https://www.datatobiz.com/blog/machine-learning-in-ecommerce/ Machine Learning or ML is an AI technology that has been dubbed as the next “big” thing in the modern business landscape. While the e-commerce businesses keep on getting complex and highly competitive, technological disruptions have made it a highly volatile space for business strategies and tactics. However, with the smart ML-based tools delivering actionable insights, such as demand forecasting into the unstructured heaps of data gathered over time, businesses are beating the odds everywhere. As per the recent stats, 45% of the retail industry employed Machine Learning for online as well as offline customer engagement. Take a look at the 6 Innovative Ways of Using Machine Learning in E-Commerce

1. INNOVATIVE WAYS OF USING
MACHINE LEARNING IN
E-COMMERCE
6

2. Role of AI in E-
commerce

3. Chatbots – 24/7 assistance, guided shopping, contextual information gathering and
tackling repetitive queries
CRM – Better decisions stemming from advanced and in-depth analysis of customer data
via AI tools
Customer service – Deliver services and recommendations in a highly targeted and
personalized manner
Automation – Operation and process automation in multiple scenarios – warehouses,
data management, inventory management, KPI, performance metrics, etc.
AI is one of the key enabler technologies in e-commerce and is facilitating various services
and functionalities for both, the vendor and the customer.
AI-powered customer solutions allow business owners to deliver streamlined customer
experiences such as shopping inside the chat windows, query resolution, and delivering
personalized product recommendations. Because of such customer-oriented services, the
customers are able to make the most of every penny they invest and access better services.
Some other ways AI has revolutionized the e-commerce industry are:

4. ML for E-commerce: Is
There A Different
Reality for Enterprises
and SMEs?

5. One of the major concerns most SMEs have while thinking about adopting the AI
or ML capabilities in their business model is the budget. They feel that large
enterprises have the right amount of resources and budget to support this
adoption.
However, they overlook the fact that ML can be leveraged in various forms. They
need not have state-of-the-art ML tools or enterprise legacy systems in order
to opt for smart e-commerce practices. They just need to choose the best way
to leverage the technology for their business.
Hence, we are going to share 6 innovative ways to use Machine Learning in e-
commerce.
Further, we will also share how to choose the best way to use ML for your e-
commerce setup.

6. Benefits of Machine
Learning in
E-commerce

7. Natural Language
Previous shopping and search history
Showing results on the basis of search query typed in natural language (even when it
doesn’t mention the brand name or specific products)
ML boosts your conversions in two ways – product recommendations and offering on-site
search engines.
Machine Learning algorithms allow you to offer search results keeping many things in mind,
such as:
By showing search results that are more aligned to the search queries, the AI solutions, like
on-site search engines improve the overall customer experience and also increase
conversions.
Further, ML-based online shopping assistants can offer highly personalized product
recommendations, based on the analysis of visitor behavior and activity.
1. ML Increases Conversions

8. AI chatbot offering the contextual and historical data about a query to an agent for faster
query resolution
Showing website heatmaps
Recording visitor browsing sessions to show generic user behaviour and experience on
your website
AI can deliver highly smart and actionable insights into customer data, website performance
and sales or support stats. All this refined information comes in the form of reports that assist
and empower your in-house teams to streamline their operations.
Examples include:
From warehouse costs to sales and support to visitor experience – ML can help you improve
your in-house operations based on actionable data-driven insights.
2. Improves In-House Operations

9. Product viability
Audience availability for new products
Shipping and payment decisions
ML equips you with reliable and highly optimized business and market data.
This allows you to make more informed business decisions, such as:
3. Smarter Business Decisions

10. 6 Innovative Ways You
Can Use Machine
Learning in
Ecommerce

11. Special discounts
Personalized product recommendations
Personalized product catalogues
Personalized brand offerings are becoming an industry norm with more and more brands adopting smart
ways to make their customers happier.
Personalized offerings include the following:
Based on the purchase and browsing history of the consumer
Like the ones appearing on your screen when you watch Netflix
Your customers see the products in the order of their preferences and website behavior
There are endless ways to tailor your brand offerings for your customers.
However, it is also one of the core hurdles. Such granular learning stems from thorough learning about your
customers, which has its roots in customer data. One major problem with customer data is the noise. The
data is cluttered and seems to make no sense on the surface, at least when inspected and sifted manually.
This is where ML comes into the picture. It can process huge chunks of customer data, learn from it, and
adapt as per the insights. So, you can offer highly personalized brand offerings to your customers, based on
business insights.
1. Personalization

12. The purchasing power of the customers
Type of products they buy
Age
Gender
Demographics
Shopping behaviour
Shopping pattern and preferences
Previous brand interactions etc
Customer segmentation means segmenting or dividing your customers into various smaller groups or clusters.
This clustering or segmentation is done on the basis of various parameters, that can be in-built (if you are using
AI or ML-powered customer tools with pre-built features) and it can be custom segmentation (the smart
customer tools allow you to set the segmentation criteria).
Some examples of segmentation criteria are as follows:
Better segmentation allows you to learn more about your customers and target them with personalized offerings
like never before.
For example, if you are a chocolate manufacturing brand, you will know:
How many customers in which age group from which market zone have been eating which flavor of your
chocolates and in which pack sizes.
2. Customer Segmentation

13. Demographics (Location, Age, Gender etc)
Shopping preferences
Purchase histories
Browsing histories
Optimized pricing or dynamic pricing means displaying different prices to different customers based
on various factors:
Now, you might think: Why does it matter?
The answer is: To ensure that your brand keeps on getting the revenue, your customers stay
engaged and you can boost your sales.
Dynamic pricing considers all the above-mentioned and many other factors and targets the
audiences likewise.
For example, showing heavy discounts on items bought or browsed earlier by your customers.
All this is done by smart ML-based customer or business intelligence tools that are designed and
developed for e-commerce businesses. Also called Business Intelligence or BI tools, these ML-
powered tools transform your decision-making process with fantastic results.
3. Optimized Pricing

14. ML-powered tools can detect much more than just the popular search terms. They can dig into the
mounds of search data from the search engine and reveal multiple hidden patterns. You can learn
about the search, purchase, behavioristic, and preferences patterns your potential customers or
existing customers have while searching for your brand offerings.
Further, these are not just the raw numbers, but actionable insights with proper nudges to draft your
market strategy on.
4. Optimized Search

15. ML-based customer support allows you to automate your customer service and offer
immediate, smarter, and better support. You can offer canned responses, bot-based
support, self-service support, and lots of other such capabilities to make your support
system excellent.
5. Customer Support

16. ML can help you conduct sentiment analysis over the entire length and breadth of your
customer base. Sentiment analysis allows you to identify how your customers perceive
your brand and its offerings. You can dig into the customer comments and data collected
over time to find out what makes them feel so about your brand.
6. Sentiment Analysis

17. WANT TO KNOW HOW TO LEVERAGE
ML FOR YOUR E-COMMERCE
BUSINESS?
https://www.datatobiz.com/blog/machine-learning-in-
ecommerce/