Customer Service Analytics: Benefits, Challenges, and Tips for 2024

min read
Customer Service Analytics: Benefits, Challenges, and Tips for 2024

Technology has changed the way businesses operate. Companies in the past did not have the tools they have today, nor did they have easy access to all the information needed to make important decisions. 

Modern companies have these capabilities, and that’s why the days of making “gut feeling” business decisions are a thing of the past. Today, companies want to make informed decisions and execute tasks to increase their revenue while keeping costs low. 

Data analysis has become a crucial business process, just like product development or accounting. Today we’ll talk about how business data can help you set the best customer service strategy in place. 

What Is Business Data Analysis?

As part of business analytics, data analysis includes a set of technologies and disciplines used to solve various business problems. There are several different analysis methods, and all of them are used for specific tasks. 

  • Statistical analysis: involves researching relationships, patterns, and trends within quantitative data. It can help companies uncover things that weren’t evident and find new opportunities.
  • Predictive analysis: this process involves using modeling and statistics techniques on datasets to make likely predictions about future performance or outcomes.
  • Consumer data analysis: customer data analysis or customer analytics is the process of gathering and analysing customer data to learn about customer behavior, interests, likes, and dislikes. 
  • Competitive analysis: gathering information about some of the biggest competitors in your target market to learn about different aspects of their business, including marketing, sales, product, services, customers, and more.

Why Data Analysis Is Crucial for Your Business

Data analysis is vital in acquiring valuable data while helping users set the proper methods for analysing the information. 

With data analytics methods supporting relevant and accurate data, companies can: 

  • Recognise their strongest and weakest points throughout all departments, processes, and workflows
  • Determine what the best solutions for overcoming current challenges are
  • Establish better business plans or update existing ones 
  • Create, set, and execute more effective marketing and sales strategies 
  • Strengthen and modify the company mission, vision, and values to align with current market trends, employees, and customers. 

The Benefits of Customer Service Data Analytics

Data analytics and customer service are a match made in heaven. Customers have higher expectations for the experience than ever before, and when you quantify your experience with data, you can make smarter optimisations to improve it. Collecting customer data also allows you to personalise the experience for your customers to surprise and delight them.

Identify and improve weak points

Data can help you determine crucial process metrics to learn what you might be doing wrong and improve. Customers won’t always give feedback on what they dislike, but data analytics can discover hidden patterns and trends.  

Offer personalised customer experiences

Data turns into insights, and insights allow companies to deliver better experiences. Modern customers want personalised experiences, services, products, and offers. Data allows companies to learn about customers and what part of the website they like the most, how they react to ads, which products they like best, etc. 

If you want to learn how data helped us determine how inflation affects customer experience, check out Invoca’s 2022 Buyer Experience Benchmark report. Here are some highlights from the report:

  • 76% of consumers will stop doing business with you after only one bad experience.
  • Consumers nowadays actually prefer to talk over the phone than via chat.
  • High prices are the primary reason your customers will stop doing business with you.

Read the full report for more details.

Having all this information empowers customer support staff to approach every person as an individual and personalise the experience to meet their unique needs. 

Be well-versed in your product or service

Data analysis helps you learn how customers use your products, what they like about them, why they purchased them, and what common issues they face. Over time, gathering this kind of data can help your customer service staff prepare for all customer queries. 

You can also use data analytics to see what kind of help or service turns into positive outcomes and add the best approaches to your library process documentation so that everyone knows what they need to do in different scenarios. 

Better response times

With clean and updated data, the best customer service staff can react instantly in real-time to provide quick answers. Having valuable data allows customer service teams to be proactive and avoid unpleasant situations. Identifying the problems early on enables you to keep customers happier and keep them loyal for a longer time. 

Common Challenges of Customer Service Data Analytics

Analysing the vast sea of customer service data can be a complex task. Here are a few common hurdles that customer service teams face when trying to glean valuable insights from their data:

1. Data Silos and Integration Issues

Customer interactions occur across multiple channels — phone calls, emails, social media, live chat. Unfortunately, this data often resides in separate systems, creating "silos." Extracting and integrating this fragmented data into a unified view presents a significant challenge. Without a complete picture, it's difficult to understand the full customer journey and identify areas for improvement. For example, a customer expressing frustration on Twitter might also have called with the same issue, but without integrated data, these interactions appear disconnected, hindering a holistic understanding of the problem.

2. Data Quality Woes

Data quality is paramount for reliable analysis. Inaccurate or incomplete data, like typos in names or inconsistent product codes, can lead to misleading insights. Manual data entry errors and outdated information further muddy the waters. Ensuring data accuracy requires ongoing vigilance and potentially implementing data cleansing techniques. Without clean data, even the most sophisticated analysis will yield skewed results, potentially leading to wasted resources or missed opportunities to improve customer satisfaction.

3. Difficulty Isolating the "Customer Voice"

Customer service data often gets tangled up with agent actions and internal processes. Separating customer sentiment, feedback, and pain points from internal notations and troubleshooting steps can be a challenge. This requires effective data filtering and categorisation to isolate the true "voice of the customer." For instance, distinguishing between a customer describing a product defect and an agent logging troubleshooting steps requires clear data categorisation, ensuring you're analysing customer sentiment and not internal procedures.

4. Keeping Up with the Speed of Change

Customer behavior and expectations are constantly evolving. The data analytics tools and techniques that worked yesterday might not be sufficient today. Staying abreast of the latest trends and adapting your approach is crucial to ensure your data analysis remains relevant and actionable. New communication channels emerge, customer preferences shift, and data security measures evolve — all requiring ongoing adjustments to your data collection and analysis methods.

