The adoption of Artificial Intelligence and Machine Learning in Marketing has revolutionized the way marketers approach business challenges. There is no area where AI and ML cannot play a role in the entire spectrum of Marketing. When machine intelligence through AI/ML is combined with human EQ, marketers can actually approach any business problem from a more customer-centric mindset. After all, we are serving actual humans and not machines. A marketer must strike the perfect balance between the job’s emotional and logical data-driven parts. Just like finding the right balance between Captain Kirk’s compassion and Dr Spock’s cold and hard logic. The combination of human intelligence and machine intelligence can help in finding that balance. Just to give you an idea about the scale of impact AI/ML has brought in Marketing-
- According to McKinsey Global Institute, AI and Machine Learning are on track to generate between $1.4 to $2.6 Trillion in value by solving Marketing and Sales problems over the next few years.
- According to Salesforce Research’s most recent state of Marketing Study, the AI application in Marketing jumped to 84% in 2020 from just 29% in 2018.
- According to Drift’s 202 Marketing Leadership Benchmark report, AI, Machine Learning, marketing & advertising technologies, voice/chat/digital assistants, and mobile tech & apps will have the biggest impact on the future of marketing.
Telecom marketing, as covered in one of our old blogs, requires in-depth insights into customer behaviour. The AI-ML capability helps campaign managers design and execute highly relevant and contextual campaigns and improves the campaign uptake rate, ARPU, retention and CLV.
How AI/ML is making Contextual Marketing in Telecom more dynamic, agile and customer-centric.
- Helps in understanding the Customer’s Intent
- Enable Multi-dimensional Customer Profiling
- Let’s deep dive into the Customer Journey
- Provide Intelligent Insights/Customer Analytics
mViva- the AI/ML driven Customer Engagement Hub
Pelatro tries to follow a guideline that Analytics’ integration in mViva mesh well and unobtrusively with the client’s business-oriented perspective – these include the ability to design campaign experiments by creating different configurations of target groups, methods to sample out control groups, various statistical KPIs, preferred channel identification etc.
In addition to the above, the mViva platform can also ingest certain types of models that can then be used to make predictions. The model output can then be used in campaigning like any other KPI.
mViva also includes other modules that are more obviously AI/ML. These include a Next Best Offer module that can choose the offer that will best align to a stated and configurable business strategy; a Customer Lifetime Value model that can be used for targeting subscribers appropriately; an Analytics Workbench that has been designed to help citizen data scientists build and deploy their own models; additional segmentation methods such as RFM analysis and Segment Shattering which permit users to automatically break down and describe larger segments so that they can be targeted differently.
Want to know more about the AI/ML capabilities of mViva Customer Engagement Hub?
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