Customer sentiment analysis is an automated process that uses artificial intelligence to help brands analyze data to determine how customers perceive their brand at any given point in time.
This is an important practice to help you gauge your customers’ brand affinity, both individually and in aggregate, and can give you insights to help you respond quickly to negative feedback or provide reinforcement for positive reviews. By using customer sentiment analysis, you can get a better picture of your company’s overall performance with customers, and generate personalized marketing strategies based on how they feel.
Here’s a look at how to manage your customer sentiment analysis process.
How Customer Sentiment Analysis Works
Customer sentiment analysis uses natural language processing (NLP) to measure a customer’s brand affinity and brand sentiment—providing measurable metrics for assessing which customers feel positively or negatively towards your brand.
It analyzes a customer’s overall engagement with your brand, as well as the use of particular words, and even emojis, in brand-related conversations and reviews to create a score for each customer and uncover data trends across your entire customer base.
For instance, if a customer’s product review uses words like “amazing” and “best” with a smiley emoji, your sentiment analysis tool will translate that into a positive sentiment for that customer. If another customer leaves a review including words like “glitchy” and “confusing,” this review will be scored as a negative sentiment.
The scores can be consolidated in different ways. You’ll be able to gather user sentiment on the individual level, and store it in each customer’s profile within your marketing automation solution. You’ll also be able to aggregate all of the data and use it to analyze trends, such as tracking brand affinity over time within a set timeframe of a new product launch, or tracking customer sentiment tied to a particular product or concern (i.e., shipping, customer support).
Customer Sentiment Analysis Data Sources
Most customers leave large digital footprints, and you can use the wealth of information they share across various channels to help you understand their attitudes towards your brand. Below are some of the potential data sources you can use for customer sentiment analysis.
User Surveys
One of the most straightforward ways to learn how customers feel about your brand is by asking them directly—a.k.a. gathering zero-party data. While some surveys may simply offer a multiple choice or point scale, you can get more insight from your responses by offering open-ended questions where customers are free to write in detail about their experiences. However, depending on the quantity of data, it may take weeks to manually review all of their responses, and you may not prioritize responding to customers who have shown strong positive or negative emotions. By using sentiment analysis to mine the data, you can easily flag the comments that are high priority for a manual review and take subsequent action.
Product Reviews
You can use customer sentiment analysis both on reviews left on your own site and on third-party reviews sites, helping you identify general trends in opinion around specific products, your customer service, and your brand at large. You can also flag specific reviews to follow up on, based on the tone of their comments.
Customer Support Communications
Regardless of the mode customers use to get in touch with your customer support department, you should be able to monitor their conversations and use the data to help you collect important insights. Beyond the data that’s available from each closed support ticket, you’ll be able to use sentiment analysis to track their feelings towards your brand based on the words they used in their messages or calls.
Social Media
Your brand’s social media accounts can provide valuable insights into how customers are responding to you. Beyond tracking individual likes, follows, and other social interactions, you can follow other metrics—like the use of hashtags or certain keywords in comments—to determine how a customer feels about your brand.
Marketing Engagement
Looking at your marketing communications is another way to gauge sentiment by focusing on a customer’s engagement behavior. If they’re interacting with your marketing more frequently, their sentiment is positive. Some marketing automation tools, like Iterable’s Brand Affinity, track customer sentiment throughout their journey with your brand and attach their sentiment level directly to the user profile for easier access and segmentation.
While these are some of the most common channels to use for customer sentiment analysis, data from any channel your customers engage with can be integrated into your evaluation. And once you’ve developed a sentiment analysis score for each customer, you’ll be able to use those insights to improve your personalized marketing strategies.
Improving Your Marketing with Brand Affinity Scores
By using customer sentiment analysis, within a cross-channel marketing solution like Iterable, to track each customer’s brand affinity, you’ll be able to create segmented lists for your marketing campaigns, alongside behavioral and other forms of data.
For instance, you’ll be able to identify customers who’ve had a strongly negative customer experience, and target them with a winback campaign that could offer a sincere apology paired with a free product. You can personalize the campaign with dynamic content on an individual level. If a customer has previously purchased a pair of shoes and wasn’t happy that the color was different from advertised, you might send a coupon to get another pair of shoes for free.
Likewise, if a customer has given you glowing reviews, you might segment them for a high-value customer track, and offer them a free gift to thank them for being a loyal customer.
Cross-Channel Customer Sentiments
By using a cross-channel marketing strategy to send customers personalized messaging sequences across all of the marketing channels they use, including email, SMS, in-app messages, push notifications, direct mail, and social, you can maximize engagement and deliver relevant content that will help to either win back customers who’ve with negative brand sentiment or reward customers with positive brand sentiment. Your messaging sequences should adapt in real time based on customer actions.
Additionally, the more channels you use to engage with your customers, the more data you’ll be able to collect, helping you to continually improve the accuracy of your sentiment analysis.
Customer sentiment analysis is an important tool to help you evaluate the health of each customer relationship. By understanding those metrics on an individual level, you can work to foster a stronger connection with your brand that will keep your customers engaged throughout the customer lifecycle.