*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
If you want to improve your customer experience, like a data scientist, make data-driven decisions. By making decisions backed by data, you’ll be able to serve customers quicker and with more accuracy than ever before. This blog will tell you all you need to know about leveraging customer data to make data-driven decisions and help keep your business ahead of the game.
Data-driven decision-making is the process of using insights gathered from data to inform your business decisions. Utilizing data allows businesses to make smarter decisions backed by data rather than solely operating on gut instincts or assumptions. Businesses that make data-driven decisions can make decisions faster and with more accuracy than those that aren’t leveraging data.
Data-driven decision-making can revolutionize virtually every aspect of your business, including the general customer experience. From streamlining the process to improving connections between your business and customers, making decisions based on data can significantly improve your customer service offerings. Read on to learn how businesses are making data-driven decisions to level up their customer experience and how you can do it, too.
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There is so much data out there just waiting to be tapped into. Customer data specifically can be a goldmine for your business, especially when dealing with e-commerce. If your business and its interactions with customers primarily happen online, you’ll be capturing loads of data from your customers that can be useful in your operations.
While shopping online, several competitors are fighting for a person’s attention at once. This means you need to find ways to set yourself apart from the rest. Collecting and analyzing customer data can help you do that by presenting insights about your customers that allow you to serve them better, offering a more enhanced customer experience than your competitors do.
Customer data can help you identify customer segments, personalize the shopping experience, improve product offerings and recommendations, and generally get to know your customers better, which helps you provide better customer service and improve the experience of interacting with your business.
There are various types of customer data you can collect from different sources, but the most important types include:
- Personal Data
- Behavioral Data
- Engagement Data
- Attitudinal Data
Also known as “demographic data,” personal data includes biographical information about a customer, like their name, birthday, email address, and phone number.
Behavioral data holds some of the most valuable information that can help you make data-driven decisions. Behavioral data reveals how your customers interact with your brand and how they respond to social media posts, ads, and emails. Essentially, it details key information captured along the customer journey, like cart abandonment rates, average order value, and customer lifetime value.
Engagement data can also be very insightful when looking to make data-driven decisions as it reflects how your customers interact or engage, with various channels related to your business, like your website, social platforms, and emails. This data is gathered from sources like website visits, social media shares, and email open rates.
Attitudinal data can be used to help you understand how your customers feel about your business. Using attitudinal data to gauge your customers’ opinions of your company can help you determine how well certain products are performing and what the public opinion of your brand is. This data can be gathered through feedback surveys, focus groups, and customer complaints and reviews.
Suppose you want to leverage customer data to make data-driven decisions and improve your customer service. In that case, you should get familiar with data analysis and the techniques and tools that make it possible. There is no shortage of data analysis methods, tools, and software out there to help you make good use of the data you gather. Some key data analysis techniques include:
- Regression Analysis
- Factor Analysis
- Descriptive Analysis
- Cohort Analysis
- Cluster Analysis
- Time Series Analysis
Regression analysis is used to show the connections between a dependent and one or more independent variables. The goal of regression analysis is to determine how many factors — independent variables — may be influencing the dependent variable, keeping an eye out for patterns and trends. Regression analysis allows you to make predictions about the outcomes and helps you make data-driven decisions by understanding each variable’s relationship.
Factor analysis takes a large set of data and shrinks it down to a smaller, more manageable set that can be used to determine whether a group of variables has any link or connection. Factor analysis is very useful in the way that it makes large data sets more understandable, but throughout the process, it can also help you to discover hidden trends in the data.
Descriptive analysis sets out to answer the question “What happened?” in a data set — which is the foundation of most analytic processes. Descriptive analysis organizes, processes, and analyzes raw data from different sources, taking into account historical data and any KPIs. Descriptive analysis won’t necessarily help you forecast trends or future behaviors, but it will help you organize your data, turning it into information that can be used in further research.
With cohort analysis, you take a group of users, or customers, with shared characteristics and analyze their usage patterns. This can be useful as it shows you how impactful your marketing efforts are on specific customer segments and allows you to ask targeted questions to your cohorts which can help you make informed product decisions that reduce churn.
Cluster analysis is the process of organizing data into groups, or clusters, based on how similar they are. Clustering is useful in comparing groups to one another and identifying patterns. It can also be used to optimize your marketing efforts and the customer experience by dividing customers into clusters based on metrics like demographics or shopping behaviors to then send out personalized marketing materials.
A time series analysis is used to analyze a collection of data points over a specific period to identify patterns and cycles that occur over time. This could be data like monthly email sign-ups, weekly sales figures, or monthly rates of social media engagement. By reviewing patterns, you can start to anticipate how the variables you’re interested in will perform in the future and adjust to take full advantage of the data.
When it comes to building brand loyalty and securing long-term growth for your business, focusing on improving the customer experience is key. When your customers are happy, they’re more likely to become repeat customers and even recommend your business to their friends and family.
There are several different ways to make improvements to the customer experience and build lasting relationships with your audience. Some of the ways your business can identify areas of improvement in the customer experience and make data-driven decisions to solve the problems include:
- Identify Pain Points in the Customer Journey
- Map Out the Customer Journey
- Identify Customer Preferences and Behaviors
Pain points in the customer journey include any friction or unpleasant experiences your customers may face when they interact with your brand. There are typically four types of customer pain points, including financial, process, productivity, and service-related pain points. These can include things like the prices being too high, features on your website being difficult to navigate, or offering poor customer service.
To identify pain points in the customer journey, you, or any data scientist you hire, need to gather data from your customers. This data can come from a variety of sources like focus groups, surveys, and customer reviews. Once you’ve gathered the data, you can start identifying areas of the customer journey that need a revamp.
