In partnership with

Improve your marketing with Ahrefs’ AI features!

Transform your marketing game with Ahrefs!

Ahrefs’ new AI features can supercharge your workflow:

AI Keyword Suggestions: Find high-value keywords effortlessly.
AI Search Intent Analysis: Understand user intent like never before.
AI Content Grader: Optimize content for top-ranking results.
AI Translations: Scale globally with ease.

Plus, enjoy free AI tools for copywriting, content creation, and more.

Start now and let Ahrefs be your ultimate marketing copilot!

Learn more

Welcome back!

Happy New Year B925 readers! We hope your resolutions are off to a great start. If you’ve got any resolutions related to growing your business this year, we’re here to help with fresh marketing strategies for 2025. Data is the lifeblood of modern marketing, so this month, we’re zooming into data-driven optimization techniques, from CRM to content strategy.

Let’s dive in! 🏃‍♂️🚀

News

Tools + Productivity

Two steps ahead

In marketing, predictive analytics uses historical data to forecast customers’ future actions, allowing marketers to anticipate needs and optimize campaigns based on those needs, rendering marketing strategies more targeted and effective.

Imagine if your local supermarket could read your mind and offer exactly what you need— perhaps before you even know you want it. That’s essentially what Amazon does on a massive scale with its recommendation system.

First off, Amazon makes use of collaborative filtering, which analyzes customer purchase history, browsing behavior, and ratings to identify commonalities between users. If a customer exhibits similar behavior to other users, Amazon recommends highly rated products by customers with shared interests.

There are two types of collaborative filtering— one is user-based, and the other is item-based.

Here’s a visual to represent the user-based concept:

Although both the green and blue girls bought the apple, the green and red girls have both apples and kiwi in common, so the system recommends products the red girl bought to the green girl.

On the other hand, item-based collaborative filtering can be represented with this visual. Here, instead of matching users to other users, products that are bought simultaneously with another product are recommended to users purchasing one of the products in isolation.

Red and blue choose both the pear and the kiwi, so since green buys the kiwi, the system would recommend the pear based on common buying patterns.

The best approach depends on your brand. User-based collaborative filtering may be more suitable in situations where the item catalog changes frequently, user preferences are the primary driver of recommendations, and the user base is smaller and more stable than the item catalog.

Still, item-based collaborative filtering usually makes more sense for most companies, as item characteristics tend to be more consistent over time compared to user preferences. A movie’s genre or style doesn’t change even if user tastes evolve.

Item-based also helps avoid the “cold start” problem, which occurs when the system lacks sufficient information to make reliable predictions or suggestions for new users and items.

When a new user joins a platform and has not yet interacted with any items, it’s difficult for the system to understand their preferences. With item-based collaborative filtering, the system can recommend items based on users’ initial interactions, even if that’s only a single item.

Slowing the churn

Predictive analytics can also be a tool to proactively prevent customer attrition once user data starts suggesting it might happen.

For example, maybe you’ve received a notification from your internet service provider along the lines of “We noticed you haven’t been using your internet as much lately. How about a special offer to keep you connected?” That internet provider uses predictive analytics to target disengaged customers and prevent churn.

How can you apply this to your own business? First, start by determining what indicators signal churn for your business— perhaps decreased usage frequency, negative feedback from customer support interactions, or changes in purchasing patterns.

Gather data from your CRM systems, transaction histories, customer feedback surveys, and social media interactions to identify patterns that indicate churn risk, and set up alerts for high-risk customers so you can act quickly with targeted retention strategies.

These might include tailored email campaigns including discounts on products they’ve been eyeing, or suggesting new arrivals based on past purchases to reignite interest.

Personalized loyalty programs (which offer exclusive discounts on specific products or types of products a customer buys) can also combat churn.

Pet supply company Chewy.com does this— if a customer frequently buys organic pet food, for example, Chewy might offer exclusive discounts on new organic products.

Is this acquisition worth it?

Predictive analytics can help you forecast Customer Lifetime Value (CLV), which allows you to allocate resources more effectively and make informed decisions about campaign investments.

Information on purchase history, engagement levels, and demographic information can identify a company’s most valuable customers— and more importantly, what behaviors and preferences characterize them.

With this knowledge, marketing campaigns can target the customer segments that share these characteristics to get more value from each dollar invested.

Similarly to slowing churn, start by collecting comprehensive data on customer interactions and transactions across touchpoints, and look for patterns that indicate which customers are likely to become high-value clients based on their purchasing behavior, frequency of purchases, and engagement levels.

From there, email communications, loyalty rewards programs, and exclusive offers can be adjusted to the preferences of high-value customers.

It’s important to continuously track the performance of these campaigns, as new customer segments that represent higher value customers may emerge over time.

Looking Ahead

Predictive analytics transforms raw data into golden insights that fuel campaign success, supercharging campaign performance, and lead conversion. It’s not just about forecasting but about creating self-optimizing ecosystems where campaigns learn and improve in real-time. The most successful marketers use this power to stay ahead of trends, consumer behavior, and competitors.

If you want to automate your marketing processes to grow faster, click here to learn more about how Loopgenius can help you.

Stay tuned for next week, when we’ll revisit SEO optimization for the season!

Data-driven Marketing

Week 1: Predictive analytics for campaign optimization
Week 2: Sentiment analysis
Week 3: Data-driven content strategy
Week 4: Hyper personalization

REFERRAL PROGRAM

Hey! Don’t keep us a secret! Refer your friends and start earning rewards! Share this link with your friends: https://beyond9to5.beehiiv.com/subscribe?ref=PLACEHOLDER

If you get just 3 friends to subscribe you’ll get our exclusive sticker pack OR refer more and earn a trip to Vegas!

Your Progress