When you run a business, it’s important to gather feedback from your customers and analyze data.
Customer feedback helps you identify where your company could improve. Plus, it shows what aspects of your business need attention. Collecting this information in addition to data visualization can help you make well-informed decisions about scaling.
It tells you what customers like about your brand and how they feel about your customer service. In today’s world, you can find customer feedback data in many places, such as social media and online reviews.
In this blog post, you’ll learn:
● Why you should analyze data,
● Where to collect data,
● How to analyze data in four steps.
Why Should You Analyze Data?
● Data analysis helps you see where your business needs improvement. Customers might be complaining about the same thing. Or one product might be more popular than another.
● Data analysis lets you determine how well your competitors are doing. Examining your competitors’ customer reviews shows how you can be better.
● Data analysis tells you what customers don’t like. Analyzing data lets you uncover your brand’s weak points. For example, customers may be complaining about high prices. If so, you could experiment with new pricing models for a limited time.
Or they’re saying your product doesn’t deliver on its promise. In this case, consider changing suppliers or improving your production team.
● Data analysis tells you what customers want more of. If customers ask for more features, you can consider adding new ones. For example, a brand like HubSpot constantly sends out surveys to its customers asking them what features they would like to see in the next HubSpot updates.
They then focus on adding those features to their software. This is why we constantly see feature-packed updates from HubSpot.
● Data analysis tells you what customers love about your brand. Knowing what customers love about you is powerful. You can use their favorite features as a differentiation factor. This sets you apart from the competition. You’ll also learn which features you should promote in future campaigns.
Where to Collect Customer Feedback Data
You can collect customer feedback in various ways. The first step is to decide what type of customer feedback you’re looking for and where to look. Here are some ideas:
Online Reviews
Thousands of people use review sites like Google and Yelp. Spending 10 minutes reading reviews will show you the public’s opinion of a business.
Google also lets you interact with customers by responding to their reviews. Take advantage of this — customers love feeling heard. And responding to reviews is an easy way to show them that you care.
Social Media
Social media is a powerful tool for data collection and brand awareness. In 2022, DataReportal reported that 75% of people use social media to research products. Plus, you can generate new leads by interacting with your audience more on social media. It builds trust with your audience and increases brand awareness.
Surveys
Surveys have been a go-to method for feedback collection for decades. They can be effective as long as you approach them the right way.
Keep your surveys short – it should only take the participants 1-2 minutes to complete them. And always offer incentives. For example, discounts on a future purchase or a giveaway entry.
You can spread the survey to your email list. Consider embedding a pop-up after customers buy your product. Ask customers to leave reviews after they’ve received orders. The options are endless.
You can simplify the process by using a QR code generator. This lets customers scan the code on their phones, which takes them to the survey site.
Product Reviews
Checking your product reviews is an easy way to collect customer feedback. You can also check out your competitors’ reviews on the review sites we mentioned earlier. Or check networking platforms like LinkedIn to see what people are saying about them.
These teach you what customers love (or hate) about your competitors’ offerings. This information then gives you an edge when improving yours.
Look for trends in what customers say about your product or service- are they happy with their overall experience? Was customer service up to the mark? Have they recommended your service to a colleague? Would they like to see a new feature added? Have they positively or negatively compared you to a competitor in some way?
Identifying these trends tells your team what’s most important to customers. And in the end, you avoid your competitors’ mistakes.
How to Analyze Data
Without further ado, let’s elaborate on how to analyze data in 4 easy steps:
Step 1: Identify Your Goals
Before you begin, define your goal and identify the problem before looking for a solution.
It’s easy to get distracted when analyzing big data. And it’s tempting to explore every possible result. But going down rabbit holes will only make finding the solutions take longer.
You must also avoid falling into the trap of competitor comparison too soon. Your goals should reflect what you need to discover about your business. This will likely differ from anyone else’s.
