5 ways ML can assist in customer engagement

Machine learning has shown enormous promise and will only improve in efficacy and evaluation over time.

Machine learning approaches are helping businesses overcome challenges presented by COVID, especially in the healthcare sector. Below are 5 ways ML can assist your business.  1. Provide data and help in the creation of ‘virtual’ groups or communities. With value-based selling gaining ground, creating communities is a great asset for any business. By data integration across a variety of platforms, it is possible to identify stakeholders and customers with similar personas and buying behavior leveraging data-driven capabilities   2. Extract insights from the data to expedite the design and execution of key initiatives such as recruitment plans, distribution plans, sample sizes and campaign allocations
With value-based selling gaining ground, creating communities is a great asset for any business.
Machine learning approaches are helping businesses overcome challenges presented by COVID, especially in the healthcare sector.

Below are 5 ways ML can assist your business.


1. Provide data to create ‘virtual’ groups or communities.

Provide data and help in the creation of ‘virtual’ groups or communities. With value-based selling gaining ground, creating communities is a great asset for any business. By data integration across a variety of platforms, it is possible to identify stakeholders and customers with similar personas and buying behavior leveraging data-driven capabilities


2. Extract insights from the data

Extract insights from the data to expedite the design and execution of key initiatives such as recruitment plans, distribution plans, sample sizes and campaign allocations.


3. Insights

Improve the insights derived from random samples by designing adaptive campaigns and analysing results to aid decision making


4. Patterns and behaviours

Assist in drawing customer patterns and behaviours, thereby facilitating in identifying and prioritising plans and strategies, as well as validating cause and effect patterns.


5. Interpretations from raw data

Analyse large volumes of data generated through the new digitized lifestyles to select plans for growth. In particular one can leverage ML methods to draw casual interpretations from raw data.


6. Manage complex tasks

Break complex tasks and processes into simple, efficient, and adaptive recurrences. By using ML methodologies it is possible to learn commonalities and behaviors otherwise unnoticed, thereby reducing learning time and fast-tracking decision making.


Machine learning has shown enormous promise and will only improve in efficacy and evaluation over time.


How to create data-driven customer experiences. Read on.