Your app has changed. Has your feedback strategy?

0:06
Hi, everyone, welcome.

0:07
I’m Kaylee Beller, Senior Customer Success Manager here at Alcamer, and I’m joined today by my colleague Mackenzie Shoemaker as CSM on our Strategic Accounts team.

0:16
Mackenzie and I both work closely with mobile apps team teams every day, and today we’re going to talk about something that comes up in those conversations a lot.

0:25
And that’s what happens when a feedback setup that worked great at launch quietly falls out of step with how your app actually works today.

0:31
And sometimes it’s not obvious that anything is drifted until metrics have really taken a turn.

0:36
So today, we’re going to walk through how to take a look and what to do when you find something worth updating.

0:44
So here’s what we’re covering today.

0:45
We’ll start with the love dialogue and rating dialogue.

0:48
What they are, why they matter, and how the decision to couple or decouple them affects your data.

0:54
From there, we’ll get into targeting what it is, why it’s the foundation of your whole feedback program, and what happens when it goes stale.

1:01
We’ll walk through how to update it, look at reports that can inform those decisions, and close with a quick bonus tip on annotations.

1:08
We also have two real customer stories woven in that make all of this really concrete.

1:15
Awesome.

1:15
Let’s dive in.

1:16
So before we define what love dialogues and rating dialogues actually do, I really want to zoom out for a moment on why any of this matters.

1:25
The answer is App Store ratings, and the data here is really striking.

1:28
Moving from a moving a rating from three to four stars can lead to an 89% increase in conversion, and 9090% of customers say star ratings are an essential part of their decision to download an app.

1:40
You pair that with 79% of users check the rating before downloading.

1:44
If your rating is sitting below 4 stars, a meaningful portion of potential users are making a decision before they even open your app.

1:52
And love dialogues and rating dialogues are the primary lever you have to influence that number, but only if they’re set up well.

1:58
That’s really what today is all about.

2:01
So let’s define the two.

2:03
The love dialogue is your internal sentiment.

2:05
Check.

2:06
It’s that simple.

2:06
Do you love our app Yes or no Prompt.

2:09
It tracks what we call fan signals over time, and it’s a jumping off point for other interactions.

2:14
The rating dialogue is external.

2:16
It’s the prompt that asks users to leave a star rating and optional written review right from inside the app without having to leave it.

2:23
So that’s what shows up publicly on the App Store or Google Play.

2:26
So the love dialogue is for you, internal data, and the rating dialogue is for the world, your public score.

2:33
So they’re related, but they are distinct and how you connect them or don’t is what we’re going to get into next.

2:43
So to couple or decouple, this is probably the question we get asked most often.

2:49
Coupled means the rating dialogue launches directly off a positive love dialogue response.

2:54
The user taps yes I love the app and immediately gets prompted to rate it in the store.

2:58
A straight line from love to rating.

3:01
Decoupled means they’re completely separate interactions, each with their own independent targeting logic.

3:07
The coupled setup is simple, intuitive, and gets you to your App Store rating goals quickly because you’re only prompting people who’ve said they love the app.

3:15
The trade off is volume.

3:16
If your love dialogue reach is limited, your rating dialogue reach is limited by definition.

3:28
There are some compliance dimensions worth understanding, and the FTC has issued guidance that essentially says don’t ask for reviews only from people you think will leave.

3:37
Positive ones don’t incentivize customers to write reviews, and don’t prevent or discourage negative reviews.

3:43
So that’s where the coupled setup gets a little tricky.

3:46
By design, it routes only happy users to the public reading prompt.

3:50
It’s worth noting this doesn’t prohibit asking happy users to rate your app.

3:56
Customers can always go directly to the App Store on their own.

3:59
What the FTC discourages is systematically suppressing negative voices from public channels.

4:05
On the platform side, Apple limits in in app rating dialogues to a maximum of three times per year per user.

4:12
Google Play enforces a time time bound quota that can change without notice.

4:17
So those constraints are real regardless of how you’re configured.

4:21
So Alkimer’s decoupled approach with internal survey routing for users who don’t love the app is really designed to align with this guidance.

4:29
But we always recommend looping in your own legal and compliance teams, especially if you’re operating in multiple markets.

