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.

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.

How to Use Sentiment Analysis to Transform Your Digital Marketing Strategy

The goal of sentiment analysis is to increase customer acquisition, retention, and satisfaction. Moreover, it helps put the right brand messaging in front of the most interested eyes.

Sentiment analysis is a fascinating concept.

Brands use it to better understand customer reactions, behaviors, and opinions toward their products, services, reputation, and more. The goal of sentiment analysis is to increase customer acquisition, retention, and satisfaction. Moreover, it helps put the right brand messaging in front of the most interested eyes.

Before the digital age, gauging and understanding sentiment was an incredibly cumbersome process. It typically involved sending out surveys manually, going to the streets and asking people, or gathering focus groups in one place at one time. The big data-infused model of sentiment analysis we know today hit its stride on the political scene in 2010. Since then, it has morphed into a key tactic in marketing plans. These days, most of the grunt work is automated.

However, even with all of the advances in areas like martech, voice search, conversational commerce on social media, virtual assistants, and big data analytics, understanding how to actually use sentiment analysis to improve the bottom line is a complicated task.

Here are a few key approaches to help you get the value you need.

Know the Terms and Phrases That Indicate Intent

Most businesses today (hopefully) don’t even begin their digital branding and marketing efforts without a list of keywords relevant to their industry and a plan on how to target their audiences. You should have a good idea of the terms and variations that bring you traffic to your website, when used in conjunction with your brand and products. If you run an auto repair shop, people are likely finding you on the web through terms such as: body shop near me, auto repair, replace brake pads, etc.

Google Search Console gives you a great, fairly accurate idea of what’s bringing people to your website:

google search console
Credit: Author’s own

In terms of sentiment analysis, to gain actionable insight, you need to know how people are using these keywords in a way that indicates interest and engagement potential. Now, this is perhaps the biggest gray area in sentiment analysis, because not all positive sentiment equates to sales. Just because there are a lot of positive words around luxury cars doesn’t necessarily mean people are about to buy.

However, there are certain terms and phrases that signal people have entered your buyer’s journey. Let’s say you run an SEO agency and one of the terms you’re tracking for sentiment analysis is “Google update.” If you notice that a lot of people are searching for things like “what to do after a google algorithm update?” or “how to recover from a google penalty?” it’s a good indicator that they might need your services at the moment; you should target them accordingly.

Spot Patterns in Product Reviews

At its core, sentiment analysis is a game of pinpointing patterns and reading between the lines. Simply put, the more genuine and meaningful feedback you get on your product, the better insights you will gain into your customers.

Of course, gathering such high-quality feedback is easier planned than executed; especially for newer or smaller companies. Only 10% of customers will review or rate a business after a purchase, while half of consumers will leave a review only some of the time. However, the number of reviews jump significantly to 68% when a company asks the customer directly to leave one.

In order to find fruitful, up-to-date patterns, you need to make it a marketing process to consistently seek out new reviews. Then, you’ll want to start by searching for common adjectives. These should include words like:

  • great, simple, easy,
  • or awful, difficult, poor, etc.
trustpilot review
Credit: Capterra.com

In the above image, there are a good amount of reviews that include the word “great” for this product. Looking at the context around this term, we notice recurring patterns around components, like features and usability, and “not so” great opinions on customer service.

Finding recurring themes in customer sentiment will give you a better picture into the positive and negative aspects of your business or product. These can indicate the level of trust people have in your brand and how likely they are to give you a recommendation. When you are looking for patterns, try to come up with several adjectives that shed light on both sides of the spectrum.

  • What words are commonly used to describe their experience?
  • Is there an issue that forces multiple people to leave negative reviews?
  • What part delights them the most?
  • What’s preventing you from solving common problems?
  • Which products or solutions are users comparing yours to?

The answers to these important questions can help you understand user sentiment better and build a customer-focused marketing strategy.

Look to Social Media for Unabashed (Unfiltered) Opinions

Oftentimes, social media is one of the best places to get raw opinions, where people don’t hold back —  both in positive and negative lights. Knowing how people feel in an unfiltered environment can be a great way to tell which parts of your business are working very well —  and not so well.

A social listening platform is an important tool to keep in your portfolio for monitoring online mentions and gathering important datasets. Tools like Mention, Talkwalker, and Brand24, not only keep an ear on social mentions, but also turn these comments and hashtags into valuable customer analytics to help your marketing team understand your customers even better.

For instance, the online gaming developer Wargaming used brand monitoring techniques to analyze its customer’s desires and see which products performed best. The company tracked its users’ social media conversations to see what they were looking for, what parts of the games they liked or disliked, and any suggestions they offered for improvements.

Similarly, you can use a social listening tool to combine all your brand mentions into one database, giving your marketing team a bird’s eye view of audience sentiment on social platforms and identify areas to work on.

talkwalker
Credit: Talkwalker.com

While gathering this sentiment is good, the most important thing is knowing what to do with it. About 83% of customers who make a social mention of a brand —  specifically, a negative one —  expect a response within a day, and 18% want one immediately. Unfortunately, a majority of these mentions go unanswered, which can really impact a brand’s image. By utilizing an effective real-time social listening program, you can not only stay on top of social buzz, you can intervene and reply to any negative sentiment right away.

