Customer service agents are the brand ambassadors of a company. In my opinion, in terms of reputation, they are even more important than any marketing or advertizing strategies companies run – they have the power to make or break a company’s image. You may spend a lot of money on marketing campaigns to capture the attention of customers, but sooner or later it will be the job of the customer support agents to maintain your brand.
Why? Let’s focus on when the customer service agent comes into the picture – when your customer has a problem with your product or service. At this point, the customer depends on your support team to resolve their issue. A good experience could make them a customer for life, whereas a bad experience could not only lose you the customer, but also other potential customers in their circle.
With the rise of social media, a negative customer support incident is amplified far more (in both speed and reach) than a positive experience – organizations need to be extra careful and make resolutions as quick and seamless as possible. A full and fast ‘fix’ can transform dissatisfied customers into advocates of the brand.
Based on my own personal experience of dealing with customer support agents, I am more likely to recommend a brand if I received good customer support (despite an initial hiccup) than a brand that didn’t provide support in the first place. How a company takes ownership of and solves their issues can leave a positive and lasting imprint.
If customer service agents are so important, can advanced analytics be used to better prepare them for providing great customer service? The answer is yes. According to this McKinsey report, only 37% of organizations feel they are using advanced analytics to create real value – a lot is being left on the table.
This article will explore some of the ways that companies can leverage advanced analytics to improve the performance of customer service representatives – thereby improving the brand’s reputation and overall customer satisfaction. First, we need to find out what’s happening with your customer service representatives, their workload, and their key performance indicators.
Descriptive analytics helps us answer this important question: “What happened?” This is the place to start, as knowing what is working (and what is not) is crucial to improving the performance of your service representatives.
Let us start by asking some more questions:
- Which product/service is seeing the maximum volume of cases?
- Which service agents are performing better and which agents need additional help?
- Are the skillsets of service agents correctly mapped to the issues they are being assigned to?
- What is the average time for handling the cases and can it be reduced?
- How many customers are leaving positive feedback and how many had an awful experience?
List as many questions as you can think of. Next, begin to find the answers to these questions. Hopefully your organization is using a CRM tool that captures all service-related details in the system. If not, take a step back and think about implementing a tool for service.
We can use any business intelligence tool to analyze and capture insights from the data. My favorite is Tableau CRM if you are using Salesforce Service Cloud for your service needs. It has built-in templates that can quickly spin up an app containing all the essential dashboards for my analysis. You can use other BI tools too as per your convenience.
We first look at the high-level numbers:
- What is the backlog?
- What is the average time to close?
- How many are first contact resolution and average CSAT?
You can set a goal against each and check whether your team is improving week on week or month on month.
Next, we drill down into agent performance and segmentation.
We can drill down on the number of CSAT per agent and cases per agent. We can then decide which agents to applaud and which ones need help. Segmenting by product, reason, and origin helps us to make more proactive decisions; you can find out which products are problematic, what the most common pain points are for customers, and which channels they prefer for contacting your representatives. Accordingly, you can take resource allocation decisions, create knowledge articles for training reps, or hire new reps.
This section has focused on descriptive analytics – we find out what has happened and make changes to our processes accordingly. Let’s see how predictive analytics can make your service team deliver exceptional customer service.
Predictive analytics is the practice of using past data to determine future patterns with a certain degree of accuracy. Forecasting and predictive modeling etc. are some of the ways you can predict future outcomes based on your historical data. These predictions come with a probability score – they are correct to a certain degree but not set in stone.
We can use predictive analytics to help deliver superior service to customers. Predictive analytics can help us take a proactive approach to customer service. This, in turn, results in shorter waiting time, quicker service, opportunities to upsell, and so on. It is an ever-growing field with new and sophisticated predictive models making it possible to deliver superior customer service.
Predictive analytics can solve challenges for customer service teams:
- Predict how many agents per channel are required for a future date.
- See which channels are likely to grow in demand and which are likely to fall out of fashion.
- Hire and prepare for big events such as shopping sales or other promotional events.
- Predict which skills will be in demand in the near future and whether you have any potential staffing gaps.
The list is not exhaustive, and the use cases are growing day by day.
So how can you get started with predictive analytics to help predict the future requirements? First you will need access to service data from your service console. Using this data, you can create your custom predictive tool for each use case. Take the case volume and agent availability data to create a model that can predict how many agents you will need for handling calls next month.
The alternative is to leverage the citizen data scientist tools that platforms are creating for specific use cases.
Salesforce Service Cloud Workforce Engagement
Salesforce Service Cloud has recently announced a new product called Service Cloud Workforce Engagement. This helps companies deliver exceptional service by using artificial intelligence (AI) to predict customer service demand, enabling them to equip the right agents with the right skills at the right time.
This tool uses machine learning (ML), which is based on data already present in Service Cloud, as well as data from other third-party tools. ML can help to prepare companies for any spikes in customer enquiries, providing a smoother all-round experience.
The Service Cloud workforce engagement offers a feature called Intelligent Forecasting. This feature helps to prepare for surges in demand by predicting contact center case volumes across all channels, regions, and expertise levels.
In order to predict future case volumes, we need historic caseload data – we need to create a workload history from past workload volume data. Using the app launcher in Salesforce, open Intelligent Forecast. Then select Omnichannel History and click Start. This will create the workload history needed to generate the forecast.
Now open Intelligent Forecast again from the app launcher, and click New. Select the workload history you created. Follow the on-screen flow to complete the setup and create your forecast. Then save the changes. Run the forecasted data to check the forecast.
From the figure above, you can see the predicted volume of cases per day. Using the filters, you can adjust the main graph by channel, region, and skill. This will help you decide how many agents to put on each channel in each region.
The model is flexible and can be adjusted for any special launch events or promotions. You can also adjust the forecast by adding the spike case.
Workforce engagement has an omnichannel capacity planning tool which can help with:
- Balancing staffing needs across any digital channel, including phone, email, web chat, text, and social channels.
- Assigning the right agents at the right time based on their skills and availability.
This will reduce the friction faced by customers when passed from one agent to another, increasing overall satisfaction from your service team.
Providing quality service to your customer is as important (if not more so) as marketing and selling your product and service. Using descriptive analytics, you can find out exactly what is going on in your customer service department to prepare and adjust for your future needs. Using predictive analytics, you can prepare for the future based on past trends to proactively offer better customer service.