Inbound marketing isn’t the only field that has been touched by big data. Big data is making waves in the customer service professional as well. Unfortunately, the hype over big data has often distracted customer service professionals from the steps that they need to take to actually use it to their advantage.
Many companies have a treasure trove of customer service data. In any given hour, Walmart collects over 2.5 petabytes of customer data, which can be invaluable for their customer service strategy.
When used properly, big data can yield some fascinating insights into customer behavior. The problem is that analysts often succumb to a variety of heuristics that cause them to draw the wrong conclusions. Big data can tell multiple stories, depending on how well it is interpreted.
Here are some common big data mistakes that analysts make in the customer service field. You want to avoid them at all costs.
Not trying to segment customers
It is impossible to draw meaningful conclusions by looking at raw, aggregate customer service data. Far too many variables affect customer satisfaction, initial queries and other variables, including:
Collect and act on NPS-powered customer feedback in real time to deliver amazing customer experiences at every brand touchpoint. By closing the customer feedback loop with NPS, you will grow revenue, retain more customers, and evolve your business in the process. Try it free.
- Customer age and gender
- Nature of the problem
- Region the customers based in
- Medium or outlet the customer was first reached
- Previous experience with the customer service department
Try to segment customers as carefully as possible. You need to see how different customers respond to various customer service approaches.
Treating every support channel the same
Omni-channel customer service is changing the relationship between customers and brands. Some customers prefer to seek support via email, while others prefer online chat or speaking with a customer service representative over the phone. It is a mistake to evaluate each of these channels the same way. Not only are many of the variables different, but customers have different responses to each channel.
On average, customers spend more time speaking with a customer service official over the phone than a chat bot. You can draw some very erroneous conclusions if you don’t break down your data by support channel. For example, you may notice that the engagement time with your customer service representative has dropped 30%. Without evaluating your data by support channel, you may be tempted to conclude that your representatives are handling customer increase more quickly due to a more effective training program. In actuality, the change could be driven solely by more customers seeking support through online chat rather than over the phone.
Not Sharing Customer Information Across Teams
Data about customers is collected by marketing, sales, IT and more. If customer service teams don’t have a holistic view of a customer’s history then servicing that customer can feel clunky and impersonal. Big data and machine learning can be used in today’s technology environment to provide a much higher quality customer service engagement. To accomplish this, companies are integrating disparate systems like sales, customer service management and internal communications to know how each customer has engaged with the business in the past and what issues they are having today.
Chitra Dorai, Ph.D. told Forbes that big data and AI is transforming the way brands respond to customer inquiries.
“Things humans do and excel at are to do with decision-making. If the work is routine, we have automated,” said Dorai. “Even in decision-making, in today’s world of the digital explosion of data – the volume, variety, and velocity – all make it impossible for humans to comb through them and extract the relevant information to aid in their decisions. We have begun to rely on AI systems to collect and combine, and analyze diverse data sources to extract new insights that can augment our intelligence and expand our knowledge.”
Neglecting the factor for recent promotions and one time events
Leon Sun, the owner of SocialLia states that big data provides great insights on customized promotions and offers.
“Big Data is extremely helpful for insights related to customized promotions and special offers…
Since each customer is going to have their own individual preferences, personalization is key. This can have a very big impact on overall sales and profitability in the long run, not to mention customer loyalty. Leveraging big data can give businesses the tools they need to analyze customer and market data in order to create personalized offers that are targeted to the correct audience. This customer data is especially useful for real-time insights which allows for real-time decision making,” Sun told CallMiner.
The flipside to this issue is that these irregular events can skew the results of your data. You may notice a large spike in calls to your call-center or emails during this period. Many of these customers may have just discovered your brand through the promotion, so they may be unfamiliar with your product or procedures. As a result, they may need more extensive feedback than the regular customers you interact with throughout the year.
You should pay close attention to the data from recent promotions. It can answer important questions such as:
- How many yearly promotions is the customer service team equipped to handle?
- Does the company need to take preemptive measures to avoid unnecessary customer service calls, such as providing better literature to new customers?
- Does the company need to scale the customer service department before announcing new promotions?
You need to look for patterns with your product offerings and use predictive analytics to make forecasts for future promotions and offerings. If your promotions are highly accurate, then you know that you are developing an effective system for handling customer service issues.
Featured Image from Shutterstock / By Alfa Photo