Power BI Reporting: Mining Customer Behavioral Data for Results

The continued success of your business depends on whether you're able to adapt to your customers' wants, preferences, and expectations. And since you don't have a customer-centric Geiger Counter to use, you'll instead need to rely on mining customer data. Through the discovery of metrics and data points related to your target audience, you can conduct customer behavioral data analysis, allowing you to understand what data you do have. And when executed correctly, these insights can make it much easier to see how to improve your products, services, and customer experiences. 

Learn more about mining customer data and interpreting those points to better serve your customers. In a way, this is probably the closest we can get to having a customer-centric Geiger Counter. It's the only data-driven method that will allow you to remain agile, adapt to emerging customer behavioral trends and stay relevant in your market.

Collection of Customer Data

You're always looking for more efficient and effective ways to engage new customers. You can take a peek into target customer preferences by mining and collecting data at every touch point and effort. Collecting data is the first step. Explore these sources and methods of customer data collection. You may find that you already have some in place. Follow the steps for identifying what data to collect, enacting processes for data collection, categorizing the data collected, and interpreting customer behavioral data to discover insights your business can use.

What Are the Sources of Customer Data?

There are four types of customer data your business should seek to collect. These include basic data, interaction data, behavioral data, and attitudinal data. Any engagement a customer makes with your business, online or otherwise, presents an opportunity for data collection. Purchase histories, support requests, and browsing history all provide unique metrics about what a potential customer looks for, struggles with, or needs. So, start by outlining every source your business has that interfaces with your audience. Think online, like your mobile app, website, and social media. Think offline, like phone calls, store visits, or customer care inquiries. 

Methods of Data Collection

Your company can explore the various methods of data collection. These methods are usually broken down into three core initiatives. You can ask customers directly for their feedback or opinions, you can indirectly track customer behavioral data, or you can append other sources of consumer data to your own. These methods could look like:

1. Direct: Surveys and focus groups
2. Indirect: Social media listening
3. Other sources: Web analytics

Once Collected, the Data Analysis Begins

Once your customer data begins rolling in, you'll need to create a series of processes for analyzing the results. Aim to develop a system for collecting and storing the data as it presents itself. Assign team members the core responsibilities of categorizing, cleaning, and processing the data in such a way that you can make interpretations for improvements, suggestions, and next steps. The data you collect will remain stagnant until you process it and find the story it's telling.

Data Cleaning and Processing

With your customer data collected, establish a cleaning process that allows you to remove unnecessary values, duplicate entries, unwanted outliers, and typos. Part of your processing might also include converting data types. For example, you might collect relevant demographic information about your customers from two different sources. In one survey, your participants answer true or false. In another dataset collection, customers typed out full answers. Combine those results into one consistent format for easier interpretation overall.

Descriptive Statistics

Part of your data processing actions will include assigning descriptive statistics to help translate raw data into customer behavioral insights. Look for ways to describe what your collected data suggests. These might include:

• Frequency analysis.
• Cross-tabulation and inferential statistics.
• Correlation analysis.
• Regression analysis and data visualization.
• Charts and graphs.
• Heat maps.

Interpret Results

Once your data is collected, cleaned, and processed, it's time to start interpreting the results and identifying the unique stories your data is telling you. Create a process and use a team approach to find your interpretations. Keep the following steps in mind:

Identifying Patterns and Trends in Customer Behavior

Look for customer patterns or emerging trends in their behavior. For example, maybe your data suggests more customers are using your mobile app than your website to place orders. Or, maybe you spot a surge of new customers coming from a specific part of the world. 

Determining Customer Preferences and Attitudes

Identify customer behavioral data insights that speak to your core audience's preferences and attitudes. For example, maybe one of your products suddenly becomes more popular than another. This could mean customers have a new problem that they are trying to solve. Or maybe your social media engagement metrics reveal a growing conversation about a common, new challenge your customers are facing.

Evaluating the Effectiveness of Marketing Strategies

Interpreting the behavioral data of your customers can be game-changing, especially when applied to your marketing strategies. Use the data to tell you which of your marketing campaigns are working effectively and which are not. The data can tell you if paid ads are increasing conversions. It can tell you if your email marketing efforts are leading to improved sales funnel activity.

Identifying Areas for Improvement in Products and Services

Data can also tell you which of your core offerings needs improvement. Look at metrics from customer support requests, for example, to spot common issues with ordering or shipping. Look at data from reviews and surveys to see what complaints customers might be making about your product or service.

Application of Results

Once you've identified the stories behind the data, spotted trends and found intel your business can use, it's time to actively implement those necessary changes and improvements. Knowing what you need to fix will only be helpful if you take the steps necessary to make those fixes. These steps might include:

• Personalizing products and services.
• Developing targeted marketing campaigns.
• Improving customer experience and satisfaction.
• Enhancing overall business strategy.

Challenges of Customer Behavioral Data Analysis

Customer behavior analysis is critical to your business's ability to remain dynamic and flexible in an ever-changing marketplace. Of course, despite all of the advantages of collecting and analyzing customer data, there can still be a few challenges, such as: 

• Recognizing that not all the data you collect will be actionable. 
• Being mindful of privacy and ethical data collection practices. 
• Knowing that there will be limitations in terms of data analysis tools and techniques.

Conclusion

With much of today's customer interactions going global and online, businesses have access to an incredible amount of data. Mining customer behavioral data is a proven method for any company to stay in tune with their industry, understand changing customer dynamics, and continue to improve. Start implementing a customer behavior analysis process for your business using the steps outlined in this article. It's the best way to inform the improvements you make rather than making educated guesses. You'll find it's the Geiger Counter you've been looking for to help you serve your customers better and improve your bottom line.

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