Reportedly, 69% of shoppers wish to receive personalized experiences and 40% of the brands are actually meeting the expectations. How does it become possible? Inarguably, customer data analytics which is the most commonly used branch of data analytics provides suitable predictions regarding customer behavior and experience. As a result, the technology is potent to improve customer service and related operations. Based on historical data, powerful AI-driven technology, and statistical modeling, the market of the predictive tool is growing 21% every year since 2016. Consequently, Customer-focused data analytics has geared up the market competition too.
What is Customer Data Analytics?
The concept refers to the overall data sourced from key segments of customer relationships. Since the customer’s overall journey is quite complicated, therefore it becomes difficult to map out bulky data towards developing meaningful insight. As a result, customer analytics help to frame and support strategies, establish product roadmap, strategize market campaigns, and initiate sales efforts.
Customer-driven behavioral analytics is categorized into three major types based on the organization‘s requirements.
|Customer Experience Analytics||The organization uses experience analytics to upscale the revenue drive by facilitating the descriptive study. Customer Satisfaction, First Response Time, Total Time to Resolution, etc. stand as the key performance indicators here. As a result, the accurate measure of the staff performance helps to understand the customer experience and accelerate sales rate.|
|Customer Journey Analytics||Here, the journey refers to the overall behavioral trend of the customer throughout the association with the respective company. It includes customer’s product usage, purchase history, and responsiveness towards the brand’s offerings, discounts, and rewards. Such data is sourced from outbound emails, CSAT ratings, social media pages, and conversations with support. The analytics help to understand and analyze the pattern and drive towards success.|
|Customer Retention Analytics||Customer retention comes from brand loyalty. The study of Zen desk CX reports, 74% of the customers feel loyal to the brand using data analytics in their operations. To attain such brand loyalty, the company shall ensure replacing customer efforts with a healthy customer experience. Thus, the analytics helps to retain existing customers besides getting the new ones in the spectrum.|
Use Cases of Customer Data Analytics
Regardless of the company type, the importance of the customers and clients remain intact. As a result, business entities have to draw their major attention towards these precious stakeholders. Keeping the consideration in mind, customer data analytics comes into the picture and leverage some progressive use cases mentioned below:
|Personalized Marketing||It refers to the delivery of the right message at the right time through the right medium. Thus, servicing is a blended concept of art and science where customer data analytics creates magic. Based on the data-driven technology personalized options for the customers are created leading to the creation of unparalleled opportunities for the customers.|
|Customer Forecasting||As the name suggests, customer data analytics play an essential role in forecasting future behavioral patterns and preferences of the customers using the customer data available with the organization. Such accurate predictions allow brands to be more proactive and responsive towards customers’ demands and desires. The AI-driven predictions are based on customer effort, retry rates, cycle time, etc. followed by constant learning and algorithm tuning.|
|Virtual Concierges||AI-driven customer analytics plays a significant role in providing an immersive experience to the users. The virtual-try-on options especially for clothes and cosmetics have turned out to be the gratified approach towards customers. Moreover, recommendations update in the online social platforms and entertainment industry is bringing satisfaction to the customers.|
|Customer Churn Reduction||Customer data analytics supports the reduction of churn risk, also referred to as customer attrition. It serves as the critical metric and is the least expensive method to retain existing customers. As a result, the customers with dissatisfactory experiences can be lured back with healthy incentives and better payment plans.|
|Streamlining Shipping Activities||The data analytic concept allows organizations to keep up with delivery time and day by coordinating well with delivery partners. With the effective process of forecasting maintenance issues and pinpointing transport routines, delivery becomes manageable|
|Expanding Internal Team Support||The customer experience largely depends on the nature of the service provider; therefore it is essential to have a strong support team to resolve customer issues and queries. Using data analytics, the MasterCard representatives can determine the future customer demands and be prepared to address them.|
|Effective Resource Planning||With the help of data analytics, resource allocation gains better productivity and intelligence. The derived insights help to store staffing levels and consider the requirements well in advance. Allocation of required resources at the right place and time help to deliver a smooth, faster, and better service experience to the clients. The concept gets life with the continuous monitoring and resource mapping processes.|
Common Metrics Used for Customer Data Analytics
The prediction of customer behavior and experience depends on two major factors –performance of the service provider and required customer efforts. Thus, considering the fact, major types of online Key Performance Areas (KPIs) are studied in the form of data analytics.
|Nature of Purchases||With the use of data analytics, business entities can thoroughly analyze customer preferences, demographics, and areas of interest. The complete understanding of products and services makes it easier to tailor suitable offerings for the clients/customers.|
|Dwell Time||It indicates the time spent by the customers on the website. The longer the consumers dwell on the website, the better are the chances of making purchases. Thus, it acts as the suitable application of consumer data analytics leading to enhanced customer satisfaction.|
|Type of Content Consumed||The accurate prediction helps to understand what type of content customers prefer consuming. Some of the most common content types are videos, blogs, download assets. podcasts, quizzes, etc. With the customer-centered prediction attracting new customers and retaining the existing ones become possible.|
|Heat Maps||This web customer analytics tool is similar to traffic monitoring patterns where organizations can view their navigation progress on the website. As a result, heat maps help to optimize the website and make it more user-friendly.|
|Number of Active Users||The online existence and activeness of the customer add better value to the brand identity. Such progressive approach from both ends helps to create additional insights and create suitable offers and promotional tactics with the use of data analytics.|
|Rate of Brand Loyal Customers||Reportedly, retaining the existing customers cost five times more than the new customers. Irrespective of the heavy cost involvement, customer retention is of the greatest and data analytics serves the purpose well with great cost-efficiency.|
Examples of Customer Data Analytics
The major applications of customer data analytics extend to almost all kinds of industries. Moreover, the profound technology has brought significant differences in the performance of business entities throughout the world.
