Narrowing Down To A Particular ML Algorithm Is Largely Centred On Business Decisions

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With artificial intelligence (AI) businesses can identify and unearth hidden revenue streams, run highly personalised campaigns at a large scale and garner many meaningful insights unlike traditional methods. Sudeshna Datta, co-founder and executive vice president, Absolutdata, explains to Deepshikha Shukla how AI can eliminate the need for manual intervention, while enhancing productivity and giving businesses access to massive data sets in real time.


Sudeshna Datta, co-founder and executive vice president, Absolutdata
Sudeshna Datta, co-founder and executive vice president, Absolutdata

Q. What role do technology stacks play in enterprise architecture?

A. Technology stacks help in the successful and timely development and deployment of AI. These are needed for businesses to pick the right combination of AI development tools at the early stages of a project. Technology stacks contain fundamentals on which a comprehensive solution can be built to ensure rapid AI/ML (machine learning) development and cause real impact across the enterprise. These also help the enterprise mine existing sales data using analytical frameworks and natural language processing.

Enterprise architectures traditionally have three layers: data stack having new and existing data, execution stack having tools, techniques and algorithms, and human talent that converts these into actionable solutions. Today’s landscape requires addition of an intelligence stack that can provide actionable insights and smart recommendations to enterprises. This has resulted in the creation of a new enterprise stack that has data stack, execution stack, intelligence stack and human talent to support it.

Q. How is an ML algorithm choosen for a specific analytical purpose?

A. Narrowing down to a particular ML algorithm is largely centred on business decisions. The right model can help enterprises discover existing connections that are beyond the reach of human teams. A precise model can help organisations accurately identify viable opportunities while avoiding risky areas.

Among factors that help businesses choose the ideal algorithm are understanding the amount and type of data they have at hand and categorising the problem. They also need to check the requirements pertaining to input and output, as well as to keep in mind their limitations.

A highly-scalable, fully-automated, self-learning solution has solved one of the most burning problems in analytics, that is, striking an optimal trade-off between existing and new product recommendations in a widely varied landscape. This was achieved using a custom cost function in the recommendation model.

Q. How can an AI-driven platform empower the salesforce?

A. Sales professionals need to make critical decisions every day, and many tend to make these decisions based on intuition or by following an organisational playbook. AI provides a buyer-centric alternative while applying its uses on data.

While traditional playbooks mostly comprise predictive selling, AI transforms these from set and static to dynamic and living. AI playbooks keep adjusting over time with deep learning, which uses outcomes and new data to adjust algorithms. Such flexibility for adjustments enables playbooks to continuously learn, improve and become more accurate.

Also, sales personnel get adaptive guidance throughout each buying journey with AI. For instance, they can be prompted to adjust the message, timing, product offering and channel of communication when in progress, thereby earning the business a competitive edge even in a tough sales situation.

Our product, NAVIK SalesAI, has increased sales by providing predictive data on buyers, identifying most promising prospects and aligning purchaser needs with products as well as communication channels.

Q. What are the benefits of a customer-directed AI marketing platform?

A. It allows companies to conduct marketing in a personal manner. A digital twin presents a deeper, more accurate and comprehensive picture of a consumer, thereby helping brands pitch relevant messages to customers on a big scale.

Information gets delivered to the marketer through an AI bot, which also provides recommendations to the marketing team regarding campaigns that are more likely to succeed, how churn and other negative changes can be prevented, and what actions should be taken to mitigate migratory behaviour.

Q. How do AI-enabled algorithm platforms advance market research?

A. With the power of AI and ML, research projects can be carried out without human support. Also, this gives enterprises the ability to create autonomous adaptive surveys that can automate addition, removal and changes to questions in the data collection process. This creates a solution that can share insights instantly, while actively combining research data with other data sources to provide recommendations that drive daily decisions, as well as specific strategy-oriented decisions for the future.

AI algorithms create robust research designs using analytical frameworks and business content, and can make questionnaires, analyse large data pools and generate automated insights.

These help companies reduce their manpower requirement. Once built, the algorithm gets smarter with time.

Q. How does an AI-driven analytics engine works?

A. An AI-driven analytics engine is based on its unique decision engineering methodology and the human-machine model. It builds on this with automated integration and harmonisation of internal and external data sources, as well as automated data ingestion.


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