Snowflake: Generative AI Key Driver of Change For Marketers

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Snowflake has published its Modern Marketing Data Stack. It says generative AI is one of the key drivers of decision making in marketing.

Snowflake has published its annual Modern Marketing Data Stack report, which identifies the best solutions for marketers by tool type, alongside analysis of the current state of play of the marketing industry, and any new pressures or economic forces which may influence decision making. In this year’s edition, Snowflake mentioned for drivers of change in the marketing industry. The use of large language models (LLMs) and generative AI was an obvious addition, given the growth of the industry in 2023. Convergence of advertising and marketing, regulatory and data privacy requirements, and the need for a single source of truth were also considered to be key drivers. 

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“Despite navigating macro-economic pressures and decreased budgets, it’s an exciting time for marketers given the pace of technology innovation and the forthcoming impact of AI and LLMs to disrupt, automate, and enhance the productivity of marketing teams,” said Denise Persson, chief marketing officer at Snowflake. “Our Modern Marketing Data Stack report highlights the best tools and solutions available to marketers so that teams can supercharge their ability to execute and drive results.”

Gain comprehensive insights into your data stack to improve data and pipeline  reliability, compute performance, and more. [Learn More]

Generative AI Usage

Artificial intelligence is expected to have an effect on every industry, and in marketing specifically there are a lot of functions and roles which could see some form of automation in the next few years. LLMs and generative AI are all the rage at the moment, with businesses looking to see how they can utilize generative systems to gain a competitive advantage and improve productivity. 

Snowflake highlights five functions and goals that it expects will be automated by large language models: 

  • Ad campaign optimization – LLMs fed on successful and unsuccessful ad campaigns alongside audience data will be able to analyze ad placement, time, color, and other metrics and provide optimization tips for more successful campaigns.
  • Content generation – This is already being discovered by workers in many fields, as a way to reduce the amount of time creating new content. Generative AI systems can ingest previous campaigns and write blog posts, ad copy, emails and more in a a very similar style, or be used as an assistant to brush up final copy or edit out grammar mistakes. 
  • Natural language processing – LLMs were built out of natural language processing tools, meaning they should be more adept at being virtual assistants and chatbots than the current crop of services. Marketers can deploy LLMs to quickly analyze sentiment and opinion on a recent campaign, and some LLMs are able to interpret text, audio, and video.  
  • Market research – Even though OpenAI and others have opted to have their LLMs only use data from a few years ago, once LLMs become more commonplace, we should expect them to crawl the web every day or every week to discover new material. Once that is the case, marketers should be able to gain insights into consumer opinions, and new market opportunities. 
  • Personalization – Marketers can feed LLMs data on specific customers and generate ad copy, emails, and other promotional material optimized to improve campaign results. 

With generative AI and LLMs still being a nascent technology, there may be new functionality that we are completely overlooking at the moment which will enable more productivity and financial gains. There are many startups working on LLMs specifically for marketers and advertisers, taking advantage of the amount of data that marketers have access to and the need to generate a lot of content to meet the demand. 

“When we can simply ask our data tool to explain something to us, and the answer comes to us in natural, conversational language, we’ll trust the output more because it will feel more human,” said Ryan Doctorow, corporate account executive at Snowflake. “Depending on how generative AI models develop, this heightened tendency to trust could be either helpful or problematic. But without a doubt, this trust will further accelerate the adoption of AI and change the way we work.”

Advertising and Marketing Convergence 

From the outside looking in, advertising and marketing may be considered one and the same thing. However, the two practices have been largely independent of one another, with teams, channels, and applications specifically created for advertising or marketing. This has caused is heavy fragmentation in some businesses, making it difficult to follow the entire customer journey from first impression to purchase or subscription. 

That appears to be shifting, as marketers look to reduce the amount of tools and services they use and integrate advertising into their workflows. It is also by necessity as marketers and business leaders add artificial intelligence and other analytics systems to marketing and advertising campaigns, which work better with fewer intermediaries. 

This inefficient way of working, which may have been tolerated and necessary pre-internet, is also hurting the bottom line of businesses, due to the high costs from connecting hundreds of links in the marketing chain each time a new campaign is launched. In the current market, where businesses are being pushed to focus on profitability over growth, the likelihood of these two practices merging is more likely than ever before.

Data Privacy and Regulatory Issues 

Data privacy is becoming more of a complex issue to deal with, as national and intranational bodies create new laws on data collection, usage, and storage. Marketers need to be aware of these regulations, but there are ways to reduce the amount of manual time spent reorganizing campaigns, targeting strategies, and data management through the use of automation. 

Snowflake said it is seeing users tie all data management layers together under one unified system, to generate a holistic view of the customer and to simplify data privacy requests. When a customer asks for data to be removed, which will be their legal right in the EU by sometime next year, businesses need to be able to automatically grant that request. 

Single Source of Truth

What Snowflake has been attempting to push through with its key factors is the need for a single source of truth, or applications which build directly on the data layer and leverage modern data tools to work inside an organization’s data platform without any friction. This single source of truth is one of the key elements in Snowflake’s Modern Marketing Data Stack, with most of the leaders in its categories delivering a unified data store that does not require multiple ecosystems to function. 

As mentioned in the advertising and marketing convergence section, having fewer barriers gives marketers more clarity to see the customer’s journey. It also provides marketers with more data on each customer, which can be pulled through to one dashboard for easy access.  

Gain comprehensive insights into your data stack to improve data and pipeline  reliability, compute performance, and more. [Learn More]

David Curry

About David Curry

David is a technology writer with several years experience covering all aspects of IoT, from technology to networks to security.

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