The Ultimate Guide to AI Marketing

21st century, accessible AI is here for the taking. Let’s break down the nuts and bolts of AI, how you can use it in your day-to-day, and how marketers are already seeing amazing results.

20 Min Read


The Ultimate Guide to AI Marketing

21st century, accessible AI is here for the taking. Let’s break down the nuts and bolts of AI, how you can use it in your day-to-day, and how marketers are already seeing amazing results.

20 Min Read


AI Marketing

Today’s customers have sky-high expectations for marketing — and these expectations extend to a growing number of channels and devices that they access almost 24/7. What’s more, marketers are also expected to have a greater hand in crafting customer experiences (CX). So how are marketers supposed to deliver personalized messaging across a myriad of channels while remaining agile and responsive to customer preferences? The answer lies in AI marketing.

In this guide, we’ll introduce you to some AI basics, how they relate to marketing (and just how easy they are for marketers to leverage), and why AI marketing presents a fantastic opportunity for your business, no matter your budget, size, or vertical.


How AI Can Revolutionize Your Marketing

Over the past decade, marketers have become increasingly swamped. As the channels piled on, customer expectations grew, and data sources expanded, marketers have risen to the occasion — but at the cost of being stretched way too thin. With the help of AI, you no longer have to be bogged down with manual tasks with AI determining the optimal WHO, WHAT, WHEN, and WHERE (segment, recommendations, time, and channel) to reach your goals. This enables you to explore your dream ideas:

  • Discover new, high-value segments within your customer base and target with precision
  • Autonomously personalize every message customers receive to their explicit and implicit preferences
  • Sending at the perfect moment and on the best channel for engagement with campaign optimization
  • Easily optimize individual messages with automated messaging and creative

Unlike the highly manual (and time-consuming) methods most marketers are forced to rely on today, AI offers:

  • Less guesswork, providing more signals to trigger next-best action
  • Reduced time to create segments, recommendations, and optimize campaigns
  • Built-in, continuous self-learning
  • Easier customizability to your unique needs


AI-Driven Marketers

Improved Performance

AI helps you better engage and convert customers through relevant, timely interactions. Built-in optimizations continuously improve campaigns as they run.

Increased Productivity

You can streamline manual, time-consuming processes around data gathering, segmentation, customer insights, campaign orchestration, and execution.

Upleveled Day-to-Day Responsibilities

AI frees marketers from mundane and tedious tasks so they can focus on creativity, strategy, and new revenue-generating ideas.

Became Self-Sufficient

With AI tools built specifically for them, marketers no longer need to rely heavily on engineering and data science resources to run campaigns.

The Secret Sauce of AI Marketing

Breaking Down
the Science

01 / 08


AI Workflow

At its core, AI is simply a process of learning meaningful predictions or patterns through data and using the predictions to automate business decisions. Collectively, AI is a set of computational algorithms that takes data as inputs and produces insights in the form of predictions (scores) or patterns (segments or groups). We can then use these predictions to make automated decisions, and it’s key to distinguish between the two.

  • Predictions are insights
  • Decisions are actions

Once AI reaches a conclusion as to its prediction and decision, it’s critical to run experiments to test the validity of these statements.


Supervised vs Unsupervised

AI is here to bring meaning to vast amounts of data for marketers. Here are two popular ways that AI can derive meaningful insights.

Supervised Learning

Supervised learning, a common and effective approach, are AI algorithms that learn meaningful relationships in data using labeled (outcomes) examples — you can use any dataset that can be labeled in this approach.

The examples (called Dataset) contain data (called Features) and outcomes (called Label).

The dataset is fed into an AI algorithm that learns how to map Features to Label. This mapping function is called a Model. This is the learning (training) stage of supervised learning.

Important Tips

  • The quality of the model and its predictions are only as good as the training dataset. The old adage of “garbage in, garbage out” applies here.
  • Supervised learning is computationally scalable to large data sets.

Unsupervised Learning

Unsupervised learning is a class of AI algorithms used to identify meaningful patterns in the data — most commonly in grouping use cases. The data here does not have labels, so the model doesn’t have any clues on how to divide and categorize the data. Instead, it needs to infer its own grouping rules. The process of grouping is called Clustering.

Clustering helps group similar items together in a cluster. The input to clustering is a dataset, and the output is a cluster name assigned to each item in the dataset.

Important Tips

  • Clusters are hard to interpret and require BI analytics to make sense of.
  • Clustering algorithms are computationally expensive to train.
  • Clusters can be used to assist humans in manually labeling examples so supervised learning could be used on top of the labeled clusters.


