Published on June 1st, 2017 | by Guest


How Machine Learning Will Change Your Day-To-Day Digital Experience?

In the business world and especially when it comes to the innovative digital experience, machine learning is certainly creating a foothold. Across the industries, the WCM players are completely into machine learning and aiming to support a smart experience. The recent industry overview of Forrester stated that “as more companies determine the need for circumstantial digital experiences, the Web content management market is transforming. In this landscape, every vendor is tracking towards this goal.” As the contextual experiences progressively become the brand differentiators, the machine learning’s capability to deliver these experiences at scale is highly advantageous.

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If we put in simple terms, machine learning is an analysis of data in the same way you would do manually. The computers today are capable of doing it many times faster. Let us imagine that data is a paper – it comes in different sizes and shapes. You can serve numerous purposes with it like writing on it, wrapping up a present and many more. The machine learning is equivalent to bundling huge amounts of paper, wrapping it and making it a cardboard which is stronger. You can probably make a strong cardboard but a machine definitely makes a strong cardboard. A cardboard has numerous so as machine learning. Today, every individual, every industry is molding it according to their purposes.

Below here, I will discuss how 3 kinds of industries can create a more contextual digital experience with machine learning.

The Financial Service Industry

Considering the digital transformation initiatives, the financial services industry is raging forward and the machine learning acts as a catalyst here by helping the customers acquire more transparency, more clarity and increased access to tools and services.

Understanding the Needs of Visitor

Through examining the click behavior across all the visitors, the machine learning can help mapping out an information path and offer the most helpful and relevant content along the financial path. The current standing and the financial history can be analyses on a deeper level of penetration to pinpoint precisely where someone is in the financial journey of the visitor and provide the information automatically to prepare the visitor for the next step.

Offering the Right Information

Chances are that the same educational content helps the person who searches for the bond growth beyond 18 years and the other person searching for college fund for a new baby. Your digital experience engine with the help of natural language processing will learn the search intent continuously to provide the ideal information to your users, and it will notify you when a usually searched-for category requires further content.

Automatic Advising

As a number of personal factors go into each and every financial decision and as from start to finish, no two paths are exactly the same, creating a financial plan seems difficult. Although, by examining the financial choices of all clients, the machine learning can determine better where every individual is currently in his financial journey and predict automatically where they can succeed financially down the road.

Manufacturing Industry

Analytic and Data insights have been a central part of manufacturing and with machine learning, the information can be processed more effectively in order to allow the business to streamline the supply chain management and scale production.

Product Innovation and Refinement

The Purchase data not only notifies you of the features and models your customers are prioritizing, but also recognizes what features are not performing. If offering 5000 variations can give the customers the same amount of satisfaction as focusing on 500 options with features that are of high demand, then the processes can become more efficient and streamlined at a very low operating cost.

Personal Performance Forecasts and Metrics

Through machine learning, the multifaceted analyses of data will not only increase efficiency, but also can keep the stakeholders informed and in control of their supply lines. The automated performance reports provide real-time insights on every stage of supply chain and offer further suggestions regarding cost- and time- saving. For potential clients, the metrics from the existing clients of similar location, size and industry can be aggregated to generate a highly accurate speculative report.

Intelligent Supply Chain

The machine learning takes the massive data that is collected along the supply chain and utilizes it to deliver insight in order to drive creation engine that is highly intelligent. Analyzing the information across the weather conditions, transportation, assembly line equipment, raw materials and inventory availability will enable the manufacturers to automate and map out an end-to-end journey with unparalleled efficiency. The low inventory at a customer can be automatically flagged and activate the optimal supply chain timeline providing new inventory with no gaps in supply.

Retail Industry

Helping the customers get what they are exactly looking for and making the experience more delightful can be attained through machine learning at scale.

Contextual Experience

The contextual content can be intertwined with products through metadata for the retailers who are ready to go beyond the product pages. For instance, the same copper lamp is seen by the two visitors – visitor A then looks for “coffee table with light wood” and visitor B searches “steel counter stools”. In these choices, your content engine will observe the overlapping themes and provide “the beginners guide to Scandinavian design” to visitor A and provide “top 10 industrial apartments” to visitor B. Now, a question that how will this technology match the content to products? The machine learning automatically reads your present content, add the metadata and match it with the product metadata, all while learning constantly through a crawling external domains and through a feed of internal data.

Digital Brick and Mortar

Today, when customers pay a visit to a physical store, their digital experience is disconnected from their brick and mortar experience. The machine learning can make the digital and in-person experiences cohesive. With the help of viewing history and previous purchases, you can know their preferences, understand that they are present in the store depending on their location, scan the presently available inventory and suggest them the items in stock, in their usual price range, their size and style. A digital display can make checkout much easier at the point-of-purchase feeding the information regarding purchase back to their digital profile in order to help deepen personalization in the future.

Knowing precisely what they want

The NLP (Natural Language Processing) can take a normal site search bar and create a marketing machine that is highly effective. NLP learns to continuously identify and connect the intent. Insights on more than just the search items that are trending are offered by these high-powered analytics. They can also determine what overarching topics and categories are likely to be the next big thing and display your existing catalogue that can fit nicely into these trends.

A Personal Shopper for everyone

The journey of every customer is an unique one. Even though people search in same categories like running shoes, holiday decorations, DIY kitchen supplies, they will have different preferences at different stages of buying process. For instance, a customer who purchases repeatedly on every second friday? So, we will be having an idea on which day the customer pays. So, you can offer them content focusing more on lifestyle in the first week and send them a good discount code on second thursday, i.e, before payday. So, this kind of personal experience can be automatically cultivated through machine learning at scale.

A world of convenience

Customers reward the organizations at the end of the day that anticipate and satisfy their wants and needs. Making the maximum use of machine learning provides businesses with the power to determine and personally cater to the needs of the customer at an unprecedented scale.

Everyday, we do many things repetitively like using a toilet paper roll, drinking coffee and so on. This is how Machine Learning and AI will invade our lives by utilizing the data of our choices to make everyday a bit more convenient, across every industry.


About the author:

Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working as a Content Contributor at Tekslate.com. His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He enjoys spending time with his friends. He can be contacted at [email protected]. His LinkedIn URL is https://www.linkedin.com/in/savaram-ravindra-48064641 and Twitter URL is https://twitter.com/s11ravindra.

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