Have you heard about the new buzz term in analytics, “Deep Learning”? Although it might seem like some obscure, abstract addition to the already cluttered analytics space, Deep Learning is actually more of a combination of other analytics methodologies that tries to accomplish something better.
Blockchain technology has been all over the news recently. With the explosion of bitcoin usage for digital transactions, blockchain is required to track and verify every transaction made.
After a sale, it is critical to ensure every touchpoint with the customer is handled skillfully. It is only possible to understand and improve a customer’s journey by tracking key customer metrics and run analytics. Organizations that know how to identify such gaps in the service provided using analytics, to further enhance customer satisfaction, reduce churn, increase revenue, and greater employee satisfaction, will see the rewards.
Customer service is essential to any organization, especially for product-based companies offering support to customers around the clock. When an organization offer such services, it becomes pertinent to keep the customers happy and that is only possible if the call centre operation is fully optimized. Now, the question is what makes a customer happy?
In Part 1 of our blog, we explored how to get started with BI Analytics in your organization, including the primary business challenges and how to overcome them. BI Analytics is just the start though – most organizations are moving to Advanced Analytics with features such as forecasting, predictive modelling, algorithms, machine learning, and artificial intelligence (AI). Advanced Analytics is all about foresight and getting accurate predictions of what is likely to happen to your business.
Companies across many industries have already adopted BI and Analytics to streamline day-to-day operations, customer support, accounting, sales, marketing and other business functions. If your company or department hasn’t adopted BI and Analytics, you may find that this post can help you or your team move into the analytics space – starting with BI and Analytics and evolving to consider leading-edge Advanced Analytics solutions with AI and Predictive Analytics.
Over the past few years, you’ve probably heard plenty of talk about Machine Learning and its business applications, including increased revenue, better customer service, fraud detection, and inventory/supply chain management. However, do you know how Machine Learning works and how it, and its many algorithms, can actually help your business?
IoT (Internet of Things) Analytics is revolutionizing service delivery in the hospitability industry. Many hotels already use IoT devices, including voice assistants, smart thermostats, automated door locks, to greatly enhance guest experience. With the right IoT Analytics tools, you can also use the data collected by these IoT devices and sensors to drive down costs and improve maintenance while making your guests happier from the moment they book their stay.
Modern social analytics tools expand the insights you can get into the performance of your products, services, and related marketing campaigns. Looking beyond internal data sources, these tools can analyze external sources, including social media, chats, and forums, to reveal what your customers really think about your company’s services.
Visualization helps you put huge sums of data gathered from web analytics tools into a visual format that’s easier to understand. Spreadsheets and lists just can’t convey the meaning of data the way a chart can. It helps you gain insights, find trends and patterns, show large amounts of data at scale, and identify items for further analysis. One way to use visualization with web analytics is through “heatmaps” that reveal insights about user behavior.
