With the growth and access of technology and social media, today’s organizations and businesses are generating vast amounts of data from all aspects of their operations and workflows. Apart from handling the data, organizations have started realizing the potential of harnessing the power of big data and to utilize the same for overall business improvement and growth.
In this blog, I wanted to highlight some of practical big data use cases and the benefits for various industries. They showcase how structured and unstructured content processing, NoSQL databases, predictive analytics, machine learning, and advanced search relevance ranking techniques have made search and big data analytics a strategic part of the businesses vision and strategy.
Recent advertisement by Cadbury was released targeting hundereds of Geo-location based customers to relate them to the local businesses was a great campaign and was the real use of big data and data analytics.
Use case #1: E-commerce personalization and customer experience
Retail and e-commerce has been biggest benefactor of Big Data and advanced data analytics, and they have been utilizing the power of analytics for targeting the potential customer. Remember when you were leisurely browsing online looking for a travel destination or shopping sites to find that surprise for your family or your wife (or yourself)? How often do you type in the search box, click on the navigation bar, expand product descriptions, or add a product to your cart? Every one of these actions are the key to understanding the customer behavior, requirements, and can be used to optimize the entire shopping experience. This means the company has to have means for collecting, processing, and analyzing shoppers’ behavior and transaction data which can open up enormous opportunities for big data in e-commerce.
A powerful search and big data analytics platform allows e-commerce companies to (1) clean and enrich product data for a better search experience on both desktops and mobile devices; and (2) use predictive analytics and machine learning to predict user preferences through log data, then personalize products in a most-likely-to-buy order that maximizes conversion. There has also been a new movement towards real-time e-commerce personalization enabled by big data's massive processing power.
Use case #1: Dynamic Pricing and discounting
With competition, companies want to tap all kinds of customers and realize that they might loose price sensitive customer. Also with the scale of SKUs, it is difficult to keep a tab on the competition pricing and hence can loose the customers. Pricing as well as Strategy teams wants Identification and categorization in product pricing derived from pricing trends of products and competition. With data analytics, this has been made easier to manage and control.
Identification of Discounting pattern VS Commerce data, Discount pattern Vs clickstream, Conversion Funnel for discounts (LC-->PDP-->ATC) can be easily viewed through advanced analytics. Identification of Price gap, Pain Point, Sweet Spot between competitor and Marketplace is now easy for the marketplace owners and even the sellers on the marketplace.
Big Data and Data analytics has changed the way customers are offered and the way brands are pricing the products online, it has not only improved the overall customer retention, but also helped organizations realize higher sales with much higher returns on promotions.
Use case #3: Recommendation engines
We all encounter the benefits of recommendation engines, whenever we visit websites for buying items or for online media streaming platforms, we may have noticed those “recommended for you” videos, movies, or music even ads running on our facebook, games or even in-mobile advertisement also recommending the similar products. Even while browsing through movies or songs, we get recommendations which is personalized only for us and as per our search history, isn’t that cool? It’s easy. It’s time-saving. Overall, a satisfying user experience, right? Have you also noticed that the more videos and movies you watched, the better those recommendations became? As the media and entertainment space is filled by strong competitors, the ability to deliver the top user experience is becoming the winning factor. Even for products, based on recommendation, the companies can attract buyers to visit the website and then clubbed with different promotions and discounting can ensure the sale happens.
Big data, with its scalability and power to process massive amounts of both structured (eg. video titles users search for, music genre they prefer) and unstructured data (eg. user viewing/listening patterns), can enable companies to analyze billions of clicks and viewing data from you and other users like you for the best recommendations. Over time, through machine learning and predictive analytics, the recommendations become better tailored to the user’s taste.
Use case #4: Location based SEO and customer targetting
Location-based marketing isn't a new concept. Businesses have always understood the value of marketing to people based on their location. This is why many brick-and-mortar businesses still send direct mailers to locals, and why people who live in frigid climates are more likely to be persuaded to buy snowshoes in winter than those in warmer climes. Recent advertisement by Cadbury targeting multiple GEO-locations is the example and power of location based customer targeting.
With, the growth of mobile integration, big data management, and improvement in technology with consumer access to technology has led to making location-based marketing more advanced and efficient. The data for customer location is collected through consumers' smartphones or wearable tech via GPS, Wi-Fi, Bluetooth, IP addresses, or cell tower triangulation, as well as through zip code when they were last in the store. If the customer is in a location where the business deems them to be receptive to a marketing message, be it inside or in the general vicinity of the business, the business will deliver a targeted message to them, such as a coupon or other custom offer, or news of a promotion or in-store event.
Based on the location based data and customers the organizations can manage promotions and product offerings as per the location and customer profiling.
Use case #5: Insurance fraud detection and credit management
Banking, financial services and Insurance organizations that handle large amount of financial transactions keep looking for more innovative, effective and efficient approaches to fight fraud. For e.g Insurance agencies want to manage fraud, which can cost the industry up to multi-billion dollars annually, and similarly banking companies want to manage credit being offered with minimal risks. With Traditional fraud detection models, fraud investigators need to work with BI analysts to run complex SQL queries from bill and claim data, which used to take weeks or months to get the results. This process sometimes causes lengthy delays in legal fraud cases, thus, huge losses for business.
With big data technologies, billions of billing and claim records can be processed and pulled into a search engine, so that investigators can analyze individual records by performing intuitive searches on a graphical interface. Predictive analytics and machine learning capabilities enable a big data platform for fraud detection to provide automatic red flag alerts as soon as it recognizes a pattern that matches a previously known fraud scheme.
As social, cloud, and information have become the driving forces of the modern business, we expect to see more and more innovative use cases that leverage search and big data analytics to make sense and make use of the vast amount of data. Like the cloud, big data is here to stay and continue to enrich the business technology ecosystem in the coming years.