Did you make it to the end of the blog title or stop after “short attention spans?” You’ve likely heard that highly circulated statistic that the average attention span of a human – eight seconds – is now shorter than that of a goldfish. And you probably wondered the same thing I did when hearing this statistic: what exactly were the people being asked to pay attention to? That matters, doesn’t it? Were they fully engaged with the task at hand, or were there external distractions? For instance, you wouldn’t ask someone to read the first chapter of Neil DeGrasse Tyson’s “Astrophysics for People in a Hurry,” while “Star Wars: The Force Awakens” enveloped them in surround sound and expect their recall to be good. Contextual relevance is king when it comes to presenting people with messages you want to resonate and drive them to respond.
In debunking whether goldfish truly have better attention spans than humans, the psychology professionals interviewed for this wonderful BBC article said that attention really isn’t something that can be objectively measured. It depends heavily on context – on the individual and what that individual is being asked to pay attention to. For our purposes in product management, I’d like to focus on the importance of presenting contextually relevant information to users of our software applications.
What does it mean to be contextually relevant in the context of product management? It’s delivering the right message to the right user at the right time in the user’s journey. To be most impactful, the message should be delivered when the user is engaged with the application itself. When it comes to seeking the attention of our users to inform product development, the goldfish example reminds us that we must present information within a specific context to maximize its validity, meaningfulness, and utility.
Keep that in mind when you send an email with a survey for a user to complete. Are they at home, distracted, and ticking away boxes without a lot of thought? How confident are you in the reliability and accuracy of those responses? That same logic can even be applied to personal outreach to users.
Overreliance on a handful of “friendly” users to inform development can lead to selection bias and obscure the needs of the majority of users. The channels we use to reach out to users for information are crucial to weighing the relevance of the information we receive from them. For example, after the release of a new piece of functionality, you reach out to super users (perhaps the same ones you looked to during development and beta tests) and ask them how things are going. They report back that things are good, yet a marketing campaign is failing to convert the larger user base or gain traction with net new accounts. Your team is scratching their heads, sales is reeling, and marketing is left to develop strategies in the dark to boost adoption.
Now consider that scenario where context is available. By leveraging anonymous usage data, you’ve identified attributes of users who are light in their use of the new functionality. Using this anonymous data to segment your users, your team can reach out in a more personalized manner leveraging in-app messaging. This enables you to more deeply uncover the “why,” and create data-informed strategies to drive adoption, conversions, and long-term value for your customers. Data-driven in-app messaging can also be used to push relevant educational content that guides the user experience – deployed at the precise time in the workflow that the unused feature would deliver the highest value. These in-the-moment prompts show users the usefulness of your software, building adoption and satisfaction.
You can learn more about how to leverage an integrated in-app messaging and usage analytics strategy in the third installment of our “Building Better Applications with Software Analytics” series. This short, highly readable ebook, “A Product Manager’s Guide to In-App Messaging to Engage, Convert and Delight Users,” will show you how this integrated strategy can increase adoption of key features, conversion rates, and long-term customer value. You will also learn how this strategy extends beyond product management and engineering to deliver actionable data throughout your organization.