Surya (20+ yrs) is an Industry, Tech and domain expert in SAP, Retail Media, Marketing Science and Analytics, Generative AI, Data Clean Rooms, Graph and other cutting edge tech. Connect & learn more!
Google recently announced a game-changing feature for Large Language Models (LLMs): context caching. This innovation significantly enhances the efficiency and scalability of LLMs, allowing them to process information more effectively and cost-effectively. But what exactly is context caching, and how does it work? Think of an LLM like a human
Ready to unlock the true potential of your marketing data and transform your customer retention strategies? In Part 1 of this series, we demystified the process of finding the right data with the intuitive combination of BigQuery Canvas and Gemini. Now, prepare to dive even deeper and harness the magic
Earlier this year, Google Cloud introduced BigQuery Data Canvas, a Gemini feature within BigQuery. This innovative data analytics tool streamlines the entire data analysis process – from data discovery and preparation to analysis, visualization, and collaboration – all within the BigQuery platform. Leveraging natural language processing, BigQuery Data Canvas enables users to
Google Cloud just released Spanner Graph and it’s AWESOME news for Marketers and Retail Media Networks Let me explain this from the context of a Retail Media Network. I will present my POV from a Marketers perspective in a future article Unleashing the Power of Graph Techniques for Retail
Clean Room
Google did a recent webinar, where I had the privilege of leading a discussion on the cutting-edge of data collaboration, diving deep into Google Cloud's innovative tools: Analytics Hub and Data Clean Rooms. I had the pleasure of joining Google Product Leaders Pascal Trang , Spencer Janyk , Robert (Bert)
Retail Media
Retail media networks (RMNs) offer brands a powerful platform to reach targeted audiences. But with a multitude of shoppers, how can you ensure you're attracting the ones most valuable to your brand? Here's where Lifetime Value (LTV) steps in, becoming the Key for your RMN strategy.
Machine Learning
In my Day to Day Job, most of my conversations are around Data Driven Marketing and leveraging the power of Advanced Analytics in the field of marketing. One of the most talked about KPI’s in the world of Marketing is R.O.A.S as well as Predicted ROAS
After the successful launch of Light Weight MMM Model, Google launched its latest advanced MMM model called Meridian. It is a pioneering open-source MMM that helps tackle critical challenges in measurement. With Meridian, you gain complete control over your marketing data and insights. This empowers you to make informed decisions
Digital marketing has relied heavily on Return on Ad Spend (ROAS) as a primary metric and I believe that focusing solely on ROAS can limit our perspective on marketing success. I'd like to introduce a new concept called ROAS Distribution. This approach involves assessing the distribution of ROAS
At Google, experimentation has consistently been a guiding principle behind our marketing strategy. In the unprecedented and unique times we are living in, the significance of experimentation has only become more important. Unique times = Sum (Tech Environment + Data Privacy Laws + Consumer Behavior) The present moment is a direct result of
Google Ads
This is a continuation of the article that I wrote before and is for practitioners who want to get into more details, please continue to read… Here is a TL:DR version of the pivotal reasons why Google Ads data integration with BigQuery can revolutionize your marketing strategy are as
Google Ads
This article will have two parts. The first piece serves as an executive brief, aimed at giving you a digestible snapshot without descending into the complexities of code. For those who are keen on exploring the technical depth, do dive into this article and its subsequent part. In the sequel,