Facing the highest competitiveness and being under the constant risk of losing clients and money, businesses are strongly focusing on the creation of a client-centric strategy. If earlier it was enough to offer some kinds of goods at a lower price, now getting success is much more difficult.
To stay competitive and boost sales, companies have to add value to customers, provide them with the personalized experience, and solve their challenges. Customer data is taking the center stage: when understanding the audience needs, a company can deliver the best service possible and thus receive loyal customers and increase income.
Why use Big Data
Big Data is about large data amounts that can be found in various sources, including social media channels, user comments, website testimonials, the results of polls and surveys, and many others.
Representing a set of tools for capturing, storing, analyzing, and visualizing data, Big Data is used in different industries for a wide range of purposes: risk management, company performance and SWOT analysis, business strategy development, predictive modeling, improved targeting, and more.
Companies need Big Data in order to find out what should be improved, develop an effective advertising strategy, and improve the customer experience. Big Data is crucial for customer in-depth analytics and getting the value of data insights.
The improvement of customer experience management (CEM) is one of the most important objectives companies can achieve by using Big Data solutions. CEM takes the center stage in a variety of domains, from insurance and retail to banking and healthcare.
By processing customer data, estimating customer loyalty and discovering the reasons for negative feedback, companies get the ability to create a successful action plan and improve customer experience and boost sales as a result.
How to use data for improving customer experience
1. Define customer needs and preferences
An understanding of customer problems, tasks, and expectations comes from a thoughtful analysis of customer behavior, which may find its reflection in user testimonials, social networks, purchasing history, duration of customer interaction with your company, lifetime value, results of polls and surveys, and mobile app analytics.
By delivering the product/service that customers really need, you convert them into loyal clients, who return to you again and again. Moreover, most of Big Data solutions provide such useful features as data visualization, real-time reports, and integration with other software systems, for example, CRM systems.
2. Ask questions & analyze answers
By asking questions – in polls, surveys, feedback forms – and processing the answers you get to know what things you should enhance (e.g., reduce average handling time or focus on first contact resolution) to increase customer loyalty and receive income. With Big Data Analytics you can estimate the following indicators:
- the current level of customer satisfaction
- metrics that have a major impact on customer loyalty
- factors that affect customer negative feedback (reasons)
- how to improve customer/employee performance
- things that need to be improved
- things enjoyed by customers most of all
3. Conduct Linkage Analysis
Linkage Analysis is the process of linking disparate data sources (e.g., customer, employee, partner, financial, and operational) to uncover important relationships among multiple variables (e.g., call handle time and the level of customer satisfaction).
So, Big Data processing tools can combine a wide range of sources with customer feedback and analyze various metrics, for instance, financial and performance indicators, employee activities, and then provide advanced reports and smart recommendations.
Depending on the issue required to be solved, you connect different sources of data, e.g., customer feedback to financial metrics, customer feedback to employee and partner variables, or customer feedback to financial metrics.
For example, when having an issue referred to financial metrics, you can integrate financial data (a wide range of financial indicators) with customer feedback (social media channels, surveys, polls, testimonials) and receive a holistic picture of how they are related (e.g., explore the causes of negative feedback).
4. Use data to deliver targeted messages
Big Data Analytics is crucial for delivering targeted messages and improving advertising campaigns. When collecting, for instance, data about the results of email marketing activities (message openings ratio, click-through rates of links inserted in the messages, etc.), you can then design the message that will delight and engage your customers.
Also, you can use geolocation data to better target customers near brick-and-mortar stores or during their in-store shopping experiences. Companies may use such information to send push messages about sales events, shares, gifts, and discounts in the stores, thus motivating them to make purchases.
It can work as follows: motion tracking embedded in beacons, instantly detects when the customer appears near a certain store and automatically notifies him or her of some special offer with a text message that comes on a user smartphone.
The personalization is ensured with what the retailer already knows about the customer, based on profile information, loyalty card data, purchasing history, and stated attitudes. It is called a single view of the customer, which is created with the use of Big Data solutions.
5. Create a customer-first strategy
When building a plan for improving customer experience management, remember that loyal customers spend 2-3 more than clients with a low loyalty level. At the same time dissatisfied customers often leave negative comments and bad testimonials, send complaints and tell their friends stories about terrible services.
So, in the business development, the main focus should be made on the creation of a customer-centric strategy, which implies quality services, products, well-trained staff, and meeting customer needs and preferences as well. The achievement of these goals will enable to build strong customer credibility and significantly boost sales.
How companies take advantage of Big Data
Retail companies have already learned the advantages of monitoring customer behavior in their stores. One successful example of a company that uses Big Data is RetailNext, which offers intelligent solutions for analyzing customer data and tracking customer traffic.
When collecting and processing information about all customer visits, behavior patterns, and shopping habits, retailers get the ability to define areas that need improvement and provide customers with the best customer experience.
What’s more, the measurement of customer traffic enables retail companies to enhance employee working hours and optimize resourcing-related operating expenses. This way, they successfully reduce waste and increase the overall performance.
Hotels are another kind of organizations that can significantly benefit from using Big Data software. Since there are thousands of hotels in each particular region, it’s difficult to attract travelers and maximize profit.
Duetto specializes in helping hotels define ideal timing for price surges and ensure that each rate quote offers the best deal both for customer and hotel. For that, it gets a deep insight into large amounts of data, which involves dates, channels, room types, market demand, and other important factors.
The integration of customer data allows hotels to better anticipate customer needs and preferences and personalize every aspect of the guest stay. The system applies machine learning algorithms which are constantly being updated to provide the best results for each hotel using this solution.
Customer experience is a top priority in any company that provides some kind of service. When delivering the personalized engaging experience and offering customers a solution to their problems, businesses increase sales and achieve a high level of client satisfaction.
Big Data Analytics is irreplaceable in everything related to capturing data, transforming raw unstructured data into valuable information, analyzing customer behavior, defining their pain points, needs and wants, and increasing customer loyalty.