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How to Improve Your Customer Service Analytics in 10 Easy Steps

Advanced customer experience analytics enables you to gain useful insights into consumer behaviour while also assisting you in identifying customer needs and mapping customer expectations.

Customers want your firm to provide them with a tailored experience. When they don’t get it, though, around 71% of customers are likely to be dissatisfied with your company. Customer Service Analytics enables you to please your customers, resulting in increased loyalty and sales.

Customer service managers can delve deeper into buyer profiles to improve customer experiences, which could lead to lower customer turnover and more engagement by leveraging strong customer service data.

Customer Service Analytics

In this blog, RisePath discusses some methods to improve your customer service metrics right now.

1. Request customer information.

Customer service analytics are only as good as the data that is collected. Customers and businesses alike are wary of customer data collecting due to privacy issues.

With the implementation of global data regulatory standards, your customer care team has no reason to be frightened to ask for client data in order to improve the customer experience. You can easily start collecting data by sending out questionnaires to your existing consumer base.

2. Collect data from a variety of sources

The overall picture will become obvious as you collect more client data from various sources.

Don’t rely solely on one data source, like your customer’s website. Instead, take an omnichannel approach to data collection. Make sure your service teams have access to data from all of your clients’ platforms, no matter where they are. Customer feedback gathered through in-store interactions, call centre interactions, website activity, chatbot conversations, and social media posts are just a few examples.

Text analytics software can assist your customer service team in analysing and reporting on vital information gleaned from phone conversations and chats. Customer effort score (CES), customer satisfaction scores (CSAT), and net promoter score (NPS) are examples of customer service measures (NPS).

Your service operations team can also gain insights from third-party data, which can aid in data visualisation and the development of a more precise customer-centric strategy. Keep in mind that some of this external data may need payment. Other types of data, such as industry and economic statistics, may be available for free.

You may upload the data you need, to your analytics platform to get started on your data strategy once you’ve located or purchased it.

3. Create a customer journey map.

Work on a thorough examination of consumer interactions across various touchpoints. Only by looking at the full customer experience can you see the consumer pain points and opportunities that have gone unnoticed.

“Performance on journeys is considerably more strongly connected with customer happiness, revenue, churn, and repeat purchase than performance on touchpoints,” according to a recent study.

Predictive analytics may assist in strategically mapping a client’s journey from beginning to end by extrapolating real-time insights.

This method can assist you in determining:

  • The road that your most delighted customers have taken
  • Obstacles encountered by disgruntled consumers
  • The paths that are most frequently abandoned or have the worst outcomes

4. Keep an eye on data integrity

Data that is inaccurate might lead to distorted results and incorrect conclusions.

So, how can you ensure that your data is correct?

To reduce manual errors, limit the free-form selections first. On your survey, instead of traditional fill-in boxes, use a drop-down menu.

Next, ensure that your service operations team is aware of the need of precise data entry. Allow them to use customer service analytics products with built-in dashboards that are error-free.

Finally, don’t forget to conduct data audits on a regular basis. In the data, look for anomalies or outliers. You can use bell curves or graphs to identify data points that are out of the ordinary, and then dive further to find out why.

5. Make data available to everyone

The information becomes useless if your stakeholders are unable to access the data or make use of the analyses. As a result, in order to allow cross-functional data exchange, your support team must periodically detect data bottlenecks inside the system.

Making a uniform dashboard accessible to every team that interacts with consumers can help your service operations run effectively. Integrating and updating data sets may not be fast enough to meet customer engagement goals like response times without effective data-sharing practices.

Encourage collaboration across the organisation by cultivating a culture that prioritises customer interaction and promotes data analytics processes and policies.

6. Give your team the tools they need to manage big data.

Your staff must not only be able to access the enormous amounts of data available, but they must also be able to drive and comprehend useful insights from this data pool.

As the world of data analytics becomes more complicated, this can be difficult. To overcome this obstacle, you’ll need to train your customer care representatives on how to use data from advanced dashboards to improve operations and provide personalised assistance.

7. Make Mobile Services a Priority

Did you know that over 40% of all transactions are made on a mobile device? And, inside a physical store, 80% of shoppers use their phones to look up product reviews, compare prices, or find alternate store locations.

Furthermore, businesses that gather and use data from mobile devices have demonstrated a greater ability to innovate. Businesses generate significant streams of data every time they tap into mobile phones, which leads to better customer messaging and offers.

This emphasises the value of mobile data and how it can be utilised to build a comprehensive consumer profile. You can even utilise a mobile data gathering tool to capture valuable data from your clients’ phones to make this process easier.

8. Keep track of data processing time.

You and your team must be able to obtain information quickly and simply in order to implement customer service analytics.

Data processing, on the other hand, can become time-consuming as the volume of data you collect grows. You’re wasting critical decision-making time if it takes hours to prepare reports, compute variables, and run models for forecasting and trend analysis.

You may shorten the time it takes to process your data by controlling how data is entered into your database and when it is processed. You can, for example, gradually add data each day. Then, during off-hours or downtimes, you can schedule batches and process data.

To discover bottlenecks and slowdowns, your team should be able to track runtimes and dependencies. This would allow them to make adjustments to their processing timetables as needed.

9. Facilitate quick decision-making

The longer your service activities take to use the data, the less useful it gets.

To assess operational effectiveness across the organisation, we recommend selecting key metrics or key performance indicators (KPIs). This enables you to extract insights from a large volume of support tickets in order to resolve them effectively and quickly, improve analysis for faster decision-making, and empower consumers to use self-service portals in order to increase agent productivity.

It’s critical to know exactly what you’re looking for so you can concentrate on one issue at a time. When it comes to answers, the golden rule is to automate them wherever possible.

You can, for example, develop automated email offers that are triggered to be delivered to customers whenever they do a specific action. You may get your support under control by consolidating concerns into a single global inbox with customer email management software.

10. Keep track of your analytics’ return on investment.

Customer service analytics gives you access to a lot of data. However, if it doesn’t result in better customer service and more income, it’s not worth the money.

How can you know if buying more data is a good investment?

Please keep in mind that buying external data or employing professionals to collect and analyse data may end up costing you more than it’s worth. This is why it’s critical to keep track of the cost of data collecting and analysis in relation to the benefits you’re receiving.

Conclusion

Customer service analytics is the bedrock of excellent client service and is required to provide a great customer experience. The more you understand your consumers’ wants and needs, the more value you can provide. The more tailored experiences you provide, the higher your chances of customer retention are.

You can create complete customer profiles using advanced customer service metrics. As a result, your targeting improves, allowing you to give more targeted offers.