Introduction

Are you curious about what people really think of your product? Do you want to know how your customers feel about your brand or industry? Sentiment analysis is here to help! This powerful tool allows you to analyze text and understand the sentiment behind it. Whether people are raving about your product or venting their frustrations, sentiment analysis can give you valuable insights into the customer experience. In this blog, we'll cover the basics of sentiment analysis, how it works, and how you can use it to improve your product and make informed decisions.
Sentiment analysis is a way to understand what people are saying about a product, brand, or topic by identifying and measuring the sentiment of text. It involves analyzing text to detect positive and negative words, phrases, and sentences, and can be used to understand the customer experience, market conditions, and how to improve products. There are two main methods for performing sentiment analysis: machine learning algorithms and dictionary-based methods. The results of sentiment analysis can be used in customer feedback analysis, social media analysis, and market research to make informed decisions about products based on customer sentiment. In this blog, we'll explore the basics of sentiment analysis, how it works, and what it can be used for.

 

What is sentiment analysis?

 

Image source - Travel Media Group

 

Sentiment analysis is the process of identifying, extracting, and measuring the sentiment of the text. It's about understanding what people are saying about a product, brand, or topic. Sentiment analysis can be used to measure how happy or unhappy people are with a company or its products.

It’s important to recognize that sentiment analysis isn't always easy—there are many factors involved in determining whether something is positive or negative (the number of reviews, who wrote them, and when). But once you get your head around these issues it becomes much easier for you to understand why certain things work better than others in terms of improving sales conversions and increasing customer satisfaction levels overall.

Why does it matter?

Sentiment analysis is a way to understand what people are saying about your product. It can help you understand the customer experience, market conditions, and how to improve your product.

Let's start with an example: if you're building an app for people who want to eat healthier food but don't know where to find it in their area (maybe they live in an urban area with limited access), then sentiment analysis might show that there is demand for this type of service but not enough supply yet—so maybe it's time for some more advertising or promotion efforts aimed at getting more restaurant owners onboard with this new idea?

How does Sentiment Analysis Work?

Sentiment analysis is a process of analyzing the sentiment (positive or negative) in an online text. It can be used to detect positive and negative words, phrases, and sentences within a given piece of content.
A simple example: if you're browsing through your newsfeed on Facebook, you may see something that says "I hate Mondays", but this doesn't necessarily mean that everybody hates Mondays—it could just be one user who really hates Monday mornings. If you were able to get more context about this post by analyzing its content with sentiment analysis software like Watson Analytics' Sentiment Analysis Toolkit (SAK), then you'd be able to learn some important information about other people's feelings toward different topics such as holidays or sports teams.

There are several ways to perform sentiment analysis, but one common approach is to use machine learning algorithms to classify text as either positive, negative, or neutral. This is often done by training a machine learning model on a large dataset of labeled text, where the text has been manually labeled as either positive, negative, or neutral.

Once the model has been trained, it can then be used to classify new, unseen text as either positive, negative, or neutral. In some cases, the model may also be able to output a confidence score for each classification, indicating how confident it is in its prediction.

Another approach to sentiment analysis is to use a dictionary-based method, where a pre-defined list of words and their associated sentiments (e.g. positive, negative, neutral) is used to classify text. This method can be faster and easier to implement, but may not be as accurate as machine learning-based approaches, especially for more complex or nuanced text.

Overall, sentiment analysis is a useful tool for extracting subjective information from text data, and can be used in a variety of applications, including customer feedback analysis, social media analysis, and market research.

 

What can we do with it?

The sentiment analysis of your customers' reviews can help you understand how they feel about your product, competitors' products, and the industry in general.This information helps you make informed decisions on what to improve or change in your product. If the majority of people who use your service are extremely positive about it, then chances are there's something wrong with it—you might need to do some major rethinking about how things work!
On the other hand, if most users have negative comments about their experiences with certain aspects of an app or service then that means those aspects should be fixed ASAP before more negative feedback comes pouring out!

 

How to get started with sentiment analysis

Once you have a dataset, it's time to get started.The first step is to use a tool to import the text of your product reviews into your tool of choice. You can then create models on these datasets, which will allow you to train them and test them against new data as needed. The results from this process will provide valuable insight into how customers are feeling about your products—and what might be causing them problems!

Why sentiment analysis matters to product managers

When you're a product manager, it's important to know how customers feel about your products. This is especially true for companies that have a competitive edge over their competitors—for example, if you're an e-commerce retailer and one of your biggest selling points is that your products are cheaper than those of other retailers, then knowing whether people think this is accurate will be valuable information.

