A Data-Driven Consumer Experience


Caroline Swartz

Date Published:
March 16, 2023


Data-driven customer experience is changing the way that brands interact with their customers. From a marketing perspective, data-driven customer experiences are focused on using data to personalize the way you interact with each individual consumer. From an operations perspective, they’re focused on using data to improve your internal processes so that you can provide better experiences for your customers. Both approaches lead to happier—and more loyal—consumers who are willing to spend more time and money when shopping for products and services from your brand.

60% of consumers want brands to use data to improve their experience.

There’s a lot of data out there, and it can be used in many ways. You may use data to improve your customer experience within marketing or operations. The most important thing is that you have access to the right data for what you want to accomplish. You should also consider:

Where does the data come from?
How accurate is it?
Is the information accessible in real time?

Customer Data

The cornerstone of e-commerce and the foundation of any marketing plan is customer data. However, the time for collecting consumer data is running out, now it’s time to pay attention to customer wants. Third-party data is becoming less prevalent, whereas first- and zero-party data are gaining prominence.

In order to better understand their customers and deliver the individualized experiences they crave; brands must increasingly focus on building direct digital interactions. The best way to maintain this reciprocal value exchange is through mobile apps, due to shifting consumer behavior among mobile users.

Zero-party data is information that your consumers voluntarily and knowingly provide you. It’s typically obtained from surveys, user profiles that consumers make when shopping online, along with in-person conversations with shoppers when they shop in person. This data is used to build customized and targeted product recommendations, interactive experiences, and scaled targeted marketing for each customer using this data.

Consider zero-party data “conversational data”:  What would you discover about a consumer if you were conversing with them about their upbringing, beliefs, and preferences? What would you want to discover?

Data Ethics

Analysts, data scientists, and information technology professionals are all concerned about data ethics. Anybody who works with data, on the other hand, must understand its fundamental principles.

For example, your organization may collect and keep information about your customers’ journeys from the first time they enter their email address on your website to the fifth time they buy your goods. If you work in digital marketing, you probably deal with this data on a daily basis.

Cover of Responsible Data ScienceAlthough you may not be in charge of deploying tracking code, administering a database, or creating and training a machine-learning algorithm, understanding data ethics can help you detect instances of unethical data collection, storage, or use. You can preserve your consumers’ safety while hopefully avoiding legal problems.

For more on this topic, Peter Bruce and Grant Fleming’s Responsible Data Science: Transparency and Fairness in Algorithms, talks about how to implement data science solutions in an ethical manner that minimizes the risk of undue harm to vulnerable members of society – consumers are just one example.

Mobile shopping is the new norm.

Mobile phones have evolved into one of the most popular buying channels. They have transformed the way people purchase online, allowing them to rapidly discover and find what they need, no matter where they are or what time it is.

According to a Mobile Shopping Report, 68% of surveyed U.S. consumers say they are shopping more often now with their mobile phones than two years ago, with 8 in 10 of them mobile shopping within the last 12 months.

This is particularly true among Gen Z (79%) and millennial (77%) respondents.

A data-driven customer experiences starts with the customer journey.

The first step is understanding where your customers are in their journey. A product or service may be valuable, but if it can’t be accessed easily or quickly enough (or isn’t convenient enough), then it will be difficult for people to buy from your company. This is why many companies are now focusing on improving the convenience of their products and services as a way to set themselves apart from competitors who have similar offerings.

Source: Omnichannel Shopper Behavior report by Mercatus Technologies focuses on a consumer’s online grocery shopping behaviors.

Why don’t customers use (grocery) delivery?

The convenience that comes with placing an online order is the dominant reason, but this benefits is not exclusive to a grocery delivery service.

For this strategy to work, though, businesses need access to high quality data about their customers’ behavior along each stage of their journey—from before they purchase something until after they’ve used it or deployed it at home or work (or even while they’re still shopping around). Only by analyzing this information can companies determine how best to meet people’s needs across all stages of their journey.  There should be no gaps between initial interest through actual adoption (using something new) over time–and especially over multiple purchases within a single household.

Data-driven customer experiences are becoming more widely adopted by businesses.

A data-driven customer experience is personalized by the preferences and needs of individual customers. These experiences are becoming more widely adopted by businesses, as they allow brands to deliver a more relevant experience.

To create a successful data-driven customer experience, you need to be able to listen to your customers and provide them with what they want—which requires access to their information. There are many different types of data that can help you do this:

Primary Data

data collected directly from your customers (e.g., sales figures)

Secondary Data

publicly available data that has been collected by other companies or organizations (e.g., census information)

Unstructured Data

non-numeric content such as images, text, and video files

Companies that have adopted a data-driven approach to their customer experience see higher customer satisfaction and retention rates.


