Any organization that provides a service will receive a lot of complaints, enquiries and feedbacks from customers almost every day. There are numerous interactions with customers, over telephone, email or face-to-face. These scenarios provide plenty of data which would be vital when it comes to improving customer service. Unfortunately, very few organizations actually use this data or know how to use the data to improve their customer service.
Thanks to Big Data, organizations are now capable of understanding their customers’ needs. This webinar discusses how Big Data can be used the organization’s advantage. We will be discussing the following topics as well:
Rise of Big Data
Big Data and Hadoop
Major companies using Hadoop
Scenarios in customer service where Big Data can help
Rise of Big Data:
There is a lot of information floating around these days than ever before, and Big Data is putting them to extraordinary new uses. Big Data doesn’t necessarily relate to Terabytes and Petabytes of data i.e the size of the data. The term ‘Big Data’ is used to represent a collection of data sets that are large and complex and is difficult to process using available database management tools or traditional data processing applications.When speaking about the amount of data, unstructured data forms a huge chunk of the pile. The world creates about 2.5 Quintilian bytes of unstructured data per day from various sources like sensors, social media, photos, documents, etc. The number continues to grow. The big question is what is being done with it.
It is estimated by IDC, that by 2020 the number will reach 40,000 EB, or 40 Zettabytes (ZB). By 2020, there will be 5,200 GB of data for every person on Earth.
Big Data with Hadoop:
Organizations are taking fact-based decisions. In order to do this, we need lots of data. The Bigger the data, more accurate is the decision! The data is growing in terms of volume, and we need a mechanism, with which we can not only store the data but can also process the huge volumes of data and get some insight out of it.This is where Hadoop comes into play. Hadoop can digest all kinds of data and you can torture it to help you make your data-driven decisions.
Hadoop provides frameworks like MapReduce and tools such as Hive, Pig, NoSQL, HBase, and Cassandra. By using them, we can process the huge volumes of data by involving distributed computing concept, and we can process it too.
The need to process big data and get insights out of it led to the need for a technology like Hadoop. The widespread use of Hadoop has now reduced Big Data challenges to a large extent.
Major companies using Hadoop:
Here are some of the organizations that have adopted Hadoop and benefited from it. Looking at the image it is obvious that Hadoop can be implemented across various sectors and is not restricted to any particular domain.
How can Big Data improve customer experience?
Big data is all set to help out marketers to reach and engage customers and prospects in ways that businesses are only now starting to understand. It is just not sufficient to gain new customers but also retain existing ones. This can be done only when you understand customer sentiments, what the customer expects, needs and is facing.
Transaction data doesn’t improve customer experience on its own, unless you combine it with some other data to gain some insights. This data when combined with new data sources from social media to location and weather data, lets you enter the world of Big Data. These data on its own isn’t useful but when these intersect is when you begin to know more about customers.
The collected data can give you a clear picture about customers’ habits, their preferences, their interactions with the company and then analyze those data sets for predictive behavior and proactively apply those insights both to your existing and new customers.
Companies using Big Data for Prediction:
Case study 1:
Sites like Mint and LearnVest allow consumers to review their spending by category and see where their money went in a given week, month or year.
The user’s spending data is continuously monitored and analyzed to find a pattern which may help users in saving money and spending smartly.
Case study 2:
The food diary platform MyFitnessPal gives people not only detail of how many calories they’ve consumed each day but also breaks them down into protein, fat, and carbs.
These companies perform analytics on the huge data generated by the user’s mobile and they show the information extracted from customer’s raw data.
When a customer reaches out, company representatives can quickly and efficiently solve the problem if they have the right data in front of them. They won’t need to ask as many questions of the customer because they already know the answers. Companies who equip their employees with tools that provide in-depth customer data stand apart. This helps customer service executives provide great service, which only improves with each interaction.
Case study 3:
Southwest Airlines, is using speech analytics to extract data-rich info from live-recorded interactions between customers and personnel to get a better understanding of their customers. Speech analytics helps the airline to have maximum information about the customer beforehand
Whenever the customer calls, his voice is organized by speech engine and his recent queries and important data is displayed which helps in improving the interaction.
Most companies know their customers’ pain points (if they don’t, they aren’t paying attention to their customers). Those who are digging deep into the data to solve those difficulties are improving their customers’ experience.
Case study 4:
Passenger’s lost baggage is top concern for airlines. Delta looked further into their data and created a solution that would remove the uncertainty of where a passenger’s baggage might be.
Customers can now snap a photo of their baggage tag using the “Track My Bag” feature on the Delta app and then keep tabs on their luggage as it makes its way to the final destination.
Customer churn prevention:
Closing a sale and landing a new customer is great, but it shouldn’t be the end goal for businesses looking to foster long-term growth. Representatives can use response time, channel analysis and categorized tags to see exactly how to help customers as quickly and efficiently as possible. Customer service and sales teams can combine their data to see how customer-support responses contribute directly to sales efforts.