For example, your competitors might need to improve their customer service. But their product works very well. Your customer service team might be kind and patient. But the product isn’t as durable as customers would’ve hoped.
To help you identify your goals (and stay on track), here are a few questions to ask yourself:
● Why did I decide to analyze data?
● What do I want to get out of this data analysis?
● Where am I going to get my data?
● How am I going to measure the results?
You likely already know the answer to the first two research questions. But you might not have the answers to the last two yet. But that’s what these next two sections are for.
Step 2: Collect Your Data
Now that you have identified your goals, we can move on to the next step which is to start collecting your data from existing customers. The key when collecting data is to make sure you collect as much as possible so you have a lot of information to work with at the end.
#1 Quantitive Data Analysis Vs. Qualitative Data Analysis
There are two main types of data you need to know before jumping into the data analysis process:
● Quantitive data analysis. (Data that you can measure and number)
● Qualitative data analysis. (i.e., survey results and responses to open-ended questions and online reviews)
You should strive to collect both qualitative and quantitative data. But depending on your goal, you’ll likely end up with more of one than the other.
The next step is identifying the correct data analysis method.
#2 Feedback Surveys
Try asking customers for feedback via email, social media posts, or ads.
An ad’s comment section is usually a treasure chest of feedback. Scroll through the comments on your previous ads, and you can find a treasure chest of feedback. Reply to every comment to show your audience that you value their opinion.
For the survey form, focus on making the questions as easy as possible for your audience to answer. For example, ask questions that only need short responses rather than long paragraphs (e.g., ‘yes’ or ‘no’).
This makes it easier for people who aren’t tech-savvy or are very busy to answer them all. Plus, people are more motivated to take surveys when they know it will only take five minutes of their day. It also never hurts to include an optional message box. This lets people who have more time give more detailed answers if they want to.
#3 Review Sites
When using review sites, remember that more is not always better. The majority of reviews only come from four websites:
● Google,
● Yelp,
● Facebook,
● Instagram,
● TripAdvisor.
What Does This Mean for Data Analysis?
The most dependable source for customer feedback is Google. You can even get by only referring to Google for data collection. This isn’t recommended, though.
The more sites you use to look for reviews, the more you’ll find.
However, in some niches, like law or medicine, your clients are not going to be reviewing your services on Instagram which is a platform more used by clothing or beauty brands.
For example, for a law firm like Attorney Brian White’s, most people will be checking their Google reviews since that is where the reviews will be most accurate and clients will be more likely to go.
Further proof can be found by looking at the homepage of De Castroverde Accident & Injury Law Group.
Their homepage actually makes it a point to focus on the hundreds of Google reviews they have received.
All of this is to say that the platform you focus on is most likely going to be Google but it is heavily subjective on what industry you are in which is why you need to take a step back and think about where your audience is the most active.
#4 Social Media
The easiest way to collect data from social media is by checking these places:
● Hashtags (i.e., #yourbusinessname),
● Posts with @ mentions (any time someone @s your account, you receive a notification),
● Your DMs,
● Comments on your posts.
Social media is also an excellent place to advertise your customer feedback survey. Create a post about it and ask your followers to answer a few short questions. Include a link to the survey in the post and your bio so it’s easy to find.
And finally, don’t forget to add an incentive for those who take the survey. Think special discounts, a free gift, or entrance to a giveaway – the options are endless.
#5 Product Reviews
Look for keywords in product reviews to help you identify the most common types of feedback you get.
These should be the areas you prioritize improving the most. This is especially true when the feedback is negative.
Handling the most common flaws first is often more cost-effective.
This is because they don’t take as much time expenditure to uncover. And it can prevent future negative reviews rooted in the same issue.
Look for keywords in product reviews to help you identify the most common types of feedback you get.
This is especially easy to do on sites like Amazon. See how Amazon identifies keywords above the product reviews for a podcast microphone:
Step 3: Analyze Data
Data analysis is a type of statistical analysis. It involves extracting information from data and converting it into useful information. And you can use tools to understand sales performance and customer behavior patterns.
