Product Led Growth Research
Insights Derived From The Practices Of 40 SaaS Organizations
On the first part of this report, we had the opportunity to discuss fundamental KPIs characterizing current onboarding practices. The product-led turn organizations take, alter their internal and customer-facing procedures radically.
Although the transition is the one way forward all organizations have to take, the SaaS industry has still miles to walk until its practices are homogenized and stabilized. The following recommendations, sum up the learnings derived from this paper and can be used by product leaders, success practitioners, sales advocates, and SaaS founders to align their teams to a product-led agenda.
CONSIDER ONBOARDING PREVALENCE
Product Led practices redefine Onboarding. Instead of being a set of random actions causing friction, onboarding becomes a set of data-driven product engagement practices, based on behavioral notions and users’ proficiency. The exploitation of historic data, along with contextual guidance, constitute its main pillars.
Being able to be measured on every step of the customer journey, onboarding abandons the traditional sales model archetype ending its prevalence during activation. Organizations need to shift their mindset and track onboarding ROI during Activation, Retention, and Expansion.
EMPLOY A UNIFIED AGENDA
Silos abandonment, will be realized when internal teams deploy a unified agenda. Product Led practices and data analysis bring Product Management at the forefront and make its role critical on every step of the customer journey.
Product data insights, however, are not limited to Product Management activations. They need to be dispersed across Sales, Customer Success and rest customer-facing teams which need real-time insights about users’ behavior.
An internal feedback loop mechanism needs to be established and sustain alignment across and within departments.
REDEFINE ONBOARDING METRICS
Product-led onboarding is continuous, and its measurements should follow the same logic. Capitalization of POEs metrics (Breadth, Depth, Efficiency & Frequency of Use) constitute a foundation Product- Led organizations already use to estimate Onboarding ROI. Those metrics enable teams to establish in-app user segmentation criteria and derive insights via onboarding activations. Now organizations can measure which features yield more revenue, underperform, or not exploited at all.
Alignment of teams around the same onboarding KPIs, followed by business metrics, is paramount to business goals’ evaluations and internal harmonization.
In addition, the particularities following each strategy should always be considered:
A) HUMAN ASSISTED ONBOARDING
1) Sales and Customer Success interactions with buyers should focus on strategic decisions going forward. In application, onboarding should not prevail but complement them. To achieve that organizations need to invest in product engagement activations at scale that consider context of usage and end users’ characteristics.
2) Disposal of human resources should become proactive and not reactive to customers’ needs. Usage analysis and dispersion of its insights across and within teams is one way forward organizations can take to optimize acquisition and retention costs.
B) SELF SERVE ONBOARDING
1) Self Serve activations should double down on Team Onboarding from the very beginning. Internal teams should follow these notions to predict retention and evaluate internal buy-in.
2) Time to implement should be considered in conjunction with the learning curve period. The latter can be optimized by having product engagement activations laser-focused on users’ goals, type, and proficiency. Product data analysis and historic usage should indicate where and why product engagement practices should take place.
ESTABLISH USER BEHAVIOR METRICS
Emerging product-led growth metrics may seem to redefine customer satisfaction evaluations, but business & marketing metrics still prevail. Organizations should consider product activations effectiveness when striving to evaluate user behavior.
Only, product performance and customer feedback in conjunction with product data analysis can evaluate user experience. In addition, in-depth knowledge of users’ existing workflow helps to establish context behind their behavior.
REVISIT ONBOARDING TOOLSET
Organizations need to revisit their toolset and reassure; it enables them to capitalize on product data and customer feedback analysis. Feature or account/user-level measurements do not derive all necessary estimations regarding in-app behavior.
In addition, exploitation of engineering teams provide insights on past user behavior. Product teams need to acknowledge in real time the bottlenecks discouraging adoption. This is the only way to optimize onboarding flows accordingly and evaluate accounts’ status.
CAPITALIZE ON PRODUCT ENGAGEMENT EXPERIMENTATION
Heavy experimentation should follow onboarding activations, to balance context with users’ progressive route to excellence.
1) In regards to Human Assisted onboarding it will guarantee the internal buy-in from thousands of end users. At the same time, data capitalization, allow in-app education activations to be measured end to end.
2) Tailored experiences are also critical to Self Serve activations’ success, as they replicate Sales and Customer Success interactions. Iterating email activations, to bring users back in app is not enough. Experimentation levers should consider product engagement activations too.
These are, only indicative suggestions to consider. The micro view of any onboarding strategy may be the optimization of every customer touchpoint, but the big picture is far greater. It is interrelated with successful synchronization within and across departments upon product delivery. It abandons vanity metrics and focuses on the value products’ derive.
The investment in product data and the development of associated metrics is only the beginning. In a few years from now, data analysts will deliver multiple forecasts and growth scenarios having as sole denominator historic product data.
The SaaS industry may have disrupted the digital economy by capitalizing on a business model every organization will step on eventually, but in the end, it will introduce something far greater. A way to deliver a better service paramount to the monitoring of customer behavior.