It is no surprise that we talk a lot about software usage analytics, but I thought it would be interesting to take a look and see what industry analysts are saying, too.
Gartner first included software usage analytics in its Hype Cycles in 2017 and defines it as “the detailed tracking and analysis of users’ interactions within a software application. It is used by software providers and application developers to understand users’ behavior at various levels of aggregation — individual users, users within a customer account and users overall. It provides insights that are used to improve user experience, prioritize feature enhancement, measure user adoption, track compliance and provide real-time user help.”
Gartner predicts that by 2021, 75% of software providers will rely on insights from embedded software usage analytics to inform product management decisions and measure customer health (Predicts 2018: Technology Go-to-Market).
While Flexera is not an industry analyst, its recent research finds that “interest in usage data is strong and continues to grow” and that “91% of all respondents either already collect usage data or are planning to collect it in the next 24 months.”
Creating Customer Value
What is driving this interest in product usage and data analytics? IDC’s Mark Thomason noted that: “Customer lifetime value has become the crux of value generation in the software industry, so understanding customer consumption patterns is an imperative for traditional software and software-as-a-service (SaaS) providers alike. Besides monetization opportunities, the benefits of understanding customer usage can become a powerful source of competitive advantage” (Understanding Software Usage: Are Customers Finding Value in Your Application?).
This is supported by the strategic planning assumptions in “Use Gartner’s Customer Analytics Maturity Model to Create Better Customer Experiences:”
- By 2020, more than 40% of all data and analytics projects will relate to an aspect of customer experience.
- By 2022, more than 10% of customer engagement hub architectures will include real-time event streaming or streaming analytics.
In addition, Gartner recommends sharing customer data to reduce cost and improve customer convenience: “One of the fastest-growing categories of data within most organizations is detailed data about customer transactions, interactions and product usage (especially in an era of cloud-based services and the IoT). Data privacy legislation in many markets makes disclosing that this data is being held a legal obligation, generally with a right-to-access requirement as well. The twin drivers of regulatory obligation, and a cost-driven desire to enable customer self-service, make this initiative [to share data] a standard in many industries” (How Sharing Data Builds Stronger Customer Relationships).
Digital Transformation and Adoption of Cloud-Like Business Models
The “SaaSification of software” is also leading on-premise software vendors to leverage software usage analytics to drive decisions. Flexera noted from its research that “suppliers are often using a hybrid approach, using a mix of deployment models. Companies of all types are leveraging usage-based monetization models to some degree. SaaS-based companies lead the way, while on-premises companies remain exposed, when compared to the competition.”
In the recent webinar, “Your Product is Perfect, Now Change: Product Innovation Strategies for Desktop Software Vendors in a Cloud World,” IDC analysts Mark Thomason and Jeffrey Hojlo noted that:
- By 2023, 46% of Software revenue will be from SaaS business models
- 42.5% of software publishers offer both public cloud and on-premise software (hybrid) and that percentage is expected to grow
They broke the software market down by application type and revealed the growing importance of understanding product usage in markets where the revenue attributable to on-premise software is high, and there is significant opportunity to inform cloud strategy:
Data-Driven Product Management and Development
In the webinar mentioned above, IDC analyst Mark Thomason highlighted a formula to achieve actionable product insights by combining software usage data with segmented customer demographics data and entitlement data (version, tier, etc.). He also noted how usage analytics can yield monetization and business model innovation by:
Revenera’s Monetization Monitor
Our latest research reports explore trends around software monetization, usage analytics, piracy, and compliance.
- Creating data-driven editions/subscription tiers using feature usage
- Leveraging usage data to understand value and pricing for renewal contracts
- Driving usage driven upsell/cross-sell recommendations
- Benchmarking usage to understand when customers might churn
- Enabling feature level consumption pricing
Additionally, results from a recent Gartner survey of product managers at high-technology industries show that growth companies appear to believe that investing in advanced analytics software tools for product management is likely to facilitate identifying new opportunities, developing differentiated products, thinking outside the box and performing outside the norm (Reignite Growth Through Targeted Investments in Software Tools for Product Management).
Growth respondents were significantly more likely to invest in systems and tools than nongrowth respondents (73.9% vs. 55.3%). Gartner recommends investing in tools that “stimulate, capture and interpret the ‘voice of the market’” such as usage analytics.
Gartner includes software usage analytics and in-application messaging and polling among the types of in-application feedback product managers should use to improve product-market fit and drive product leadership (Use In-Application Feedback to Continuously Improve Product-Market Fit). It notes that these tools can be used to understand feature consumption, deliver quantitative and qualitative feedback, and monitor usage continuously or episodically, both for existing customers and prospects. Gartner recommends that product teams implementing in-application feedback tools identify “attention-worthy signals buried within the noise of feedback” to:
- Focus on feedback that can test the explicit and implicit feature-level assumptions regarding product-market fit contained in each iteration.
- Set expected thresholds for feature consumption and explore the data stream further if expectations are not met.
While this represents just some of the discussion of software usage analytics by industry analysts, it does underscore the growing role of data-driven decision making by product and management teams.
