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What We Can Learn From Our Competitors' Customers

Posted by Matt Desilet on Fri Sep 15, 2017 09:30 AM

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I Can’t Get No: What To Learn From Our Competitor’s Online Customer Satisfaction

We can learn volumes from our competitors' customers. Customers that take the time to say something about a company online likely have strong opinions. Customer sentiment can be good, bad, and ugly. To learn from those reviews and rants, we must take the time to understand the varying levels of online customer satisfaction, and what they mean. Once we have a baseline for customer satisfaction, it's time to focus on collecting online feedback. How do we best gather online feedback intelligence? What types of online feedback are strong signals for our business? What kind of noise is out there, and how do we look past it?

In this post, we will:

  1. Outline the spectrum of customer satisfaction, and what different sentiments mean
  2. Create a plan to collect and categorize competitor customer feedback data
  3. Propose action items that help you strategically use customer satisfaction insights

 


 

The Customer Satisfaction Spectrum

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What embodies a happy customer? What’s the anatomy of an angry customer? Why is it that some customers don’t seem to care? By the time we get to an online review, we’re already analyzing the symptom, not the disease. We start by establishing a baseline understanding of customer happiness so that when we collect data, we make smart decisions. Let’s look at potential causes, which we’ll call Themes, that exist within the customer satisfaction spectrum.

 

Themes For Happy, Unhappy, and Unmotivated Customers:

Happy Customers:

  • Consider their expectations met
  • Have confidence in their ability to use the product
  • Feel a sense of attachment to your brand
  • Are motivated to promote and praise the brand

Unhappy Customers:

  • Consider their expectations unmet
  • Lack confidence in their ability to use the product
  • Feel a sense of attachment to your brand
  • Are motivated to share discontent with your brand

Unmotivated Customers:

  • Lack or are unaware of baseline expectations
  • Are disinterested and/or unaffected by learning the product
  • Feel disassociated with your brand
  • Prove difficult to connect with

 

We can pull out a couple interesting insights from this perception of customer satisfaction. Notice how both happy and unhappy customers have a sense of attachment to your brand. At first glance, it’s tempting to dismiss the idea that unhappy customers have a close attachment to your brand. How could they care? These customers took the time to bash our competitor online, where’s the connection? In order to become upset over a situation, humans have to care first.

Unmotivated customers feel a certain level of disassociation with your brand, and thus, their happiness loses relevance. As you read the themes for each type of customer satisfaction, think about how you behave as a customer. We have all jumped into a purchase with little expectations. Unsurprisingly, when we are dissatisfied with those purchases, we are not motivated to express those views. It is the depth of our initial expectations that drives us towards the outskirts or center of the spectrum.

 


 

How to Collect and Gauge Feedback

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Brands go to great lengths to quantify customer sentiment. We use formulas like Net Promoter Score (NPS), Customer Effort Score (CES), Customer Churn Rate (CCR), and even Customer Happiness Index (CHI - an in-house metric created by Crayon Co-Founder, Jonah Lopin). Creating insights from competitor feedback is not as simple as compiling KPIs. Truthfully, we will scarcely have access to competitors' internal KPIs. For the purposes of setting expectations, we will rely on the valuable public customer sentiments that we can access.

 

Review sites are an easy place to start looking for online feedback. Plenty of the review sites out there provide numeric rating systems. Almost all review sites adopt the 5-star format. These rating systems can provide quick insights into overall customer happiness, but be sure to combine that data with the qualitative feedback of the reviews themselves.

Social media has become a popular destination for customers to express varying levels of satisfaction. Twitter users have shown that their voices have the power to incite action from even the most prolific companies.

"Turned" users, or users that have adopted your product after working with a competitor, can be a great resource. It’s crucial to grab that feedback at the onset of the new relationship, while the old wounds are still fresh.

Case studies often act as a hotbed for positive testimonials. Case studies are not limited to positive opinions of what your competition does well. Testimonials show the world what a company's ideal reputation might look like. By making case studies public, the competition places a gold stamp of approval on the content inside the study.

 

The juicy intel lives in a targeted keyword analysis

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We have incredible access to external, public sources for gauging competitor customer satisfaction. We have to be creative in how we produce quantifiable results with anecdotal information. The plural of anecdote is not data. If we instead create a way to extract keywords from wordy reviews, we can connect the dots between intent and sentiment. That’s how we “data-ify” wordy paragraphs.

