What is our primary use case?
I typically use Appsflyer for deep linking, attributions, and tracking all the URLs that we share, such as all the campaigns that we do via QR code. When we first launched Clydo.in, which is a shopping quick commerce fashion app, we had posters and banners all over the places which have a QR code generated with Appsflyer's deep link via OneLink, and we were able to measure how much traffic was coming from scanning that particular QR code. That is one marketing campaign we did with posters and QR codes across all of Bangalore.
How has it helped my organization?
There is a very big positive impact from using Appsflyer. Instead of optimizing purely for installs or traffic volume, we start optimizing for retained users and repeat purchases, even for LTV. Overall conversion quality also increases, and one major improvement is budget allocation. Teams are able to identify which campaigns, channels, or creatives are valuable users versus low intent traffic, helping us reduce wasted spend on scale channels that actually drive revenue. Another strong impact is visibility across funnels; Appsflyer helps connect acquisition data with downstream actions such as sign up, checkout, and subscriptions. Deep linking significantly improves user experience; users can move directly into relevant product and offer pages after clicking an ad or referral link, which improves activation and our conversion rates. Fraud prevention is also an area where we have seen measurable gains by detecting fake installs, click spams, and low-quality traffic, preventing a significant amount of wasted acquisition spend.
Implementing Appsflyer gives us much clearer visibility into campaign quality and user behaviors, with a significant improvement in ROAS optimization. Earlier, we scaled campaigns mostly based on install volume and CPA, but after attribution and cohort analysis, we identified channels that brought low retention users. Reallocating budget helped improve overall ROAS by around twenty to twenty-five percent over the past two quarters, and we also saw retention improvements. For example, D30 retention for users acquired through our best-performing campaigns improves from roughly fourteen percent to around twenty-one percent. After optimizing targeting and creatives based on Appsflyer data, deep linking helps improve onboarding and conversion flow; install to purchase conversions in some campaigns improve by nearly twelve to fifteen percent because users land directly on a relevant product page instead of the generic home screen. On the fraud side, Protect360 helps identify suspicious traffic patterns from a couple of acquisition sources, allowing us to reduce wasted acquisition spend by close to eighteen percent in a few months. Another significant impact is decision-making spend; earlier campaign analysis involved pulling data from multiple dashboards and internal systems, but with Appsflyer acting as a centralized attribution layer, the growth and product teams can react much faster to campaign performance and user quality signals.
What is most valuable?
The best feature of Appsflyer is the attribution engine itself, where we can do ROAS per campaign, and we can calculate the retention sources. It works across Meta ads, Google ads, and OEM trafficking, and OneLink deep linking is also the second most useful feature we found. Irrespective of which platform it is, whether Android or iOS, with just one link we can redirect them to the Play Store or App Store, which is very helpful. These two are the best features that we use from Appsflyer.
The biggest shift is that before attribution, the teams optimized for installs, while after attribution, teams optimize for profitable users. For example, if we run Meta ads, which are expensive installs, we can easily attribute whether Meta installs are expensive or Google installs are expensive. We also see CAC, which is very meaningful. Another example is calculating retention-based optimizations; we run campaign A with one hundred thousand installs and campaign B that gives us forty thousand installs. Without attribution, campaign A looks really good because it pulled one hundred thousand installs, but retention is very important for us. Campaign A had an eight percent D30 retention, while campaign B had a thirty-one percent D30 retention. This is how important it is to check retention-based optimization, where we can connect ad creatives, installs, and all downstream events. We can discover trends such as women's fashions and their average order value, or even discount-led creatives which attract low retention users, including influencer traffic which converts better for prepaid orders.
There is even geographic decision-making that we can do, and with geographic decisions, we can see whether Bangalore users have high retention or whether any tier three cities have high acquisition and low activations. We can also analyze platform basis, whether iOS has two times the ROAS versus Android. Geo-segmenting allows us to spend, create region-specific onboarding, and optimize all logistics costs and change inventory availability. The fraud detection feature, Protect360, helps us discover install form, click spam, bot traffic, and suspicious CTIT patterns, saving us a significant budget, as it can literally save lakhs of rupees per month for a scaled app. We see a meaningful ROI after implementing Appsflyer properly, with the biggest impact on marketing efficiency and decision-making spending. By identifying low-quality acquisition channels, reallocating spend improves overall campaign efficiency by roughly twenty-two to twenty-five percent.
What needs improvement?
Overall, the platform is really strong, especially for attribution and deep linking, but there are definitely a few areas where it could improve. One challenge is attribution discrepancies; sometimes the numbers among Appsflyer, Meta, and Google internal backend events do not match, especially after iOS ATT changes. I understand the technical reasons, but it creates confusion during reporting and decision-making. Another pain point is the learning curve; while the platform is feature-rich, onboarding new growth or product team members can take time. Some dashboards and configurations could feel more intuitive, and SDK integration and debugging can also be slightly painful, especially in a hybrid stack like Flutter. Event validation and deep linking debugging sometimes require a lot of manual testing. Reporting flexibility is another area that could improve. Although the dashboards are powerful, many teams still end up exporting raw data into internal Power BI tools for more customized analysis and visualization. Pricing is discussed quite a bit; for smaller startups or early-stage products, Appsflyer can feel a bit expensive compared to the scale they operate at. Lastly, I think predictive insight could become stronger, such as more AI-driven recommendations around campaign optimization, fraud anomalies, or retention forecasting directly inside the platform, which would make it even more operationally valuable.
For how long have I used the solution?
I have been using Appsflyer from past six months.
What do I think about the stability of the solution?
Appsflyer is very stable and its performance has always been reliable.
What do I think about the scalability of the solution?
Appsflyer handles growth and data needs very well.
How are customer service and support?
Overall, customer support has been amazing. We receive support quickly, and there is a very good experience in terms of support; they respond to emails very quickly, and premium support is very helpful for us.
What was our ROI?
We see a meaningful ROI after implementing Appsflyer properly, with the biggest impact on marketing efficiency and decision-making spending. By identifying low-quality acquisition channels, reallocating spend improves overall campaign efficiency by roughly twenty-two to twenty-five percent.
What's my experience with pricing, setup cost, and licensing?
My overall experience with pricing, setup cost, and licensing is good; however, the initial pricing is slightly high. Eventually, we manage because visibility into ROAS, retention, and fraud prevention directly impacts marketing efficiency and budget optimization. That said, for smaller teams or early-stage startups, the licensing cost feels quite high initially, especially when adding advanced modules such as Protect360, raw data export, and premium support. One thing we appreciate is scalability; as campaign volume and analytic maturity grows, the platform integrates well with broader growth and BI workflows.
What other advice do I have?
For others considering Appsflyer, I mainly suggest not to scale rapidly; instead, scale slowly and only as needed. It should be on-demand, starting small and then moving to higher and better. This interview was awesome and amazing; it was very realistic. There is nothing that I have to add to this. Overall, I would say my experience with Appsflyer has been very good. It did not reduce headcount, but it definitely improved team efficiency. A smaller growth and analytics team can manage campaigns more effectively due to clear attribution and automated reporting pipelines. Overall, ROA becomes visible through reduced wasted spend, improved ROAS, better conversion quality, faster decision-making, improved retention insight, and less manual reporting overhead. My review rating for this product is eight point six out of ten.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other