Skillshare Improved Enrollment Rates by 89%

Tests showed AI helped students discover the right classes.

“AI marketing helps Skillshare’s marketing team appear larger than it is. We are a lean team and having Blueshift helps us present ourselves and speak to our customers in a more sophisticated way like companies 10X our size.”


Getting Granular with Segmentation

Selecting Your
Perfect Audience

02 / 08


Who to Target

The days of “spray and pray” campaigns are far behind us. Most of us today now use some form of segmentation. Brands may start out with simple segments based on demographic information, but that isn’t the most effective way to segment — and trying to do anything more involved manually is nearly impossible.

Marketers have access to a growing amount of customer data which makes maintaining segments difficult. Segmentation is traditionally an offline, time-intensive process and by the time a segment list comes back, it’s already outdated and has grown in size and complexity. But luckily, this complexity and volume of data are perfect for AI to tackle, as the more data you have the better AI will function. Let’s examine 2 ways we can use AI to better understand the Who of our marketing.


Segmentation Techniques Using AI

Segmentation, or how we group customers to message, can be optimized and made easy with AI. Here are several techniques AI marketing can use.

Propensity Scoring

Old-school segmentation techniques might call for you to segment customers based on their journey stage — but with a million different experiences in each stage, this leaves a ton of room for your message to fall flat.

AI marketing goes beyond this and uses Propensity Scoring to calculate a customer’s probability to perform a specified action (think conversion, engagement, churn, etc.) by examining activities common before the desired action is taken to find useful patterns.

This is achieved through supervised learning and these scores can range anywhere from 0-100 with 100 being the highest likelihood. To segment high-intent customers, marketers can simply pull a continuously-updated list of customers with a score of 80-100 to complete their desired goal.

Lookalike Audiences

AI can also help bring new customers to your brand with Lookalike Audiences. This method takes high-performing segments from your customers such as category affinity, LTV projections, and journey status, and then finds new customers with similar attributes.

The “ideal customer profiles” can then be used to identify, target, and acquire customers from a third-party data source, like Facebook or Google, who look similar to your champion customers.

This method is fantastic for marketers who have a ton of customer data at their disposal but want to increase their brand reach and customer base. These segments are great for finding new customers based on seasonality, sale affinity, weekend vs weekday shoppers, and more.


Tuft and Needle Improves Email Revenue by 181%

Increased Email Revenue Within the First Year

“Blueshift’s Segmentation Engine is so much fun to use. I have the ability to quickly build extremely granular segments within a matter of clicks. I haven’t seen a more powerful segmentation tool before, it’s better than anything else I’ve used.”



Serving Up
Hyper-Accurate Recommendations

03 / 08

AI-Powered Recommendations

What to Recommend

Your customers are looking for a seamless omnichannel experience that incorporates meaningful personalization throughout. This requires you to serve up personalized content across every channel — which is extremely challenging given how much data customers leave behind and the sheer size of product catalogs. The key to moving past generic batch recommendations is through using AI-powered Predictive Recommendations.

Luckily for marketers, recommendations happen to be some of the most well-researched areas in academia — which means there are many algorithms out there that produce extraordinary recommendations. Predictive Recommendations will take the input of your catalog content, customer data, and customer interactions with your catalog and produce recommendations based on a number of algorithms. Let’s examine ways AI can determine the best What for your messages.

Trending Content or Items

Scans all recent sessions to determine which content or products are trending for your brand. These recommendations work well for broader segments and are useful for inactive or new users that aren’t leaving behind a ton of data.

Recent or Expiring Content or Items

Uses your catalog to find new content or products, or content soon to expire to be used as recommendations to let customers know what’s new (and what they need to grab ASAP).

Collaborative Filtering

Analyzes all customer sessions and compares those against individual sessions to produce recommendations such as “customers like you loved” to further guide new/active customers to the next stage in their journey.

Similar Content or Items

Use tagging to group similar products or content, by using a similarity metric. This allows you to easily recommend products similar to what customers have interacted with or purchased in the past.

Next Best Product or Offer

Based on behavioral data AI can predict customer affinity to certain categories and products to generate recommendations.


Zumper Scales Lead Submissions by 384%

Incorporated Advanced, Predictive Recommendations

“Blueshift’s AI-powered recommendation engine allowed us to better serve our customers with targeted marketing and personalized campaigns at scale. The platform enabled us to turn user behavior into experiences that capitalize on actionable insights that were critical to customer experience.”


