Knowing what your customers (or even potential customers) have to say about you can be a godsend. But does it involve deep layers? Does it require special expertise to translate insights from the dashboard into actionable steps?
For all your questions, sentiment analysis has the answers. But before that, let’s walk you through the definition.
What is customer sentiment analysis?
Customer sentiment analysis is an approach to gauge and understand what your customers think(opinions), how they behave(attitudes), and how they react(emotions) towards a product or service.
It does this by studying different forms of customer feedback including but not limited to reviews, social media comments, customer interactions such as sales calls, support calls, and surveys.
But, why do I need it?
Using sentiment analysis, you can measure customer sentiment, identify the weak points, and monitor customer sentiments from time to time.
And finally, build products that are aligned with the product roadmap and development team. This is possible thanks to AI and ML which classifies sentiments into positive, negative, and neutral.
Use cases for sentiment analysis
1. Make smart product decisions
Sentiment analysis in customer feedback can help you make data-driven decisions instead of relying on gut and intuition.
Sentiment analysis can help you in the following ways:
- Collect and analyze feedback based on performance, users, and features—you get what’s causing problems and how it can be improved
- Determine which feature has the most value—understand positive and negative sentiments and focus on iterating it better
- Learn the actual opinions of customers—With sentiment analysis, you can interpret survey results better, you know how to engage with loyal and unsatisfied customers, improve customer support efforts, and reduce customer churn
- Help in monetizing the product—helps understand the sentiment around your current pricing, especially cost-to-benefit ratio, examines price sensitivity around different customers with their usage, determines pricing better on usage levels, etc
2. Helps differentiate feedback
Customer sentiment analysis goes beyond the classification of sentiments. It helps differentiate feedback based on feature requests, bug reports, and usability issues.
Not just this, it can focus on the intensity based on aspects such as need of the hour, can’t be delayed for long, needs cohort analysis to conclude, etc
It also understands the nuances of contexts such as identifying sarcasm, puns, humor, etc. Further, it helps in identifying intent such as the likelihood to churn, discontinue, or recommend.
Let’s say, you come across a review where the customer talks about the bad product experience. Subsequently, emphasizing the customer support follow-ups that still haven’t been able to resolve the issue.
This calls for allotting the best person for the job and immediately taking the best action. It could be the founders doing it. Maybe, the VP of customer support jumping into action.
Based on the outcome, you’d either have a churned customer or a customer who’s willing to continue, giving you a second chance.
Its other application is topic modelling where issues of the same kind are grouped under a particular topic such as:
- Mobile app problems—crash, battery drain, offline mode, notifications
- Data visualization request—graph types, custom reports, export-import, CSV, share options)
- Integration issues—API, third-party integrations, sync, Zapier, webhooks, and middleware
- User interface feedback—cluttered, no lazy loading, carousels not working, small thumb zone
3. Evaluate new product potential
Launching a new product is always a bit risky. You never know how users are going to perceive it. But, you can minimize it using TAM size with a little help from sentiment analysis.
It can help you understand:
- The opinion of a product in a certain demographic—understand the needs, problems, potential to adopt it, etc
- Multi-tiered approach—Not all demographics are the same, for instance, if the demography in Arkansas is keen to adopt a feature, but Oklahoma may not
- It can help benchmark against competitors—identify areas where you have an edge over your competitors and areas to improve
- Helps understand time to first value(TTFV)—which feature will help potential users turn them into habitual users because of a benefit
- It can help in anticipating problems—by identifying problems and trends early and fixing them
4. Helps understand the customer journey better
Customer sentiments evolve as they pass through different stages of their journey. With sentiment analysis, you can get a pulse of varying opinions.