4:36
So when does each approach make the most sense?

4:39
Let’s go ahead and jump to the next slide.

4:40
Perfect.

4:41
I usually frame it around where you are in your App Store journey.

4:45
If you’re just starting out, your rating is low.

4:47
You need to, you need to build that volume fast.

4:49
Coupling is a reasonable place to start.

4:51
It’s simple, gets you moving, and then once you’ve hit a rating that you’re happy with or you’ve maxed out your love dialogue reach, that’s when decoupling becomes the right move.

4:59
It opens up the audience, stretches rating opportunities throughout the year, and gives you a broader view of sentiment.

5:06
The trade off is that it can increase your total feedback record since you’re now prompting double the number of feedback mechanisms, which may be a metric you need to watch.

5:15
But really the quality and the representativeness of the data usually improves, which leads us right into why your setup has a direct impact on the data you’re collecting.

5:25
Yeah, volume is a data quality issue.

5:28
When you’re rating dialogue reaches low, your sample size is low, which makes your App Store score more volatile.

5:36
A handful of negative reviews can move the needle significantly when you only have a few 100 total ratings, and the routing question matters just as much as the prompting question.

5:46
When users who don’t love the app are routed to an internal survey instead of a public store prompt, you turn a potential bad review into a Direct Line of what needs to improve in your app.

5:56
That’s a fundamentally more useful outcome, but the longer a setup or survey has been running unchanged, the more likely it is that the data you’re seeing actually reflects who your users were and not who they are today.

6:08
Which is exactly what where it what targeting is designed designed to solve.

6:13
So let’s talk about what targeting actually is.

6:17
Targeting is the logic that controls who sees in each interaction, where in the app they see it and when, SO3 dimensions, who the user attributes, event history segments, where in the specific app event or screen that triggers the interaction, and when, Frequency, timing, and cool down logic.

6:38
Here’s the key thing to understand.

6:40
Once a user meets your WHO criteria, they’ll see the interaction the next time they hit a defined where event.

6:46
So if your where is pointing at an event that barely anyone hits, it doesn’t matter how well crafted your WHO logic is, the interaction just won’t surface.

6:56
Targeting is the difference between relevant feedback and noise.

6:59
Get it right and your response rates go up.

7:01
Your data is actionable and your team makes decisions based on signal.

7:05
Get it wrong and you’re collecting a lot of data that doesn’t tell you anything.

7:09
If you leave with nothing else today, leave with this.

7:12
Targeting isn’t just a set up step, it’s the foundation everything else sits on.

7:17
Your response rates, your App Store rating, the quality of your data, it all traces back to whether you’re targeting continues to work as your app evolves.

7:27
So why do so many teams end up with stale targeting?

7:30
Because initially, it works great.

7:32
You figure it, you can figure it at launch.

7:34
Interactions run passively, there’s no ongoing maintenance required, and teams stay focused on building the app.

7:40
You get good metrics right out of the gate.

7:42
And then a few years go by, the app has changed, the team has changed, and the feedback starts feeling a little off.

7:48
Response rates are dropping, comments that don’t quite match what your teams are working on, and there’s a rating dip you can’t explain.

7:55
There could be a few common culprits.

7:57
App updates or shifts in user behavior change, which events are relevant or business goals change and art reflected in the old configuration.

8:05
And targeting criteria like count is at least X can create long term effects where users become permanently eligible or locked out, especially if those counts include deprecated features.

8:16
Yeah, you definitely want to be make sure that you’re being really conscious of making up making updates to your targeting to reflect those types of changes.

8:25
So some best practices we’ve developed around when you should think about making these different updates to your targeting after a major app release, when ratings and response rates decline noticeably, when that feedback starts to feel off topic.

8:41
But at a minimum, once a year as a routine hygiene check to make sure everything’s in line to ensure you’re capturing feedback to where your users are now and your app is at now.

8:52
So let’s dive into some real customer examples that have gone through this.

8:57
So our first customer case study is a large rural lifestyle retailer.

9:02
Their app is a core part of their sales funnel, emphasizing buy online, pick up in store, as well as their loyalty program.

