How to Make Actionable Sense of Customer Sentiment Analysis

Creating a better customer experience is a top priority for most businesses, with 72% of companies saying improving CX is their No. 1 goal, according to data from Forrester. However, figuring out what drives a better user experience is a total guessing game, unless you take a deep dive into customer sentiment analysis.

customer sentiment analyis

Creating a better customer experience is a top priority for most businesses, with 72% of companies saying improving CX is their No. 1 goal, according to data from Forrester. However, figuring out what drives a better user experience is a total guessing game, unless you take a deep dive into customer sentiment analysis.

Understanding the responses and reactions that customers give out after using your products can help your brand immensely. Of course, conducting market research and surveys, and gathering feedback from customers are all small but essential steps toward improving your product or service, as well as its user experience. However, these reports are mostly a whole lot of confusing numbers and statistics; they offer no action plan or recommendations, or even insights on what to do next.

Making actionable sense of the numbers can be tricky, especially if there are no clear problems or opportunities that were identified through your research.

So, what should you do? Let’s go step-by-step.

Pinpoint Common Threads in Customer Reviews

While it’s typically a company’s first reaction to try to remove negative reviews that could deter future customers, these actually may be your best resource for fixing hidden issues.

About 25% of consumers have left a review for a local business because of a bad experience, but this doesn’t mean that 100% of these reviews are helpful to either companies or other customers. It’s best to turn to a reliable system here that can sift through emotionally exaggerated (and practically useless) or downright fake reviews and uncover valuable information that could point you toward better solutions.

A review platform, such as Bazaarvoice, allows brands to collect genuine ratings and reviews from customers, respond to their questions and concerns about their products, display moderated content created by customers on social media, and even implement a product sampling program based on the reviews you’ve collected.

Similarly, an interaction management tool, like Podium, gets you in the game earlier, helping you connect and interact with prospects on multiple channels. It enables team collaboration on lead generation and nurturing, as well as solving customer problems, leading to a consistent customer experience.

Customer Sentiment Analysis image
Credit: Podium.com

More customers tend to leave reviews with brands that use customer review management tools. This results in more data for your sentiment research, eventually ensuring better targeting and success of your product marketing campaigns.

Watch out for repeated keywords throughout these reviews, such as issues with customer service, packaging, delivery, or pricing. Looking for patterns in your customer reviews lies at the core of identifying the problems and coming up with solutions.

Use Smart Segmentation

Customers never fit into the one-size-fits-all category. Even if you cater to a small niche or if your product has a very specific use, there will be subsets, segments, and cohorts, all influenced by varying demographics and regulations, who could affect opinions of your business. This is why smart segmentation is important when reviewing customer sentiment analysis.

Again, these segments may need different targeting strategies, depending on whether your company is a B2C or B2B entity.

B2C

B2C marketers need to look at the:

  • age:
  • location:
  • income: and
  • in-the-moment needs of their customers.

B2B

B2B marketers, on the other hand, need to address non-personal variances, such as:

  • company size:
  • budget; or
  • objectives.

By pairing demographic and quantitative data, customer sentiment may make more sense and provide even deeper insight than before. For instance, customers who are motivated by finding the best deal may say that your shipping costs are too high; whereas, customers with FOMO may be ready to pay extra for next-day delivery. When you have multiple datasets of behavioral data that you can compare against one another, your team can understand how to cater to various customer segments by understanding their motivations.

Note that customer “segments” vary from “profiles” or “personas.” They are not as specific, and typically only focus on one or two variables rather than a list of unique qualities. There are countless ways to segment your audience, so be sure to find the segmentation model that best fits your business.

Customer Sentiment Analysis photo
Credit: MeaningCloud.com

Identify Engagement Intent

Understanding the “why” behind your customer’s actions will shed some light on their sentiment reactions. Your expectations always influence your experience, so a customer’s engagement intent could play a part in their response.

The rise of search as a marketing channel has made it clear that there are essentially four engagement intent categories that consumers fall into today:

  • informational;
  • navigational;
  • commercial; and
  • transactional.

Each of these steps correlates well with the traditional AIDA sales funnel model.

Informational

The first is searching for information on a particular subject that may or may not be a problem for them. These are typically prospects who are just entering the marketing funnel. They simply want to know more, so if your website does not offer the information they are looking for, their interest in your brand or product will not develop at all.

Navigational

People in the navigational category are looking for a specific product, service, or piece of content. This group knows what they want, and they will be easily frustrated if they can’t find it.

Commercial

The commercial investigation intent group is interested in buying, but they just aren’t quite ready yet or aren’t convinced that your product offers the best solution for them. They fall just above the action segment of the sales funnel and are often looking for the last bits of information before they make a purchase.

Transactional

And finally, the transactional group has the intent to buy. They have already made their decision to buy a specific product; however, any hiccups in the buying or checkout process could deter them.

Identifying Engagement Intent

Of course, identifying their engagement intent is a little tricky, especially after the interaction has been completed. But with some digging and martech tools, there are ways to figure out the motivations behind every brand-customer engagement.

One of the clearest ways to identify engagement intent is through carrying out intent research, attribution modeling, and analyzing their behavior on your digital property. If they just read a post on your blog, chances are they were looking for more information on a topic related to your industry. If they clicked an ad and filled up a form on your landing page, they are probably interested in availing themselves of your service.

Once their intent has been identified and understood, it will be much easier to understand their sentiment post brand engagement or product usage.

Experiment With Changes

Finally, the only way to make customer analysis actionable is to, well, take action. However, just switching things up without constantly analyzing the results will only put you back at Square One.

Many marketers rely on A/B/n or multivariate testing strategies to compare different changes, whether it be in the design or layout to an entire product or service experience. However, A/B testing can be a long and arduous process that yields murky results. It may even mislead you, if you over-rely on seasonal or contextual variables. Unsurprisingly, AI technology has been a huge help in the A/B testing realm by improving the accuracy and reliability of the process, resulting in few conversion opportunities lost.

AI-based algorithms are able to gather and analyze massive amounts of data at a time. They can compare results of multiple tests against each other simultaneously at various interaction points along the buyer journey.

Tools like Evolv use machine learning (ML) to find which experiences and customer journey paths work best (make profits) for you and nudge customers down those paths accordingly. You can set up experiments on your landing pages with goals and KPIs, and let the algorithm tweak the UX for each customer by presenting various combinations. The data from these experiments help you understand how satisfied the customer is with the interaction, and also develop new hypotheses to keep testing further or make decisions related to product development or service delivery.

The Way Ahead

By understanding the root causes behind your customer’s reactions and feelings, you can go as far as to influence sentiment, improve brand loyalty ,and encourage advocacy. Always be looking for overlaps and commonalities among complaints. This will help you avert PR disasters, deliver exceptional customer service, and stay ahead of the competition.

Use sentiment analysis to understand where your customers are coming from by segmenting them and uncovering their intents at every interaction. Finally, track the effects of all your initiatives and take action responsibly to ensure they stay delighted at all times.

Author: Rohan Ayyar

Rohan Ayyar is the regional marketing manager for India at SEMrush. His blog, The Marketing Mashup, covers digital marketing from the perspective of B2B, B2C, lead generation, mobile marketing, SEO, social media, content marketing, database marketing including predictive analytics, and conversion rate optimization. In addition, he'll look at emerging marketing technology and how marketers can use it. Reach Ayyar at searchrook@gmail.com.

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