Everything Is a Recommendation: Personalization for Every Touchpoint

Think Beyond the “You Might Also Like”

When most media executives hear the word personalization, they immediately think about content. “What articles should we show next?” or “What podcast should we recommend?”

But here’s the truth: everything is a recommendation. Payments, service experiences, delivery preferences, and even product bundles can all be personalized using the same principles.

This article is designed like a guided workshop. Share it with your team. Use it to break down silos. And most importantly—start treating every touchpoint as an opportunity to recommend the right experience at the right time.

Step 1: Personalize Content and Checkout

Exercise: With your team, map out where personalization stops in your customer journey.

• Does it end after the paywall?

• Does it extend into the checkout page?

In eCommerce, checkout is a recommendation engine: “Add this, upgrade that.” For publishers, it can be the same. Imagine showing a subscriber a discounted bundle at checkout, or letting them choose between monthly Apple Pay or annual direct debit depending on their past behavior.

Data to ask for:

• Conversion rates by payment type.

• Which upsell offers convert best at checkout.

• Drop-off points in the payment flow.

Step 2: Make Service a Retention Recommendation

Exercise: Ask your customer service team to list the top three cancellation triggers. Now, brainstorm what “recommendations” could change those conversations.

Example: Instead of processing a cancellation instantly, the system could recommend a “pause plan” or offer a different delivery method (digital vs. print).

Data to ask for:

• Average save rate per cancellation offer.

• Customer sentiment scores post-interaction.

• Repeat cancellation attempts vs. successful saves.

Step 3: Use Delivery as an Experience Lever

Delivery isn’t just logistics—it’s personalization in action. Think about push notifications, print frequency, or newsletters.

Exercise: With your audience development team, run through every delivery channel. For each, ask: “How can we recommend a better cadence, format, or time?”

Example: A subscriber reads long-form content on weekends. The system could recommend pushing weekly digests on Saturday mornings.

Data to ask for:

• Engagement by delivery channel.

• Open/click rates by time of day.

• Churn risk for subscribers receiving too much vs. too little.

Step 4: Bundle Like an eCommerce Brand

Just as retailers recommend “Frequently Bought Together,” publishers can recommend bundles across their portfolio.

Exercise: With product managers, create a “bundle map.” Pair content verticals, newsletters, or event tickets that naturally align.

Example: A sports subscriber might also be interested in lifestyle or travel content. A family magazine subscriber might be a candidate for kids’ event promotions.

Data to ask for:

• Bundle performance by demographic.

• Correlations between content categories and subscriber lifetime value.

• Uptake rates on cross-brand or cross-title offers.

Step 5: Think Like a Retargeter

Streaming platforms promote their own shows between episodes. eCommerce retargets abandoned carts. Publishers should treat their own content and memberships the same way.

Exercise: With marketing, run a “retargeting brainstorm.” For every property, ask: “If someone reads this, what else should we promote?”

Data to ask for:

• Top referral pathways across content.

• Audience overlap between properties.

• Cost savings of house promotions vs. paid ads.

Data + Collaboration = Personalization Everywhere

Personalization is not just about recommending content. It’s about making smarter recommendations across every subscriber touchpoint—content, checkout, service, delivery, and beyond.

The magic happens when teams break down silos: editorial, marketing, finance, and customer service working from the same data. Technology like Darwin CX surfaces the insights, but it’s the people and processes that bring recommendations to life.

Everything is a recommendation. The only question is whether you’re making the right ones.