How to get meaningful, actionable insights from customer feedback
Your customer feedback is a gold mine. The rock walls are your raw feedback, and the gold hidden within represents insight into your business and customer experience. These actionable insights have the potential to revolutionize the way you do business.
Of course, not all business data generates actionable insights, and not everything within that rock wall is the pure metal you’re looking for.
Imagine yourself chipping away at the rock. Two gold nuggets fall out. You analyze them for purity. One nugget is small, and low quality. We’ll take it anyway. Another, is large, and rich in purity. We’ll tell headquarters about this one.
In this metaphor, the difference between these two nuggets is what makes a finding generic (non-actionable) or actionable. Differentiating between the two enables you to empower your decision makers with all the data they need to make informed decisions, without cluttering up the table with large amounts of junk.
If you keep analyzing your feedback but are still unsure how to take action, this post is for you. Here we’ll share how to differentiate actionable data from non-actionable data, and explain three types of actionable insights.
What are actionable insights?
Actionable insights are meaningful findings that result from analyzing data. They make it clear what actions need to be taken or how you should think about an issue. Organizations use actionable insights to make data-informed decisions.
Not all insights are actionable though. Actionable insights don’t come from having more information, or more data. To point out the obvious: insights, information, and data, are not created equal.
In short, data is raw and unprocessed information, in the form of numbers and text. Data can be both quantitative and qualitative, and can be found in spreadsheets or computer databases.
Information is data that has been organised and contextualized into a user-friendly format. This can be in the form of reports, dashboards, or visualizations.
Insights are created by analyzing information, and then drawing conclusions, and making decisions from it.
Why actionable insights matter
Actionable insights matter because you can use them to make strategic, well thought-out decisions. These decisions can drive positive outcomes specific to your business. Unlike generic coaching or advice, your actionable insights are custom-made for you: derived straight from your actual sales data or your customers’ feedback.
For a data first organization, actionable insights are the key to improving product and operational processes. When a company claims to be data-driven, it should mean that all executive decisions are based on real-life data.
Progressive companies today say they want to be data-driven. Forrester reports that 74% of companies say this is a goal - although only 29% of these companies are actually successful in generating actionable analytics. But it's worth pursuing.
Businesses with data driven strategies have been shown to have five to eight times ROI as businesses without. The missing link for companies wanting to drive business outcomes from their data is actionable insights.
3 places to get actionable insights
Qualitative data, such as customer feedback, is full of deep actionable insights. Compared to numeric data, it gives you the answers to why customers behave a certain way.
Here are 3 sources that you can use to gather customer feedback:
1. Net Promoter Score surveys
Net Promoter (NPS) surveys allow you to ask what your customers think about your products and services. These surveys can sit permanently on your website, or be an interactive form at your event. Since you can decide what questions to ask on surveys, you have the potential to target them at potential trouble spots. The danger, of course, is in asking leading questions that don’t provide any real information. The right questions will always be open and unbiased!
2. Online reviews
Online reviews are a great place to collect feedback. Text analytics solutions such as Thematic are invaluable for analyzing this type of data and turning it into insights. You can also use your competitors online reviews to derive insights. First find your competitors public product reviews online. Then upload the reviews to your text analytics product, and analyze the reviews. In a product like Thematic, you can also compare the results against your own analyzed feedback.
3. Social media
Another great place to find valuable insights is your social media mentions. Analyze these alongside what your customers are saying on relevant forums and websites.
How to get actionable insights
Whether you’re using an NPS survey to gather customer feedback or something else, you will need a reliable solution for finding actionable insights.
Insights are hidden in verbatims, or free-text responses, where customers explain why they gave you a particular score, or what they dislike about your product or service.
Verbatims can be hard to analyze manually. This is why people use Natural Language Processing (NLP) methods to ensure analysis is as accurate as possible.
But no one’s going to take your place at the wheel. Only a real person who understands your company’s context can make the final decision about what data is actionable and what is not.
The difference between insightful and non-insightful data
When it comes to making sense of data, getting actionable insights is the holy grail. But what can be considered an insight?
Of the findings in your data, which ones are actionable? Can data analysis accurately deliver actionable insights? Let’s get to the bottom of this by looking at some examples from a fictional school.
Non-insightful data is everything that’s old news to you; something that you already knew was an issue.
If you’re running a school, the fact some students are struggling with exam overload wouldn’t be considered insightful data. This is common knowledge and doesn’t help you in any way.
Insightful data, or ‘insights’, is everything that you did not know, or that you only had a hunch or a suspicion about. Insights are findings that confirm or contradict your existing knowledge. They can confirm your suspicions, or quantify the importance of existing knowledge with deeper context.
