5 Questions on AI in Marketing, Answered by Google 

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Scienz AI Blog- 5 Qs on AI in Marketing

Demystifying AI in marketing can be a long process, particularly among CMOs and marketing directors who want strong evidence of their utility and impact on long-term marketing efforts. Yet there has been an exponential rise in the use and interest in AI applications in marketing, with a significant 99 percent of marketers currently using AI every day in some way.  

Tech giant Google is one of the leading adopters of AI in marketing and advertising, experimenting with its own Gemini in its SEO content production and positioning Google Ads products as “AI-powered.”  

Google’s AI marketing strategies involve launching Performance Max (P-Max) as an AI-driven campaign, leveraging real-time data to adjust bids and targeting, and using Google conversational AI to enable users to edit and manage their ad campaigns via chat.  

In a blog, Google answers five frequently asked questions about AI in marketing. Here are AI marketing FAQs from the tech ad powerhouse:  

Q: What can we do to run effective AI-powered marketing campaigns? 

A: Fuse human expertise and strategic thinking, said Oliver Borm, Google director for performance solutions in Europe, the Middle East, and Africa.  

Get into a partnership mindset: Set the right goals, and as an example use value-based bidding combined with new customer acquisition goals if you are looking to acquire new customers in your campaign. This way, AI algorithms will bid higher for new customers than for returning ones or show your ads only to new customers.  

Q: How can we share the most relevant data with AI as data limitations or fluctuations exist? 

A: Advertisers often face data challenges, such as clunky CRMs, long sale cycles, and fluctuating prices. Invest in making consented first-party data work for you as you face data challenges such as clunky CRMs or long sales cycles, urged Jahnvi Shah, global product lead for smart bidding.  

As you share data with AI, optimize the process by paying attention to areas such as conversion minimums, data modeling, offline conversion tracking, and seasonality adjustments.  

Quality reigns over quantity, too; AI-run campaigns stand to be successful with the right data rather than collecting a large volume of them.  

Q: As AI learns from my marketing data, can my competitors also benefit from or harvest this data? 

A: Google asserts that it does not share an advertiser’s data with other advertisers except if it comes with explicit permission. Yet Pascal Trang, product lead for first-party data solutions in EMEA, explained that all AI algorithms train on huge datasets to be precise, meaning they are also trained on billions of signals to constantly improve.  

Q: How do we make sure GenAI assets are safe and legal to use from a copyright perspective? 

A: It’s a must to safeguard legality, copyright adherence, and brand alignment with GenAI-produced ads and work, said Pallavi Naresh, group product manager for ads creatives. Start with your branding guidelines – AI collaborates with your existing assets and aligns with your brand identity and vision. Conduct thorough reviews involving your legal team, too.  

Q: How can AI marketing insights inform our business strategy? 

A: AI optimizes toward specific goals set by its human users, said Hashim Syed, product lead for growth and insights in EMEA. Constant learning and improvement enable it to find better ways to reach such goals.  

In making data-driven decisions in your marketing, it’s good to look at insights around campaign performance history, assets that resonate most with the audience, audience personas driving the most conversions, and your performance vis-à-vis other advertisers.  

Related Readings: 

AI-Powered Advertising 101: Benefits, Use Cases, and Prospects  

AI Supercharges Email Marketing Via Hyper-Personalization, Audience Segmentation   

How AI Is Used in Data Analytics for Marketing 

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