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Why listening to customers matters: AI in product success

AI enables deeper understanding and anticipation of user needs, making product management even more efficient. Learn directly from a Workable product manager how you can do so in your own job.

Anthoula Poniraki
Anthoula Poniraki

Anthoula is a Senior Product Manager at Workable with a diverse background in the tech industry.

Making a product that people love isn’t just about putting in cool features. It’s also about making sure the product does what users want and need. That’s where listening to customers comes in.

By hearing what customers have to say, product managers can figure out what changes to make to keep everyone happy.

But listening to lots of customers can be hard work! That’s where AI comes in. AI is like a super helper that makes it easier for product managers to understand what customers are saying and use that info to make products even better.

Let’s talk about why it’s important to listen to customers and how AI can make a big difference in making products successful.

1. Customer-centricity

We – as product managers – always need to hear what customers think to keep our product in tune with what they want. When we ask customers for their thoughts and really listen to what they say, we learn a lot about how they use our product and what they expect from it.

By putting customer feedback first, we can make changes that fix their problems and make our product even better. This isn’t just about making customers happy and increasing product satisfaction (PSAT) – it’s also about keeping our product relevant in the market.

2. Validation and insights

Feedback from customers tells us a ton about how they feel when they use our product. It’s such a rich source of information and insights into their experience. When we look at this feedback, we can see what’s working well and what needs improvement. We can find out trends, patterns, and common issues among our users.

This helps us decide which changes to make first so we can keep making the product better for everyone who uses it. In this way, the development efforts can be focused on addressing the most pressing needs first.

3. Competitive advantage

Listening to customers and fixing problems quickly helps our product stand out from others. When a product manager takes action based on what customers say, it really makes a difference among competitors.

4. Product iteration

Getting feedback from customers is a big part of making our product better over time. It’s a very important part for iterative product development.

It helps us find problems early and figure out what to do next. By using feedback, we can keep making our product better and better so it meets the changing user needs and market dynamics.

5. Customer loyalty

When we listen to customers and prioritize changes based on what they say, it shows them we care about their opinions. This makes them more likely to stick with our product and tell others about it. Happy customers who feel heard and appreciated help us drive organic growth and expansion.

6. AI in the game

Customer feedback is super important for product managers and the whole company, really. It tells them what customers think about the product – what’s good, what’s not so good, and what could be better.

But there’s a lot of feedback to go through, and it can be hard to know where to start. That’s where AI comes in. AI helps product managers by sorting through all the feedback and figuring out what’s most important.

But how does AI make such a big difference in using customer feedback to make products better?

7. Automated data collection

AI-powered tools can automatically collect and aggregate customer feedback from different places like surveys, social media, online reviews, and customer support interactions.

This makes it easier for product managers to have access to a comprehensive dataset of feedback without doing it themselves.

8. Sentiment analysis

AI algorithms can analyze the sentiment of customer feedback. They can understand whether it is positive, negative, or neutral. Sentiment analysis helps product managers quickly see what’s important to customers and what they’re talking about.

9. Natural Language Processing (NLP)

NLP techniques allow AI systems to understand and interpret the meaning of written or spoken feedback.

By analyzing the content of customer feedback, NLP algorithms can extract valuable insights, identify recurring themes or issues, and categorize feedback into relevant topics or categories.

10. Predictive analytics

AI-powered predictive analytics can forecast future trends or customer behavior based on historical feedback data.

This helps product managers see challenges before they happen, identify opportunities and find new ways to make customers happy.

11. Personalization

AI can help tailor the feedback collection process to individual customers based on their preferences, behaviors, and past interactions with the product.

This means product managers can get feedback that’s more useful and relevant for each person.

12. Recommendation systems

AI-driven recommendation systems can suggest relevant actions or improvements based on the analysis of customer feedback. This helps product managers decide what changes to make first, address common pain points and give customers a better overall experience.

Wrapping it up

So, when we put together listening to customers and using AI, we can understand our users better and improve our product faster. It’s a win-win for everyone involved.

By listening to what customers say and letting AI help understand it all, product managers can make smart choices and create products that people really like.

With this approach, companies can stay ahead of the competition and keep their customers happy for years to come.

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