Data analysis in customer service is a topic that we have lightly touched upon. Here, we will go into more detail. We will explore different types of data analysis, and show you how it adds yet another vital tool to your larger CRM. With proper analytics platforms and usage, you provide another boost to your efficiency. Further, you increase the likelihood of delivering a positive customer experience.
A Genesys Global Survey found that 37% of customers find personalization the key component of a positive customer experience. Personalization stems from data analytics – so let’s get started on filling you in on the details.
What is it? And how do I use it?
These are fair questions. Especially for the innovative company seeking customer service solutions, there’s a lot to keep up with. The purpose of data analysis is threefold: first, you use it to attract, identify and cater to your most high-value customers (while not neglecting smaller-spending customers)! Secondly, you use predictive analytics to optimize your service and speed. Finally, you combine analytics with cross-department data (marketing and sales), to deliver future solutions based on said data.
Identify and classify
The term “data-mining” can certainly be used here. But, to break it down even more simply, we can say that identifying high-value customers can be done via their purchase history. This also ties back into our exploration of the integrated help-desk. A CSR (whether live-chat or phone) who has access to a customer’s full history can quickly identify them as high value. While that’s good for real time support, you can also utilize that data for marketing purposes.
For example, a high-value customer who is “shopping” on your website should be subtly directed to relevant products or services that cater to his or her interests. This form of gentle advertising can be very useful, but be sure not to cross into the realm of spam or intrusive advertising. It can be a fine line to walk. You use personalization to market directly and uniquely with your customers, all of whom have different budgets, spending habits and tastes.
Predictive analytics can be a very useful tool because it serves to give your agents a “heads-up” to know what’s coming. Also, that sort of prior preparation leads to reduced response times and less need for research holds and escalations. This, in turn, leads to more satisfied customers. Predictive analytics are developed from hard data relating to how many visitors your website gets at certain times, where a customer is most likely to need support, etc.
In the latter, you will want to put more resources (like chat representatives), on a page or feature of your website that tends to lead to customer inquiries. A simple example – let us say that on your subscriptions tab, where customers choose their plan and pricing model, you encounter customers who need assistance. They may backtrack, looking for a phone number for support. Here is where you will want to place a good number of chat agents or bots, to anticipate this need and stand ready to offer a solution. The result: a customer who did not have to do more work - and was not inconvenienced - to find help.
Let us also say that on a holiday (barring the obvious ones), you tend to get an influx of calls, and your queues tend to back up. This is a straightforward scenario in the customer service industry. Still, using predictive analytics allows you to keep this data on hand, and thus be prepared for the onslaught of calls.
Cross-departmental collaboration (yes, we go there again)!
We backtrack to cross-departmental collaboration for one simple reason: efficiency. Your marketing and sales departments already have their own methods of data mining, analytics, and advertising through multiple channels. So, it makes sense that you combine resources from other departments, if you haven’t already done so. The hard numbers, or data, acquired from either individual customers or larger groups, can be utilized by the entire company.
The application may be different, that is true. However, what remains the same is the value of that data. If you can hone in a specific customer’s typical time and place where they need support, you can use that for marketing, too. Combine your resources, exhaust them, and extract the most value out of them. With this full spectrum of data analysis, you will be working towards optimizing your CRM.