When consumers make purchases online, it is often the case that they wish to save time and money by seeking out the best price and quickest method of payment and delivery. Take the example of Amazon.com, where one can buy a myriad of products at discounted prices and easily qualify for free and even expedited shipping with a rather minimal purchase. How often, however, have you ended up buying more than you had intended because an additional product was recommended for you, also at a decent price and with a constant guarantee that your shipment would arrive quickly and cheaply? It’s a frequent scenario for consumers and no accident, either: the business predicted in advance what you, the consumer, would want to buy.
Such is the power of big data analytics, which allows enterprises to analyze a mix of data sets from various sources to gain important insights into customer behavior and apply those insights to business strategy and providing better customer service.
For example, transactional data-documenting the history of a consumer’s payments-may be analyzed to determine how much and how often a consumer made purchases. By determining a consumer’s spending habits-as the Amazon example illustrates-a business may predict what services or products the consumer will like and offer excellent deals. When the customer is satisfied with a good deal, brand loyalty is likely to increase, making this a win-win situation for both businesses and consumers alike. Some products and services may also provide data as a means of further personalizing the customer experience. For example, personal finance websites may track a consumer’s spending habits and even offer reward incentives for spending money-think of cash back credit cards-while health-management sites can provide feedback and encouragement for people who are monitoring their diets. Big data therefore offers a chance for companies to connect with the consumer by making the product or service about the consumer, not the company. This translates into customer satisfaction and once again, increased brand loyalty.
Then there is the opportunity for delivering excellent customer service. Another valuable data set involves the analysis of social network activity reports-showing how frequently consumers visit social media sites–and feedback gained from SEO (search engine optimization) audits which allow companies to know which webpages are most frequently visited or ignored and thus most critical to business success. In addition, traditional customer service surveys, customer service call transcripts, and most any text produced by the customer in exchange with customer service agents (SMS, social media posts, and chats included) are vital sources of big data which allow for optimized customer engagement. Businesses may pinpoint frequent sources of customer issues and resolve them in advance, or simply use customer feedback to create a more pleasant engagement experience. As customers are also connected to brands on many channels, it is vital to collect, analyze, and treat data across all channels-provide employees with the right tools for understanding, so they in turn may provide optimum customer service. By understanding customer behavior, companies can resolve issues more efficiently as big data will enable representatives to quickly and accurately offer solutions without a need for asking many questions.
Understanding customer behavior is essential to providing excellent customer service and fostering brand loyalty, and big data analysis plays a key role in making this happen.
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