5. Making Data Accessible and Actionable

Even with clean, integrated data, the challenge remains of transforming insights into actionable steps. Presenting complex data in a readily understandable format for both technical and non-technical teams is key. This might involve creating user-friendly dashboards or reports that highlight trends and guide decision-making. Crafting data visualisations that clearly depict common customer complaints or areas with high call volume empowers managers. These visualisations provide a clear picture of customer pain points and areas needing improvement, allowing managers to prioritise effectively and allocate resources strategically.

8 Ways to Analyse Your Business Data and Achieve the Best Customer Service

1. Distinguish the most important KPIs

The first thing you need to do is list your most important KPIs and focus on them. Many organisations try to “fix everything at once” and don’t get anywhere. For example, if you focus on your average response time, you can see what kind of problems are putting customers on hold for too long. 

Is the problem the lack of employees, is your customer support staff too slow in resolving issues, or are your tickets not appropriately assigned? You can see that a single metric could be the cause of many different factors you need to analyse and improve, so take one step at a time. 

It’s also essential to optimise for customer-centric KPIs that measure the quality of your brand experience. For example, the net promoter score is more important to measure than the average handle time. If you spend too much effort trying to lower average handle time, your customers may feel like they’re being rushed off the phones.  A high net promoter score, on the other hand, more often correlates to better experiences — it’s a far more effective metric to optimise for. 

Unsure of which KPIs to measure? Invoca’s blog, 10 Contact Centre Metrics to Monitor to Ensure a Top-Tier Customer Experience is a great place to start!

2. Establish short and long-term goals

Once you’ve determined critical KPIs, you can see how they relate to the customer service process and set up both long-term and short-term goals. You can improve specific KPIs relatively quickly, while others require more time so align them with your goals.

For example, if your long-term goal is to improve customer retention, aiming to increase the net promoter score is a great KPI to measure in the short term. When your net promoter score is high, it means your customers are likely to recommend you to a friend or colleague. As you increase your net promoter score, you’ll gain more loyal customers who are likely to buy from you in the long term, thus increasing retention.

3. Recognise key trends

Having a complete overview of your customer service process makes it easier to make the right decisions. Use data analysis to identify recurring patterns and trends, both good and bad. 

Customer service software, for instance, can provide a wealth of customer data. Analysing support ticket history or generating reports and studying statistics can reveal all kinds of trends. Aside from that, you can study customer behavior to develop a business strategy that suits them. 

You can also use conversation intelligence software like Invoca to recognise trends that are occurring in phone conversations. With Invoca’s Signal AI, you can automatically detect certain words or phrases that you’ve deemed important. For example, you could set up a Signal that identifies when customers ask questions about pricing or when they mention an issue with your website. This will allow you to quickly address the key points on your web pages.

To identify topics and trends you may not have known to look for, you can use Invoca’s Signal Discovery. This tool uses unsupervised machine learning to discover macro and micro trends that are happening in your consumers’ phone conversations. As a result, you can detect new customer issues at scale, without having to listen to hundreds of call recordings to find them.

4. Know who your customers are

Data analytics helps you learn about your customers, gather feedback, see how they interact with your brand, gather demographics, get technographic data, and much more. For example, knowing that your customers are 20 to 30 years old will help you adjust how you communicate with them. You can use their preferred channels like live chat to provide the best customer service. 

With Invoca conversation intelligence, you automatically capture data about each caller, such as their phone number, name, geographic location, and the digital ad or webpage they engaged with before the call. You can automatically send this “PreCall” data to your agents just before the call is connected as a screen pop so they can tailor the conversation according to the caller’s needs.

Sample PreSense data from Invoca, passed to agents before they answer the call

5. Map out the customer journey

Visualising the customer journey can help you see what inhibits or motivates customer actions. This journey can also show you how your customer service fits into the overall picture and help you recognise ongoing issues. It’s important to recognise that, in today’s modern world, customer journeys are fractured — your audience is switching from mobile to desktop to phone calls as they engage with your brand. You need to track and understand the full omnichannel experience to improve your customer service. Data analytics can help you solve all those problems and improve the overall journey. 

6. Tapping into Sentiment Analysis

Sentiment analysis uses artificial intelligence to understand the emotional tone of customer interactions. Tools like Invoca can analyse phone calls to identify positive, negative, or neutral sentiment expressed by both callers and agents. This allows you to improve your scripts and performance, and identify coaching opportunities. By pinpointing phrases or topics that trigger frustration, you can revise scripts and train agents to address concerns more effectively. Sentiment analysis can also highlight areas where agents might need additional training or support, leading to improved customer interactions. 

7. Embrace the Power of Text Analytics  

Text analytics tools analyse unstructured data like chat transcripts, social media comments, and email exchanges. This powerful combination helps you in two key ways. First, by analysing the language used in customer inquiries, text analytics can uncover recurring issues, allowing you to proactively address common customer pain points. Second, it reveals customer preferences and past interactions, empowering agents to personalise communication and provide a more relevant experience for each individual.

8. Focus on Agent Performance

When focusing on agent performance, it's crucial to thoroughly analyse their interactions to identify areas ripe for improvement. This involves doing quality assurance on calls to track each agents' performance. AI-powered tools like Invoca can automate quality assurance, grading 100% of phone calls at scale. By harnessing this data, you can provide targeted coaching tailored to each agent's needs, fostering continuous growth and ensuring consistent delivery of high-quality service throughout your team. You can also enjoy significant time savings, which allow you to spend more one-on-one time with each agent.

Conclusion

With a lot of hard work, practice, and data analysis, you can learn to recognise issues in your customer service process. It might take some time to develop a sound data-gathering workflow. However, in the long run, this data will fuel your growth and help improve customer-centric processes in your business. 

Additional Reading

Want to learn more about how you can analyse business data to improve the contact centre experience? Check out these resources:

Want to see Invoca in action? Request your personalized demo.

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