6 Ways To Level Up Your Customer Service with Data Analytics

84% of customer support executives believe customer data and analytics are "very or extremely important" for accomplishing their corporate objectives in 2023. And it’s pretty apparent—everything starts with awareness. 

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People who don’t use data analytics to improve customer support usually:

- Waste resources on things that don’t actually bring the desired outcomes

- Make the situation even worse without knowing what customers actually need

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But THIS will happen if you start using data analytics to come up with customer support enhancement strategies:

🚀 You'll KNOW what to do to improve first contact resolution, increase customer satisfaction, and improve customer loyalty

🚀 Your customer support team will have a more positive MINDSET. So that when you fail, you will learn rather than be worried.

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And beyond. 

Are you feeling inspired? If you want to include data analytics in your customer support strategy but don't know where to begin, we've put up this list of 6 ways to use data analytics for customer support success.

Using predictive data analytics to prevent issues

Is your customer support team still facing the same issues and similar support requests? If yes, then these actionable tips might change the game for your team. 

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Predictive analytics is the practice of forecasting future outcomes using data. To uncover patterns that may anticipate future behavior. Companies that use predictive analytics for proactive support may spot trends and patterns in support requests. This knowledge enables them to handle issues before they escalate, resulting in happier consumers.

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Here’s a 4-Step framework: 

1. Collect relevant data: Begin by collecting information from emails, phone calls, live chats, and social media. More data implies more accurate projections.
2. Clean and preprocess data: To ensure trustworthy analysis, eliminate duplicates, correct errors, and fill in missing figures. This method enhances data quality.
3. Identify patterns and trends: Examine your support data for patterns and trends that may indicate problems. To foresee and avert future problems, keep an eye out for patterns in support requests, customer complaints, and other issues.
4. Utilize predictive analytics: Examine prior data to forecast customer behavior, support requests, and potential issues. Artificial intelligence and machine learning algorithms may also detect and predict trends.

Using data analytics to enhance personalization

Personalized service entails not just knowing your customer's name and history but also providing solutions targeted to their individual situation.  Customer data analysis helps you tailor your communication style and channel. Personalizing communication may considerably improve the support experience, whether a consumer prefers email to phone conversations or formal language over informal.

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To enhance personalization in customer support with data analytics, try the following approaches: 

1. Implementing CRM systems: Customer Relationship Management (CRM) systems may help you exploit data by generating full customer profiles that include prior interactions, preferences, and purchasing habits. These analytics enable your support staff to tailor interactions to each individual consumer.
2. Utilize AI and Machine Learning: Artificial intelligence and machine learning can evaluate enormous amounts of data and find patterns in consumer behavior. These technologies have the potential to deliver useful information for highly tailored customer support.
3. Analyze customer journey data: You can uncover significant touchpoints for customer satisfaction by mapping and analyzing customer journeys. This may help you provide customized assistance when and where it is most needed.
4. Segment your customers: Divide your clients into categories based on characteristics like as purchasing history, demographics, or support history. Customer segmentation allows you to give each group a service that is unique to them.

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Customer profiles require a uniform format. You must update all the details. This is because consumers' demands, tastes, attitudes, and behaviors vary over time. Thus, updating client profiles is crucial. Irrelevant data in consumer profiles may impair your goals and decision-making.

Identifying Trends in Customer Expectations 

Adjusting to ever-growing customer requirements is a must-have in an era of ever-growing trends. Businesses may discover emerging or predicted trends using advanced analytics approaches such as predictive analytics, machine learning, and artificial intelligence (AI). These advanced tools can scan massive volumes of data and identify patterns that were previously unknown.

Potential questions to answer with data analytics: 

• What are the potential future needs and challenges that customers may face? 
• How are client requirements and expectations changing? 
• Which customer service channels will continue to be the most popular?  
• What is the industry's average projected resolution time? 
• What kinds of problems could lead to consumer churn? 
• Are there any methods to improve the effectiveness of current assistance strategies? 

Real-Time Analytics for Issue Resolution

In today's fast-paced business environment, customers are 2.4 times more likely to stay with a business when their issues are solved quickly. Real-time analytics for issue resolution gives customer service personnel direct access to relevant information, helping them to solve problems in a matter of seconds.

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That's why it makes sense to invest in real-time analytics tools. Select a dependable analytics solution that can process data in real-time. Such tools assist you in identifying and resolving issues as they arise and providing insights for proactive action.

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Make sure your analytics technology is capable of integrating data from all customer touchpoints. This provides agents with a comprehensive perspective of consumer interactions, allowing them to address issues more comprehensively.

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Also, you need to ensure sufficient training to your support agents on how to use real-time data. This includes knowing how to interpret data and apply it effectively to solve problems. One of the pro steps is setting up alerts for specific scenarios or thresholds. For example, if a specific type of issue arises frequently or resolution times lengthen, an automatic alert can prompt immediate action.

Tracking Customer Satisfaction Metrics

Companies must track customer satisfaction metrics such as first contact resolution (FCR), average handling time (AHT), and Net Promoter Score (NPS) to determine the efficiency of their support operations. Companies can use data analytics to monitor these KPIs and find areas for improvement, assuring ongoing advancement in customer care.

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First encounter Resolution: This metric is a customer service metric that gauges the proportion of customer inquiries resolved by service professionals on the first encounter. The goal is to resolve any problem or sales inquiry without requiring the consumer to contact the organization again.

Average Handling Time (AHT): This metric covers the time spent talking to the customer, the time spent waiting, and any after-call actions such as sending emails or updating the CRM.

Net Promoter Score (NPS): This is a customer satisfaction metric that assesses the likelihood of a customer recommending a company, product, or service to a friend or colleague. Scores range from -100 to 100, with values greater than 0 reflecting a positive attitude.

Improving Self-Service with Data

67% of customers choose self-service over speaking to a company representative.

Self-service alternatives are growing increasingly popular among customers as digital channels expand and the desire for rapid support grows. 

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Data analytics may aid in the improvement of data self-service by identifying commonly asked questions, common challenges, and preferred support methods. Companies can use this data to optimize their self-service portals for optimal efficiency.

Bottom Line: Don’t Forget About Data Privacy 

Businesses that embrace supporting data analytics may benefit from data-driven insights, proactive problem resolution, and tailored support experiences. The end result is a more successful customer care approach, which leads to happy customers and a profitable company.

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However, remember that data privacy in support of analytics is always a priority. Companies should follow data protection standards and be transparent with their consumers about the acquisition, processing, and usage of their data.

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And, YES, you can create a customer support strategy by yourself and constantly enhance it with analytics.

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BUT...

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Would the results be what you wanted? How long would it take you? How much energy would it cost you? How many new employees do you need to hire? Those are the questions you'd definitely want to ask yourself if your customer support team is not keeping up with KPIs, or if you are just at the beginning of building it. 

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If you think you can do it and get the results that you wanted, then great! But you can always save time and energy, and most importantly - get the results that you ACTUALLY are looking for with Ever-Help Customer Support Outsourcing. Just book a call with us, and we’ll see whether your business and our approach are a great fit.

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