Pratik Jain from morph.ai on how bots transform marketing

This episode of AppliedAI is on an AI vendor for product marketing that specifically focuses on solving issues in critical metrics like lead quality, engagement, drop-offs, user retention, return rates. We hosted Pratik Jain, co-founder of Morph.ai. Morph.ai is leveraging chat to help marketers solve inefficiencies in the marketing process for large enterprises. Pratik was one of the early hires of Sprinklr, a social media SaaS tool which grew from 200 million to 2 Billion valuation while Pratik was part of the team. After Sprinklr, Pratik and a few of his colleagues from Sprinklr founded Morph.ai. Now there are ~1500 businesses using the ~2 year old chat marketing platform. Pratik explains that the rapid growth  is a result of focusing on one single usecase in marketing that is valuable for corporations and consciously ignoring the glamor Their focus areas are lead generation and engagement on Facebook. They have seen great success with their workflows which is a mechanism for following up with customers that have not converted as leads. The approach has been successful also because older approaches for engaging drop-offs such as emails and calls are not effective. Email open rates are low and calls are annoying and expensive. Chat is a seamless, real-time way to engage customers that have indicated interest but not yet become leads. A carefully placed series of followup messages is their unique selling point. Their implementation can be set up in 2 weeks even for an enterprise if the customer can prepare everything they need upfront. To launch, they need:
  • 4-8 hours of the marketing responsible to identify current issues
  • Some images and videos to use in their chat campaigns
  • Approvals such as operations and security teams’ approvals which becomes the main bottleneck in most cases.
Most of the time their enterprise implementations can take 3-4 months as it takes time to get all approvals and finish all the integrations. They are working on improving how they convey marketing insights so companies can directly get these insights. Currently, analysts are helping brands understand these insights. They also plan to expand into other geographies including UK and the customers’ demands in new geographies will help determine their product roadmap. With half the world’s population on chat, Pratik believes that chat will be a channel at least as important as social media in the next few years. And brands need a chat marketing strategy.
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Synthetic Data: An Introduction & 10 Tools [07/2018 Update]

Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI needs.

The questions that this post sets out to answer include:

Why is synthetic data important and what are some use cases for it?

How does synthetic data perform compared to real data?

What are some benefits associated with synthetic data?

What are some basics of synthetic data creation?

What are some challenges associated with synthetic data?

What are some tools related to synthetic data?

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Retail Analytics: A Guide for Growing Businesses [07/18 Update]

Retail analytics is a process that helps to provide crucial data for businesses with regards to inventory, sales, supply chain activities, consumer demand, and more. By implementing an analytics-driven retail environment, organizations can make better decisions for procurement, marketing, merchandising, and other operational considerations. This, in turn, enables retailers to create a better buying experience and for identifying opportunities for organizational improvement. 

Some questions that we will answer in this blog include:

What are the benefits and importance of retail analytics?

What are some current trends in retail analytics?

How can AI impact retail analytics?

What are some challenges to effective retail analytics?

What are some common tools associated with retail analytics?

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Data Governance in 2018: A Comprehensive Guide

Businesses around the world are striving every day to become more data-driven, and as such, how they collect and manage this data is evolving. One important topic that has arisen out of this shift is data governance. In this post, we set out to answer the following questions:

What is data governance?

Why is it important?

What are the benefits of effective data governance?

What are some key tasks associated with a data governance strategy?

What are some best practices for data governance?

What are the related challenges and pitfalls?

What are some common data governance tools?

What does it all have to do with the recent GDPR?

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How to Choose the Right Data Quality Tool [06/2018 UPDATE]

Data cleaning is part of a greater effort to achieve the highest data quality possible in used in business decisions and operations. It requires organizational effort and participation throughout a business and when done correctly, can help to provide valuable insights and analytics for decision making. A few additional benefits associated with data cleaning include:

  • Streamlined business practices
  • Increased productivity
  • Faster sales cycle
  • Better analytics

Given the ever growing quantity of data for many businesses, automation is required in data cleaning. The right data tool can fill in these gaps and manage a number of issues automatically before they have a chance to become truly problematic. This can ultimately help businesses to become more efficient and more profitable in their efforts.

Choosing the right data cleaning tool for your organization is essential to getting the most utility for your investment. To help in your decision making, this post answers the following:

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