10 Ways to Level Up Your Customer Support Analytics
Customers expect a personalized experience from your company. And when they don’t receive it, roughly 71% of customers1 will express frustration with your business.
So how do you create personalized offers that wow your customers and lead to improved customer loyalty and greater sales?
By tapping into powerful customer service analytics.
Advanced analytics allow you to gather more valuable customer information. Which results in deeper and more accurate customer profiles that better support personalization and customization.
Improved analytics allow you to make better decisions, which will enhance customer service experiences, leading to less customer churn, and greater engagement.
Here are 10 ways you can start leveling up your customer support analytics today.
1. Ask for Customer Data
Your analytics is only as powerful as the data it’s capturing. Yet for some reason, many people still believe customers don’t want to share their data. Or they’re afraid to ask for it due to privacy concerns.
That’s not the case. If you want to use the data to improve your customers’ experiences, don’t be afraid to ask for it. To collect the data, you can allow customers to create their own profiles or send them surveys and questionnaires to complete.
2. Capture Data from Multiple Sources
The more data you collect from a greater number of data points, the clearer the larger customer picture will become.
Don’t rely on just one source of data, such as your customer website. Instead, gather data from all platforms and channels that your customers use. These include in-store interactions, call centers, your website, chatbots, and all social media platforms.
Text analytics software can help capture free form data from calls and chats so that you can convert it into a form that can be analyzed by your customer analytics software.
External data can also help create a more in-depth image of your customers and their needs. Some of this data, such as third-party data, requires 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 software to enhance your analyses.
3. Map the Entire Customer Journey
Looking at and analyzing single customer interactions doesn’t provide the same level of value as looking at the big picture. It’s only when you view the entire customer journey that real pain points and opportunities arise.
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.”
Incorporating customer journey analytics into your support strategy will allow you to build end-to-end views of how customers interact with your business.
This can help you identify:
– the typical paths taken by your most satisfied customers
– bottlenecks experience by customers
– the paths that are most often abandoned or have the most negative results
4. Monitor Data Integrity
If your data is not accurate, it can lead to incorrect assumptions and negative interactions with customers.
So, how do you make sure your data is accurate?
First, limiting free-form options can reduce errors and omissions. Replace fill-in boxes on profiles with drop down selections.
Next, make sure your customer service team understands how critical accurate data entry is. If they understand what the data is used for, and how it benefits the company, they are more likely to take more care when entering it.
Finally, conduct 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. Create Access Across the Organization
If your employees cannot access the data or make use of the analytics, the information becomes useless.
It’s vital to make sure every team that interfaces with customers has access to the same centralized data source.
Without effective data-sharing practices, integrating and updating data sets may not be quick enough to achieve customer engagement goals, such as optimal customer-service response times.
Therefore, the company management needs to regularly identify data bottlenecks within the organization and support cross-functional data sharing by establishing a centralized data warehouse that all key team members have access to.
A culture that emphasizes customer engagement, and clear processes and policies around data and analytics can also encourage sharing and collaboration.
6. Make Sure Your Team Understands the Data
Not only do employees need to access the data, but they also need to understand what it means and know how to implement it.
Unfortunately, this can be challenging as the world of data analytics becomes more complex. Analytics talent remains a constraint for businesses, particularly as data is now available in areas that have lacked data in the past.
To overcome this challenge, you will need to create a robust human resource plan for analytics.
7. Embrace Mobile
Mobile purchases account for almost 40% of all transactions3, and 80% of shoppers use their mobile phone inside a physical store to look up product reviews, compare prices, or find alternative store locations.
Plus, companies that collect and use data from mobile devices have been shown to have a higher ability to innovate. Every time businesses tap into mobile phones they are generating valuable streams of data that can lead to better customer messaging and offers.
All of this highlights how important data from mobile devices is for creating a detailed customer profile. You can use a mobile data collection app such as Collect to capture valuable data from your customers’ phones.
In order to use your customer support 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 or to 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 batching and processing for off-hours or down times.
Your team should monitor runtimes and dependencies to identify blocks and slowdowns. Then they can adjust processing schedules as needed.
9. Support Fast Decision Making
The longer it takes a company to put the data it has to use, the less useful the information becomes.
You should select key metrics to monitor across the organization. This will allow you to limit the data included in reports for faster creation, improved analysis, and quicker decision-making.
Many people cast too wide a view and are don’t focus enough on a particular problem. Instead, be clear on what you’re looking for; focus on one problem at a time.
Automate responses whenever possible to create greater efficiencies. For example, you can create automated email offers that are triggered to be sent to customers whenever a certain action is taken.
10. Monitor the ROI of Your Analytics
Your analytics are only as good as your outcomes. You could have a wealth of information, but if it’s not resulting in improved customer support and increased sales, it’s not worth the investment.
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.
How do you know if purchasing more data is worth the price?
This is why it’s important to continuously monitor the costs of acquiring and analyzing your data against the benefits you’re receiving.
Customer service analytics are the foundation of exceptional customer service. The more you know about your customers wants, needs and habits, the more value you can offer them.
Advanced analytics allow you to build in-depth profiles which will result in better market targeting and improved offerings, including the ability to create the personalized offers that your customers really want.
With these 10 methods, you can level up your customer service analytics and provide the best possible customer service experiences for your loyal shoppers.
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