9:09
After about three years since they last made an update to their targeting, their iOS rating was sitting at a 4.7, but their Android had dipped down to a 3.3 which was causing some real concern.

9:21
They were using a decoupled setup, which is the right call for them, but they hadn’t made any intentional changes and in 2025 only 14,000 rating dialogue surfaced on Android, despite over 500,000 monthly active users.

9:36
And so when we started to dig in, two issues stood out.

9:40
1st, The Who criteria was requiring users to tap search at least five times within the current app version, which sounds reasonable for when they set up the targeting.

9:51
Until we started to look at the usage data of where their users are now, most users weren’t searching at all.

9:56
They were reordering products that surfaced right on their home screen, and search was being bypassed almost entirely.

10:03
Second, their team releases a new app version, roughly about two to three weeks.

10:08
Requiring 5 search taps within a single version created an extremely narrow eligibility windows that most users never had enough time to hit that threshold before the version turned over and the criteria reset.

10:20
So those two factors combined into a much smaller eligible audience than anyone had realized.

10:26
So we made some key strategic changes to their targeting.

10:29
And with one week of adjusting, the Google Play rating had actually jumped from a 3.3 to over a four point O and went up further to 4.5 after just a few weeks.

10:40
The number of times the rating dialogue surfaced daily went from under 50 users per day to over 500.

10:47
IOS went from under iOS went from under 100 to over 2000.

10:53
And with that increase in volume, the percent of five star ratings also dramatically increased.

10:58
That’s what that’s what targeting aligned with actual user behavior looks like.

11:06
That’s great.

11:07
And our second customer case study is cloud based property management platform for landlords, property managers and community associations.

11:17
The App Store ratings are critical for them because trust drives adoption.

11:21
That also seen a decline in ratings, especially on Android.

11:25
But the root cause was different.

11:26
Their trigger logic had accumulated complexity over time.

11:32
You could switch to the next slide, please.

11:34
Trigger rules were counting user actions across all app versions, not just the current 1.

11:39
So users were hitting prompts based on behavior from months or versions ago in contacts that were no longer matching the app.

11:48
The result was unpredictable prompt delivery, over prompting, user fatigue, lower sentiment, and worse ratings.

11:55
The fix was switching to version specific counting, which reset the logic to align with the current behavior.

12:01
They also brought in the Love dialogue qualification from exactly once to at least once, so users weren’t permanently locked out after a single response.

12:10
The result was reduced prompt fatigue, more accurate targeting, improved feedback quality and recovery in App Store ratings.

12:19
2 very different situations, Same underlying lesson.

12:22
Targeting that’s out of sync with how your app works will cost you.

12:27
So these are just a few examples of the dramatic impact a few simple updates to targeting can make.

12:33
So let’s get into the mechanics.

12:34
So you can apply similar changes to your app, starting with who you can divine define eligibility based on metadata, things like device type, operating system version, app version, or even location.

12:47
Or you can bring in specific user attributes like loyalty program status or behavioral criteria, which features they’ve adopted or how they’ve responded to past interactions, or even how many times they’ve used a particular feature.

12:59
When it asks you for count, it’s asking you to define how many times a user has seen an interaction or an event.

13:05
It’s a way to target based on behavior.

13:07
And you can differentiate behavior by all time utilization across all versions, which creates that permanently permanently eligible or permanently locked out problem we’ve been talking about.

13:18
And using count and installed criteria allows you to reset behavior for each new app release, ensuring feedback reflects your current your users current experience and IS versus IS at least IS creates A brief eligibility window that closes the moment the user exceeds the exact threshold.

13:36
IS at least keeps them eligible once they’ve crossed it.

13:40
For most cases, IS at least combined with account and installed version is the right combination.

13:47
One of my favorite criteria to recommend is ensuring users are on the right app version before prompting them.

13:52
It means the feedback you’re getting is actually about the experience they’re having right now, not an issue that may have already been fixed 2 releases ago.

14:04
So to get into the where targeting, where is often where the quickest wins are.

14:09
This is the specific app event or screen that triggers the interaction.

14:13
Pull the events report, find your top five to 10 most triggered events, and make sure your targeting reflects where users actually are in the app.

