Which is data that has been organized structured and presented to provide additional insight into its context worth and usefulness?

Your customer feedback is a gold mine. The rock walls are like your feedback, and the gold hidden within represents insight into your business and customer experience.

Now 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 the boss about this one. In this metaphor, the difference between these two nuggets is what makes a finding generic (non-actionable) or actionable.

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 one 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, on the other hand, 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, however, 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. This is because you derive the insight straight from your data such as your sales data or your customers’ feedback.

If your organization is truly data-driven (“data first”), actionable insights are the key to improve your product and operational processes. All executive decisions should be based on 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 actioning their analytics. It’s clear that 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 places that you can use to gather customer feedback, to turn into actionable insights.

1. Net Promoter Score surveys

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 an event.

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.

Thematic, for example, uses a combination of NLP and Machine Learning to analyze verbatim feedback responses.

Although only a real person who understands your company’s context can decide what data is actionable and insightful.

The difference between insightful and non-insightful data

WWhen 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

Non-insightful data is everything that’s old news to you; something that you already knew was an issue. Using the school example: The fact some students are struggling with exam overload would not be considered insightful data. Why? Because this is common knowledge.

Insightful data

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 reveals that not only some, but 90% of students report exam overload. This is insightful data, and worth further thought. Some students might say that 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 translate into concrete actions that lead to adaptation and action. Or confirmation of the fact 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 the first 3 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.

1. Insight > Adaptation > Action

Critical thinking is vital in turning insights into actions. For example, you could remedy a lack of parking on campus by working with city council to improve public transport options. In contrast to the obvious option of providing more parking spaces.

This could offer a stipend to environmentally conscious students as it cuts 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 to remedy this.

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, none of the software solutions offered 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:

1. 1. Objective knowledge of difficulties associated with different actions,
   2. Subjective knowledge of what’s old news and what’s genuinely insightful.

Eventually, AI agents 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. Unfortunately, we are still so far from these inventions that they may as well be science fiction.

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. 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:

• Will 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?

Check out our free guide we built specifically for asking the right questions when evaluating feedback analysis solutions.

At Thematic, we help companies find insights, by turning customer and user feedback into easy-to-use data visualizations. You can start a free trial of Thematic by clicking the button below this post.