Advanced customer experience analytics allows you to gather valuable insights around customer behavior and helps you realize customer needs and map customer expectations.
Customers expect a personalized experience from your company. However, when they don’t receive it, roughly 71% of customers1 are likely to express frustration with your business. Customer Service Analytics empowers you to delight your customers, leading to improved customer loyalty and greater sales.
By tapping into powerful customer service analytics, customer service managers can delve deeper into buyer personas to enhance customer experiences, which may result in decreased customer churn and higher engagement.
Here are 10 ways you can start leveling up your customer service analytics today.
- Ask for Customer Data
- Capture Data from Multiple Sources
- Map the Entire Customer Journey
- Monitor Data Integrity
- Make Data Accessible for all
- Empower your Team to Manage Big Data
- Embrace Mobile Services
- Manage Data Processing Time
- Support Fast Decision Making
- Monitor the ROI of Your Analytics
1. Ask for Customer Data
Customer service analytics is only as powerful as the data being captured. And privacy concerns have made organizations skeptical and customers cautious about customer data collection.
However, with the enforcement of global policies around data regulation, there’s no reason for your customer service team to be afraid to ask for customer data for an improved customer experience. You can simply initiate data collection by sharing surveys with your existing customer base.
2. Capture Data from Multiple Sources
The more customer data you collect from varying sources, the clearer the larger picture will become.
Don’t rely on just one data source, such as your customer website. Instead, adopt an omnichannel approach for collecting data. Wherever your customers are present, make sure you enable your service teams to gather data from all those platforms. These might include customer feedback collected through in-store interactions, call center engagements, activity across your website, conversation with chatbots, and posts across social media platforms.
Text analytics software can help your support team analyze and report valuable insights from calls and chats. These can yield customer service metrics like customer effort score (CES), customer satisfaction scores (CSAT), and net promoter score (NPS).
Your service operations team can also garner insights from third-party data which can help you with further data visualization for crafting a more accurate customer-centric strategy. Remember, some of this external data may require purchase. Other forms, such as industry and economic data, may be free of cost.
Once you’ve found or purchased the data you need, you can upload it to your analytics tool to get started on your data strategy.
3. Map the Entire Customer Journey
Work on an in-depth analysis of customer interactions across different touchpoints. It’s only when you view the entire customer journey that you realize the customer pain points and missed opportunities.
A recent McKinsey study2 found that “performance on journeys is substantially more strongly correlated with customer satisfaction, revenue, churn, and repeat purchase than performance on touchpoints.”
Extrapolating real-time insights with the help of predictive analytics can help strategically map a customer journey end-to-end.
This approach can help you identify:
- the path taken by your most satisfied customers
- bottlenecks experienced by frustrated customers
- the paths that are most often abandoned or have the most negative results
4. Monitor Data Integrity
Inaccurate data can lead to skewed results and false assumptions.
So, how do you make sure your data is accurate?
First, limit the free-form options to reduce manual errors. Replace conventional fill-in boxes with a drop-down selection on your survey instead.
Next, make sure your service operations team understands how critical accurate data entry is. Enable them with customer service analytics tools that offer built-in dashboards with no room for errors.
Finally, don’t miss out on regular data audits. Look for anomalies or outliers in the data. You can create bell curves or graphs to pinpoint data points that fall outside the norm and then dig in further to look at the causes.
5. Make Data Accessible for all
If your stakeholders cannot access the data or make use of the analytics, the information becomes useless. Therefore, your support team needs to regularly identify data bottlenecks within the system to support cross-functional data sharing.
Your service operations can function smoothly by making a unified dashboard accessible to every team that interacts with your customers. Without effective data-sharing practices, integrating and updating data sets may not be quick enough to achieve customer engagement goals like response times.
Encouraging a culture that emphasizes customer engagement and promotes processes and policies around data analytics can also encourage collaboration across the organization.
6. Empower your Team to Manage Big Data
Not only do your employees need to access the large volumes of data available, but they also need to be empowered to drive and understand valuable insights from this data pool.
This can be challenging as the world of data analytics gets complex over time. To overcome this challenge, you will need to train your customer service teams so they can leverage data from these advanced dashboards, optimize processes and offer personalized experiences.
7. Embrace Mobile Services
Did you know that mobile purchases account for almost 40% of all transactions? 3. And 80% of shoppers use their mobile phones inside a physical store to look up product reviews, compare prices, or find alternative store locations.
In addition to this, companies that collect and use data from mobile devices have showcased a higher ability to innovate. Every time businesses tap into mobile phones, they generate valuable streams of data that lead to better customer messaging and offers.
This highlights the importance of mobile data and how it can be used to create an intensive customer profile. In order to facilitate this process, you can even use a mobile data collection app such as Collect to capture valuable data from your customers’ phones.
8. Manage Data Processing Time
In order to use customer service analytics, you and your team need to be able to access the information quickly and easily.
However, as the volume of data you collect increases, data processing can become time-consuming. If it’s taking hours to create reports, calculate variables and run models needed for forecasting and trend analysis, you’re losing valuable decision-making time.
You can reduce the time it takes to process your data by managing how you add data to your database and when you choose to process it. For instance, you can add data incrementally every day. Then you can schedule batches and process data during off-hours or downtimes.
Your team should be able to monitor runtimes and dependencies to identify blocks and slowdowns. This would help them adjust processing schedules as needed.
9. Support Fast Decision Making
The longer it takes for your service operations to put the data to use, the less useful the information becomes.
We recommend you select key metrics or key performance indicators (KPIs) to monitor operational efficiency across the organization. This helps you to extract insights from the high volume of support tickets for an effective and timely resolution, improve analysis for quicker decision-making and empower customers to make use of self-service portals to promote agent productivity.
It’s important to be clear on what you’re looking for so that you can focus on one problem at a time. The golden rule: You should always automate responses whenever possible.
For example, you can create automated email offers that are triggered to be sent to customers whenever a certain action is taken by your customer.
10. Monitor the ROI of Your Analytics
Customer service analytics equips you with a wealth of information. However, if it’s not resulting in improved customer support and increased revenue, it’s not worth the investment.
How do you know if purchasing more data is worth the price?
Please note that if you’re purchasing external data or paying people for the collection and analysis of data, it could end up costing more than it’s worth. This is why it’s important to constantly keep tabs on the cost of acquisition and analysis of your data against the benefits you’re receiving.
Conclusion
Customer service analytics is the foundation of customer service and is imperative to deliver an exceptional customer experience. The more you know about your customers’ needs and expectations, the more value you can offer them. The more you offer personalized experiences, the better your odds are at customer retention.
Advanced customer service metrics allow you to build detailed customer profiles. This results in improved targeting, helping you to deliver personalized offers.
With these 10 methods, we hope that you can level up your customer service analytics and provide the best possible customer service experiences for your loyal shoppers.
Originally published on May 28, 2019. Updated on Dec 14, 2021.
Source:
1. http://grow.segment.com/Segment-2017-Personalization-Report.pdf
2. http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/from-touchpoints-to-journeys-seeing-the-world-as-customers-do
3. https://www.outerboxdesign.com/web-design-articles/mobile-ecommerce-statistics
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