For example, say the data shows that your customers are unhappy with your customer service department and that it’s too time-consuming to wait on hold to talk to a representative. To remedy this, you may consider adding AI chatbots to your website that are available for quick help 24/7.
Mapping out the customer journey is another way to identify areas of improvement in the customer experience. Mapping the customer journey allows you to get understand how your customers are thinking and feeling across all their interactions with your brand, from the time they become aware of your business to the time they make a purchase decision.
Mapping the customer journey ensures your business is in control of every customer touchpoint throughout the journey. The customer journey is complex, but by mapping it out, you’ll be able to visualize each stage and gain insight into how customers are going through the stages and where there may be friction so you can fix it to make the process as seamless as possible.
Another good way to tell where you could make improvements to the customer experience is by learning about your customer’s preferences and behaviors. Analyzing customer behavior can give you insights into your customers’ wants and needs, allowing you to serve them in the best ways possible, which boosts customer retention and satisfaction.
Through your analysis, you may find that your customers want more email communication from your business and that the click-through rate on emails is higher than on other platforms, so you can adjust and deliver more effective, tailored experiences that your customers actually want.
Once you’ve identified areas of improvement in terms of the customer experience, you can start using data-driven insights to make changes.
There are a variety of ways your business can implement those changes depending on the insights you’ve gathered, the type of business you’re running, and what your customers want.
For example, you could implement changes that improve your customer service offerings by offering 24/7 chatbots to keep up with customers’ requests. Or you could use customer data to predict trends and stay on top of your customers’ wants and needs by sending them the right thing at the right time, whether that’s a product recommendation or promo email.
Arguably the biggest change you can implement is the ability to make data-driven decisions that are strategic and effective. Insights from customer data will uncover pain points, allowing you to make changes to prevent customers from being driven away and can also reveal opportunities to bring on new customers you may have been missing out on.
But regardless of the changes you’re looking to make, you need to be able to measure their impact.
There are several different ways to measure impact. You can measure changes over time, like month-to-month or between seasons; you can also measure behavioral changes, performance measures — like the efficacy of a new marketing strategy — or you can monitor business performance changes in metrics like revenue or customer retention.
While leveraging data can be a valuable tool to level up your operations and can be automated to make the process more streamlined, it’s important to maintain a sense of empathy and human touch in the customer experience. You should strive to make data-driven decisions to inform, not replace the human touch.
There are several ways you can balance making data-driven decisions with a human touch to craft the best strategies. Some examples of achieving this balance include:
- Maintain Human Positions
- Ensure New Technology is Adding Value
- Allow for Personalization
When you make data-driven decisions and implement new tech, you want to ensure you still have humans working behind the scenes. In a lot of cases, humans are needed to train AI you may be adding to your operations.
There are also some instances where your customers will either want or need a real person rather than a chatbot or some other kind of tech. There will also be problems your business faces that can only be remedied by a human. By implementing a hybrid model that combines both tech and people, you’ll get the best of both worlds.
There is a lot of cool, innovative technology out there, and while it may seem like a good idea to implement something extravagant, you need to ensure that any new technology you include is adding real value to the customer experience.
You can do this by focusing on adding value to existing interactions. AI chatbots are a good example of this because they can enhance the customer service experience by allowing customers to easily access quick help around the clock.
Your customers have a strong desire for personalization. They want their experiences with your business to feel unique to them, and they want to feel valued as an individual rather than feeling like a number. Allowing for personalization and making it a priority can humanize your business.
You should strive to add a personal touch to all your customer interactions, from email blasts to customer service and beyond. This will help make the interactions feel more natural rather than cold and robotic, making your customers feel more appreciated.
Data is a powerful resource, and with that comes responsibility. You must have measures in place to protect customer data, as privacy and security are big concerns for most people. Not only is it an ethical obligation on behalf of your business, but violations of privacy could lead to legal trouble, too.
You must be aware of the ethical considerations or principles when collecting and analyzing data, including:
The individual you’re collecting data from is the owner of their personal information, and it’s unlawful and unethical to collect someone’s data without consent. You can obtain consent through signed written agreements, reviewing privacy policies, or by getting customers to agree to your company’s terms and conditions, but never assume you have the person’s permission without checking first.
When collecting and analyzing data, you should be transparent with customers about how you’re collecting, storing, and using their data. Customers need to have this information when deciding whether to agree to your terms and conditions or accept your site’s cookies. Withholding information about your intentions is unlawful and unethical and could tarnish your reputation and whether your customers trust your business.
One of the biggest ethical responsibilities of collecting and analyzing customer data is ensuring privacy. Just because a customer gives you consent to collect and analyze their personal data like their name, birthdate, and credit card information, doesn’t mean they want that information to be public. You should ensure you’re storing data in a secure database to prevent leaks.
Data-driven decision-making is the process of using insights gathered from data to inform your business decisions. Utilizing data allows businesses to make strategic decisions rather than relying on assumptions. Businesses that make data-driven decisions can make decisions faster and with more accuracy than those that aren’t leveraging data.
If you begin to make data-driven decisions, you can revolutionize your entire business, including the customer experience.
By gathering various types of data and putting it through an analytic process, you’ll be able to map out and identify pain points in the customer journey and learn more about your customers’ preferences and behaviors, which provides you with insights on the best ways to serve them.
If you’re not making data-driven decisions, you could be missing out on actionable insights that can help you improve your customer experience, so it’s time to get started today.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with an agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and real-world applications. As a published author, coursework developer, and Dissertation Committee Chair, Tiffany still finds time for family and hobbies. Her non-linear career path has given her an exclusive skill set that is virtually impossible to reproduce in another individual.