SaaS feature adoption: discovery, not persuasion

Aug 22
4 min read

Updated: 4 days ago

Introduction

A team ships a feature, announces it, and watches a small fraction of users try it. The instinct is to promote it harder.

Before that, there is a question worth answering: do users not know it exists, or do they know and not want it? The two require opposite responses, and confusing them wastes a great deal of effort.

1. SaaS feature adoption is a discovery problem more often than a value problem

In most products, low adoption reflects users never encountering the feature rather than rejecting it.

Which is unsurprising. Users learn a product to the depth required for their immediate task and then stop exploring. Anything outside that path is effectively invisible, regardless of how prominently the team believes it is placed.

Establishing which situation you are in is the first task, because promotion only helps with the first.

2. Distinguish the two with a simple funnel

Three numbers separate discovery from value: how many users could see the feature, how many opened it, and how many used it more than once.

Low open rate against high availability is discovery. High open rate with no repeat use is value — they found it, tried it, and it did not help.

That distinction takes an afternoon to establish and redirects the entire effort. Products that promote a feature users have already tried and abandoned generate irritation rather than adoption.

3. Fix discovery in context, not with announcements

Announcements reach users who are not currently doing the relevant task, which is when they least care.

The effective mechanism is contextual: surfacing the feature at the moment the user is doing the work it improves. A user manually repeating something sees the automation option; a user exporting for the third time sees the scheduled report.

That timing converts far better than an email, a changelog or a modal on login, because the value is immediately obvious rather than hypothetical.

4. Recognise that a feature might genuinely not be wanted

The uncomfortable possibility, and the one adoption campaigns are often designed to avoid confronting.

If users find a feature, try it once and never return, the honest interpretation is that it does not solve a problem they have — or that it solves it worse than what they were already doing.

Talking to ten of those users is more valuable than another round of promotion. Sometimes the answer is a small change that makes it usable; sometimes it is that the feature was built on an assumption nobody tested.

5. Segment adoption, because averages mislead

An overall adoption percentage across all users is close to meaningless.

Most features are relevant to a subset. Measure adoption among users for whom the feature is actually applicable — by plan, by segment, by whether they do the underlying task at all.

A feature used by five per cent of all users and sixty per cent of the segment it was built for is a success being reported as a failure.

6. Reduce the cost of the first attempt

Adoption is frequently blocked by setup rather than by interest.

If trying a feature requires configuration, a data import, an integration, or a decision the user cannot make confidently, most will defer it indefinitely — including users who would benefit.

Lower that barrier: sensible defaults, a working example, the ability to try it on existing data without committing, and a clear way to undo. First-attempt cost matters more than the feature's eventual value.

7. Watch for adoption that does not help retention

Not all adoption is worth pursuing, which is easy to forget once it becomes a metric.

Check whether users who adopt a feature retain better, expand more, or use the product more. Some features are used and change nothing, and driving their adoption consumes attention that a retention-linked feature would repay.

Rank features by their association with retention, then concentrate adoption work on the top of that list rather than on whatever shipped most recently.

8. Retire what nobody uses

The conclusion adoption work should sometimes reach.

A feature with negligible use among its target segment, after discovery has been fixed and the value question asked, is a maintenance cost and a source of interface complexity.

Removing it makes the product easier to learn, which helps adoption of everything else. Announce it properly, migrate the few users who depend on it, and accept the finding rather than promoting it for another year.

Conclusion

Establish first whether low adoption is discovery or value, using availability, open rate and repeat use.

Fix discovery contextually at the moment of the relevant task rather than through announcements, take try-once-and-abandon as evidence about the feature, measure adoption within the applicable segment, reduce the cost of a first attempt, prioritise features whose use correlates with retention, and retire what remains unused.