Evaluating Product-Led Experience...Via the Lens of Data-Driven Guidance
Despina Exadaktylou (She/Her)
Building ΑΙ communities at Product-Led Hub - The #1 Hub for AI-Driven Tech & Product Leaders
This article is a short abstract of a comprehensive guide, first published on ReinventGrowth.Co site and is an attempt to analyze methodologies introduced on “The State of Product-Led Experience” report. A thorough report based on the findings of the most comprehensive Product Led research to date. Authorship: Despina Exadaktylou CEO & Founder at ReinventGrowth the Product Experience Agency.
Introduction
"You've got to start with the customer experience and work back to technology not the other way around" Steve Jobs
Stop for a minute and think. How many digital experiences do you encounter daily? At work, on your mobile, when you read the daily news? Our life is quite literally full of them. Daily millions of interactions are taking place by the second. Whether you are a SaaS owner, Product leader or Success practitioner you feel every minute how critical it is your product to deliver stellar product experiences. If you are a user, you experience first hand the frustration a broken experience carries and the satisfaction product delights deliver.
With Product-Led practices on the rise, competitive businesses need to fall the weight of responsibility on internal teams shoulders. Silos abandonment make very clear that customer experience does not have a single owner but it is compiled by the sum of touchpoints that originate and end up to the product itself.
Sales practitioners need to close deals reflecting on organizations’ vision. Product leaders need to deliver calculated results. Customer Success needs to act proactively, focus on customers’ growth and educate at scale.
We may think that we are in a transitioning era, but the truth is Product-Led practices have already sow their seeds. Their magnitude spreads like wildfire and their profound influence pass a very clear message: SaaS organizations need to either evolve by developing a customer-centric approach, empowered by the product itself or become obsolete.
In this guide, you will learn firsthand how data-driven engagements can be assessed, yield better results, and defined via the lens of Product-Led onboarding practices. We encourage you to share it with your internal teams and put to work those suggestions by always considering the particularities your products’ have.
The Road so Far..
The entire SaaS industry has been sold a false dichotomy, namely that a Sales strategy can either be delivered self serve or human-assisted – there is no in-between, or so we’ve been told. The optimization of product engagement practices is a discussion that is not going away anytime soon. It seems that human nature is still either too afraid to accept that products’ superpowers can fill the customer experience gap or does not really know how to handle them.
On a self-serve onboarding, “the machines” prevail among the various product experiences, while on human-assisted they come secondary to customer-facing teams activations. While in the latter instance, the replacement of product engagements with human-assisted activations partly resonates, since multiple stakeholders are involved, Product Led practices flip the script and project the product as the main growth lever.
It is obvious that the realm of stellar customer experience is not solely defined, anymore, by the buyer-vendor relationship dynamic. Serious Investment in targeted, data-driven product experiences are here to successfully bridge the experience gap humans leave behind. A gap that has nothing to do with the lack of exceptional strategic skills. On the contrary, it relates to the undeniable fact that meaningful interactions are driven by the product features’ in conjunction with product guidance that leverages context of usage.
On the flip side, organizations in favor of a self serve approach tend to throw messages at random and force users to follow a predisposed route. A fact that discourages activation rates and makes accounts susceptible to churn. Organizations ingrained into Product-Led practices, on the other hand, already allow product data to dictate the “where” and “why” product engagements should take place.
Make no mistake, this investment does not indicate displacing tooltips or in-app guides just for the sake of experimentation per se. Any iteration should follow a specific logic and executed when the onboarding team is able to justify that change. In the opposite case scenario, displacement of users’ attention will lead to more confusion and friction, while keeping at the same conversions and retention rates low.
The third leg of the tripod, Product Management’s “agile” DNA followed by ongoing feature releases, impracts radically the onboarding experience too. The emerging role of product leaders enables product guidance to introduce changes on time, flawlessly by providing the right context. Product Management needs to measure those interactions daily to reassure their investment yield the necessary results.
Currently, each department holds a different set of business metrics accountable for the productivity and input to the customer journey. Following this logic, product metrics need to be established and add value to existing KPIs by measuring activation, retention, and engagement levels.
Product-Led Experience
Unlike conventional onboarding practices, Product-Led Onboarding is driven by product data and can be evaluated on all the stages of the customer journey.
The ongoing feature releases discourage the iconic sales funnel taxonomy. On every release, the onboarding process is reactivated to deliver initial value, lead to upgrades, and further account expansion. A process that abandons the traditional sales model archetype, ending onboarding prevalence during activation.
The sales funnel has evolved into a circle where onboarding stands in its epicenter waiting for the next feature release to be triggered again. Furthermore, capitalization on product data enables onboarding owners to derive insights on every move a user makes in-app. Those learnings can harmonize, in the long term, the high touch & high tech interactions and make onboarding a tailored process able to be measured end-to-end.
