Data-First Strategies for Improving Customer Adoption
For software vendors, fast and easy user adoption experiences are key to generating the kind of immediate and long-lasting business gains that keep customers using their product.
According to Forbes, by 2018 the volume of data generated over the previous 2 years is more than in the entire human history before that! With this mind boggling volume of available data from many different sources, software vendors now have an unprecedented opportunity to gain line of sight into how customers are navigating their product adoption experience - and how to improve it.
And yet, despite this proliferation of data, it is estimated that practically all collected data (99.5%) never even gets used or analyzed. That’s an enormous well of customer information gold going to waste.
So what can software vendors do to actually use this abundance of data and design experiences which accelerate user adoption and consequent gains?
Here are some selected emerging best practices:
Most companies are still working in functional silos. Consequently the data available to teams is also in silos, meaning companies lack a holistic view of customer insight. With increased volumes of non-exploited data, this siloed way of working further amplifies the challenge of making sense of all the data.
As product adoption is a fundamental milestone in generating business gains, vendors are investing in adoption data strategies to gain more pertinent data insights. This includes:
- Designing more streamlined product adoption workflows, e.g. product funneling according to customer persona needs.
- Clearly defining what characterises user adoption and where and when this data is collected.
- Educating customers on how to measure adoption and offering access to key adoption data such as user engagement dashboards
- Customer success teams are investing in data specialist roles, such as Customer Success Operations and CS Data Analysts who help design, monitor and refine adoption data.
Vendors are creating adoption dashboards to monitor usage patterns and KPIs such as time spent in the product, monthly active users (MAU) or license utilization rate. While these were often only accessible to vendors, they are now also being made available for customers. Customer administrators and key stakeholders have become autonomous in measuring the adoption patterns of their teams. Instead of relying on vendor reports to be periodically sent to them, customers can now monitor usage and adoption data in real time and create immediate corrective action plans. For many vendors, the monitoring of usage data points is designed natively in the code. For others, usage plugin tools are used to build such adoption dashboards.
The more insights that are gained in real time around usage, the more that users can be guided in real time on how to optimize their experience. Automated workflows are used in-app to generate user guidelines based on behavioural triggers. For example, when new users are onboarding, content workflows can be configured to encourage or correct certain usage behaviors. For example, if a user adopts a certain feature X times within Y period, an in-app communication can be triggered automatically, recommending using feature Z in conjunction with that feature. This ensures users are given the most relevant and helpful guidance based on their own personal experience.
Big data has enabled the “datafication” of information. This means that data is enabling the translation of experiences into value. For example, in years gone by we would simply go for a walk! Now, we still take walks but in addition, we also know how many steps we took (adoption) and how many calories we burned (performance). Indeed, several industries are using datafication to understand their customer’s usage and adoption patterns to predict their likelihood of performing, achieving gains and continuing their subscription or churning. With enriched data insights on characteristics of customers that have previously stayed or churned, predictions can be made on current customers more likely to be successful and reach their objectives or those likely to churn.
The combined forces of big data and AI are unveiling new insights into more subtle aspects of adoption, notably around language and the use of Natural Language Processing (NLP). NLP interprets and analyzes customers’ verbal statements, providing smart answers without human intervention. Some typical use case examples of NLP in product adoption are: customer feedback analysis, routing support tickets, customer satisfaction, search bars in knowledge bases and chatbots. Indeed, Gartner predicts that by 2022, 70% of all customer interactions will involve machine learning, chatbots, and mobile messaging.
AI is also being used to analyze non-verbal communication, particularly as customer engagement is increasingly virtual in a post-COVID-19 landscape. During product adoption sessions, (e.g., beta testing, training sessions and onboarding activities, etc..), body and facial behaviors can be analyzed to decode nonverbal sentiment and reactions. This allows vendors to detect signs such as frustration, non-satisfaction, indifference, positive sentiment and real engagement.
As vendors gather and analyze more pertinent data insights about their customers, they can enhance personalized adoption and consumption experiences. This started in B2C where Netflix, Spotify, Amazon, and Starbucks have been leading the way. Starbucks has grown exponentially thanks to its smart data analytics and personalization program where one third of revenue now comes via online purchases. The B2B world is also increasingly adopting similar personalized approaches where persona-based customer journeys allow the collect and analytics of use case scenarios per user persona. Instead of a one-size-fits-all approach to B2B customers, more customised engagement models are designed and continually refined based on data insights from persona adoption needs.
Within our current data surge, the increased accessibility to adoption usage patterns will provide unforeseen opportunities for vendors to gain insights to enhance personalized adoption experiences. These will in turn create competitive advantages for vendors that embrace such data-first strategies. The key to mastering this will lie in vendor know-how around collecting, analyzing, and actioning pertinent adoption data within the data haystack. In the words of Chip and Dan Heath:
“Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.”
Request an invite to join us at the next FLOURISH Executive Roundtable on June 2nd where we will be discussing data first strategies for improving customer adoption:
Written by Sue Nabeth Moore, Co-founder of SuccessChain
