Boosting your business’ bottom line: Why your Support team is critical to success

Go to the profile of Kathryn Lye
Kathryn Lye
Boosting your business’ bottom line: Why your Support team is critical to success

Table of Content:

  1. How can Support teams help my app succeed?
  2. What metrics do Customer Service teams track?
  3. How can my Support Team improve their metrics?
    • Automation
    • Semantic analysis
    • Combining semantic analysis and automation

Providing excellent customer service is key to business growth - a truth widely recognised across sectors, from consumer goods to financial products. For anyone who needs more convincing, the facts speak for themselves: a recent Accenture study shows customers switching companies due to poor service costs U.S. companies a total of $1.6 trillion alone, while companies with a customer experience-mindset drive revenue between 4 to 8% higher than the rest of their industries.

The same is true for app businesses. Many of today’s most successful businesses - think Uber, Spotify or Headspace - are completely mobile-based. They are also undoubtedly customer-obsessed, continually pushing to create a better customer experience for their users through their design, monetization strategy or customer service levels.

While customer service for mobile businesses is just as important as for any other product, the strategy and channels used look a little different. In this piece, we’ll look at the importance of customer support in mobile, the key metrics tracked by support teams, and how they contribute to your app’s bottom line. We’ll also look into how customer service teams can improve these metrics through the use of automation and semantic analysis.

How can Support teams help my app succeed?

For app-based businesses, building a successful Support team is key to quickly and efficiently solve users’ issues - which in turn will improve brand loyalty and boost retention figures. Mobile churn rates have never been higher, with the average mobile app losing an estimated 77% of its Daily Active Users within the first three days after install.

There are many reasons for users churning: some users may not like an app, or simply have no reason to return. However, providing excellent customer service when users reach out is one way to build brand loyalty, and increase word-of-mouth marketing. Today’s users expect their issues to be resolved at the tap of a button, and a lack of efficient customer service could convince them to uninstall your app in favour of your competitors’. But provide an excellent user experience and you could build yourself a loyal following of users - who will then go on to recommend your app.

Support teams also have a unique outlook on your users and their customer journey. In fact, they are often likely to be the only team interacting with customers day-in, day-out - and have a wealth of knowledge about users’ personas, pain points, and demands. The most successful companies are the ones who make use of this knowledge, breaking down internal silos so that Support teams can actively feedback and communicate users’ needs across the company.

Building a relationship between Product and Support teams is especially valuable, as user feedback can then be used to drive product decisions. It’s a topic AppFollow explored in a recent webinar with Gram Games’ Product and Customer Service teams, who credit their close collaboration to developing smash hit games including Merge Magic!, Merge Dragons! and 1010!

What metrics do Customer Service teams track?

  • CSAT: Short for customer satisfaction score, your CSAT attaches a numerical figure to your users’ experience - both within the app itself, and externally, for example when dealing with your support channels. There’s several different ways you can collect CSAT feedback, including via email, a post-service chatbot or with an in-app survey. If using a survey, these are usually programmed and processed through the use of a third-party customer feedback tool such as HelpShift or SurveyMonkey.
  • NPS: A Net Promoter Score looks at the likelihood of a customer recommending your service or product to a friend or colleague. This usually takes the form of a single question survey, and can be triggered at various points in the customer journey - for example, just after your user has interacted with your support team, or even at the end of a certain level in a mobile game. This metric is particularly helpful for forecasting business growth and cash flow.
    NPS question example
    • Average response time: This metric looks at how long it takes from a user raising a ticket to it being addressed by your Support team. Average response time is often used in correlation with CSAT scores: the quicker you reply to customers, the more satisfied they will be - even if this is just a holding statement that you’re looking into the issue for them. When establishing your KPIs, bear in mind that different support channels also come with different expectations of response times.
    • Tickets closed: This indicates how many users’ queries have been solved, although adding more context will help paint a broader picture of your team’s success. How long do issues normally take to resolve? Once tickets are closed, how likely are users to offer a high Net Promoter Score?
    • App store rating: Unlike the metrics mentioned above, your app store rating is a public score - every user on the Google Play or App Store can submit a one- to five-star rating based on their experience with an app. Your average rating acts as essential social proof for potential new users, who will quickly go to your competitors if your score isn’t up to their expectations.
      App Store page focussing on rating and reviewsBoth stores also pay close attention to average ratings: AppFollow research shows that the average rating for apps and games in the stores’ top charts and featured sections hovers at around 4.5 stars. Critically, apps with a rating under 4 stars lose up to half of their potential downloads. For an app looking to gain more installs, ensuring high ratings is key to success.

