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.
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.
High Trajectory Customers
Self Serve Customers
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
Breadth of use product onboarding calculations formula
Product Engagement Variable: Depth of Use
High Trajectory Customers
Self Serve Customers
User Segmentation Use Case: Gainsight PX
Depth of Use- Points to Consider
Depth of use product onboarding calculations formula
Product Engagement Variable: Efficiency of Use
The difficulty level to complete common tasks is a critical evaluation of the onboarding flow. In order to be measured accurately, product teams need to be aware of the total number of users per account who begin a task, versus those who complete it.
High Trajectory Customers
Self Serve Customers
Having the product replacing human assisted activations requires continuous iterations and heavy experimentation. Being completely autonomous and subject to a faster sales process Self Serve customers should be driven directly to initial value in conjunction with the learning curve period.
Internal teams need to know when they strive to shorten trial periods, that as much as speed to implement needs to come first, products’ complexity affects the learning curve period and the onboarding flow too. As intimidating factor the learning curve period can be, users need to familiarize themselves with the product by following their own pace. Internal teams should evaluate on how many parts the onboarding flow should be broken down until adoption is realized.
Multiple Value Points Delivery (First time activation) Use Case
Effectively Reducing Trial Period (First time activation) Use Case
When Yesware, the leading email software, decided to invest heavily in a product-led, experiment-driven onboarding model, one of the first goals was to build an infrastructure allowing rapid testing. In this vein, the internal teams decided to run an experiment that halved the free trial length from 28 days to 14.
The hypothesis was that while the conversion rates would remain steady (the product team believes that 14 days are sufficient for qualified users to experience the product), the product team would benefit significantly by being able to run tests twice as fast. Additionally, there was the expectation that shortening the trial would provide a sense of urgency and spur users to take action quickly.
Because of certain technical restrictions, the test was run longitudinally as opposed to as an A/B. Functionally this means that the product team changed the trial length on a given day and compared it with the new 14-day trial cohort to the preceding 28-day ones on a variety of metrics.
After a month of testing, the results were fantastic! Although, as expected, there was a slight increase in the percentage of users who uninstalled the product during the trial (roughly a 0.5% increase), the core hypotheses were seeming valid, as an uptick in the early engagement of the product was realized. That effect took place particularly with the advanced features, which are critical leading indicators for the solution’s power users. Something assumed to have happened due to the urgency created. Moreover, conversion rates did not only maintain but actually increased by roughly 18%.
Special note: After rapid experimentation, the product and engineering team have decided to extend the 14 day trial to 28 days in Gmail if users make specific actions, based around sticky features, in the getting started guide.
High-Touch vs. High-Tech Harmonization Use Case
Userlane, the popular onboarding S/W with main target audience enterprise customers, launched a while ago an alternate asynchronous onboarding in addition to its Human Assisted strategy. The “academy” as it is called internally exploits the vendor’s features to self serve end users all the way through without eliminating the required decision making between the two parties. Customer Success calls that learning process “getting to Basecamp” where the various teams build their first Userlane guide and get them published. In the end, the users are certified and know the percentage of tasks they can complete. On top of that, the buyers’ are fully aware of the end users progress at all times.
The launch of the “Inception project”, improved onboarding process delivery tremendously. The implementation of targeted in-app guides within the product allowed a thorough personalized, albeit automatic onboarding process that didn’t require the active participation of different units. Additionally, time to initial value was decreased and after launch, buyers could go through the set up completely autonomously.
Inception Project Desired Deliverables
Onboarding Completion (D1-2)
Daily Sessions Per User
Daily Sessions Per Account
Breadth On Account Level
Key Actions Per User (W1)
In App Engagement (W1)
Key Actions Per User (D1)
*Onboarding Costs were reduced by 63%*
Across the spectrum, Userlane managed to optimize product delivery without compromising onboarding execution. Increased adoption rates (+45%) confirm that this scalable approach yields the expected ROI while keeping end users engaged and buyers aware of end results.
The stickiness every product strives to achieve is reinforced by actions that capitalize on product educational activations, to reach desired outcomes. Userlane is a living proof that organizations requiring tailored implementation and deployment can successfully apply in-app guidance and combine scalability with the human-assisted support customer-facing teams provide.
Efficiency of Use- Points to Consider
Efficiency of use product onboarding calculations formula
Product Engagement Variable: Frequency of Use
Frequency of use is concerned with how frequently and for how long users engage with products’ features. Reminding users why a specific feature is there in the first place and how it may further optimize their workflow is also something reliant to onboarding activations.
