When Product Engagement Stalls: Who Should You Hire Before Redesigning?

Last updated October 19, 2026

If users can use your product but do not consistently act on it, hire a behavioral product design consultancy that combines behavioral science, UX research, product design and experimentation in one team. At The Decision Lab, we diagnose the behavioral barrier holding engagement back, then build the intervention and test whether it improves engagement.

We built the behavioral engine behind the recommender of a digital wellness platform serving 25 million members, and our more than 60 published case studies include product work for national health platforms and a global consumer electronics company.

Before hiring anyone to redesign a product, find out which of five behavioral causes is holding engagement back, because each one calls for a different first hire.

In our behavioral product design work, stalled engagement almost always traces back to one or more of these five causes. Each leaves a different pattern in the data, so the diagnosis can usually start with numbers you already collect.

1. Customers do not understand the value. They can see what the product does, but not what it will change for them.
2. The first action takes too much effort. The cost of starting arrives today, while the benefit arrives later, if at all.
3. The product asks at the wrong moment. Prompts and reminders reach people when they cannot or will not act.
4. Rewards reinforce shallow activity. Points and streaks grow the activity metric without growing the behavior that keeps people coming back.
5. Users do not trust the recommendation or automation. They view what the product suggests, then override it or ignore it.

The right first hire depends on what you already know about the drop-off. Work through these situations in order and stop at the first one that matches yours.

1. You do not know where or why users drop off: hire UX research first, alongside product analytics.
2. Users cannot complete the task: hire a product design or UX team.
3. Users can use the product but do not follow through: hire behavioral scientists to diagnose which of the five causes is at work.
4. More than one barrier applies, or the fix changes what the product recommends: hire an integrated behavioral product team.
5. You have viable fixes and enough users to compare them: add experimentation and analytics.
6. You have a fix but too few users for a fair comparison: validate it in user tests or simulation before building it.

In our experience, most stalled products have more than one of these barriers at work, so the hire that pays off most often is a team that can diagnose the behavior and then design and test the fix without handing off between firms.

This cause shows up as wide, shallow drop-off. People sign up and look around, and then usage thins out across the whole product rather than at one step. When you ask them about it, they describe the product by its features and struggle to say what it changed for them.

Value is a judgment people make about their own situation, so they look for signs that the product was built for someone like them. We saw this in work with a global rideshare platform on driver safety communication. Through 31 interviews with company executives and surveys of more than 1,350 drivers and riders, we found that whether drivers engaged with safety guidance depended partly on whether they believed it served them or the company. Drivers and the platform defined safety differently, so we built a framework that aligned the two definitions before any message was rewritten.

To confirm this cause, ask recent sign-ups to explain in their own words what the product will do for them, and compare the answers of people who stayed with those who left. UX research is the right first hire when you cannot yet describe the gap. Behavioral science comes in when you know the gap and need to decide how value is framed in the product and when it is shown.

Effort problems leave a sharp edge in the funnel: most of the loss happens at one step, such as linking an account or setting a first goal, and people who get past it tend to stay. The effort is often mental rather than physical. A choice the user does not feel equipped to make costs more than an extra tap.

Telling people about the benefit does not remove the cost. In a randomized experiment run with H&R Block, Eric Bettinger, Bridget Terry Long, Philip Oreopoulos and Lisa Sanbonmatsu gave one group of low- and moderate-income families personalized estimates of the financial aid they qualified for. That information alone had no significant effect on whether they submitted the federal aid application, compared with a control group. Families who also got help filling in the form were substantially more likely to submit it and to enroll in college the following fall.

We design for this cost directly. In the behavioral engine we built for a digital wellness platform, every action the recommender might suggest is scored for how much effort it would take a specific member to start, so the platform offers each person a first step they are likely to take. For the MyHeart Counts Canada fitness app, our user research found that guilt over missed goals pushed people to avoid and then uninstall the app. We recommended asking users to report their current activity just before setting a goal, so their first goal started close to what they already did.

To confirm this cause, find the step where most users stop and remove or pre-fill it for a test group. Product design fixes steps that are mechanically hard. Behavioral science is the better fit when the effort lies in deciding or committing.

Timing problems show up as prompts people open but do not act on, and as opt-outs that follow a burst of notifications. People often do the behavior later, on their own schedule, which tells you the motivation exists and the request missed it.

Motivation follows the calendar more closely than most products assume. Across three field studies, Hengchen Dai, Katherine Milkman and Jason Riis found that people pursued goals more often right after temporal landmarks such as a new week or a birthday. Visits to a university gym rose at the start of each week and rose further at the start of each semester. A product that sends its biggest ask on an ordinary Thursday is competing with that pattern instead of using it.

The moment that decides engagement can also come long before the drop-off. For American Financial Solutions, a nonprofit debt management program, we modelled more than 100 characteristics per client and traced dropout risk to elements of the first onboarding call, months before clients actually quit. The barriers we identified explained about 4 in 5 dropouts, and the program's dropout rate fell by half once the redesigned call scripts and follow-up messages were in place.