Scoring highly in these metrics will go on to play a huge influence on your company’s north star metric: ie, increased revenue. Satisfied customers are generally much more willing to spend in-app, tolerate additional ads, or recommend your app to potential new users.

How can my Support Team improve their metrics?

Now that we’ve looked at how quality customer support feeds into a company’s success, let’s investigate how you can help improve your Support team’s metrics - particularly when it comes to responding to app reviews. Two of the most common, and most effective, ways to do this is through semantic analysis and automation.


Once you start receiving a certain number of reviews a day, it becomes increasingly difficult to keep up and respond to users at scale.

One of the easiest ways to manage a big increase in user reviews is through automation. This is usually done through a third-party platform, where you’ll be able to:

  • Set certain rules to automatically reply to messages
  • Automatically report offensive or spammy reviews
  • Use auto-tags to group similar responses together
Using auto tags to group responses

All of this will help your team save hours of work, reply more promptly to customers, and allow them to focus on business-critical reviews - thereby improving your users’ experience. For more on how to manage user reviews at scale via automation, head over to our Automation Guide.

Semantic analysis

Beyond replying to reviews, Support teams also need to keep a finger on the pulse of customers’ problems and expectations. This offers the entire company important insights on performance, and can help mould future app versions. But manually sorting through reviews to find those that offer genuine insight can be slow and ineffective, especially when you’re receiving hundreds or thousands of reviews a week.

Semantic analysis tools provide an easy way to keep track of user reviews at scale: powerful machine learning algorithms can analyse hundreds of reviews within seconds, grouping similar subjects together, and providing a 360 degree overview of sentiment analysis and current hot topics within your user community.

Semantic analysis is key to helping you:

  • Gain a deep understanding of your users and their pain points
  • Keep track of new bugs within your app
  • Expand internationally - helping you evaluate app performance by country or language
AppFollow's Semantic Analysis

Digging into your users’ issues and integrating their feedback into your product roadmap are two key ways of improving their customer experience - and will help you continue to innovate and improve.

Combining semantic analysis and automation

Perhaps the most powerful way to improve your Support team’s metrics is by combining both semantic analysis and automation, which can help you automate up to 90% of your review management. This depends on setting up various semantic tags (ie, sorting reviews into various buckets or types depending on the subject matter), and combining them with bulk actions - such as replying with a specific template, or reporting spam or offensive reviews.

One example could be wanting to update all users about a specific bug that’s now been fixed. From the AppFollow dashboard, simply choose all the relevant reviews within the “Bugs” tag, and reply to them with a folder of templates. Alternatively, if you want to mass-report all your offensive or spam comments to the app stores for deletion, navigate to the “Report a concern” tab. This will show a breakdown of all your problematic reviews, and from there you can combine this with an automatic rule to flag these reviews for removal - for example, if a review is semantically tagged with “Spam”, “Offensive”, and contains any swear words. 

Both semantic analysis and automation are huge boons for Support teams, who otherwise would need to manually sort through and reply or report every single review. By optimizing this workflow, you can reply to users faster than ever before - and make sure you’re not missing out on any critical feedback regarding your app. This will have a domino effect on your customer satisfaction KPIs, and help you attract and retain high-quality brand ambassadors.

Looking to learn more about AppFollow’s Semantic analysis 3.0? Read more details in our latest guide to semantic analysis, or reach out to us for a demo.

Read other posts from our blog:

How to manage user reviews at scale: Three tactics for success

How to manage user reviews at scale: Three tactics for success

Struggling to keep up with a sudden upkeep in user reviews? Our latest blog post has all you need to...

Kathryn Lye
Kathryn Lye
Semantic analysis: How to understand user reviews at scale and drive customer satisfaction

Semantic analysis: How to understand user reviews at scale and drive customer satisfaction

Learn about the ins and outs of semantic analysis, and how leading developers Gram Games and PicsArt...

Kathryn Lye
Kathryn Lye
Audiomack leverages automation with AppFollow and gets 501% ROI

Audiomack leverages automation with AppFollow and gets 501% ROI

Find out more about how automation brings high ROI

Nurzhan Ospanov
Nurzhan Ospanov
subscribe success
subscribe fail

React to user feedback and market trends faster