Here are the most useful data analysis tools and how to use them.
Types of Data Analysis Tools
There are many types of data analytics tools that you can use. The most common ones include:
● Statistical data analysis tools. These include programs that allow you to analyze data, such as Microsoft Excel and SPSS.
● Machine learning tools. These analyze data and large amounts of unstructured text or images. For example, Google Cloud Natural Language API or Amazon Rekognition.
● Predictive data analytics tools. A predictive analysis tool uses machine learning algorithms to predict future events. It’s based on past behavior patterns. Examples of these tools are the Salesforce Einstein Analytics Cloud Platform.
How to Use Data Analysis Tools
Data analysis tools analyze data and derive insights from it. Data analysis tools come in these categories:
● Data collection. Collecting relevant information is always the first step. You can do this by asking questions using surveys. But you can also use scraping websites and social media platforms.
● Data cleaning. You have to clean your data so only accurate information is in your dataset (the set of all observations). For example, if someone said they are ’23 years old,’ you’d need to change it into an integer between 0-100. This is so your program understands what they meant.
Business Intelligence Tools
Business Intelligence (BI) tools analyze data and create reports, dashboards, and data warehouses. They’re usually divided into reporting tools and dashboard software.
● Reporting tools. These tools create quick analysis reports from raw data sources. Examples include Excel spreadsheets and Google Sheets. Analysts often use them to generate ad hoc reports for organizational stakeholders.
● Dashboard software. Dashboards let you check key metrics in real time. They use interactive visualizations like graphs, charts, and tables. You can access these through desktop computers or mobile devices.
Step 4: Interpret the Results With Data Visualization
Data visualization makes information easy to understand. It does so by displaying it in a type of graph. In turn, this highlights patterns and potential future trends in the data, making it easy to read.
You can use it in many ways. For example, simple charts, graphs, and complex interactive maps. But the goal is always the same: to help you make better decisions by making sense of your information.
Data analysis results are often complex and difficult for non-experts to interpret. But you can make it easier with the right tools. For example, business intelligence and data analysis tools.
Types of Data Visualization
Data visualization tools display data science findings in a visual and easy-to-read format. The different types of data visualization include:
● Heat maps (a map where colors represent data values),
● Scatter plots (a graph that plots two variables along two axes),
● Line graphs,
● Bar charts,
● Pie charts and histograms,
● Bubble charts.
Experiment with these using a small sample size of data. This will let you identify which types of visualization work best for you.
Analyzing Data Improves Your Business and Increases Revenue
Feedback data is an excellent way to see how your customers feel about your brand. And you learn what you need to improve.
If you follow these steps, you can analyze data with ease. And then use it to drive results-based decision-making.
Always start with clear goals. Then, choose the right data analysis technique and tools for the job. Finally, turn your findings into easy-to-understand information for content analysis. Then, you can make results-driven decisions that keep improving your business.
FAQs About How to Analyze Data
In this section, we’ll address some common questions about data analysis:
Q1. What Are the Benefits of Data Analysis for Businesses?
Data analysis helps businesses identify areas for improvement, understand customer preferences, benchmark against competitors, and make informed decisions that drive growth and efficiency.
Q2. How Do You Choose the Right Technique to Analyze Data?
Choosing the right technique to analyze data depends on your goals and the type of data you have. For example, if you need to understand correlations, regression analysis might be suitable. For categorizing data, clustering methods could be used.
Final Words
In conclusion, learning how to analyze data is crucial for making informed business decisions. By setting clear goals, collecting comprehensive feedback, using the right tools, and visualizing results, you can uncover valuable insights about customer preferences and areas for improvement.
This data-driven approach helps you understand your market better, innovate effectively, and enhance overall performance, ensuring your business continues to grow and succeed.