Timing is cheap to test, so experimentation should come in early here. Behavioral science decides which moments are worth testing, and product analytics shows when people are already most active.

This cause hides behind good numbers. Daily opens and streak counts rise while the outcome the product exists for, such as savings built or sessions completed, stays flat. When a promotion ends or a streak breaks, activity falls below where it started.

Rewards change what people think they are doing an activity for. A meta-analysis of 128 experiments by Edward Deci, Richard Koestner and Richard Ryan found that tangible rewards given simply for taking part in or completing an activity reduced how often people chose to keep doing it on their own afterward. Positive feedback had the opposite effect and increased it.

The fix is usually to reward the behavior that matters, in proportion to its value and difficulty. In the wellness platform's behavioral engine, the points an action earns come from multiplying its clinical weight by how hard it is for that member, adjusted for the sponsor's priority. A hard, clinically important action earns more than an easy tap, so the reward system points members toward the behaviors that change their health.

To confirm this cause, compare people's activity before and after a reward pause, and check whether rewarded actions predict the outcome you care about. Behavioral science and data science together are the right team: one designs the incentive logic, the other checks that rewarded activity tracks real value.

Trust problems look like recommendations that are viewed but not followed, and usage that drops sharply after one visible mistake. They matter most in AI products, where the product's value depends on people acting on its output.

Use and trust can move in opposite directions. In Stack Overflow's 2025 Developer Survey of about 49,000 developers, more than 4 in 5 said they used or planned to use AI tools, while almost half said they did not trust the accuracy of those tools' output, up from just under a third a year earlier. A usage dashboard would show only the first number.

People forgive an algorithm's errors less readily than a person's. In experiments by Berkeley Dietvorst, Joseph Simmons and Cade Massey, people who watched a forecasting algorithm make mistakes became less likely to rely on it than on a human forecaster, even when the algorithm had been more accurate. In later work, the same researchers found that letting people adjust the algorithm's forecasts, even slightly, made them more willing to use it.

How a product presents its output matters as much as how accurate the output is. With Mila, we helped turn COVI, an AI-based COVID-19 risk app, into a consumer product by replacing blunt exposed-or-not alerts with graded risk guidance, tailored to each person's region and health history. That work contributed to Apple and Google changing their exposure notification system from binary alerts to multi-level risk.

To confirm this cause, measure what people do right after a recommendation and right after an error. Behavioral science should lead, with UX research to see how people check and second-guess the output in practice.

We would want any buyer to hold us to this checklist, and every other firm on their shortlist.

• Behavioral scientists who can interpret product usage data, with surveys as one input among several.
• UX research and product design capability in the same team as the behavioral scientists.
• A clear experimentation plan tied to a product outcome, such as retention or completed sessions.
• Case studies from digital products with measured results, alongside any public policy or marketing work.
• A handoff model that leaves your internal team able to run the approach after the engagement ends.

Whoever you hire, ask to see a case where the diagnosis changed the original brief, and a test that did not work along with one that did.

Ask too how the team decides what users should be steered toward. Behavioral design that serves the product's metrics at the user's expense tends to show up later as churn and complaints, so the target behavior should be one users would choose for themselves.

The Decision Lab is a fit when engagement problems go deeper than interface clarity. Our team combines behavioral science, UX research, product design, data science and experimentation, so we can find out why users stall and then build and validate the intervention before costly production work.

We also test designs before they reach production. For a global consumer electronics company, we built a behavioral design process with the client's own team and used it on a new mindfulness service inside a health app used by 60 million people. Every target behavior was tested through experimentation before production code was written. Design time fell by three quarters, even though the new process added research steps, and the team's confidence in its design decisions doubled, measured by self-report before and after the engagement. For the wellness platform, we ran the full behavioral engine against synthetic members first, so design flaws surfaced in an afternoon instead of months into production.

In both cases the client owns what we built. The wellness platform's team extends the engine to new actions and member groups on its own, and the electronics company's team now runs the design process without us.

We are not the right first partner when the core issue is a straightforward usability defect or a feature users cannot find. In those cases, a focused product design studio may be faster and more cost-effective.

How do you know if low engagement is a UX problem or a behavioral problem?

Watch a handful of users try the key task. If they get stuck or cannot find the feature, it is a usability problem and product design should lead. If they complete the task easily in a session but rarely come back to it on their own, the cause is behavioral.

What does a behavioral scientist do on a product team?

A behavioral scientist works out why users do or do not act, using research on motivation and decision-making alongside your product data. They turn that diagnosis into specific design changes, such as a different first step or a reward tied to real outcomes, and set up tests that show whether each change worked.

Should we hire a behavioral scientist in-house or work with a consultancy?

A consultancy is usually the faster start when you need a diagnosis and a first round of tested fixes, or when the work spans several disciplines. An in-house hire makes sense once behavioral questions come up in every product cycle. Many teams start with a project and hire once they know which skills they use most.

Can A/B testing alone fix stalled engagement?

A/B testing tells you which of two versions performs better, but only among the versions you thought to build. If both versions miss the real cause, the test will pick the less bad one. Diagnosing the cause first gives your experiments better candidates and a clearer measure of success.