High Trajectory Customers
In regards to high trajectory customers, where the stakes are higher, usage levels are being closely monitored mostly by Customer Success. Product-Led practices embrace Product Management involvement in the process too by holding it accountable to deliver quantitative measurements explaining levels of adoption or churn.
Both teams should create common benchmarks and be alerted whenever features’ usage drops. Respectively, targeted product activations should be released to restore engagement levels before churn prevails. Despite the fact that this practice is also leveraged for Self Serve customers, in this instance, it should be monitored when human assisted activations prompt users return to the product.
Self Serve Customers
Having Self Serve customers returning to the product autonomously is something reliant both to product and email activations. In this instance, email practices at scale replicate human assisted activations, so it resonates that customer-facing teams should adopt the right tools to monitor this correlation. Solutions like Gainisht PX mapping extensively those activities is an optimal choice to consider.
In this instance, internal teams should invest in customer feedback early on:
Frequency of Use- Points to Consider
Frequency of use product onboarding calculations formula
All things being equal, Breadth, Depth, Frequency, and Efficiency of use form an adoption loop where its implementation is viable when the maximum number of an account’s users exploit a product, use its features extensively and repeat those actions frequently.
Again, the decisive role initiating this circle of events is (team) activation. But for the loop to be consistent, on every stage of the customer journey, all four metrics should be considered. Depending on the onboarding strategy at play, the circle is being supplemented by additional business KPIs and parameters.
Product Onboarding Efficiency (POE) - Breakdown
Activation stage (Indicative)
The product team should be highly involved in the activation process, no matter the onboarding strategy at hand.
The sooner Sales’ decode the messages withheld into product data the better the conclusions drawn, in regards to the harmonization of human-product acquisition activations.
Depth of use has some variables to consider:
1) Whether or not during activation period users can realize initial and true value. This is most likely to occur on high-velocity customers where onboarding in many departments takes place post-purchase.
2) Users’ proficiency levels need to be defined early on. An advanced user may explore more key features during trial, but this is also subject to a product’s complexity.
3) On Self Serve onboarding, Retention initiation point varies. Thus Depth of Use may not be measured on Activation (during trial) but only post-purchase.
Retention stage (Indicative)
Customer Success & Product Management
Both departments are the rightful owners of accounts retention and customer journey optimization.
The retention stage defines which features prevail over the others. It is ideal at this point internal teams to define when accounts reach satisfactory levels of adoption (depth) and when they show indications of expansion.
Whenever frequency of use increases so do the indications of those accounts reaching long term retention.
Expansion stage (Indicative)
Accounts reaching expansion meet all four POEs standards. This the point where all variables following the adoption loop take full effect.
Frequency of Use
Despite the importance usage frequency has on all customer lifecycle stages, renewals and expansion teams should also consider:
1. Exploring usage-based pricing when the majority of accounts have surpassed nominal expected levels.
2. Innovating features that would help increase upsell and cross-sell opportunities within a customer’s contracted term.
What should you do next? Whether you are deeply invested in providing data-driven experiences or not, at the end of the day your users perceive the end result as your company’s main value. A bad experience is still an experience, following customers’ growth or just preventing it.
Transforming your product experiences toa data-driven force is not optional anymore. It is the only option if you want to reach and surpass the growth levels your organization has set.
Product-Led practices may constitute a challenge, as they dictate alignment across all organization practices, but at the same time, they can leverage and map product experiences to the very end. Take a closer look at the examples provided above and try to go out of the conventional ways you use so far. Experiment, iterate, repeat until you reached the point where you can proactively predict the anomalies following the customer journey.
Try to remember that while there is no panacea on evaluating customers’ behavior, capitalization solely on business & marketing metrics leads internal teams to miscalculations that compromise product delivery. Only, product performance and customer feedback in conjunction with product data analysis can give accurate estimates on product experiences.
We are convinced that Product-Led practices are the key to achieving company-wide alignment and product success and this is not possible if specific benchmarks driven by users’ actions are not in place.
Revisit your product delivery, by putting to work some of the practices discussed above when striving to evaluate user behavior and feel free to reach out if you want to continue the discussion.
We are always keen on taking your feedback, brainstorming and helping you deliver Product-Led experiences that can lead to measurable growth outcomes.