Authored by Apoorv Pandey, Head of Growth, Moloco India
The advertising industry has undergone a seismic shift, transitioning from traditional ads to the algorithmic era of AdTech and now to advanced platforms backed by operational Machine learning. This shift has been fueled by advancements in technology, the internet, social media, and digital platforms. The previous model relied heavily on mass media, making it difficult to reach target consumers and measure campaign effectiveness.
Today, Machine Learning (ML), algorithms, data-driven insights, precision targeting, personalization, and automation play a central role in crafting and delivering advertising messages.
Operational ML is a key driver of innovation in advertising. It will revolutionize the industry by enabling real-time decision-making, scalability, and industry-specific problem-solving; helping brands efficiently acquire and engage the right users.
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*Source Data.ai
In the context of India’s dynamic digital landscape, boasting a current population of 700 million and set to reach 900 million in 2024, the impact of operational ML becomes even more significant. The thriving ecosystem includes 470 million social media users and 350 million digital payment users. In the domain of online shopping, 220 million individuals engage in e-commerce activities, including 65 million who order food, 110 million purchasing games (RPG and Match will each fuel $1 of every $5 of aggregate growth in spending), and 85 million settling utility bills.
According to a global research study, among 1,675 marketers, 37% of respondents identified advanced ML as the secret to success in choosing advertising platforms. “Advanced ML” was considered the most important factor in choosing an advertising platform, followed by “broad reach” at 23% and “high quality” at 18%.
In 2024, operational ML will play a crucial role in growth and profitability, analyzing user behavior and preferences, and providing highly personalized product recommendations. This boosts user purchases and increases their lifetime value. E-commerce has also seen significant growth, with small and mid-sized companies leveraging operational ML to acquire high-value users. E-commerce companies can use operational ML to optimize real-time pricing, ensuring competitiveness and healthy profit margins.
To thrive, businesses must balance growth and profitability. Operational ML is pivotal in achieving this equilibrium. Success relies on retaining profitable customers, not just acquiring new ones. Leveraging operational ML for customer acquisition can significantly impact overall revenue.
In the upcoming year, brands will explore inventive approaches to enhance business performance, including monitoring and enhancing Return on Ad Spend (ROAS) and breakeven timeframes. Recency Frequency Monetary (RFM), Quick Ratios and Share of Voice (SOV) to Share of Market (SOM) analysis helps discern the most favorable marketing channels, and campaigns. In turn, brands can refine their advertising tactics accordingly.
ROAS and breakeven periods, though separate, are intertwined and influence a brand’s strategy. Connecting these metrics provide a holistic view of channel performance and marketing efficacy. Prioritizing these metrics enables informed decision-making, resource allocation, and improved business performance.
In the data-driven landscape, companies are adopting intricate metrics like Quick Ratios and RFM (Recency, Frequency, Monetary) Analysis for a competitive advantage. These metrics offer insights into business performance and customer behavior, empowering better-informed choices and sustainable growth.
The advertising landscape has transformed in recent years with the rise of Connected TV (CTV) ads, also known as Over-the-Top (OTT) television. CTV offers higher mental ability, cost-effectiveness, advanced measurement capabilities, extended reach, higher engagement rates, and improved targeting options for advertisers.
Unlike traditional TV advertising, CTV allows advertisers to reach a targeted audience without expensive TV network contracts. Advertisers can track metrics such as performance, CPA, ROAS, ad impressions, view-through rates, completion rates, and audience demographics. This data-rich environment enables advertisers to refine strategies, understand audiences better, and optimize campaigns in real-time.
This presents an opportunity for advertisers to create memorable brand experiences, boost user engagement, and increase brand retention and conversion rates. Understanding the capabilities and constraints of CTV platforms is crucial for maximizing its potential in 2024.
The AdTech industry is poised for robust growth and resilience, particularly through the integration of ML technologies. ML-powered predictive models are revolutionizing the global landscape, promising more efficient conversions despite evolving restrictions on consumer data accessibility.