Some of the next steps will be fairly obvious, especially when you’re dealing with negative feedback. For instance, if your customer sentiment from social listening reveals that people are having trouble updating their software or there are issues with the product itself, this indicates that some redesign is necessary. However, don’t get too comfortable when you are getting positive reactions —  these tend to trick companies into thinking that no improvements are needed.

This kind of feedback can support a stronger marketing strategy. Let’s say your business sells pool supplies. While your customers may not be tweeting about your great chlorine chemicals, they are more likely talking about the fun pool floaties and games your website sells. Therefore, it would be helpful to highlight these fun accessories, as well, by listing them more prominently on your page and even including UGC to promote them.

poolfloatz
Credit: Instagram

Use Predictive Analysis to Spot Trends and Automate Actions

Now that you have all these valuable insights, you need to know how you can use them to shape your current and future business strategies.

Plugging your sentiment analysis into a predictive model is crucial for spotting trends, getting a feel for how opinions are progressing, and determining your next steps. Predictive analytics use machine learning and AI technology to not only gather, but analyze loads of consumer data and make accurate projections. These systems gauge historical behavioral data to help determine the best plan of action in the future.

In fact, customer segmentation and targeting (which is the logical next step after you analyze your audience’s sentiments) is one of the areas where applying AI and predictive analytics has the highest chance of working well for business.

applications of AI
Credit: Emerj.com

In order to develop an optimal predictive model for sentiment analysis, ask yourself:

  • What do you want to know?
  • What is the expected outcome? What do you think your customers are thinking?
  • What actions will you take to improve overall sentiment when you get the answers? How will you automate these actions?
  • What are the success metrics for these actions?

The Wrap

Chances are, your customers are already telling you what you need to make improvements to your business. By gathering as much data as possible on customer sentiment, your marketing team can understand just what needs to be done to provide a better experience, tweak campaigns accordingly, and acquire and retain more customers in the process.

Be sure you know what to data to collect, how to mine it, and how to apply it to keep raking in the revenue.

Can Software Really Predict Our Emotions?

Technology experts and sentiment analysis software developers are claiming that we can now infer people’s feelings by analyzing big data. It’s based on what we say in social media. As direct marketers, we know our copy and content are most successful when we tap into the emotions and lift the feelings of our customers and prospects that motivate them to

Technology experts and sentiment analysis software developers are claiming that we can now infer people’s feelings by analyzing big data. It’s based on what we say in social media. As direct marketers, we know our copy and content are most successful when we tap into the emotions and lift the feelings of our customers and prospects that motivate them to take action.

While I’m skeptical how sentiment analysis can be used without provoking consumer backlash, maybe we should reflect on this claim that software can predict people’s feelings.

In my last blog, I shared this thought-provoking quote from contemporary literature author Maya Angelou:

“I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Let’s take a deeper dive to see if this claim of inferring feelings from social media posts is not only possible, but if it’s smart. Or shameful.

A recent Wall Street Journal article on the topic of big data (“Marketers Want to Know What You Really Mean Online: Sentiment Analysis Aims to Decipher the Nuances of Social-Media Posts“) cites several examples of how it works. The article goes into more detail, but in summary, the process works like this:

  1. Software now can break down tweets and status updates to extract the literal meaning of what’s being said. This step is called natural-language processing.
  2. The software determines the emotion behind the statement. Was it written in earnest, or was it snarky? Was the emotion strong? That is: enthusiastic, angry or sad?

This technology has been used by pharmaceutical companies, hair product companies, food companies, political organizations, and even for the State of the Union address.

What the article doesn’t tell us is if the technology actually worked to increase engagement and ultimately sales.

The resulting analyses of sentiment analysis can be far from 100 percent accurate, but could be one of many resources used in your messaging strategy. Context, cultural and colloquial nuances, and length of message can lead software algorithms astray. The shorter the message, the more difficult it becomes for algorithms to correctly interpret intent. As we all know, people often misinterpret sentiment when reading each other’s messages (consider how many times you’ve read an email that was intended to be cute or poke fun, but backfired).

The CEO of a sentiment analysis software company is quoted in the WSJ article as saying that, “right when a person is first diagnosed with cancer, they are the most optimistic. So he advises pharmaceutical clients to target ads based on the emotion the person is experiencing in the moment.”

Is this smart, opportunistic, creepy or offensive? My mother is currently dealing with cancer and this feels to me like an example of cold-hearted marketers tapping into raw emotions and feelings of a vulnerable person’s emotional state-of-mind. I’m more personally involved, obviously, but using big data on someone just diagnosed with cancer feels shameful (and notice I’ve used the word feel or feelings three times in this paragraph).

On a different and more appropriately used level, sentiment analysis can be effective when monitoring social media for complaints. It enables marketers to more quickly address a complaint and correct a problem for the customer. This feels like a powerful and appropriate use of sentiment analysis.

If we take to heart Maya Angelou’s quote that people will always remember how you made them feel, taken across an emotional line in the sand, marketers would be well served to remember that the good feeling of the moment could quickly turn into a negative your customers and prospects will never forget.