Strike While It’s Hot

Optimizing Send Time for Success

04 / 08

send Time Optimization

When to Engage

The always-on customer is constantly being bombarded with thousands of marketing messages across multiple devices. The key to rising above the noise lies in meeting that customer in the right place and at the right time to optimize for engagement and conversion. That won’t happen if you’re simply guessing at the best time, or going with popular times like 5 PM. AI-powered Engage Time Optimization is the best way to ensure you’re messaging each individual at their best time, every time.

Predictive Engage Time Optimization helps you optimize send times for downstream behaviors that lead to revenue, rather than initial open rates. It takes into account that people today are much more likely to have many frequent bursts of activity around the clock. From analyzing past messaging activity, customer attributes, and site activity, AI identifies and optimizes send times for each customer to when they are most likely to deeply engage with a brand — effectively pinpointing the perfect When.

What Feeds these Predictions?

A likelihood to engage prediction will examine at the recency and frequency of interactions with your brand such as:

  • Session length, time spent, and depth of downstream activity
  • Specific content pages viewed and associated UTM parameters
  • Seasonality, time zone, and location attributes

These techniques then predict the likelihood of doing “goal” behaviors (defined by marketers) for each hour of the day and day of the week.

These AI-powered predictions easily fit into existing campaigns and autonomously select send times to be at each customer’s optimal engagement time. As the campaign runs, messages will send based on each customer’s engagement affinity window that AI has predicted.


LendingTree Increases Revenue 35% with Send Time Optimization

“We saw a 35% lift in revenue per send using engage time optimization to send ‘Savings Alerts’ that are personalized to each user based on their credit history.”

End the Constant Hide and Seek

Selecting the Most Effective Channel

05 / 08

Channel Engagement Scores

Where to Message

As the number of channels you message on grows, so does the pressure to get channel selection right. Though customers often use all of these channels, there are a few favorites that are more optimal for engagement and conversion — and these might change as they continue on their journey and new trends emerge. For marketers, it’s impossible to guess what channel is best at scale without the help of technology.

Similar to send time, you can also now take advantage of AI to optimize your channel selection with Predictive Channel Engagement Scores. These scores help marketers autonomously select which channel is best for marketing to each customer by predicting the likelihood of a user engaging with a message on each channel — email, push, in-app, SMS, etc. These scores are fed by:

  • Historic behavioral data
  • Catalog interactions
  • User demographics and lifetime attributes
  • Additional information pertinent to your business

These factors inform AI to produce scores from 0-100 for each channel, with higher scores indicating that a customer has a higher likelihood of engagement on that channel and can be set up to autonomously trigger the best Where within a campaign at any given stage.


Applying Engagement Scores

Determine Channels

Choose the preferred message channel(s) based on the individual’s channel engagement likelihood scores.

Create Triggers

Set up an omnichannel trigger that targets a customer in the decreasing order of their channel engagement scores.

Control Volume

Set customer messaging limits based on channel engagement scores.

Build Engagement and Reputation

Suppress a list of least likelihood customers to increase email engagement and increase IP reputation.


CarParts.com Increases Customer Engagement 400%

Personalizing Experiences Around Each Customer’s Vehicle

“Blueshift’s platform flexibility addresses our complex data structure and makes it simple to deliver personalized messages and grow our channels. The people behind Blueshift really care about our success and are one of the main reasons we’re at where we are today.”


Hold AI Accountable

Testing and Experimentation
with AI

06 / 08


Explore-Exploit Framework

To fully ensure you’re using AI to its fullest and test efficacy it is useful to understand the experimentation lifecycle in the explore-exploit framework.

  • Explore — Create a hypothesis about which interventions to use (e.g. sending promotion, display specific content), set up metrics (higher retention, higher engagement), then design and run an experiment until the results are conclusive.
  • Exploit — If the hypothesis is true, use the hypothesis for long term business decisions. If not, repeat the experiment to keep exploring.

Designing sound experiments to test the efficacy of the predictions is vital to understanding if the intended intervention leads to the desired outcome. As the scale of data and campaigns grow this can become a challenge to do it manually, which is where AI comes in.


Experimentation with AI Solutions

AI marketing platforms that have built-in solutions for automated testing save enormous amounts of time in running experiments, tracking metrics for each test case, and reporting the significance of the observed results. There are several popular ways to run, measure, and report on these experiments.

A/B…Z Testing

Use A/B tests to split the audience and send different messages to each subset of the audience. Remember, A/B Tests need to be statistically significant for their results to be valid.

Automatic Winner Selection

As the scale of A/B testing increases, marketers often find themselves doing repetitive analyses often in spreadsheets outside their marketing tools. Using automated winner selection with the pre-set criterion on desired goal behaviors — click rates, number of orders, revenue, etc — the system can run A/B testing on a portion of the audience and automatically choose the winner and send the winning variation to the remaining audience. This can save countless hours of analysis and reporting.

