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Technology & SaaS
SaaS user activation is where retention is won or lost. We help technology companies identify the behaviors that predict lasting value, design onboarding and adoption around them and build teams that run disciplined experiments.
The people side of technology & saas.
Software companies collect more behavioral data than almost any other industry, yet many still struggle to explain why some accounts thrive and others churn. Product analytics show clicks and sessions, but SaaS user activation depends on whether users reach a meaningful outcome early enough to form a habit. Teams often define activation by convenience, such as completing setup, rather than by the behaviors that actually predict retention and expansion. The result is onboarding optimized for the wrong moment.
Adoption is also an organizational behavior. In business software, an individual champion may love a product while colleagues ignore it, and renewal depends on whether usage spreads across teams. Administrators, end users and economic buyers have different motivations and constraints. Inside the company, product, design, customer success, sales and marketing each hold part of the adoption story and are measured on different numbers, which makes it difficult to agree on what to fix first.
Current forces sharpen these questions. AI features are being added rapidly, but users adopt them only when they trust the output and understand where it fits their work. Buyers scrutinize spend and expect clear value. Product-led and sales-led motions blend, raising questions about handoffs. Growing companies also face leadership challenges as teams scale. Behavioral science offers a disciplined way to connect product data, user research and experimentation around the behaviors that drive durable growth.
What leaders are up against.
Activation defined by setup, not value
Many teams count a user as activated after completing onboarding steps. Those steps may have little to do with the value that keeps customers. Without validating activation against retention, companies optimize flows that look successful while users leave before experiencing what the product is actually good at.
Features shipped but not adopted
Roadmaps deliver new features, including AI capabilities, that only a small share of users discover or use repeatedly. Announcements and tooltips rarely change established workflows. Adoption depends on relevance, timing and trust, and teams often lack research into how users decide whether a new feature is worth the effort.
Account adoption beyond the champion
In business software, a single enthusiastic user does not guarantee renewal. Value must spread to teams and become embedded in workflows. Customer success teams may see usage concentrated in a few people without knowing which behaviors signal healthy account adoption or what would help it expand.
Experiments without behavioral hypotheses
Many SaaS companies run A/B tests, but tests often vary button colors or copy without a clear theory of behavior. Results are inconclusive or fail to generalize. Experimentation becomes more valuable when hypotheses are grounded in behavioral models and connected to predictive KPIs that matter for retention.
Scaling teams and decision quality
Rapid growth adds people, layers and handoffs faster than leadership practices mature. Product and go-to-market teams may disagree about priorities, and decisions slow or become political. Leaders who were effective at small scale must learn new ways to align teams, delegate and decide under pressure.
Where we focus.
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Activation and conversion analytics
We analyze product event data to find the early behaviors that predict retention and expansion, then define activation around them. Conversion behavior analytics identifies where users stall on the path to those behaviors, and behavioral segmentation shows which user types need different paths. The outcome is a validated activation metric and a prioritized list of hypotheses.
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Onboarding and activation design
We redesign onboarding around the first meaningful outcome rather than the full setup checklist. Approaches include progressive setup, templates that deliver early value and prompts timed to real work. Usability testing with representative users catches confusion early, and experiments confirm which changes improve activation and early retention.
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Feature and AI adoption strategy
We research how users decide whether to adopt new capabilities, including AI features where trust and transparency matter. Adoption strategies focus on relevance, ease of first use and in-context prompts. We also help teams set responsible design standards so AI features support user judgment rather than replacing it without explanation.
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Experimentation program design
We help product and growth teams build experimentation programs grounded in behavioral hypotheses, sound statistical design and predictive KPIs. That includes prioritization frameworks, test documentation and review routines that turn individual tests into cumulative learning about how customers behave and what they value.
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Leadership for scaling teams
We coach founders and executives through the leadership transitions that come with growth, from delegating decisions to aligning product and go-to-market teams. Team coaching builds the trust and decision routines that let scaling organizations move quickly without losing clarity about priorities.
The social good at stake.
Software increasingly shapes how people work, learn, communicate and access services. When technology is hard to adopt, it wastes time, frustrates users and widens gaps between those who can navigate complexity and those who cannot. Behavioral science advances the social good in technology by designing products around real human attention and ability, encouraging persuasive design that serves users' own goals and helping teams adopt AI responsibly. Companies that measure activation by genuine value create products people choose to keep using, which is better for users and for sustainable growth.
Frequently asked questions.
How do you define SaaS user activation?
We define activation by the early behaviors that statistically predict retention and expansion for your product, not by setup completion alone. That requires analyzing historical cohorts, testing candidate definitions and confirming them with user research. A good activation metric is specific, observable early and clearly connected to the value customers pay for.
How is this different from what our product analytics team does?
We complement internal analytics rather than replace it. Our contribution is a behavioral science lens: theories about why users act as they do, qualitative research to explain patterns, measurement design and experiments built around clear hypotheses. We often work alongside product analysts to strengthen the link between data, design and decisions.
Can you help with AI feature adoption?
Yes. AI features raise distinctive adoption questions around trust, explainability and fit with existing workflows. We research how users evaluate AI output, design onboarding and prompts that build appropriate trust and help teams define standards for responsible use. Adoption is measured by sustained, valuable use rather than first trials or one-time experiments.
Relevant services.
Conversion Behavior Analytics
Find the specific moments where prospects commit or drift away across digital, phone and in-person channels, and design tested changes that improve conversion.
ExploreOnboarding & Activation Design
Find the early activation moments that predict long-term retention, then redesign the first experience so new customers, members and users reach value quickly.
ExploreTechnology Adoption Strategy
Help customers, clinicians and employees actually use the tools you invest in, using behavioral science, adoption signals and targeted change design.
ExploreExperimentation & A/B Testing
Design experiments and pilots that produce clear, trustworthy answers, with proper power, guardrail metrics and behavioral hypotheses, in digital and offline settings.
ExploreBehavioral Segmentation
Group customers, users, members and patients by how they actually behave, so every journey, message and service moment fits the people it is meant to reach.
ExploreResponsible AI Adoption
Help people adopt AI tools with sound judgment and trust, while weighing the ethical, cultural and human consequences of how those systems are used.
ExploreFurther reading.
The Psychology of Technology Adoption
The psychological forces that determine whether people adopt or resist new technology, and how leaders can design rollouts that turn installation into lasting, meaningful use.
Read
Habit Formation for Products and Organizations
How habits form, why they matter for products and organizations, and how to design cues, routines and contexts that help valuable behaviors persist over time.
ReadLet’s talk about the people problem in front of you.
Every engagement begins with a conversation. Tell us what you are facing and we will tell you, candidly, how we would approach it.
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