How Consumer Startups Can Drive Growth, Manage Users At Scale Using AI/ML

Post the Covid crisis, the sweeping societal and tech changes have transformed consumer behaviour in a way that few could have possibly imagined. Take, for instance, the rise of quick commerce, popularly known as the 10-minute delivery. Consumers today look for on-demand products and services delivered at the earliest, without compromising on quality or experience. 

In fact, the new normal is all about fast-evolving technology, pushing life towards an ‘experiential’ world and compelling brands to ditch the spray-and-pray approach — a typical marketing tactic where companies communicate with as many prospects as possible and hope for conversions. Instead, personalisation and engagement are emerging as critical metrics for growth and scale. 

This is understandable as repeated and customised interactions (through personalisation) help brands target the right set of consumers, create seamless experiences and enable them to forge stronger ties with people. Therefore, startups today are actively tracking consumer metrics such as shopping habits, preferences, drop-off rates and conversions across various touchpoints to monitor each customer’s unique journey.

But even after amassing valuable consumer data, not many can convert the same into actionable insights. According to a Gartner report, 63% of digital marketing leaders struggle to deliver personalised experiences. As the tech research and consulting firm points out, part of this problem stems from the lack of adoption of cutting-edge technologies like AI and ML, which are still at a nascent stage. Its findings reveal that merely 17% of digital marketers leverage such technologies across marketing functions.

In such cases, how can startups drive growth in an ever-evolving internet landscape, use the right AI/ML tools and manage humungous user data?

Speaking at the second edition of Inc42’s Makers Summit in April this year, Rajesh Ramdas, Google Cloud’s head of customer engineering (digital natives) said, “Personalisation is based on data-driven segmentation and sentiment monitoring that helps brands create a personalised engine and target customers better. Cloud computing offers an intelligent and secure platform to deliver next-generation products and services to customers.”

Watch Rajesh Ramdas explain how consumer startups can drive and manage consumers at scale.

What Startups Must Focus On

Building Products For Bharat Users

According to Inc42’s State of Indian Ecommerce report, India is home to more than 834 Mn internet users, of which 337 Mn come from rural India. The uptick in smartphone adoption, falling data prices and the pandemic-induced digital adoption have pushed digital-first brands to look beyond urban India and serve Tier 2 and Tier 3 regions and the hinterland with equal gusto.

Ramdas emphasised that companies should look at some critical trends while developing their products. And one of these should be the rise of vernacular digital content. Consider this data by Google: 95% of online video consumption happens in vernacular languages, and 560 Mn Indians are local language users.  

“Companies today need to invest in natural language processing, have the capabilities to convert text to speech and speech to text and add vision and video intelligence tools. Leveraging these technologies can help them scale at multiple levels,” said Ramdas.

Data Visibility Is Essential For Identifying Key User Metrics

Startups today are sitting on a data gold mine. But this data is stored across multiple systems, making it tough for companies to derive a comprehensive understanding of their customers. Mass data fragmentation also increases storage costs as unnecessary data accumulation burdens the system. According to Ramdas, the ability to look at data in multiple clouds with ease is crucial. 

“When dealing with data and query loads, a next-generation platform scales transparently. It consistently maintains high performance regardless of concurrent queries. And you pay for exactly the data you store,” added Ramdas.

Embracing AI/ML To Enhance Security 

Privacy and data security are as important as data extraction for sustainable growth. Any data breach or data theft can financially impact a company and damage its reputation. 

According to Ramdas, organisations should ask themselves some pertinent questions before choosing privacy-protection tools. First, can these tools facilitate human oversight/monitoring? Are they regulation- and audit-compliant? What is the redressal framework if a problem arises? 

He suggested that robust AI/ML mechanisms can bolster data security and help organisations identify cyberattack patterns. For instance, Google Cloud’s data warehouse called BigQuery is built on ML capabilities wherein data is encrypted end-to-end, on the wire and the disk. 

“Integrated capabilities like model monitoring for performance monitoring, explainable AI for analysing predictions and machine learning for auditing are important for ensuring security,” he said.

Catch all the sessions and the insightful conversations from The Makers Summit 2022. The takeaways from some of the startup community’s most prominent product minds, makers, leaders and founders can be found right here at The Makers Academy.