So why should product managers care about sentiment analysis? Because it can help them understand what customers think of their own company's products and improve them by making changes where necessary (or by at least giving them some direction). Sentiment analysis also gives insight into which features or changes are working well and which ones aren't working so well—this can help keep everyone on the same page when developing new features or changing existing ones!

As a product manager, understanding the sentiment of customer feedback is important for a number of reasons:

 

1. Customer Satisfaction: By analyzing the sentiment of customer feedback, product managers can get a better understanding of how satisfied customers are with the product. If a large portion of the feedback is negative, it may indicate that there are issues with the product that needs to be addressed. On the other hand, if the feedback is largely positive, it may suggest that the product is meeting customer needs and expectations.
    
2. Product improvements: Feedback that is overwhelmingly negative can be a valuable source of information for product managers looking to make improvements to the product. By analyzing the sentiment of this feedback, product managers can identify the specific areas of the product that are causing the most frustration or dissatisfaction for customers.
    
3. Prioritizing development: Product managers often have to prioritize which features or improvements to focus on first. By analyzing the sentiment of customer feedback, product managers can prioritize the features that are most important to customers, or that are causing the most frustration.
    
4. Marketing and branding: The sentiment of customer feedback can also be useful for product managers in the context of marketing and branding. Positive sentiment can be used to create marketing materials and messaging that highlight the positive aspects of the product. On the other hand, negative sentiment can be used to identify areas where the product or the company's brand may be falling short and to make improvements in these areas.
    
5. Competitor analysis: In addition to analyzing customer feedback, product managers can also use sentiment analysis to track the sentiment of feedback about competitors' products. This can help product managers understand how their product compares to the competition and identify areas where they may have an advantage or disadvantage

How can product managers use Sentiment Analysis?

Sentiment analysis helps you understand what your customers are saying about your product. Rather than spending hours trying to make sense of tons of valuable feedback, sentiment analysis allows product managers to leverage AI to look into and capture the valuable parts of the feedback that they can then quantify for implementation. It can also help you understand why customers are buying or not buying your product. Sentiment analysis can help you understand how customers feel about competitors, too!

1. Identify pain points: Analysis of feedback by feedback analytics software identifies, understands, and quantifies user pain points. An underperforming product can be identified and users can be addressed where they have issues.

Discovering pain points in your tool can be achieved by looking at your themes by volume and sentiment.  For example, if you are an exercise tracking application and you receive a high volume of negative responses to GPS, you will need to investigate further to determine what common issues users are experiencing.

If you ask your platform for a summary of these negative responses, it may respond with "GPS does not work on iOS" or some such. Moreover, you should quantify the problem before asking the dev team for support. You’ll be able to communicate:  

- How many users are impacted by the issue.

- Estimate the impact of the issue moving forward (if it’s not fixed). 

- Cost to your company for supporting users with the issue. 

- The potential opportunity of addressing the issue.

- Benefits to other parts of the company for solving the issue. 

 

2. Measure the effectiveness of product updates and new features: By tracking customer feedback over time, product managers can see how well certain updates or features are received, and adjust their roadmap accordingly.
 

3. Improve customer satisfaction and loyalty: By using feedback analytics software to address customer needs and pain points, product managers can improve customer satisfaction and loyalty.
 

4. Enhance collaboration and communication within the product team: Feedback analytics software can help product managers share customer feedback with the rest of the team, promoting collaboration and improving communication.
 

5. Make data-driven decisions: Feedback analytics software provides product managers with a wealth of data on customer needs and preferences, which can be used to inform product development and marketing decisions.
 

6. Monitor the competition: Feedback analytics software can also be used to track how customers perceive the product relative to competitors, providing valuable insights for the product team.
 

7. Data-driven conversations with stakeholders: Data can be your greatest asset when collaborating and communicating with your stakeholders.

Remember to:

- Tell a story with your data, (past, present future) to bring your internal stakeholders along on your journey.

- Justify your decision-making with data-driven logic to inspire confidence in your investors.

- Use data to be as transparent as possible with your regulators, engaging them in problem-solving with you.

- Keep customers happy and informed because your data gives you the confidence you can deliver on customer promises.

Conclusion

Sentiment analysis is an important tool for product managers. It helps them understand the customer’s perspective and identify trends that can inform their decisions about product features and pricing. It can be used as a stand-alone tool or integrated into other business intelligence tools so that it’s accessible to all employees within your organization.