Consumer electronics case study

Managing the customer feedback process for over 20 apps and languages

In early 2019, Wonderflow started working with a major consumer electronics and hardware company. The scope included coverage of over 20+ local language apps and platforms like Amazon. We speak to Marissa, currently the lead product marketing manager for a range of different apps. We discuss what her work looked like before and how it has changed since she started working with Wonderflow’s AI-driven NLP solution.

Summary

  • The company is now able to analyze all customer reviews, rather picking samples
  • Increased coverage of sources to include 20+ local language app store sources (as well as Amazon for their hardware business)
  • Increased product sales through predictive analytics, highlighting potential improvements
  • Streamlined market feedback process for product launches

Why should you read this case study?

Product or consumer marketeers, customer care/experience managers, business analysts, customer insights managers can benefit from the use-cases shared in this case study. It gives a clear example on how AI-driven customer feedback analysis can help you building a better experience for your clients, and can serve many different levels within your company.

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20

apps are covered in the analysis process

7,500

new reviews are gathered and analyzed every month

Being a core source of customer insight for a global consumer-focused software provider is a challenging job, even on a good day. Marissa, product marketing manager for an entire range of apps, knows this like no other. Not only she’s asked for insights and reports daily, but she also needs to find ways to continually improve the customer experience and the insights gained from it.

Marissa started working with the AI-based NLP solution from Wonderflow in early 2019. We spoke with her to discuss what her work looked like before and how it has changed.

“Going through the reviews was also a pretty depressing task... People write nasty things when they do one-star reviews.”

The most time-consuming tasks…

“I had to cover between 1500 to 2000 new reviews every month in the app stores. I used to collect, categorize, and analyze the reviews myself, back when I only had one app to manage. My most innovative solution was a scraping tool that exported reviews to an Excel sheet. Unfortunately, that didn’t work for the Google Play store.” Marissa recalls days on end where she was scouring app store reviews. The fact that she speaks multiple languages made her job a bit easier, but that was an advantage most of her colleagues did not have.

Insights were slipping through the cracks

When Marissa started working her job, a lot of valuable feedback was slipping through the cracks. “I was looking at reviews for four hours per week before the Wonderboard, at a minimum, and those hours did not properly cover everything. I didn’t feel it was sufficient, as I wasn’t actually able to do proper analysis and giving feedback to the team. Certainly not per country.”

“Going through the reviews was also a pretty depressing task. There was an issue with a migration process, so our rating dropped. People write nasty things when they do one-star reviews…”

One of these is that we leverage the Wonderboard to keep track of how competitor’s products are performing.

Picking the right solution

Multiple solutions were considered to replace (parts of) the existing process and manual labor. The decision for the Wonderboard was one with an eye on the future. The most significant improvements were the ability to increase the number of business users dramatically and scale the tool throughout the enterprise. Being able to digest such a large amount of reviews from such a diverse group of sources, has given the enterprise a significant advantage.

Strategic insights

Marissa is now the lead for a range of consumer apps. With this new role, she is now responsible for more strategic decisions, for which she leverages the Wonderboard data.

“Ramping up the number of reviews we analyze to include over 20 different local language apps stores, that’s an improvement we can’t even put a number on. We spot trends, and the predictive analytics capability shows us how much app sales can potentially be improved if we improve specific functionality.”

It is also the leading tool we use when we launch a new app or functionality because we get deep market feedback, so quickly.

“We have added a ton of new users to the Wonderboard, from different teams. Each new user is on-boarded with care, allowing them to use the tool when they need insights. The variety of users brings forward many use cases, too. One of these is that we leverage the Wonderboard to keep track of how competitor’s products are performing. We have learned a ton from new feature launches by competitors, giving us insights for product development.

It is also the leading tool we use when we launch a new app or functionality because we get deep market feedback, so quickly. Being able to distribute this information to the relevant people within our company, in a form they understand, matters a lot. We have, in general, become much more market-driven and deal-driven.”

Read about the different use cases for the Wonderboard:

Conclusion

The Wonderboard is used by different departments within the company. People save a lot of time on different processes. To sum up a few of the key results:

  • The company is now able to analyze all customer reviews, rather picking samples
  • Increased coverage of sources to include 20+ local language app store sources (as well as Amazon for their hardware business)
  • Increased product sales through predictive analytics, highlighting potential improvements
  • Streamlined market feedback process for product launches
    • A recent report by independent research firm Ovum highlighted the quick adoption by business users.

Future plans

  • Increase the number of local language app stores
  • Ramp up the number of trained business users with 200% in the next 6 months
  • Ramp up the competitor analysis to cover all products from their 10 biggest competitors
  • Closing the feedback loop with the hard- and software development teams

The names in this case are anonymized.

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