Analyse and leverage incoming customer feedback

From tantrum to triumph

Road trips are fun, except when you do it without a GPS, aircon, or a spare tyre. They’re even less fun when you have a hot, bothered toddler throwing tantrums and toys in the backseat. You see, some things are necessary when it comes to arriving safely with your family (and sanity) in check.

But no GPS, cool wind, or spare tyre can book your accommodation, pack the right clothes, or remember the sunblock. You also need to research and prepare before you hit the road.

Imagine your business is like a family on a road trip and your customers are the toddlers in the back. Instead of a GPS, you must follow the toddlers’ instructions.

Techniques to analyse and interpret customer feedback data

Look, no one is saying that toddlers or customers are horrible. They just need the right interpreters. So how do you know what they need when they need it?

Believe it or not, this is where toddlers are easier to read than customers. Parents understand their children’s language and social cues, while each customer is different and prefers a particular platform.

In other words, customers don’t just scream, “I want a cookie.” They ask for things in various forms and formats, including surveys, reviews, ratings, comments, complaints, suggestions, social media posts, etc.

Customer feedback data can be qualitative or quantitative, structured or unstructured, solicited or unsolicited. That’s why you need to use diverse techniques and tools to process and analyse customer feedback data.

Some common techniques include:

• Text analysis: Text analysis helps you categorise feedback into topics, themes, aspects, attributes, options, and emotions. It uses natural language processing (NLP) algorithms to extract meaning and sentiment from textual customer feedback data. In a nutshell, text analysis helps you understand what your customers are talking about and how they feel about it.
• Data visualisation: This technique uses graphical elements like charts, graphs, maps and tables to visually and interactively present customer feedback data. It will help you spot patterns, compare different segments and see the big picture of customer data.
• Statistical analysis: Statistical analysis uses mathematical methods and formulas to calculate and interpret numerical customer feedback data. This way, you can make hypotheses, make predictions, and quantify and validate your customer feedback data.

How to identify recurring issues and pain points

What’s a common reason for tantrums in toddlers and customers? Teething pains. Literal for toddlers and figurative for customers.

Unless you understand each customer’s daily routine, you won’t know anything without analysing their peeves.

Here are some indicators of recurring customer pains:

• Frequency: How often do customers mention the same issue?
• Severity: How serious is the impact of the problem on customers?
• Consistency: How consistent is the pain point across different sources?

Using data-driven insights to improve customer journeys

Remember the road trip? In this scenario, we call it a customer journey.

Customer journeys are the paths that customers take when interacting with your business, from the first contact to the final purchase and beyond. These journeys can be complex and are influenced by customer needs, preferences and emotions, as well as touchpoints like websites, apps, social media, and email campaigns.

So, in our road trip scenario, it’s not just how hungry, tired, or bored your toddler is but the comfort of the car seat, the interior temperature (this is where the aircon comes in), and the effectiveness of the sun visor.

Here’s how data can make the customer journey worth it:

• Find and fill gaps: Find out where your customers are dropping off, facing difficulties, and feeling satisfied.
• Design solutions and experiments: Generate ideas for solving issues, testing, and validating your solutions.
• Measure results: Track and evaluate the performance of your experiments and compare it to previous customer journeys.

Tips to filter through the noise

1. Have a clear purpose and direction for what you want to achieve with customer feedback data. Do you want to measure customer satisfaction? Or do you want to identify customer preferences?
2. Choose the right methods and channels to collect feedback. This will determine the response rate and quality of the answers.
3. Use techniques and tools to analyse the data. Will it be software applications, online platforms, cloud services, something else?
4. Interpret the data with context and perspective. Consider the relevant factors and circumstances influencing customer feedback data.
5. Leverage feedback for decisions and actions to create better products or services that deliver more value to your customers and business.

Not up for a road trip with a crying toddler? InteractRDT’s teams are master interpreters; give us a call.

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