|Sprint –Lower Churn Rate||Sprint’s interactive culture helps to effectively interact with the customers and leverage a better experience. The company’s agents use customer data to identify customers with higher churn rates and provide suitable offerings. As a result, AI-driven Sprint culture has reduced its churn rate dramatically.|
|Gogo Air – Trend forecasting||The major airline provides internet access and in-flight entertainment to its customers. It is a result of accurate prediction of customer’s desires and their response to the facility. The right prediction has paved the way for establishing a healthy customer experience while flying.|
|Progressive – Policy creation||The company has collected miles of driving data to understand the factors contributing to driving issues. As a result, with data analytics, Progressive identifies customers with a higher risk of accidents. It has led to accurate policy formation, cost-saving, and a better understanding of the insurance marketplace.|
|PayPal – customer satisfaction||The renowned company has always been on the top list of data analytics applications. Recently, PayPal has incorporated a robust system in its operations to track the intentions behind customers’ complaints and study their sentiments. It has led to the better categorization of top promoters and detractors.|
What is Data Driven Customer Experience?
The data-driven customer experience refers to the customer’s interaction with the brand at all available business touchpoints. In recent times, the major touch points are website, online ads, social media handles, email, store visits, mobile applications, and contact over the phone, joining seminars and webinars. Since all products and services are made up of price, quality, and right market reaches, therefore it becomes important to gather precise and relevant data for making the customer experience worthwhile. Moreover, it focuses on seamlessly adapting to the customers’ changing preferences. Thus, the inclusion of the right data helps to refine the overall user experience using data analytics. Hence, the 3 key pillars of data-driven customer experience are – People, Data, and Process.
- People: The major crux of the company lies in gaining a 360-degree view of the people it interacts with. Such deep insight related to customers like demographics, digital habits, buying preferences, etc helps to personalize the customer experience through profound market campaigns.
- Data: Effective implementation of data analytics help to organize, contextualize, and derive relevant information to shape a healthy customer experience. With the help of a data-driven approach, business entities are able to make customer-driven decisions.
- Process: The development of customized customer data profiles plays an essential role in modifying customer experience by executing the required process. Thus, a data-driven approach helps to build a frictionless customer experience.
Evidently, the combination of data and analytics is changing the way a business operates today in leveraging a healthy customer experience. The data-driven customer analytics contributes immensely towards a responsive, personalized, and dynamic customer ecosystem. According to Walker’s study, 86% of customers rate customer experience as the most important factor for taking the final call to action.
Using Big Data to improve Customer Experience
The introduction of big data analytics has paved its way in assessing and monetizing unstructured and scattered customer-related data. There are several key roles big data play in improving the overall customer experience and let organizations stay centered on the customer’s demand and expectations.
Provides insight into customer behavioral approach
The bid data helps to build and transform customer and company relationships. The complete understanding of the customer behavior helps to minimize operational and experience gaps leading to the formation of a better market strategy. As a result, big data acts as a proficient tool for accurately targeting customers and leveraging satisfaction.
Facilitates tracking trends
In the dynamic business ecosystem staying rigid with the action plans all the time is the major limitation an organization binds itself with. Thus, to get an ace over the market structure big data comes into an active role and provides a complete analysis of the market trends. Staying updated to the trends helps to attract a larger customer base and better sales turnover in return.
Developing personalized approach
A thorough understanding of customer taste and trends help to create a healthy environment for developing a personalized customer approach. With the adoption of personalized product and service delivery customers feel valued and appreciated. Thus, the creation of a more rounded picture through big analytics ensures effective communication of ideas addressing the relevant customer issues and accelerating the development of motivational market campaigns.
Building brand loyalty
Building a brand and brand loyal customers is not an overnight game, it begins with a thorough market study and continues growing with effective maintenance. Thus, big data considers both the major aspects of the customer journey and provides a qualitative and quantitative view of the relevant customer aspects. Using profound data analytics organization engages meaning and productive conversation with each customer which makes them feel privileged and preferred in the decision-making process.
Evidently, customer data analytics has brought a huge revolution in the business ecosystem and acts as the difference-maker. The customer-oriented policies and predictions have bridged the gaps in customer experiences. As a result, customers feel valued and well-treated with a personalized approach. A significant report from Zion Market Research has stated that AI-driven technology is projected to reach $ 10.95 billion by 2022, which is an achievement in itself. So, ensure utilizing the complete potential of data analysis to excel in the customer-driven market.
Topics in Data Analytics
- Advanced Data Analytics
- Clinical Analytics
- Credit Risk Analytics
- Cyber Risk Analytics
- Data Analytics for Customer Behavior and Customer Experience
- Data Analytics for Customer Journey
- Data Analytics for Fraud Detection
- Data Analytics for Human Resources
- Data Analytics for Logistics and Supply Management
- Data Analytics for Risk Management
- Data Analytics for Talent Acquisition and Management
- Data Analytics in Asset Management
- Data Analytics in Digital Marketing
- Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
- Data Analytics in Manufacturing
- Data Analytics in Pharmaceutical Industry
- Descriptive Analytics – Definition, Types, Examples, and More
- Digital Analytics
- Financial Data Analytics
- Financial Risk Analytics
- HealthCare Claim Analytics
- Insurance Risk Analytics
- Insurance Risk Analytics
- Population Health Analytics
- Portfolio Risk Analytics
- Revenue Cycle Analytics
- Risk Assessment Analytics