. The loop is key to continue fine-tuning your product, responding in meaningful ways that address user expectations. With usage analytics solutions that feature in-app messaging, you can survey users in the moment to gain clarity on the validity of the developed features, the usefulness of those features as implemented, and any missing elements that were not delivered as expected.
Being Useful Versus Being Annoying
Integrating analytics with a real-time communication channel will help your product team make data-driven decisions based on customer needs and provide contextually relevant information to further engage those customers. Compare this to Clippy, which offered hard-coded responses based on anticipated use cases. If Clippy were driven by usage analytics, it would know that you don’t typically write letters and that typing the word “dear” should not trigger an offer to help you write a letter—such a message would be out of context.
Ultimately, the value of an integrated usage analytics and in-application messaging strategy extends beyond its abilities to ease onboarding, increase adoption, and lay the groundwork for upsell and cross-sell opportunities. It helps your team build products that resonate with your audience and increase loyalty from customers who view your team as a partner in advancing their business goals.
Keith Fenech is vice president of software analytics at Revulytics and was the co-founder and CEO of Trackerbird Software Analytics before the company was acquired by Revulytics in 2016. Prior to founding Trackerbird, Keith held senior product roles at GFI Software, where he was responsible for the product roadmap and revenue growth for various security products in the company’s portfolio. Keith also brings 10 years of IT consultancy experience in the SMB space. He has a master’s in computer science from the University of Malta, specializing in high-performance computing. Contact Keith at email@example.com.