Uber is a strong illustration of how to deliver a positive customer experience since it demonstrates the key steps in detail.

All the difficulties users had while calling a cab have been recognized and eliminated. You are no longer blindly waiting for a cab since you can see where your car is along with an estimate of how long until it arrives.

Additionally, visible ratings for drivers help make sure that their services live up to your expectations (and similarly, drivers are helped by seeing a rider’s rating and preferences). Only if you’re in the mood for a chat should drivers engage you, and some may even offer a treat or a phone charge while you are on your journey. Lastly, in-app credit card payment saves time (for travelers that are trying to get to their destination quickly), and it caters to fewer people carrying cash with them.

Behind the scenes, a massive framework of data collection, transmission, processing, and analyzing makes this possible.

Data-driven customer experience is a competitive advantage.

Companies that have adopted a data-driven approach to their customer experience see higher customer satisfaction and retention rates. This is because the benefits of using data in your customer experience extend beyond improving the customer journey, including:

Customer Acquisition

Use data to help identify new audiences and to target them with personalized messaging.
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Increasing Sales

With access to robust datasets, you can make more informed decisions about which products or services will best suit each individual’s needs.
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The benefits of using data in your customer experience extend beyond improving the customer journey.

Once you have a data-driven consumer experience, it can be used to improve your customer journey. By correlating customer behavior with the relevant events in each interaction, you can identify opportunities for better service and thereby increase sales. You can also reduce operating costs by optimizing processes and eliminating redundancy, or you can create new revenue streams from existing data assets.

Consumer expectations for brands have changed over time, and they’re now only satisfied if you can provide them with a personalized experience.

The consumer experience has changed over time. In the past, it was enough for brands to simply provide products or services that met consumer needs. However, now customers want more from the brands they choose to support. They want to feel like they’re part of something bigger than just buying a product or service—they want to feel like they’re part of a community that shares their values and interests. And today’s successful brands are capitalizing on this trend by giving consumers personalized experiences that make them feel special and appreciated as individuals.

The most forward-thinking companies have learned how valuable data can be when used as an asset in building these relationships with customers. By leveraging data collected via email campaigns or website visits (such as IP addresses), companies can use behavioral insights about who their customers are and what type of content resonates with them to deliver an experience tailored specifically towards each person’s needs and preferences—which is key if you want to build your brand at scale!

Consumers want to feel like they're part of something bigger than just buying products; brands that use data are capitalizing on this trend and gaining an advantage by providing better experiences for their customers.

Using data to personalize the customer experience.

Data can help you improve your ability to personalize the customer experience by making recommendations based on customers’ preferences. This includes recommendations for products or services that are similar to items that customers have purchased in the past. Or, making recommendations for products or services that are related—for example, if a customer has expressed interest in a specific type of car, then suggesting other cars in that category may be valuable.  Or, suggest products that add onto products the customer is known or presumed to have purchased.


To create a personalized experience across all channels, the Whole Foods app combines the mobile experience with in-store shopping. The app keeps track of each item a user buys and arranges them so it’s simple to search for and place another order. Recommendations for new products and recipes are based on each customer’s past purchases. When users are in or close to a Whole Foods, they even get personalized notifications and offers for that particular store.

Using data to optimize advertising campaigns.

You can use data-driven customer experiences to optimize your advertising campaigns by identifying which audiences are most receptive to particular ads or marketing messages at any given time—and targeting them with specific messages through email blasts, web pages, and social media updates, as well as traditional advertising channels like Google Adwords and Facebook Ads Manager.


Age, gender, occupation, and location are just a few examples of demographic information that can disclose a lot about a person’s wants and requirements. Naturally, such knowledge contributes to a more targeted marketing message and a higher campaign return. To attract new clients and grow its user base, DirectTV identified a particular market to target — people who have recently relocated to a new home – using a variety of data points. Additionally, it was shown that, when people relocate, they frequently test out new services. Many also choose to change service providers.

In the past few years, data-driven customer experiences have become more widely adopted by businesses, since those companies see higher customer satisfaction and retention rates than companies who don’t.

The benefits of using data in your customer experience extend beyond improving the customer journey: It also has an impact on how you market and sell products to customers through personalized promotions and offers based on individual preferences; analytics can inform how you allocate resources (i.e., budget), what technologies to use, and improve internal processes like hiring decisions; etc.


Data-driven customer experiences are a key way for businesses to keep up with changing customer expectations. By using data effectively, brands can create personalized experiences that make their customers feel like they’re part of something bigger than just buying products.