Customer Analytics using Data Science – Improve Customer Experience
Since the last decade, customer expectations have been changing drastically. Customers are no longer satisfied with just receiving good quality goods and services. They are mindful of their experience during every stage of doing business. Gone are the days of one-size-fits-all marketing. Companies have realized that the more personalized service they offer to their customers, the more likely they will be to bring in more business. According to a study conducted by Experian Marketing Services, it was seen that 84% of customers did not give repeat business to a company that failed to take their personalized needs into consideration. Here is where predictive analysis comes in helpful. Predictive analysis allows businesses to understand their customers. It allows businesses to use customer data to understand current consumer behaviours and predict future consumer behaviour.
Here’s how different businesses are meeting consumer expectations and how they can adapt to tomorrow’s demands.
BANKING AND FINTECH
Banks and other financial institutions are very focused on providing improved service to their customers. Some banks are seeing a definite increase in their overall profitability by implementing analytics solutions that have helped them identify the right-fit customers and helped them with cross-selling and upselling opportunities. One of the largest banks in India implemented an analytics solution for pre-delinquency management, to help identify the payment propensity that is the likelihood of repaying of their customers. The predictive insights gained by this particular bank enabled it to increase its collections by more than 50%! Banks have been able to see significant improvements in other areas of business as well. They have been able to improve lead generation, retain loyal customers, improve credit decisions, and improve overall operational efficiencies.
Airlines are using predictive analytics to improve customer service interactions. One airline, for example, uses speech analytics to extract important information from customer interactions with their call-center personnel. By collecting customer data, the personnel can solve the customer problems quickly and more efficiently because they have all the right data in front of them. They don’t need to ask as many questions of the customer to be able to resolve their issue. Another airline came up with an innovative solution to track passenger luggage through their app. This app allows passengers to upload a photo of their bags on the app and track it as it makes its way to the destination. In the backend, this app uses big data to track down the passenger’s baggage, irrespective of whether the baggage is on the same flight as the passenger or not and updates the passengers on the status of their bags. This helps improve the passengers experience flying with this airline.
Big retail brands are using predictive analytics to predict exactly what customers want even before they ask for it. These companies gather information about their customers, such as where they live, what they have purchased, what websites they visit, and if they have interacted with their brand on social media. This data is properly mined to derive useful insights and build relationships with their customers. These companies then promote the right products to the right customers on the right channel.
Amazon has been collecting such information for years-not just addresses and payment information but the identity of everything that their customers have bought or added to their cart or even looked at. They then recommend books, toys, or other specific items that their customers might be interested in. In fact, Amazon has generated 29% of sales through their recommendation’s engine. Other companies have followed suit. Netflix assesses customer viewing preferences and makes suggestions for watching particular movies. Streaming services, such as Spotify and Pandora have not only played a major role in enhancing customer engagement and providing a personalized experience but also helped discover upcoming artists and make forecasts on their potential for success.
Marketing companies are increasingly using predictive analytics to get their brand message out to the public in a more personalized manner. One such example is Google Trend analysis, a tool that marketing experts are using to forecast and indicate a certain trend.
Some of the big telecom players are using contextual marketing to upsell offers that are relevant to their customers, thereby transforming marketing campaigns from a cost-intensive effort to a sustainable profit centre. In addition, these companies are using predictive analytics to gain real-time insight into potential failures and also equip their call centre representatives with actionable insights to deal with customer backlash during such outages. They are able to minimize recovery time during network outages without sacrificing service quality.
Customers are the most important entity for any business to survive. This means that focusing on retaining customers and ensuring customer satisfaction is primary to all businesses. But in reality, it is difficult to predict which customers will bring in more business and which of them will switch over to the competitor’s business. Predictive analytics plays an important role in helping businesses improve the overall satisfaction of their customers and reduce customer churn.