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Sushant puri

Sushant Puri, Co-Founder ANS Commerce.

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Machine Learning: Transforming the way businesses interact with customers

As one of the most popular new-age technologies, 61% of business professionals point to AI and ML as their company’s most significant data initiative over the next few years

Photo Credit : Shutterstock,

Over the last few years, two of the most commonly heard and used words are Artificial Intelligence (AI) and Machine Learning (ML). With these novel technologies, businesses across verticals, particularly e-commerce, have witnessed a remarkable growth trajectory. Despite the ongoing pandemic’s continued onslaught, these technologies have been central to business continuity to an exceedingly large extent. Now that the economy is gradually reopening, technology remains as a core enabler of success for players in the e-commerce industry.  

As e-commerce businesses begin to restart full-fledged operations even as the pandemic rages on, ML has expedited the progress. A subset of AI, the technology has been at the core of several modern business applications. Customer experience and business-consumer interactions are two critical aspects that can make or break business growth it has revolutionized. ML has been at the root of enhancing the way brands in the industry interact with customers. Here’s how.


Marketing and conversion

Regardless of the nature of a business, marketing and conversion are certain crucial facets. ML can be used to identify and improve low-conversion products and help businesses devise strategies based on the attributes of those who engaged with the product but did not complete the purchase. Furthermore, it can also help identify a potential early buyer/adopted of the product. How? The answer lies in providing faster, more intelligent, and relevant feedback. This buys more time for businesses to experiment with their creativity and technical expertise to bring about a more appealing product/solution for customers. 


ML also assists brands in segmenting customers and breaking consumer data down to a granular level to help detect various customer responses to a particular interaction, an important feature for e-commerce players who largely depend on consumer insights to augment their services. ML models also offer effective target prediction based on the customer segments discerned to help companies target their ads more accurately. This has proven to be extremely useful since a vast majority of brands, especially those in the e-commerce sector take to social media to identify target customers to display their ads. 


Customer engagement

Merely selling a product doesn’t complete the functions of a business. Today, customers prefer personalized products and services that will cater to their requirements. ML-led algorithms can assist brands in developing customized loyalty and referral programs to better understand the complex customer life cycle, thereby helping increase customer acquisition and retention.


While shopping online, it is often a challenge for customers to find products that fit them as they can be uncertain about their sizes. With ML, shopping apps will finally be able to allow customers to virtually try apparel by analyzing user preferences. ML algorithms assist in generating customer-centric and highly engaging content in the form of blog posts, FAQs, and identify the right person to market the content to, making the customer’s experience more personalized, thereby increasing customer engagement.


Personalization


Customization has the unique ability to connect with the consumer on a personal level. In an e-commerce business, personalization often makes the customer feel more valued, strengthening the loyalty of the customer. ML helps build a personalized shopping experience for customers by responding to real-time data and keeping track of browsing history to guide the consumers better in their purchases. Additionally, ML also enables the discoverability of complementary products based on the user’s latent preferences such as size, shape, color, brand, etc.


Chatbots and social media interactions


For e-commerce businesses, chatbots have been the backbone in facilitating efficient business-consumer interactions. ML helps brands build chatbots that are available 24x7 without requiring manual assistance, creating the holy grail for businesses – a satisfied customer. ML algorithms also gather and store data to learn from user interactions and help customers find more accurate items over time. Apart from these, ML plays a pivotal role in processing data to understand user engagement with the brand’s content on social media platforms. This will help identify the kind of content and platform that resonates the most with consumers.


Omnichannel strategy and cost optimization


For businesses in the retail/e-commerce segment, adopting an omnichannel strategy will boost the brand’s presence and increase its customer base. That said, managing an omnichannel strategy efficiently involves high cost and manpower, and is time-consuming. ML manages and optimizes the omnichannel pricing and when coupled with cloud-based solutions, they help decide the optimum pricing. Besides, ML also determines if there is excess inventory of a product through pattern recognition and even help in finding the right courier partners basis factors such as weight of the package, routes, past SLAs, and pin codes.


For decades, the optimum utilization of ML has been a pipe dream for the business. However, as the e-commerce sector begins to discover the technology’s true potential and understand it better, they are exploring ML algorithms on a deeper level to solve several challenges. Technology, which forms its very foundation from receiving orders to fulfilling deliveries, has played an instrumental role in helping the e-commerce industry grow exponentially. To sum it up, ML has undeniably become a fundamental aspect that holds modern businesses together and a well-balanced combination of man and machine will help businesses achieve rapid growth.





Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house


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