Let’s see what these numbers say!
91% of shoppers said they were likelier to buy products from retailers that use their name and provide personal recommendations.
Meanwhile, 80% of companies saw increased sales after implementing personalization in their CX strategy.
Around 87% of consumers said they would not do business with a company if they had concerns about its security practices.
We surely live in an era when customers expect businesses to understand their needs, sometimes even before they realize the need for something themselves.
As buying becomes less about what consumers buy and more about how companies take personalization to an entirely new level, organizations are arming themselves with the data and analytics to enhance customer experience, not just during the research and purchase phases but across all touchpoints, including shipping, returns, and post-sales service—the role of data in has become the true differentiating factor in customer experience delivery.
An organization X, for example, would boost the use of advanced data analytics to gather deeper insights into customer procurement practices and emerging product preferences. Those data and more significant mobilization across functions would help managers uncover untapped growth opportunities and crucial insights into customer preference. Now, Sales teams, R&D, and product-development functions could use real-time analytics and knowledge about customers and markets to collaborate more closely on new, higher-margin offerings aimed at nascent delivering data-driven CX.
It’s all interrelated and data in CX can really bring such a transformative change in improving customer experience. But at what level? And, most importantly, how?
This playbook uncovers all the crucial answers related to the role of data analytics in creating superior CX.
Why is Customer Experience Real-Time Analytics important?
Over the years, there has been a sudden and steep shift towards customer-centric digital experiences, which also means that companies will have more dynamic data at their fingertips that they can use to reveal what delights their customers the most.
Now, what is that “DATA”? Anything that a company knows about us is data for them.
It can be as simple as knowing our birth date or full name.
A complex form of data can be our shopping behavior, a systematic view of political parties, or, as simple as that, how we proceed with our political journey.
When this data analytics in customer service is used, it helps companies understand their customers’ journeys, allowing them to customize experiences to meet individual preferences. Through customer behavior analytics, businesses can better personalize and target their offerings.
Additionally, customer experience analytics helps companies identify their customers’ pain points.
What does Data-Driven CX look like?
As brands compete for customer attention in a crowded digital landscape, they should look for opportunities to interact with customers more efficiently and deliver digital experiences that keep them coming for more. Companies can use the customer data gathered during the customer journey and combine it with information to enhance their digital experience, engage with consumers, and predict what they want before they even look to a competitor. Like leading brands in their league, Amazon, Netflix, and Starbucks did.
Let’s examine the CX when the data is properly utilized for leading brands like Amazon, Netflix, and Starbucks.
| Amazon | Netflix | Starbucks | |
| Customer experience reimagined | Customers receive highly contextualized emails with personalized product recommendations based on customer demographics, psychographics, and previous purchase and view history. | Customers receive a highly contextualized and individual experience starting from a homepage that is based on their past viewing history and that uses viewing habits to recommend content. | Customers receive a personalized in-app experience with real-time offers based on their preferences, activities, and past purchases. |
| Approach | Amazon uses a recommendation engine algorithm called ‘item-to-item collaborative filtering’ to suggest products based on key data points to create user profiles and craft a highly contextualized email for the shopper. | Netflix uses an algorithm to predict content that users will want to see. It combines behavioral attributes with predictive learning to send 103 million users unique movies and show recommendations to increase engagement and loyalty | As part of its Digital Flywheel strategy, Starbucks uses a data-driven AI algorithm to send over 400,000 variants of hyper-personalized messages (food/beverage offers) to their customers and promote unique and compelling offers for each specific member |
| Data considered | • Search query
• Average time spent on searches • Past purchase history • Brand affinity • Category browsing habits • Time of past purchases • Average spend amount |
• Customer behaviors (including viewing history, ratings)
• Movie information (including titles, genres, categories, actors, release year, etc.) • Members with similar tastes and preference |
• Contextual data (including location data, geospatial, demographic, and traffic)
• Customer preferences • Customer activity • Past purchases |
| Outcome | The product recommendation engine generates over 35 percent conversion by creating unique, hyper-personalized experiences for each customer | The product recommendation engine generates over 35 percent conversion by creating unique, hyper-personalized experiences for each customer | 25% increase in incremental revenue via the offer of total transactions being conducted via the mobile app |
How Does Data Analytics Improve Customer Experience?
Famous American consultant Peter Drucker coined the phrase, “You can’t manage what you don’t measure.” This statement, which hints at the importance of data-driven CX, also applies to today’s digital world, in which real-time analytics are paramount in assessing customer satisfaction.
Hyper-Personalization
Hyper-personalization is the most advanced way organizations can customize their marketing and branding messages to individual customers and target them based on user preferences.
Data-driven CX personalization is another way of creating custom and targeted experiences through data, analytics, AI, and automation, where companies can send highly contextualized communications to specific customers at the right place and time and through the right channel.
Responding to changing consumer perceptions and market conditions requires leveraging customer data at the most granular level, which is necessary to achieve hyper-personalization.
The best part is that personalization can be applied throughout the customer journey, from attracting customers with personalized web pages and dynamic pricing to providing personalized services after the purchase.
However, to meet and exceed evolving customer expectations, brands must use data and real-time analytics to understand their customers better and harness the power of AI to create authentic interactions and the hyper-personalized experience that customers now expect.
For example, organizations can effectively use technologies to capitalize on data for insights-driven results, like blending AI predictive analytics algorithms with data analysis to help credit card issuers get the right card into the right hands. By drilling into the data of users’ credit reports and predict their finance management and spending prefrences, companies can also ensure they provide highly personalized credit offers to the credit card holders.
