MACHINE LEARNING – IMPACT ON MARKETING PRACTICES IN INDIA

The term ‘machine learning’ has been a buzzword over the past few years in the media due to which most people must have heard of the term. However, only a few are aware of its details and applications.
 

In its true essence, machine learning is the technology which enables a machine to learn and improvise based on the feedback it gets, and thus mimic humans in decision-making.
 

Machine learning’s application in businesses is relatively new in India. But recently, some organizations have initiated its use in scaling up their business in various areas. One significant example is its use to process the satellite images in the field of remote sensing technology under the Indian government’s authority.
 

One of the many domains that machine learning has significantly impacted and changed is the field of marketing. Machine learning has influenced the marketing practices in India by inducing the following changes:
 

1.    Marketing automation:
 

Machine learning introduced automation into the world of marketing. This replaced the manual efforts invested into the marketing by a more scalable option. It enhances the marketing efforts by coding the human logic and then executing it. This, in turn, allows more time for a business to utilize the freed-up time on more human-intelligence-intensive areas.
 

India’s largest lingerie retailer brand, Zivame, adopted the marketing automation to engage product page abandoners by using web push and onsite notifications. This resulted in a conversion uplift of 20% on their online website, zivame.com.
 

2.    Personalization:
 

One of the major realizations that have changed the way marketers approach their audience, is the view that every customer is unique, and should be treated so. However, doing this manually for millions of customers is not possible.
 

Data Science has solved this challenge by using data to identify the most personalized and relevant products for each customer in real-time across all interactions with the customer. This approach lets the retailers identify and direct only the most relevant products for each customer.
 

In one such effort, ABOF used a machine learning product to re-order the products shown on their page to each customer on the website. So, each customer saw a different website based on their preferences and tastes. This led to customers finding the products of their choice more easily and culminated into a 30% increased purchases per customer.
 

(https://boxx.ai/case/CASE%20STUDY%2002.pdf)

Likewise, Clovia.com also achieved a 30% increase in its click-through-rates on its product display page. This was attained by providing a completely personalized “recommended for you” widget for each consumer.
 

(https://boxx.ai/case/CASE%20STUDY%2003.pdf)

3.    Omni-channel reach:
 

Based on the statistics, it is observed that retailers who employ omni-channel customer engagement strategies achieve nearly 89% customer retention rate as against 33% for companies with weak omni-channel strategies. However, a manually personalized omni-channel strategy is not possible due to a limited compute power of humans. Further, humans will never be able to identify the most relevant communication for each customer and then dissipate it across channels. 
 

Machine Learning has made the process convenient by automating the algorithms and computation. This enables an online retailer to reach a wide consumer base, with the same message, across all channels viz. email, notifications, ads, onsite, offline stores, and chat-bots.
 

Nearbuy employed machine learning technology to determine the personalized product matches for each of its customers and then sent the same communication across personalized emails and app notifications with a complimenting landing page on the website and app.
 

(https://boxx.ai/case/CASE%20STUDY%2001.pdf)

4.    Marketing optimization:
 

An important marketing tactic that was induced by the machine learning technology is the optimization of the channel-mix in correspondence to the consumer’s availability. This engages and prompts call-to-action from prospective customers to maximize the results.
 

Nearbuy enhanced its marketing efforts across various channels by optimizing each personalized communication in accordance with the consumer’s behavior. This involved optimizing various features viz. scheduling the notifications for a suitable time at a recommended frequency, optimizing the channel-mix, to name a few.

Boxx.ai is an AI-driven machine learning product that helps the companies in the e-commerce space to increase the top-line of the clients by 40%. Boxx.ai uses machine learning algorithms to help the e-commerce businesses identify the most relevant and personalized products for each customer, based on their past behavior, transactions and profile summary. It then delivers these across various channels including website/app, digital marketing channels, and advertisements.

Though machine learning’s application to the Indian business market is a relatively recent development, its popularity has grown in the last few years. This is due to the success achieved by the businesses that applied machine learning technology to scale up their overall marketing efforts. As a result, the machine learning trend is expected to gain more limelight in the Indian business market in the years further.