A Data-Driven Approach to Improving the Customer Experience

What’s your business’s most critical differentiating factor? You’re in for a surprise if you think it’s your product. Great products can show that your business is innovative. However, the benefits you derive from this are short-lived. It won’t be long before a competitor comes up with a version of your idea. The true differentiating factor that any company can rely on is the customer experience. Think about Amazon and their one-click buy process. It shaves only seconds off the overall time it takes to buy something, but it seems to save a lot more time. 

The consumer benefits through a streamlined process. Amazon benefits even more because they get the client through the checkout faster. There’s less chance for us to change our minds as a result. 

In this article, we’ll look at how you can improve the customer experience to attract more clients. We’ll discuss a data-driven approach, so you have measurable goals.

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Data and the Customer Experience

Most businesses today have a wealth of data to access. Even the most basic CRM system allows you to track a customer’s movements on every page of your site. It makes sense to use this information to your advantage. 

When it comes to improving the custom experience, you’ll look at data like:

• Demographics: Understanding who your customers are as people is crucial in creating a targeted marketing message. Demographics tell you where your customers live, how old they are, how much they earn, and other salient details. This allows you to create accurate personas. 
• Behavioral Data: You’ll gather this information by watching what customers do. How do they move around your store? What do they do on your app or website? For example, checking opening and click-through rates on marketing emails is a great way to evaluate a campaign. 
• Feedback Data: You should try to gather feedback from your customers regularly. You can ask them to leave comments, reviews, or complete surveys. You should also scroll social media and put Google alerts in place for company mentions. This allows you to improve the experience. 
• Transactional Data: What your customers buy and how frequently they shop is valuable information. It shows you what their preferences and priorities are. 
• Operational Data: The data here is what you’ll gather from your service team. You’ll look at things like response time and resolution rate to evaluate efficiency.

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How to Implement a Data-Driven CX Strategy

So, where do you start with this type of strategy? There are several steps you need to follow. 

Data Collection

You’ll need to first identify what data will be the most useful for you. You should look across multiple touch points like websites, apps, social media, service interactions, and in-store visits. You should map out the entire customer journey to identify the most meaningful touch points.

Data Integration

Next, you’ll need to compile the data into a useful format. You want to create a 360° view of the overall experience. This is time-consuming because you’ll have to bring in information from various systems. However, it’s worthwhile if you want a complete view. 

Data Analysis

You can then plug the data into a CRM to identify:

• Patterns
• Trends
• Correlations

You can use an AI-based program to highlight opportunities to improve. Alternatively, you can use the information to better understand your clients and their pain points. 

Customer Segmentation

The next step is to segment your customers on criteria that make sense. This could be as simple as grouping young executives or mothers with children. Choose segments with similar pain points so that you can relook at the experience from their perspective. 

For example, a time-crunched mom might value a quick checkout process. In contrast, a young exec might have the time to examine specifications. 

Personalization

You now have the information you need to personalize your customer interactions. This goes beyond greeting them by name. It extends to making tailored recommendations and personalized offers. The advantage is that they’re more likely to buy because you’ve made the message highly relevant to them. 

Predictive Analytics

The next phase is to use predictive analytics to work out how your customer needs will change. You can then proactively adapt the experience before they feel dissatisfied.

Continuous Improvement

The final step is to continually monitor the data. Doing so allows you to zero in on successes and tweak when necessary. You can then adapt the experience as the custom needs change. 

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Tools and Technologies for Data-Driven CX

What can you use to help you? There are several systems that make the process easier. 

Customer Relationship Management (CRM) Systems

CRM systems like Salesforce help you manage your customer data. They allow you to analyze various interactions and behaviors throughout the custom lifecycle. Some systems draw data in from several sources to create a central hub. 

Data Analytics Platforms

You can use tools like Google Analytics to see how customers interact with your website. This allows you to identify bottlenecks in the experience.

Customer Feedback Tools

You can use customer feedback tools like SurveyMonkey to collect and analyze valuable feedback from your audience. This is critical if you’re serious about improving the overall experience. By leveraging these tools, you can gain insights into customer preferences and areas for enhancement, ultimately leading to better satisfaction and loyalty.

Artificial Intelligence

You can harness the power of AI to analyze data quickly and efficiently. The machine can suggest improvements to your processes and predict customer responses. 

Customer Data Platforms (CDPs)

Programs like Segment collate your data from various sources, saving you time. You can then create reports to analyze as you like. 

Case Study: Amazon

Amazon stands out as a prime example of the type of customer experience to emulate. You can take a leaf out of their book in the following areas. 

Efficient Customer Service

Amazon has an amazing support team. You can interact with customer service in several different ways. How can a smaller business compete with this level of client support? Companies like Supportyourapp offer you the opportunity to beat Amazon at its own game with outstanding outsourced customer support. 

Amazon goes a step further and identifies common issues that come up in these interactions. They then work on a solution to resolve the issue overall. This reduces the number of clients who are frustrated by the same problem.

Personalized Recommendations

Amazon uses advanced algorithms to analyze your purchase history and browsing behavior. The algorithm can then recommend products similar to the ones that you’ve shown an interest in the past.

Predictive Analytics

Amazon uses predictive analytics to forecast demand. This enables them to manage their inventory more effectively. The upside is that popular items are always on standby. This improves the customer experience by reducing wait times. 

Customer Feedback Analysis

Amazon analyzes customer reviews to identify low-quality products or vendors that need to improve. They then use this information to correct issues and improve the experience. 

Challenges and Considerations

Before you rush into a new data-driven program of this nature, you need to think about the following. 

Data Privacy

Companies that play fast and loose with customer data face strict fines and lawsuits. You have to comply with regulations like GDPR and CCPA. You also have to take reasonable steps to protect your client data. 

Data Quality

Your analysis is only as good as the data you gather. If you have incomplete information, it’s difficult to gather meaningful insights. Therefore, it’s essential to choose good data sources and oversee AI’s analysis. Otherwise, you could work on misassumptions.

Integration Complexity

It’s difficult and time-consuming to integrate data from several sources. Using the right software makes the process easier, but this is an investment. 

The Skills Gap

You’ll need a skilled team to correctly analyze and interpret the data you gather. You would, therefore, need to hire data scientists or invest in training your team. You’ll also need to consider the skills gap when implementing improvements.

Can your current customer service or sales team use new systems you might implement? Do they need further training? 

Customer Expectations

You’ll also need to account for rising customer expectations. Once you start to use the data to personalize the experience, your customers will expect you to continue. What’s more, they’ll expect you to innovate more in the future. 

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Ethical Considerations

Companies today have a lot of power thanks to the data that they gather. It’s important for them to use it ethically. This point is best illustrated by an example from Target. 

Target decided that they wanted a share of the profitable baby item market. They realized that they had to attract customers when they were thinking about having children rather than wait for them to actually be pregnant. 

They designed a highly accurate algorithm that analyzed customers’ purchase behavior. This was so efficient that it could predict if someone was likely pregnant by their purchases. The company would then send advertising for baby products to these customers. 

This backfired a little when a concerned parent saw the advertising sent to his teenager. He felt it was highly inappropriate. Unfortunately for him, his daughter was actually pregnant. 

However, it raised the issue of privacy. Did women really want Target to know that they might be pregnant? In the end, the campaign came across as more invasive than helpful. 

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Conclusion

There’s always room for improvement when it comes to the customer experience. By carefully analyzing the data and gathering client feedback, you can stay ahead of the curve in this vital area.