Did you know that most of your users are willing to pay extra to receive good customer experience?
In fact, research backs this claim – in a study, it was revealed that 86% of your users would even pay more for better services. Great, right?
Today, customer experience is the heart of every service and product. Think about it, won’t you purchase from a vendor that is extremely supportive than purchasing from a vendor that is rude and downright arrogant.
If a shopkeeper refused to exchange a broken product, would you ever go back?
We all know the answer and because of this answer, we also know that customer support is the bread and butter of every organization.
Hence, in this article, we will discuss how data analytics affects customer support.
Enhanced Data Model
Data model specifies your database, how data is stored, organized, processed, and edited. Every organization uses some or the other type of data to process data. This is true for every organization that processes or utilizes data.
If you are using data to analyze what type of products millennials like as millennial forms 90% of your customers, you are using a certain data model.
Let’s see how you can improve this data model:
If we talk about Google Analytics, then Google only considers the last interaction as the reason for conversion. For example, your user checked your products after finding your Facebook ad but left because they didn’t need the product at that time. Then, they again saw your ad on Twitter and added somethings to cart but left because they didn’t need the products then. But, finally, after 3 weeks when the user need came up, they searched Google and purchased from you.
To Google, the reason for conversion is Google Ads and not any other platform. If you follow only this data model, you may end up misinterpreting your users.
Hence, using different data models is the key to achieving success. You need to know your data extraction and how users are interacting with your brand.
Improved Availability
We have all had that experience where we bought something from a vendor but the product was not up-to-the-mark, so we complained. But, no one ever reached out to us. Even after placing several calls, we received no support.
This is because not every customer support related employee of the organization has the latest, updated data. Since they don’t have the data, they don’t know how to respond to a customer’s query.
This brings us to how data analytics can improve customer support. With customer analytics, the customer support representative can immediately understand the purchase of the customer and offer them guidance. This is based on active recommendations from analytics which also offer suggestions based on previous data.
Better Execution
Undeniably, we live in an era where every individual wants quick results so they have high expectations from everything. For example, CXOs and CEOs of the business want to see growth fast but when they don’t achieve this growth, they keep changing data models. However, it is likely for all these data models to not work.
Why?
Because the goal-setting of the businesses is flawed. Most of the businesses don’t have a clear goal so they are not able to measure these goals, which is why they are not able to change data models effectively.
Setting a clear goal such as you wish to reduce customer response time from 7 days to 1-day. That is something you can track, very easily. Data analytics can give you clear insights of your progress based on which you can keep changing your models and keep improving your customer support.
Enhanced Management
Data analytics can improve the working of your customer support department. As this technology can offer real-time updates and insights on user trends, requirements, and expectations, the customer support executives have a chance to improve post-sale support to a great extent. But, we rarely see that happening.
The reason why sometimes data analytics doesn’t work for customer support is because of the lack of new tools and qualified workforce. If your workforce is not equipped to handle data analytics, how can they possibly utilize it?
Thus, that should be the primary concern of every organization, utilizing new technology and improving the knowledge of customer support executives.
Conclusion
Data analytics is the key to creating better customer support experience for users. However, most of the times, we fail to extract value from this implementation because it is not apt. Hence, read the above data to understand how data analytics can impact your customer support and how you can improve the efficiency of data analytics.