Deep-dives on brand growth, digital marketing, analytics, paid media, and everything in between.
Meta Advantage+ Audience is better for scale and automation, while manual targeting is better for control and niche audiences. In 2025, most advertisers should test Meta Advantage+ Audience first, then compare results with manual targeting.
Conversion Rate Optimization (CRO) is the process of improving your website or app to encourage more visitors to take a desired action whether it’s making a purchase, whether it’s making a purchase, signing up for email updates, installing the app, or entering information in a form. In simple terms, CRO helps you get more value from the traffic you already have, by understanding user behavior, testing changes, and making data-driven improvements.
Artificial Intelligence (AI) is transforming how we live, work, and make decisions — from recommending what to watch next on Netflix to diagnosing diseases and powering financial decisions. But as AI becomes more integrated into daily life, an important question arises: can we trust these systems to make fair, unbiased, and transparent decisions?
In the age of data-driven decision-making, one truth stands firm: your insights are only as reliable as your data. With the transition to Google Analytics 4 (GA4), businesses are gaining deeper, event-based insights into customer behavior. But with that power comes a challenge: keeping your data accurate, consistent, and actionable. And that’s where the shift from manual validation to automated validation is making all the difference.
In the ever-evolving landscape of digital marketing, many companies focus on chasing trends, launching campaign-based content, and capitalizing on the “now.” While these strategies can generate short-term results, they often fail to deliver long-term value. At AnalyticsLiv, we believe in a different approach, building an SEO content foundation that stays relevant, authoritative, and effective for years. That’s where evergreen SEO content comes in.
As in the rest of the world, India is also experiencing a shift towards programmatic advertising, and with it, a change in the marketing approach to a much more intelligent and data-oriented one. The distaste for keyword-driven advertising is a thing of the past. It is common to now rely on technology, machine learning, and hyper data to target and connect with consumers. In the Indian context, programmatic advertising is gaining popularity with a keener focus on efficiency and return on investment. Whether it is DV360 advertising services or tie-ups with full-service programmatic advertising agencies, digital enterprises in India are adopting a more technology-driven approach for better ROI and programmatic control in their advertising spend.
Marketing Mix Modeling (MMM) is a causal inference model that helps businesses understand how their marketing investments, such as ads, promotions, or campaigns, drive business outcomes like sales, revenue, or leads. Unlike forecasting tools, MMM does not try to predict future sales or recommend budget splits for upcoming campaigns. Instead, it analyzes past data to reveal which channels or activities had the biggest impact on your KPIs and how effectively your budget was used. In simple terms, MMM explains what worked, what didn’t and where your marketing spend delivered the most value. By applying MMM, you can clearly identify the true drivers of performance and make more confident, evidence-based decisions about future budgets and have better budget allocation.
Ever had that “wait, what?!” moment when the numbers in GA4 don’t match the ones in BigQuery? Yeah, we’ve been there too. One of our clients faced this exact problem. GA4’s Explore report said one thing, but BigQuery, our trusted source of truth, told a completely different story. And sometimes, the difference wasn’t small. Here’s how we solved the mystery and what you can learn from it.
Google Tag Manager (GTM) plays a crucial role to track your user journey and build strong reporting without requiring website developers' efforts. In Part 1 of Google Tag Manager mistakes, we discussed Part a robust tool designed to manage tags and tracking codes across both websites and mobile applications. It serves as a centralized tag management system that enables the deployment of multiple tracking codes and facilitates data sharing with platforms like Google Analytics, Google Ads, SA360, Facebook/Meta, Snapchat, and many others.
In the fast-moving world of digital advertising, one-size-fits-all strategies just don’t cut it anymore. Whether you’re running YouTube ads, leveraging Display & Video 360 (DV360), or aiming for high-impact performance across Google’s network, continuous optimization is the name of the game. That’s where Demand Gen Experiments come in—a game-changing feature in Google Ads designed to help you test, learn, and grow. In this post, we’ll break down exactly what Demand Gen Experiments are, how to use them, and how they can supercharge your campaigns. No jargon, just clear steps and practical tips to help you level up.
Festive seasons whether it’s Diwali, Christmas, Black Friday, or New Year bring a surge in online shoppers ready to spend. In fact, research shows that holiday shopping contributes 30–40% of annual revenue for many e-commerce brands. But here’s the reality: only 2–3% of website or app visitors actually convert into paying customers on average. That means during peak festive traffic, 97 out of 100 visitors leave without buying anything.
Marketing data is messy; there's no polite way to put it. Whether you’re pulling reports from Google Ads, Facebook, CRM systems, or offline sales, the data usually comes with duplicates, missing values, inconsistent formats, or even outright errors. Before you can run attribution models, Marketing Mix Modelling (MMM), or customer segmentation, your data needs to be clean, structured, and reliable. Think of it as prepping ingredients before cooking. If the vegetables aren’t washed and chopped properly, the final dish won’t turn out right.
