Consumers are overwhelmed with offers for products and services, especially while shopping online. It's very common for a customer to see advertisements for products that they may have recently searched for, but also sometimes the marketing emails and pop-up ads are not necessarily suitable with the consumer's interests and may come across as more annoying than actually useful.
More of customer service is the aspect that is vital in any of the fields to rule the market - Unless your customer is satisfied your business is not going to reach the new level.
Isn't this a truth?
So, to judge that was a customer is liking and what are the kinds of stuff that are driving them to your set of products is worthy of analysis.
Big Data analytics is a term used to refer the large data sets that are too complex for traditional data-processing application software to adequately deal with. Big Data Analytics is the process of collecting, organizing and analyzing huge volume of data to discover patterns and other useful information for organization use.
Are You Not Using Big Data Analytics Yet? You are surely missing out a TON!
The gigantic data matrix helps to enhance the potential and growth of the company. Deep analysis of the data matrix helps to gain the hidden benefit from the reports. Many companies are reporting positive changes while implementing big data analytics initiatives. Researched advantages using the benefits of big data include the following:
- The capability of making better decisions
- High Productivity
- Cost-effective operations
- Personalized effect on the customers' needs
- Monitoring the sales highs and lows
- Upgraded customer service
Why for You it's a Right Time to Embrace Big Data Analytics?
With real-time Big Data Analytics, you can step ahead of the competition or the moment can be notified to your direct competitor that you are changing the strategy to be the market leader. Service improves dramatically, which could lead to a higher conversion rate and more revenue. When companies monitor the products that are used by their customers, it can proactively respond to upcoming outcomes - successes or failures.
Brands need to take a more strategic approach to drive sales and delivering excellent customer service stem to give customers an effective experience. Companies may analyze a huge set of complex data sets from various sources to gain important apprehends of customer behaviour and use such feedback to drive sales and provide better customer service with big data analytics.
Impact of Big Data on Customer Service & Experience
Better Contrive Is the Result of Better Analytics
The use of big data analytics is separating through transactional data or a customer's purchase history. Such data may reveal how much a customer has spent on something, how often he or she makes the purchase, and-most importantly-on which products or services. This kind of data is crucial for making marketing offers to customers for future purchases as well as recommendations based on customer preferences.
Nordstrom, for example, is developing stores that integrate data collections into a brick-and-mortar environment. It displays what customers have purchased before, whether at a store or online, and generates recommendations essentially serving as personal shoppers.
More Personalized Customer Experience
Big data analytics further present an opportunity to personalize and customize the customer experience. Therefore it offers a chance for companies to interact with their customers by specifying suitable products and services according to the customer's needs. This helps to expand greater sales, greater customer satisfaction, and sustained brand loyalty.
For example, financial management and personal banking websites may track a customers' spending habits and offer cash rewards or other incentives when they spend money, while health-management sites or even devices can provide feedback and encourage people who are monitoring their diets or tracking their vital signs.
To Wrench Social Media as a Power Tool
Customer feedback surveys, call transcripts, and most any text produced by the customer in an exchange with customer service agents along with the SMS, social media posts, and chats included are vital sources of big data analytics which allows optimizing customers' engagement.
Especially, big data gathered from social media activity can provide tremendous spectacle into customer concerns and directs the companies towards identifying and fixing customer service issues. Contact hubs can be reorganized to accommodate high demand channels, and managers can train their agents to work more on channels that receive the most customer contact.
For example, if Twitter proves to be a high demand channel for a certain brand, a company can heavily focus on it. Companies may also use such analytics data to carry out better marketing campaigns on the most in-demand social media sites, serving to customers' preferences.
Delivers Valuable Feedback
Big data analytics can also show which channels are most frequently used by customers and also the ones which need better engagement, thus providing an additional opportunity for brands to optimize their strategy. As customers are more connected to brands on the channels, it is vital to collect, analyze, and treat all data in order to provide employees with the right tools for understanding so they, in turn, may provide efficient customer service.
By understanding customer behaviour, companies can resolve issues more efficiently as big data will enable representatives to offer solutions precisely without a need of any queries.
For instance, big data is now integrated into CRM systems with the goals of improvised customer analysis, defining a better picture of customer-facing operations, good decision making, and helping in predictive modelling.
The concept of big data creating more personalized experiences may seem like a combination of similar technologies.
For example how chatbots are used to boost customer satisfaction. More a fashion chatbot for a brand like H&M helps a customer, the more it learns about their preference in terms of products. The chatbot is then able to come up with personalized product recommendations as a result.
While marketers aren't expected to rely on robots, they are expected to regularly gather data from customers for a more personalized experience.
Beyond the chatbots, Amazon's recommendation engine is a relative example of personalized recommendations via data collection. The personalization engages the consumers, and so the key for marketers to deliver the relevant recommendations can be efficiently achieved.