14:22
If events in your targeting are getting almost no traffic, those interactions are really rarely going to surface no matter how well the rest of your setup is configured.

14:32
One important guardrail is never place an interaction in the middle of a revenue generating flow.

14:37
So don’t interrupt A checkout or a transaction.

14:40
Think about triggering right after a completed action.

14:42
An order confirmed event is ideal, the task is done, the user is in a positive moment, and you’re not getting in the way of anything.

14:49
And that’s exactly the gap we saw with that retail case.

14:52
High volume events like order placed and app launch weren’t in the targeting at all, while low volume deprecated events were.

14:59
Fixing that was one of the levers that moved the needle.

15:03
So when we think about when, when is really all about the frequency and timing, making sure that users have had a chance to actually use the app.

15:10
Before you ask for feedback, you want to ask that you want them to have a real experience to reflect on in.

15:17
Cool down periods matter too.

15:18
If a user dismisses a prompt, give them breathing room before asking again.

15:22
Over prompting is one of the fastest ways to damage sentiment, and we saw that play out directly in the property management case.

15:29
The key take away here?

15:30
Start with where.

15:31
Updating your event targeting to reflect current app usage is usually the highest leverage change you can make, and it’s often where the biggest gaps are hiding.

15:40
So let’s take a look at one of the reports that we used to identify those key events, the Events report.

15:46
So this this report should be informing all of the decisions that you make with your event targeting.

15:54
It shows utilization for every integrated app, integrated event in your app, the most triggered, least triggered.

16:00
And critically are the events in your targeting actually seeing the the traffic you’d expect.

16:06
If an event in your targeting has 10 clicks in a year, that’s a signal.

16:10
Pull this report, sort by volume, and compare what’s in your targeting against what’s at the top of the list.

16:16
The gap between those two things is going to be your opportunity.

16:20
For the rural lifestyle brand, they’re top events in 2025.

16:23
Shopping cart tapped at 4.3 million, app launch at 4 million.

16:28
None of them were in the rating dialogue targeting.

16:31
However, a very niche event for pet health info was in the targeting and only had 10 clicks all year.

16:36
This report makes that visible in about 30 seconds.

16:39
So let’s move on to annotations.

16:44
The this is the last thing before we wrap up, and this is the bonus tip.

16:48
An annotation is a note tied to a specific date that appears visually on your reporting charts.

16:54
They matter because configure changes don’t show up in your data immediately.

16:58
It can take days or weeks to see the effect.

17:00
And without annotations, you’re looking at a chart with a spike or a drop and trying to remember what changed when.

17:06
With annotations, you just look at the chart, see the marker, and know exactly what happened.

17:11
Best practice.

17:12
Every time you update targeting, change dialogue, copy or push a major app release, add an annotation.

17:18
30 seconds of work that make you reporting dramatically more useful over time and invaluable when you’re onboarding a new team member, diagnosing an unexpected shift, or preparing for a QBR.

17:30
So moving on to the next steps and let’s go ahead and wrap up.

17:35
We covered the love dialogue and rating dialogue, what they are, why they matter, and how coupling versus decoupling affects your data.

17:42
We talked about targeting as the foundation of your feedback program and what happens when it drips.

17:48
2 customer examples, an outdoor retailer and a property management platform both saw real rating improvements from targeting updates that took less than a week to implement.

17:57
We walked through who, where, when the events report and annotations.

18:02
So really the most important next step if you’re a current customer, well, that events report into your account and compare what’s in your targeting, what’s actually against what’s actually getting traffic.

18:11
That comparison alone is going to tell you a lot.

18:14
And if you want to walk through your setup, reach out to your CSM.

18:18
It doesn’t have to be a big project, and sometimes a 30 minute conversation and one or two changes can make a real difference.

18:25
If you’re not a customer yet, reach out to us.

18:27
We’d love to have a conversation to discuss your feedback strategy and see how Outcomer can help support those goals.

18:34
Lastly, we are a feedback company and we’d love to hear from you if you have a moment.

18:39
Please let let us know if you found this session valuable or if there’s anything again you’d like to see in the future.

18:45
Thank you so much for being here today.

18:46
We look forward to seeing you again soon.

18:49
Thanks all.