Population Testing with Test and Control Audiences

In addition to A/B testing, marketers can split audiences into test and control buckets and measure the efficacy of intervention vs no intervention. Oftentimes when the unit economics of intervention is costly it’s desirable to measure and establish the soundness of intervention before scaling such interventions.


While AI algorithms have a built-in, continuous process of testing, learning, and optimizing marketers should still track and validate how their AI-powered campaigns are performing and adjust models and marketing actions as necessary. Stick to AI solutions that are transparent both on the inputs of the models and the output of the results to ensure you’re making the right investments for your business.


Discovery, Inc. Delivers Personalized Content

“Blueshift really puts the customer needs first and they have an incredibly powerful platform, yet easy to use. Blueshift helps us to be relevant, interesting, and personalized to our end-customers.”

Keep Personal Data Safe

Privacy, Transparency,
and Trust

07 / 08


Compliant Customer Data

Modern AI is able to process more data than ever before. While this provides many opportunities for marketers, it can also surface customer privacy, trust, and bias issues in how that data is sourced, processed, and used. As you begin your AI marketing journey make sure that AI marketing solutions you use and build meet data privacy and anti-discrimination standards and adhere to all legal regulations.

Data Privacy

Customers today expect brands to provide a personalized experience and those experiences require gathering data about customer preferences and browsing patterns. Customers’ willingness to share personal data comes with the expectation that it will be used for better experiences and their privacy will remain respected. Striking this delicate balance is necessary for brands to earn and keep consumer trust.

Transparency and Trust

Adopting AI requires trust in the process. Such trust can only come with having transparency into and control over its inputs and outputs. This includes having the ability to customize inputs, understand the model insights, and validate predictions. With any platform you vet, it’s important to determine each software’s “AI ethos” — do they have white-box, or fully transparent, models and openness to using AI from your other tools? Or, is their “secret sauce” truly kept secret and their AI policy restrictive, leaving your team in the dark terms of how your customers’ data is being used?


Biases are data generation processes leading to undesirable outcomes towards a subset of customers based on specific attributes and are a common side effect of modern AI models that process vast amounts of data where noisy inputs could lead to over-generalizing for certain subsets of the customers. To prevent bias, don’t use discriminative demographics and racial attributes unless absolutely required, and remain in accordance with anti-discriminatory laws that provide guidance for isolating and measuring such bias and preventing discriminatory experiences to different subsets of customers.

Your Time is Now

The Future with AI Marketing

08 / 08


Your Approach is Key

When starting on your AI Marketing journey it’s important to remember, there’s no one size fits all approach. A successful approach is a combination of smaller decisions about the Who, What, When, and Where based on the business objectives (grow engagement, reduce churn, increase purchases, etc.) and human hypothesis automated with the help of AI insights.


Securing Marketing’s Future with AI

AI is here to transform the way marketers work and it exists to elevate the marketer. AI helps marketers:

  • Be more agile, productive, and effective at their jobs
  • Free up from both mundane, repetitive tasks as well as complex computational activities
  • Launch and optimize campaigns faster by eliminating heavily reliance on data teams for models and insights.

All of this gives marketers the time back to focus on creativity, strategy, and continuously improving how they engage customers.

With AI, marketers can expect their day-to-day to be more efficient and productive, their workflows to be more seamless, and their marketing performance to improve.

The future of marketing will see marketers working in tandem with AI and using it to extend their capabilities and capacity. AI gives marketers a helping hand, providing the scale that marketers themselves could never reach because the computational power required to do so isn’t humanly possible.


Artifact Uprising Believes Personalization is the Future

“We know that personalization is the future. Blueshift is preparing us to move into the next phase of hyper-personalization — leveraging AI capabilities to really bring the message to the customer and make sure it’s timely and hyper-relevant.”


Blueshift’s Unique Approach to AI


Accessible AI

At Blueshift, our goal is to enable marketers with accessible AI — and we hope this guide has helped you along your AI Marketing journey. That being said, AI is not the full answer to creating great customer experiences and solving all of the problems marketers face. To achieve anything, you need to start with a solid foundation of customer data powering your entire tech ecosystem.

Our SmartHub CDP helps companies of all verticals and sizes do just that through leveraging unified customer data and patented AI technology across the experience building process. Through unified data, omnichannel orchestration, intelligent decisioning, and unmatched scale, Blueshift gives brands all the tools to deliver seamless experiences in real-time.