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Monkey Learn + Flipkart Integrations

Syncing Monkey Learn with Flipkart is currently on our roadmap. Leave your email address and we’ll keep you up-to-date with new product releases and inform you when you can start syncing.

About Monkey Learn

Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.

About Flipkart

Flipkart is an e-commerce marketplace that offers over 30 million products across 70+ categories. With easy payments and exchanges, free delivery, Flipkart makes shopping a pleasure.

Flipkart Integrations
Connect Monkey Learn + Flipkart in easier way

It's easy to connect Monkey Learn + Flipkart without coding knowledge. Start creating your own business flow.

    Triggers
  • New Order

    Triggers when a new order occurred.

  • New Return

    Triggers when a new return occurred.

  • New Shipment

    Triggers when a new shipment occurred.

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Create Product

    Create product listings in Flipkart’s Marketplace.

How Monkey Learn & Flipkart Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Flipkart as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to Flipkart.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and Flipkart

Monkey Learn?

Monkey Learn is a machine learning platform that allows users to create their own machine learning models. It supports the framework of Python, R, Java, C#, Swift, PHP, NodeJS and Ruby.

It provides certain open source functions which can be used to create an analysis model. The analysis model helps in analyzing different data sets and can detect patterns in the data.

It also provides the option to integrate the Machine Learning API with other applications like Salesforce, Slack, Python, Ruby etc. It converts unstructured data into structured data.

Learn more about Monkey Learn here.

Flipkart?

Flipkart is an e-commerce company headquartered in Mumbai, India.

It was founded by Sachin Bansal and Binny Bansal (no relation. in 2007. As of 2018, it has more than 100 million registered users and has raised $4 billion in funding.

The company operates 11 main categories including Books (including textbooks), Fashion (clothes and accessories), Electronics (including cell phones and accessories), Mobiles (mobile phones), Computers (including laptops), Lifestyle (home furnishing products), Sports & Fitness (sports equipment), Travel (accommodation, flight tickets), Health (medical supplies), Beauty (cosmetics and personal care items. and Durable Products (home appliances.

Integration of Monkey Learn and Flipkart

Integration of Monkey Learn and Flipkart will help Flipkart to convert the unstructured input data into structured output data. This will help to improve the accuracy of the prediction algorithm. The fplowing are some examples of the benefits of the integration of Monkey Learn and Flipkart:

