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Home » SAS’ MarTech Platform Integrates with Generative AI

SAS’ MarTech Platform Integrates with Generative AI

…New marketing assistant available to SAS Customer Intelligence 360 users enables use of generative AI models

Techeconomy by Techeconomy
October 5, 2023
in Commerce
0
SAS Generative AI
SAS

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SAS’ marketing customers can now enjoy the power of generative AI. SAS Customer Intelligence 360 can now be integrated with generative AI providers for assistance in streamlining marketing planning, content creation, and journey design activities.

Customers can use generative AI capabilities for the whole customer engagement lifecycle, from marketing planning and audience creation to journey design, creating channel specific creative, through reporting and measurement.

Unlike many other vendors, SAS’ generative AI integrations are not tied to one generative AI model provider.

SAS’ marketing customers enjoy the flexibility to choose which generative AI models they integrate and use. Customers can bring their own generative AI provider, choose models from popular AI vendors such as OpenAI, or elect to use open source, privately hosted models.

Additionally, customers can choose which capabilities to integrate and interact with, how they are trained and configured, and how they are introduced to marketing users – all via a custom integration framework.

“Generative AI is an exciting emerging capability that has naturally made its way into customer engagement marketing,” said Jonathan Moran, Head of Martech Solutions Marketing at SAS. “Using this technology in a responsible manner empowers marketers to optimise time, resources and marketing budgets. Gaining marketing and advertising efficiencies and effectiveness is what SAS Customer Intelligence 360 is all about, so it makes great sense to integrate generative AI with our solutions with responsible, trustworthy guardrails in place.”

Generative AI can help marketers create marketing plans, and identify and create additional segments and audiences to target.

It can generate suggested text for content such as email or other promotional or engagement copy.

For personalised targeting, it can provide suggestions around which demographic, psychographic, behaviour, and geographic variables to include in engagement activities.

United BANK

If the marketer is not satisfied with the text or image guidelines or suggestions, the marketer is then empowered to edit this information.

Once created, approved, and activated results can be measured, reported on, and democratised throughout the organisation.

Core custom integration capabilities include:  

  • Integration via the SAS connector framework to large language model (LLM) providers to brainstorm novel campaign strategies.
  • Applying natural language to build audiences for targeting.
  • Accelerating content generation using custom models and knowledge bases.

Generative AI as a component of responsible marketing

According to a recent Gartner research report, Predicting How Major Trends Will Shape Marketing’s Future, 70% of enterprise CMOs will identify accountability for ethical AI in marketing among their top concerns by 2025. From the same report, by 2027 80% of enterprise marketers will establish a dedicated content authenticity function to combat misinformation and fake material.

“These data points emphasise the fact that using technologies such as generative AI with safety, accuracy, and honesty in mind – to empower marketers is top of mind for organisations now and into the future,” added Moran.

SAS Customer Intelligence 360 development is tightly aligned with guidance from the SAS Data Ethics practice.

The pillars of the strategy for safely and responsibly introducing generative AI technology into marketing environments include:

  • Prioritising data privacy – no sharing of sensitive company and customer data with the models.
  • Maintaining human oversight – AI-generated content should always be reviewed and approved by humans.
  • Creating interpretable and transparent output – it should always be clear to marketers how an AI algorithm arrived at its conclusions and recommendations.

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