We have created a shortlist of common keywords found amongst online reviews. We used Crayon data from the last quarter to highlight common words associated with good, bad, and average reviews. For this study, we created the following legend:

Good review = > 4 Stars

Bad review = < 3 Stars

Average review = 3.0 - 3.9 Stars

We separated our keyword analysis by B2B product/service reviews and B2C reviews:

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After processing over 17,000 online reviews, we picked up on a few anecdotal trends:

Good review keyword trends

Bad review keyword trends

Great

Time (+ Save)

Bad

Expensive

Useful

Quality

Purchase

Not (+ Worth)

Full

Strong 

Poor

Poor

Easy

Worth

Needs

Decent

 

If you’re wondering where the list of “average” keywords is, keep wondering! Average reviews do not show consistent keyword strings. Moreover, mediocre reviews rarely make it out to the public world, and when they do, they’re not very detailed. Think back to the lack of attachment at the onset of a new user-product relationship. Those users who fall into the apathetic bucket do not see the value in putting time into an online review. 

Looking for a great “hack” to spot an average review? Consider combining the recurring keywords from the good and bad reviews in one string. The most identifiable trend in average reviews is a combination of “good” and “bad” review words. Another hack to spot the average is to look for conjunctions. Conjunctions can also lean towards negative reviews. Again, we attribute this to unmet expectations that create dissatisfied customers.

 


What can we do with the competition’s customer feedback?

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It’s not competitive intel if you can’t find actionable insights. Collecting insights without acting is research. As professionals focused on competitive intelligence, we have to interpret the meaning of the data and act on it. 

Each customer satisfaction profile supports different sets of action items. Negative feedback will guide your next steps in a very different direction than positive feedback. We want to take sets of negative data and reference ourselves against that data. How can we avoid the pitfalls customers have identified in these online outlets? How can we distribute news of a new product issue trending on Twitter to our sales teams? Can we act fast to adjust our talk tracks and lead prioritization, or will we miss the opportunity?

Realizing the difficulty here, we have created a quick start guide on how you can act on different sentimental insights.

 

Use Positive Competitor Feedback to: Learn from your competitors

Use Negative Competitor Feedback to: Correct unmet expectations

 

How positive feedback creates opportunities to learn:

  • Compare the sentiments from happy competitor users with positive reviews of your own products and log trends
  • Use positive comments about competitive solutions to inform teammates in product on what’s working outside of your business
  • Generate inspiration from case studies, testimonials, and customer appreciation content on how you can bring positivity to your brand on your own terms

 

How negative feedback shows where misalignment exists:

  • Modify your positioning to attack the competition’s weaknesses - as stated by users
  • Product-related reviews can be used to help your internal roadmap, avoiding pitfalls of the competition’s design
  • Create a study on unmet expectations amongst competitors to help your customer success team train new clients 


Wait… so what do we do with “average” feedback. Can we use it?

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Drawing conclusions from positive and negative reviews is a much easier task than using "average" online opinions. Average reviews often contain both good and bad sentiments. Acting on unclear data from disinterested users is difficult. Managing expectations from average reviews is a good start.

One tactic to try when analyzing average reviews is to re-tool that negative keyword strategy. By looking at negative keywords in "average" feedback, we can find threats to competition within their common user base. If you find issues worth mentioning among average reviewers, you may have identified a trend that the competition hasn't. Small differences, when positioned, could make all the difference in winning a competitive sale down the road.

Consider how you could use positive keywords within average reviews as well. Positive keyword analysis can show emerging trends. These keywords can also identify non-mission-critical sentiments amongst average customers.

Leverage the "average":

The most valuable takeaway from “average” customer feedback is the potential to use this data to "lean in" to your own "standard" user. Knowing that “average” feedback contains some level of dissatisfaction or recommendation, consider creating a customer survey that targets the issues prevalent in average feedback. As a target audience for the survey, choose a set of “average” customers in your user base to mimic the customer feedback profile. Consider a very similar set of competitors. Let’s say you have a total of 300 + “average” reviews, via 6 competitors, over 6 months. What if you only have 50 “average” customer reviews for your company in the same timeframe? In a way, did you not 6X your data set for analyzing common customer sentiment?

Bottom Line? To get the most value out of those 3.0 - 3.9-star reviews, we must be creative.

 


 

Wrapping it all up

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Taking the time to learn from our competitors' customers is a fantastic way to find and use free intel. Strong opinions make their way online, and we can learn a lot about where our competitors win, and where we win when it comes to customer satisfaction. Knowing what different types of feedback mean, and how we can use them, help us guide the way we collect data. Well-mined data and insight into customer sentiment allow us to act with unique focus. We can target our work based on each type of feedback collected.

Want to create your own competitor customer feedback summary? As a thank you for reading the article, check out this template based on our “Customer Satisfaction Spectrum” graphic.

 


 

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