  • Identification of product pages with low conversion rates
  • Analyzing product performance over time for individual sellers/brands or entire categories
  • Analyzing product performance over time for individual sellers/brands or entire categories 4. Detecting potential fraudulent activity on the site 5. Improving search relevance by analyzing user feedback on product pages 6. Identifying potentially relevant products for an item 7. Building product recommendations using consumer ratings on Flipkart 8. Generating email subject lines by analyzing the recent actions taken by a user on an ecommerce site 9. Tracking ad click-through rates based on the content of ads 10. Understanding which images are most effective at attracting clicks 11. Monitoring customer satisfaction 12. Understanding language usage on a website 13. Adding new cpumns to data tables 14. Understanding trends by analyzing data over time 15. Understanding changes in shopping hours 16. Identifying product trends 17. Detecting exceptional products 18. Understanding the popularity and purchasing habits of customers 19. Identifying users who may need assistance with a specific task 20. Understanding customer preferences 21. Understanding customer interests 22. Limiting website access for customers 23. Understanding relationships between products 24. Identifying groups of similar products 25. Understanding customer preferences 26. Detecting customer dissatisfaction 27. Predicting price fluctuations 28. Uncovering events that may cause people to shop online 29. Exploring metrics such as customer loyalty 30. Understanding why customers come back to a brand 31. Detecting negative sentiment about a brand 32. Comparing how customers feel about different products 33. Identify which customers may be unhappy 34. Identifying customers who may stop shopping 35. Identifying which customers are most loyal 36 .Identifying customers who may leave 37 .Auditing websites 38 .Detecting negative sentiment about a brand 39 .Understanding why customers come back to a brand 40 .Improving search relevance by analyzing user feedback on product pages 41 .Analyzing product performance over time for individual sellers/brands or entire categories 42 .Predicting revenue based on seasonal sales 43 .Tracking ad click-through rates based on the content of ads 44 .Generating email subject lines by analyzing the recent actions taken by a user on an ecommerce site 45 .Analyzing product performance over time for individual sellers/brands or entire categories 46 .Identifying potentially relevant products for an item 47 .Building product recommendations using consumer ratings on Flipkart 48 .Identifying potentially relevant products for an item 49 .Generating email subject lines by analyzing the recent actions taken by a user on an ecommerce site 50 .Identifying potential fraudulent activity on the site 51 .Improving search relevance by analyzing user feedback on product pages 52 .Tracking ad click-through rates based on the content of ads 53 .Monitoring customer satisfaction 54 .Monitoring customer satisfaction 55 .Understanding language usage on a website 56 .Understanding language usage on a website 57 .Adding new cpumns to data tables 58 .Adding new cpumns to data tables 59 .Understanding trends by analyzing data over time 60 .Understanding trends by analyzing data over time 61 .Detecting exceptional products 62 .Detecting exceptional products 63 .Understanding the popularity and purchasing habits of customers 64 .Understanding the popularity and purchasing habits of customers 65 .Identifying users who may need assistance with a specific task 66 .Identifying users who may need assistance with a specific task 67 .Detecting customer dissatisfaction 68 .Detecting customer dissatisfaction 69 .Predicting price fluctuations 70 .Predicting price fluctuations 71 .Uncovering events that may cause people to shop online 72 .Uncovering events that may cause people to shop online 73 .Exploring metrics such as customer loyalty 74 .Exploring metrics such as customer loyalty 75 .Understanding why customers come back to a brand 76 .Understanding why customers come back to a brand 77 .Detecting negative sentiment about a brand 78 .Detecting negative sentiment about a brand 79 .Comparing how customers feel about different products 80 .Comparing how customers feel about different products 81 .Identify which customers may be unhappy 82 .Identify which customers may be unhappy 83 .Identifying customers who may stop shopping 84 .Identifying customers who may stop shopping 85 .Identifying which customers are most loyal 86 .Identifying which customers are most loyal 87 .Auditing websites 88 .Auditing websites 89 .Detecting negative sentiment about a brand 90 .Detecting negative sentiment about a brand 91 .Improving search relevance by analyzing user feedback on product pages 92 .Improving search relevance by analyzing user feedback on product pages 93 .Analyzing product performance over time for individual sellers/brands or entire categories 94 .Analyzing product performance over time for individual sellers/brands or entire categories 95 .Predicting revenue based on seasonal sales 96 .Predicting revenue based on seasonal sales 97 .Tracking ad click-through rates based on the content of ads 98 .Tracking ad click-through rates based on the content of ads 99 .Generating email subject lines by analyzing the recent actions taken by a user on an ecommerce site 100 .Generating email subject lines by analyzing the recent actions taken by a user on an ecommerce site 101 .Understanding which images are most effective at attracting clicks 102 .Understanding which images are most effective at attracting clicks 103 ..Identifying product trends 104 ..Identifying product trends 105 ..Detecting exceptional products 106 ..Detecting exceptional products 107 ..Understanding the popularity and purchasing habits of customers 108 ..Understanding the popularity and purchasing habits of customers 109 ..Identifying users who may need assistance with a specific task 110 ..Identifying users who may need assistance with a specific task 111 ..Understanding customer preferences 112 ..Understanding customer preferences 113 ..Identifying groups of similar products 114 ..Identifying groups of similar products 115 ..Understanding customer preferences 116 ..Understanding customer preferences 117 ..Detecting customer dissatisfaction 118 ..Detecting customer dissatisfaction 119 ..Predicting price fluctuations 120 ..Predicting price fluctuations 121 ..Uncovering events that may cause people to shop online 122 ..Uncovering events that may cause people to shop online 123 ..Exploring metrics such as customer loyalty 124 ..Exploring metrics such as customer loyalty 125 ..Understanding why customers come back to a brand 126 ..Understanding why customers come back to a brand 127 ..Detecting negative sentiment about a brand 128 ..Detecting negative sentiment about a brand 129 ..Comparing how customers feel about different products 130 ..Comparing how customers feel about different products 131 ..Identify which customers may be unhappy 132 ..Identify which customers may be unhappy 133..Auditing websites 134..Auditing websites 135..Detecting negative sentiment about a brand 136..Detecting negative sentiment about a brand 137..Improving search relevance by analyzing user feedback on product pages 138..Improving search relevance by analyzing user feedback on product pages 139..Analyzing product performance over time for individual sellers/brands or entire categories 140..Analyzing product performance over time for individual sellers/brands or entire categories 141..Predicting revenue based on seasonal sales 142..Predicting revenue based on seasonal sales 143..Tracking ad click-through rates based

The process to integrate Monkey Learn and Flipkart may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.