Identifying Trends and Patterns
With the help of real-time analytics, organizations can identify patterns, trends, challenges, and, most importantly, the correlation in customer data, allowing CX leaders to understand individual customer needs and preferences better.
The technology allows the data to be mined and spot patterns and trends through analytics, channeling more interaction around the brand, and with more and more interactions across many different platforms, organizations can leverage the opportunities to tailor their experiences better.
Let’s take an example of a popular cosmetic brand Glossier, which uses artificial intelligence (AI) to turn data on product return patterns into opportunities for better personalization and to improve the online shopping experience.
Following a spike in returns, the brand traced the reason to one particular shade of lipstick, which was often exchanged for a lighter shade. The data on which products were returned and why helped Glossier optimize the website’s color-matching technology, making the experience more personalized and boosting business.
Bonus Tip- Data is a mine of customer journey patterns. So, use it to create data-driven CX that your competitors can’t replicate. Let the customer be your guide, and they can tell you exactly where the business needs to rework. Consider not just what customers are likely to want but what is likely to spark joy.
Reduced Churn Through Predictive Analytics
The data, if analyzed well, can reduce the churn rate of the customers to a great extent.
As we just mentioned above, data and real-time analytics can identify the trends or patterns your customers follow. Using these trends also indicates why the customer might leave.
Hence, the insight into their pain points lets businesses take proactive measures to retain customers.
Businesses can identify patterns that indicate common motivators for customers to leave by identifying their behavioral patterns; while using predictive analytics, they can also identify at-risk customers and create more effective, personalized strategies for customer retention.
By integrating predictive analytics into the churn analysis framework, companies can create data-driven insights to address issues before they lead to churn.
Increased Sustainability
Even for contributing to a sustainable environment, brands can use data to their advantage.
Brands can communicate all the good they are already doing and tell their story continuously – not just as part of their end-of-year results by utilizing the data-driven approach to environmental, social, and corporate governance and measuring the impact of their data-driven CX efforts on sustainability.
How to examine Customer Experience, Metrics to Track for Data-Driven CX
Here are ways to analyze the customer experience through a comprehensive audit of customer interactions at every brand touchpoint. The CX audit considers the metrics below.
Net Promoter Score (NPS)
Net Promoter Score (NPS) is a way to measure customer satisfaction and enthusiasm with a brand, service, or product. The score is calculated simply by asking customers, “On a scale from 0 to 10, how likely are you to recommend this product/company to a friend or colleague?
NPS is a simple metric used to capture customer feedback on the overall service in terms of customer experience after the product is used or the service is availed.
Customer Satisfaction Score (CSAT)
The customer satisfaction score evaluates the degree to which the product/service/ brand fulfilled the consumer’s needs or solved their problem.
However, unlike NPS, customer satisfaction scores focus on specific products or services.
For example, the CSAT would be measured based on the question asked to the customer on
How much would you like to rate X product or service
- Very Unsatisfied — 1
- Unsatisfied — 2
- Neutral — 3
- Satisfied — 4
- Very satisfied — 5
Customer Churn Rate
Customer churn rate is an important metric for companies to track the significant impact of customers leaving the brand on revenue and profitability. By measuring the churn rate, companies can identify areas for improvement and take action to reduce churn and increase customer retention.
Calculated through customer data platforms, a high churn rate indicates product or customer service issues, while a low churn rate indicates increased customer satisfaction and loyalty to the company.
Here’s how you can calculate the customer churn rate
To calculate the customer churn rate in data-driven CX efforts, you can use the following formula:
(Lost Customers ÷ Total Customers at the start of the period) X 100
Steps to follow
- First, take a time period to calculate, like a month, a quarter, or a year.
- Now, determine the number of active customers at the beginning of the period.
- Now, track the number of customers who canceled by the end of the period.
- The number of canceled customers needs to be divided by the number of active customers at the start of the period.
- Now, multiply that number by 100 to get the churn rate percentage.
Customer Lifetime Value (CLV)
Customer lifetime value is another important metrics that businesses use to determine the total revenue they can expect from a single customer account over their relationship with the company.
It’s an important metric for businesses as it informs decisions on
- customer acquisition costs
- Profitability
- Forecasting
- Business strategy
- And understanding loyal customers.
The calculation of CLV considers the revenue generated by a customer and aligns it with the anticipated duration of the relationship to drive long-term growth and profitability.
To calculate CLV, you need the following information:
- Customer lifespan
- Purchase frequency
- Purchase value
Determining a particular segment’s CLV informs your decision to address their problems.
Moving Ahead With Data-Driven Customer Experience
McKinsey’s surveyed more than 260 CX leaders from U.S.-based companies and found that 93% use a survey-based metric to measure data-driven CX performance.
This could include a customer satisfaction score. Still, only 15% of leaders claimed to be “fully satisfied with how their company was measuring CX,” while 6% “expressed confidence that their measurement system enables both strategic and tactical decision making.”
However, as we explore the role of data analytics in CX, it’s important that CX leaders take action and begin using the data available, gaining valuable insights that can prompt alerts and guide swift action to improve customer experiences through a customer data platform. The CX platforms become the foundation to link CX to value and to build clear business cases for CX improvement.
Successive Digital assesses every customer episode through various lenses, examining factors such as market segments, regions, time periods, and more.
As your growth catalyst, we help you deliver data-driven CX, optimized through a purpose-led strategy.
By taking customer insights into consideration, we help clients develop a detailed action plan for reaching target customers and achieving a competitive advantage in the market.
