How to Improve Customer Experience with Data Mining
Data mining, unfortunately, has a very scary reputation. For example, social media companies have been able to use people’s social media activity to predict “political preference, personality score, gender, sexual orientation, religion, age, intelligence, along with things like how much you trust the people you know, and how strong those relationships are.”
As Jennifer Golbreck explains in her TED talk1, a research paper published in 2013 shows that all this information has been uncovered from likes on Facebook, applied to data as obscure as curly fries. Data mining came under further scrutiny when used for advertising targeting in the 2016 American elections2, and British referendums by Cambridge Analytica.
But data mining is more than just a method for targeting people based on their political preferences. Data mining can also help uncover trends in a large collection of data, identify opportunities to improve the customer experience, and decode what your customers really want.
While data mining has been used for some unpleasant outcomes, at the end of the day it’s just a tool, dependant on how it’s being used by people.
There’s no denying the fact that it’s one of the most versatile and important tools available to companies today.
What is Data Mining?
Data mining is the process of analyzing bulk data to find new unknown patterns and hidden correlations3. It’s not spying on people, or collecting data without users knowing about it. It’s as simple as looking at information you already have, to make predictions and spot trends based on that information. Data mining is tasked to predict or forecast future actions, or to describe and organize existing data. Both of these actions are helpful to customer satisfaction teams.
Predicting and forecasting: Data mining can help identify why customers churn, and what type of customers will spend more money on you in the future.
Organizing existing data: Spotting trends in data is tough, because as humans, we tend to let our biases get in the way. Data mining can organize data and show you your biggest opportunities to reduce frustration and optimize your customer journey.
What Data Does Customer Satisfaction Need?
Customer experience teams can learn a lot from the data that customers leave behind. In customer satisfaction, we need to know how customers think and what they consider a good customer experience. If we know that, we can do more of what they like and less of what they don’t. For example, if our customers continually tell us that they value human connection, we can prioritize outbound support calls.
There are a lot of ways we can get this data from the actions customers already take. Customers want you to listen to them, and if you look in the right places you’ll notice that they aren’t subtle about their needs. Consider:
- Customer conversations, messages and reviews can be analyzed at a content level for theme and sentiment. Natural language processing (NLP) allows data scientists to analyze customer conversations based on what the customer means, and how they feel about certain features or issues.
- Customer actions within the product can be analyzed for the most common bugs and problems, alongside what browsers or devices are causing the most issues. Air Canada used customer data during flight purchases to identify common issues in the checkout process. This saved their contact center a lot of time, and increased the number of flights that customers were able to book without running into roadblocks.
- Purchasing data holds a lot of secrets about what customers commonly purchase together, and when. Almost everyone has heard of the baby coupons sent to the house of a pregnant woman4, when she didn’t even know that she was pregnant herself. Data mining doesn’t need to be creepy, but it can help identify future buying patterns of your customers.
Note that all of this data is collected ethically, legally and transparently. The following GDPR rules for personal information5 is mandatory, so be sure to not overstep when collecting data. Let customers know what their data will be used for, and allow them to opt out if they don’t want their purchasing data (for example) stored. Most customers are happy for their anonymized data to be used to improve their experience – as long as it isn’t tied to them personally.
Customer data is like their body language. 90% of all communication is nonverbal. They are saying a lot more than what they are actually saying.
Data Mining Helps Improve Customer Satisfaction
Investing in customer experience improvements pays off. In fact, 86% of customers will pay more for a better customer experience6. Therefore, if you can use data mining to improve your customer’s experience, it makes a lot of sense to start.
Data mining helps uncover insights about how your customers interact with your company. Using information like customer conversations, user data and purchasing information, you can start to better understand your customers so that you can serve them better. You already collect this information everyday. Why not analyze it for the gold nuggets of wisdom it contains?
Better customer satisfaction keeps customers coming back for more. If you can, through data mining, uncover the top three reasons why customers churn, you can take steps to eliminate these reasons. Providing a consistently better experience by identifying common issues and rectifying them for future customers will decrease churn and increase customer loyalty.
How to Get Started With Data Mining in Customer Satisfaction
If you would like to start using data mining to get more insights the first step is to review the data you’re currently collecting to make sure it’s accurate and accessible. If you’re on Freshdesk, you can use apps like Stitch Data to get your conversation data into central data storage. Combining the conversation data with other operational data in a central place makes it easier for analysts to use the data.
Secondly, consider if your company should look into a machine learning tool to uncover customer experience trends. Machine learning can dig through data faster and more effectively than humans can. Many customer service tools have machine learning capabilities built into them to data mine customer conversation data or search data for insights. For example, Freshdesk’s Freddy chatbot uses machine learning to uncover search patterns from customers looking for help.
Finally, a data analyst will be the most qualified employee to answer specific questions and solve specific customer problems using data. Investing in hiring a data analyst, or even a consultant just for a few hours of their time, will give you the most insight from your data. When you compare the cost of an extra person to the impact a better customer satisfaction will have on your revenue, it’s a no-brainer.
Data Mining Isn’t a Dirty Word for Customer Satisfaction
For customer satisfaction practitioners, data mining will elevate your customer’s experience efficiently and effectively. Without mining information for insights, you’ll only be guessing at what customers want. When data is collected ethically, data mining is the key to success.
Knowing your customers better is the most important strategy for great customer experience. Data mining helps you dig through the information you’re already collecting, to make better decisions and serve customers better.
1 – https://bit.ly/1ehDVSs
2 – http://theconversation.com/2016-presidential-advertising-focused-on-character-attacks-68642
3 – https://snov.io/blog/data-mining-tools-the-what-the-why-and-the-how/
4 – https://bit.ly/2ltI0TH
5 – https://gdpr-info.eu/issues/personal-data/
6 – http://www.oracle.com/us/products/applications/cust-exp-impact-report-epss-1560493.pdf