It won’t be long now until new terms will come to describe the intimacy levels of the User-Product relationship. The first associated term so far is the Product-Qualified lead (PQL), referring to prospects that signed up and demonstrated buying intent based on product interest, usage, and behavioral data.
The PQL term is limited to the point where a paid conversion is made, having as main benchmarks usage and early adoption.
For prospects’ behavior to be accurately evaluated organizations should consider Product OQLs? ( Product Onboarding Qualified Leads) in their day-to-day assessments. Product OQLs? rely on POEs metrics to evaluate prospects’ usage and in product behavior. Despite the fact that there is no absolute as to which metric should prevail, balance among all those four measurements indicates that a prospect gets initial value.
Product Onboarding Efficiency (POE)
Product Onboarding Efficiency (POE) is an onboarding evaluation framework relying on four product engagement variables Breadth, Depth, Efficiency & Frequency of use to accurately monitor and guide Product-Led onboarding (PLO) efficacy throughout the customer lifecycle. In an attempt to better explain those elements’ importance we will thoroughly analyze their characteristics and associations following them.
Product Engagement Variable: Breadth of Use
An alternate form of (team) activation and retention constituent, breadth helps product managers realize the extent a product is being used on an account level. As a product metric, it monitors account health and helps product managers & data owners proactively manage churn.
a) High Trajectory Customers
For Mid Market and Enterprise customer segments, internal buy-in from end users is discouraging onboarding deployment. Heterogeneity on proficiency levels and complicated workflows decrease end users’ willingness to adopt a new solution. Focus on Team Activation (and by extension Team Onboarding), historical usage and use case particularities, is an optimal way forward, internal teams can take to overcome this barrier.
b) Self Serve Customers
1. Having Self Serve customers inviting team members during trial embrace activation rates. In the absence of Sales and Customer Success guidance, in-app flows can double down on inviting team members and emphasize on how their involvement increases perceived value.
2. Product teams should define which roles may cause various drop-offs on their first-week activation cohorts and iterate flows accordingly.
3. Training procedures need an equally concerted focus on each user role separately versus team training.
Team Activation Use Case: Hubspot
One of the key activation metrics predicting usage and retention for Hubspot, the leading automation suite, is how successfully the solution onboards teams within an account. Product teams monitor weekly team activities within accounts and whether or not the solution is driving value. We should mention at this point that team activation associates with Hubspot’s long term success and substitutes its north star metric as well.
Breadth of Use- Points to consider
1. Breadth of use importance is not limited to the activation stage. Every time a new release is launched product teams should monitor breadth in conjunction with the degree end users’ workflows are affected.
2. How many users log in when onboarding is carried out and how many after it ends?
3. How many users are activated when a new feature release is launched. (Assuming that the release is targeted to users’ finding value in this feature)?
4. How is depth defined during trial and how post-purchase? (Varying parameter per product/strategy/pricing plan)
5. What characteristics follow users sustaining engagement levels (retention) and those who abandon the app?
Breadth of use product onboarding calculations formula
Product Engagement Variable: Depth of Use
High Trajectory Customers
Points of consideration here are end users’ growth within the product itself. Daily usage should be monitored and reported on an account and individual level. Internal teams (Customer Success, Customer Support & Product Management) should focus on setting milestones per each role separately in conjunction with users’ proficiency evolution over a 12-month time span.
Self Serve Customers
1. Since Small Medium Businesses have smaller teams, Depth should be measured in conjunction with Breadth to accurately evaluate engagement levels.
2. Since Small Medium Businesses have smaller teams, Depth should be measured in conjunction with Breadth to accurately evaluate engagement levels.
User Segmentation Use Case: Gainsight PX
First Time Activation:Gainsight PX, the leading product experience solution, segment users’ behavior, and usage on first-time activation by having milestones track if PQLs complete setup by using key features during trial. When prospects use features more than the freemium version allows them to, internal teams measure conversion rates and revenue generated from the trial source.
New Product Release:On new product release, users’ behavior is segmented by measuring success in terms of adoption. Specifically, the product team releases targeted in-app guides to users based on historical product usage. The Query Builder feature, one of the key features, is presented to users who have shown interest in other analytics areas the past 30 days. If the released feature is a paid only module, the revenue is measured by attaching a rate on it.
Depth of Use- Points to Consider
1. Which levels of adoption (Depth of Use) correspond to which user role?
2. Does adoption increase due time? (aka are users growing within the service?)
3. Are internal teams able to reflect usage levels on Net Revenue and/or Net churn?
4. How is depth defined during trial and how post-purchase? (Varying parameter per product/strategy/pricing plan)
5. Which adoption levels indicate signs of accounts’ expansion?
Depth of use product onboarding calculations formula
