AI for Marketers

AI is becoming a key part of how all the tools in a modern marketing system work together. And with 89% of marketing leaders saying AI helps improve timing, content recommendations, and personalized product delivery, it’s clear that smarter automation is now essential for growth.

Companies at the cutting edge of customer engagement today are delivering 1:1 recommendations in many different forms, powered by Artificial Intelligence (AI) or Machine Learning. Today's digital marketer finally has access to data about customer behavior and preferences in real-time. However, synthesizing this data into recommendations is extremely hard. That's where AI powered systems come in.

RFP Questions for Marketer-focused AI

AI Predictors

1. Does your solution offer predictive scoring for customer actions? What scores are available, i.e. buying propensity, user engagement, and affinities? Are scores out-of-the-box and/or customizable?

2. Can predictive scores be used for dynamic segmentation and to automatically trigger communications based on a user's score?

3. Does your solution provide an interface for business users to generate, deploy, and refresh custom predictive models without involvement of a data scientist or IT?

4. Does your solution have the ability to integrate with and bring in in-house models into the system?

5. Does your solution offer transparent models that provide detail into the model feature inputs and the performance outputs?

AI Assistants

6. Does your solution utilize deep customer understanding and customer data to automatically generate personalized, brand-aligned content across channels using AI, demonstrably reducing campaign development time while improving relevance and marketer efficiency?

7. What specific marketing assets (e.g., email subjects, body copy, CTAs) can your AI Assistants generate and what channels are supported by your AI Assistants?

AI Agents

8. What specific marketing tasks can your AI Agents autonomously execute, and how are they configured to achieve predefined business goals?
9. How do your AI Agents use real-time customer data to make decisions and self-optimize campaigns without direct human intervention for each action?
10. Describe the guardrails and oversight mechanisms available to marketers when deploying AI Agents for automated campaign execution.
11. How do your AI Agents learn from past performance and evolving customer behavior to continuously improve their decision-making and task execution?

Recommendation Engine

12. Does your solution have a visual interface in which marketers can create and manage recommendation types without technical knowledge? Is this a native capability or through a partnership/ additional integration?
13. Describe what types of recommendations your solution supports (i.e. collaborative filtering, trending items, similar items, new items, expiring items, etc).
14. Does your solution ingest product, content, and offer catalog data and use dimensions of that data (i.e. product name, price, etc.) within dynamic recommendations?
15. Does your solution allow us to import recommendations from our in-house team? If yes, describe how.
16. Does your solution support combining multiple types of recommendations within a template? If yes, describe how.
17. Does your solution generate unique recommendations for every user based on real-time data? Does it offer the ability to preview recommendations for different users and add filters?
18. Can your solution generate ready-to-deliver content using templates that select different items (text, images, offers, etc.) for different individuals based on fixed rules, predictive models, or both?

Read Blueshift's responses to these questions