Using the school example, your analysis might reveal that not only some, but 90% of students report exam overload. This is insightful data, and worth further thought. Some students might say they’d like exams to be spread out more evenly. This is an actionable insight that you can take to rectify the issue.
Actionable insights can translate into concrete actions that lead to adaptation and action. They can also confirm that no action is required. Companies need to ask themselves: What can be actioned? What hasn’t already been actioned?
As a rule of thumb, if you can add “and therefore” at the end of a finding and then complete the sentence, it’s an actionable insight.
How actionable are your insights?
As an exercise, try to find examples of actionable insights in your business. To begin, we’ve come up with a few examples below.
- Our NPS score this month dropped by 15 points.
- Passengers complain at missed flight connections.
- 20% of customers talk about price.
- Buyers say that clothes sold by a competitor are better quality.
- People talk about our brand more positively following a ban on plastic bags.
- 30% of your detractors mention your competitor has an easier to use product.
Perhaps surprisingly, the first 3 of the above examples are not actionable for the business, and don’t provide meaningful insight.
This is because these findings are obvious, and don’t provide insight into the ‘why’. It’s great to know that my NPS has dropped 15 points, but why has it dropped? The why is likely the actionable insight.
Items 4-6 are valid examples of actionable insights that can lead to data driven decisions.
3 types of actionable insights
1. Insight > Adaptation > Action
Critical thinking is vital in turning insights into action. For example, you could remedy a lack of parking on campus by working with your city council to improve public transport options. In contrast to the obvious option of providing more parking spaces.
This could make it possible to offer a stipend to environmentally conscious students, as well as cutting down the number of students driving to campus.
2. Insight > No Action Required
Not everything is worth measuring, but data analysis can confirm assumptions. This analysis can lead to insights that aren’t actionable, but are vital to the context of your business.
For example, you may think that class size is an issue, but if students don’t mention this is an issue, then no action is required.
3. Insight > Rethink strategy
Data analysis can also help validate whether the implementation of a strategy is working or not.
Consider the scenario that last quarter students complained about unhelpful university staff. After taking measures to change this, the next quarter’s results will show whether the measures you took worked or not. If not, they need further thought.
Or, if your customers are saying that your competitor makes better quality clothes, that is a key insight that you can action. By asking for feedback through a follow-up survey, you can gather more information about why your customers feel this way. You can then drill down into what it is exactly that your customers like more about the quality of your competitor’s clothes.
If your company is a supermarket chain, and you ban the use of plastic bags for a few franchises, this can have a positive effect on your customers. Your customers may feel that they’re doing something positive for the environment by choosing to shop at your supermarket.
As an action, you could build on the successes of these few franchises, and enforce this change across all stores.
Can today’s software find actionable insights in data?
Despite all the promises, no software solution available today can ingest data and spit out usable, actionable insights.
This is because separating actionable and insightful findings, from other types of insights (such as non-actionable/insightful, non-actionable/non-insightful and actionable/non-insightful), requires two types of knowledge:
- Objective knowledge of difficulties associated with different actions,
- Subjective knowledge of what’s old news and what’s genuinely insightful.
Eventually, AI may be able to class what is objective knowledge by reading materials published over the course of years and years. AI agents may even build up the ability to classify subjective knowledge over time, by working alongside their software users.
Advances in AI mean we're getting closer, but it remains essential for a real person to validate analysis and decide what's actionable.
So how can today’s software help us derive actionable insights?
What AI can deliver today is the ability to sift through data more efficiently. It can dramatically cut down the time it takes to transform feedback into insights.
NLP algorithms allow us to make sense of people’s comments by turning them into themes that can be analyzed, like numbers. This data is then displayed visually to help clarify differences, uncover correlations, and detect trends.
When evaluating an AI solution, use the following questions:
- Can this solution tell you things about your business that you don’t already know?
- How easily will you be able to separate signal from noise?
- Will it be able to identify trends in data without having to specify them in advance?
If that sounds complicated, we've got you covered! Check out our free buyer’s guide and learn how to ask the right questions when evaluating feedback analysis solutions.
Make sure to book demos with providers to help guide your selection. Ideally, request a trial of the software; you want to be sure it works on your data.
Once you’ve identified your top contenders, you can start crafting a request for proposal (RFP). If you’d like some help with this, we’ve created a guide specifically for text analytics software RFPs - which includes RFP templates.
And of course, we’d be thrilled to show you how Thematic works magic on your data. Book a demo with our team and let’s get analyzing!