4 Top Ways to Use AI to Optimize Campaigns

1. Using AI to build precise, high-value segments

Batch-and-blast campaigns don’t cut it for today’s savvy consumers — so most marketers today now use some form of segmentation. And while simple segmentation based on demographic factors like age and location is a great starting point, it’s definitely not the gold standard of customer experience, especially with omnichannel marketing on the rise. Scaling personalization past demographic factors can become almost impossible to do manually, which is where AI can take the reins.

The first-party data that brands own is growing in size and complexity, which makes maintaining segments difficult and time consuming. Luckily, this complexity and volume of data are perfect for AI to tackle, as the more data you have, the better AI will function. There are two unique AI applications for segmentation that have helped marketers find the right audiences consistently.

• Propensity scoring uses AI to calculate a customer’s probability to perform a specified action (think conversion, engagement, churn, etc.) by examining activities commonly taken before the desired action to find useful patterns. This can help marketers segment high-intent customers vs. low-intent customers — and optimize their messaging, budget, and strategy accordingly.
• Lookalike audiences take high-performing segments from your customers such as category affinity, LTV projections, and journey status, then find new customers with similar attributes. These ideal customer profiles can then be used to identify, target, and acquire customers who look similar to your champion customers.

2. Building 1:1 messaging with AI-powered recommendations

Your customers are looking for a seamless experience with personalization on every channel — which is extremely challenging given how much data customers leave behind, the sheer size of product catalogs, and how often customers browse anonymously. The key to moving past generic batch recommendations is through using AI-powered predictive recommendations.

Predictive intelligence 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. Different types of personalization blocks within messages can include: Trending Content/Items, Recent or Expiring Content/Items, Collaborative Filtering, Similar Content/Items to Past Purchases/Views, and Next Best Product or Offer.

3. Perfecting send time with AI-based optimization

Customers see thousands of marketing messages each day. To rise above the noise, marketers have to message at the right time to optimize for engagement and conversion. Batch sending at popular times like lunch or 5 p.m. won’t cut it — your message will be lost in a flood of other offers. With Mail Privacy Protection launching on Apple devices in the near future (likely fall 2021), optimizing send times will be even more important for orchestrating engaging customer experiences with first-party data.

AI-powered Engage Time Optimization is the best way to ensure you’re messaging each individual at their best time, every time. Engage Time Optimization helps you optimize send times based on behaviors (like past purchases, bursts of browsing, etc.) that lead to conversions. 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 send time.

In addition to 1:1 send times, AI can also help sort and navigate sending for special occasions, lifecycle events, and much more. (How do you think Amazon always sends umbrella ads right when it’s raining in your area? That mysterious personalized content starts to seem not so mysterious after all.)

4. AI selects the best channel for your goals

Customers browse multiple channels every day, which makes choosing the right marketing channel a difficult task. Each customer often has a few favorites that are more optimal for engagement and conversion — and these might change as they continue on their omnichannel journey and new trends emerge. The only issue? These ideal channels are essentially impossible to guess.

Similar to send time, marketers can also take advantage of AI to optimize their 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, digital ads, etc.

These scores can be leveraged across your marketing in a number of ways:

1. Choose the preferred message channel(s) based on the individual’s channel engagement likelihood scores.
2. Set up an omnichannel trigger that targets a customer in the decreasing order of their channel engagement scores.
3. Set customer messaging limits based on channel engagement scores.
4. Suppress a list of least likelihood customers to increase email engagement and IP reputation.

We hope these tips have given you a clearer vision of the profound impact AI marketing can have on your marketing strategy — but this is just the tip of the iceberg. Learn more and see AI in action by scheduling a demo with one of Blueshift’s AI marketing experts today.