For instance, here’s how it looks:
- Awareness—users are looking for tools or options to solve a problem; post their requirements on LinkedIn, X, etc
- Vet the brand sentiments of your brands and those of competitors—lack of awareness, uncertainty regarding its benefits
- Consideration—Understand which features your core audience values the most; for eg, enterprise users may prefer something over small teams
- Purchase—Recognize the problems starting from pricing problems to stakeholder approval
- Onboarding—follow up regularly to see their actions including trouble moving beyond areas, and inability to solve problems after accessing training material, with increased support times
- Usage—identify features that have been utilized the most, track DAU, WAU, and MAU, and find out if they are using reactivation metrics like sending 50 zaps in a week, sending 100 emails in a week, requesting a quote for insurance
- Support—identify sentiments affecting aha moments, address feedback regarding bugs, keep an eye on metrics such as customer health score, average resolution time, etc
- Retention—Find out how far they have realized the goal, check the level of empowerment, ability to solve problems without repeated intervention, whether are they at risk of churn, and do they regret
- Advocacy—This is the stage where customers have experienced delight; look for reviews either negative or positive reviews; find out what needs to be fixed
- Look for any signs of drop-offs or churn and areas that have performed well
5. Create better brand messaging
By tracking sentiments across social media and other platforms, you can refine your brand messaging from time to time.
For instance, you could be a home decor person who also sells camping furniture but because your homepage and reputation are stuck to the former, your inventory isn’t selling out.
To find out if this hypothesis is true, you can analyze the sentiments, phrases, and perceptions of customers. It also could be due to limited options or outdated inventory.
You could also help better target your ICP by revamping your value proposition. Let’s assume you're a BNPL app that sells interest-free loans to 18-24 year olds. You could offer services to help build their credit score. By giving tips such as taking X number of active loans and repaying them before availing another.
Benefits of using Sentiment Analysis Tools
1. Overcoming Data Overload
The average brand has to sort through 500 million tweets per day, thousands of reviews, and app feedback from hundreds of customers.
The problem is just a lack of people to get the job done, have to do it manually. A luxury that most brands including yours may not have.
With a sentiment analysis tool, this can be changed. It can help by:
- Categorizing reviews and feedback into buckets or groups split into positive, negative, and neutral sentiments
- Show the number of responses, sentiment, and the action to take
- Create topic-based buckets to categorize issues for faster issues
- Gather insights in a shorter time to turn feedback into faster
- Frame better problem statements due to higher accuracy
- Present a picture via graphs to stakeholders
2. Improving Sentiment Classification Accuracy
When you’re short on staff and time, it becomes difficult to focus on things individually. For instance, you might find it difficult to differentiate between ambiguous words. They vary in their meanings in different contexts.
Moreover, the inability to identify multiple emotions from a single review. For example, The overall product is great but the heatmap feature is lackey and isn’t easy to identify when there are piles of such data.
With a sentiment analysis tool, you can:
- You can classify emotions into specific subcategories—such as annoyance, anger, gratitude, regret, recommendation, etc
- Using an ensemble network consisting of multiple language models can help you differentiate between seriousness and sarcasm
- Distinguish between words related to each other on a graph without having to train a model on a huge dataset—for eg, The product is far ahead in being backward is a sarcastic comment that might be mistaken as a positive
- Use sentiment lexicons, a dictionary with words containing polarity scores—numerical scores ranging from -1, 0, 1
3. Turning data into actionable insights
While sentiments might be the same, the context is different for each business. You’ll need a sentiment analysis tool that’ll help you:
a. Extract the right phrase & keywords—The terms have different meanings according to each industry eg. Cheap in retail might sound good but when comes to furniture, it screams bad quality
b. Identify Aspect-based sentiments—Apart from the phrases, other aspects of sentiments include picking up the verbs, adverbs, and adjectives that are unique to different industries.
Furthermore, this helps understand the contextual flow. Let’s say, This is something I will never recommend shows a negative connotation. Conversely, Rarely I like fries, but Wendy’s helped me change that is a positive remark.
c. Monitor trends—Identify any patterns talking about the inability to use a feature, or UX issues like non-functioning buttons and tie it to similar sentiments; find out its impact on customer satisfaction(repeated drop-offs), potential problem areas that could turn into pain points
Wrapping Up
Since you’re aware of what sentiment analysis can do, it’s important to use this handy technique to create products that users need while balancing the product roadmap. It should help stay away from giving in to populist demands at the cost of disturbing the product vision.
In the end, sentiment analysis will help decide if it's worth pursuing it now or making it the product backlog.
