Dotdigital Personalization: Using Data to Deliver Relevant Email Experiences

Personalisation in email marketing has a spectrum. At one end is first-name insertion — technically personalised, practically table stakes. At the other end is an email where every content block, product recommendation, and message tone is individualised based on what that person has purchased, browsed, clicked, and told you about themselves.

Dotdigital sits firmly in the capable middle of that spectrum — offering enough personalisation power to deliver genuinely relevant experiences at scale, without requiring the engineering investment that enterprise CDP-based personalisation demands. For mid-market e-commerce brands and retailers, that’s the right balance.

This guide covers how Dotdigital’s personalisation features work in practice: what data sources feed them, how to build dynamic content blocks, how product recommendations function, and how to build a personalisation strategy that’s both effective and maintainable.

Personalisation Data Sources in Dotdigital

The quality of personalisation depends on the quality of the data feeding it. In Dotdigital, personalisation draws from three primary data sources.

Contact Data Fields

Every contact in Dotdigital has a set of data fields — standard fields (email address, first name, last name, mobile number) and custom fields you define (date of birth, gender, preferred store location, product preferences). These fields are the foundation of attribute-based personalisation.

Custom fields are populated from multiple sources: form submissions, preference centres, import files, API calls from your e-commerce platform, and Dotdigital’s own automation programmes (a programme node that sets a field value based on a condition).

The practical implication is that the richer your contact data, the more personal your emails can be. If you know a customer’s preferred product category, their shoe size, or the last brand event they attended, each of those data points becomes an ingredient for relevance.

E-Commerce and Purchase Data

Dotdigital’s native integrations with Magento (Adobe Commerce), Shopify, and Salesforce Commerce Cloud sync transactional data into the platform: order history, product catalogue, average order value, purchase frequency, last purchase date, and category affinity.

This purchase data powers the most commercially valuable personalisation. Knowing what someone bought, when they bought it, and what categories they’ve purchased from allows you to send product recommendations that are genuinely relevant rather than algorithmically random.

The sync needs to be configured correctly for this to work. Ensure your integration is passing order status updates (not just initial order creation), product data including categories and images, and customer lifetime value where available.

Preference Centre Data

Dotdigital includes a native preference centre tool that lets contacts choose what communications they want to receive. A contact might opt into your weekly newsletter but not promotional emails, or indicate they’re interested in womenswear but not menswear.

These preferences become contact data fields, queryable in segmentation and available for dynamic content decisions. A preference centre-driven personalisation strategy respects subscriber autonomy while giving you cleaner segmentation — contacts who select their interests are more engaged with content in those interests.

Dynamic Content Blocks

Dotdigital’s drag-and-drop email builder includes dynamic content functionality: the ability to show different content blocks to different recipients within the same email send.

A dynamic content block is a block with a visibility condition. The condition evaluates a contact data field or segment membership, and if it’s true, the block displays. If it’s false, the block is hidden and the email renders without it — either showing nothing in that space or falling back to a default block.

Building a Dynamic Content Block

In the email builder, after adding a content block, select the “Dynamic Content” option in the block settings. You’ll see a condition builder:

• Select the data field (e.g., Gender, Preferred Category, Country)
• Set the operator (equals, contains, is set, is not set, etc.)
• Enter the value (e.g., Female, Footwear, UK)

The block only renders for contacts who match this condition. Add multiple dynamic blocks with different conditions to the same area of the email — Dotdigital will show the first matching block (evaluated in the order you’ve arranged them) and use your default fallback block if no conditions match.

Practical Applications for Dynamic Content

Regional content — show UK-specific delivery offers to UK contacts and EU-specific offers to European contacts. Particularly useful for post-Brexit shipping cost messaging.

Loyalty tier content — show a “thank you for being a Gold member” header block to loyalty programme members and a “join our loyalty programme” CTA block to non-members.

New vs returning customer messaging — in your welcome email, show a “your first order” specific offer to contacts with zero purchases and a “welcome back” message to contacts with at least one prior purchase.

Category affinity — in a promotional email, show product blocks from the category each recipient has purchased from most frequently. A customer who predominantly buys kitchen products sees kitchen-focused promotions; a customer with a home décor purchase history sees décor.

Gender-based product selection — for fashion brands with gender-specific products, show the relevant product selection without requiring separate campaigns for each segment.

Product Recommendation Blocks

Dotdigital’s product recommendation engine generates algorithmically personalised product suggestions based on each contact’s purchase history. These render as dynamic product blocks within the email — product image, name, price, and link — populated differently for every recipient.

The recommendation logic can be configured based on several algorithms:

“Customers who bought X also bought” — collaborative filtering based on purchase patterns across your customer base. Effective for cross-sell recommendations in post-purchase emails.

“Based on your purchase history” — products within the same categories or attributes as a contact’s past purchases. Effective for replenishment prompts and new arrivals emails.

“Top sellers in [category]” — bestsellers from the category a contact has most affinity with. Effective when individual purchase data is limited (new customers with only one purchase).

“Recently viewed” — products the contact has browsed on site but not purchased. Effective in browse abandonment and win-back emails where the intent signal is known.

To use recommendation blocks, your product catalogue must be synced to Dotdigital (via the Magento or Shopify integration, or via a product catalogue upload). Each product record in Dotdigital needs a category assignment, an image URL, and a product URL for the blocks to render correctly.

A/B Testing Personalised vs Non-Personalised Emails

Before investing heavily in personalisation infrastructure, it’s worth running tests to quantify the impact for your specific audience. Dotdigital’s A/B split testing functionality lets you test different versions of an email against each other.

A simple personalisation test structure:

Version A (control): A promotional email showing the same curated product selection to all recipients.

Version B (test): The same email with the product selection replaced by personalised product recommendation blocks, populated dynamically based on purchase history.

Send both versions to equal segments of the same audience. Measure open rate, click rate, click-to-open rate (the best measure of content relevance), and conversion rate. Most e-commerce brands see click-to-open rate improvements of 15–30% when moving from static to personalised product selections.

Run the test across a minimum of two or three send cycles before drawing conclusions. Single-send tests can be misleading due to day-of-week or seasonal variance.

Building a Personalisation Strategy That’s Maintainable

The most common failure mode in email personalisation is building something too complex to maintain. If your personalised email requires updating 12 dynamic content blocks and a custom product feed for every send, your team will revert to static sends within weeks.

Build personalisation infrastructure in tiers of complexity:

Tier 1 — Always on, no maintenance: Dynamic blocks powered by contact data fields that change infrequently (gender, country, loyalty tier). Once configured, these update automatically as contact data changes.

Tier 2 — Low maintenance: Product recommendation blocks powered by the always-on product catalogue sync. These update as purchase history accumulates. No manual curation required per send.

Tier 3 — Curated per campaign: Manually selected dynamic content blocks for specific promotions (a seasonal banner that shows different creative for different regions). These require per-campaign attention but are the most impactful for seasonal relevance.

Design most of your email templates to use Tiers 1 and 2. Reserve Tier 3 for your highest-traffic campaigns (Black Friday, seasonal sales) where the investment in bespoke dynamic content is justified by send volume and revenue impact.

Preference Centre as a Personalisation Engine

A well-designed preference centre is one of the highest-leverage personalisation tools available in Dotdigital. Contacts who choose their content preferences are self-segmenting — they’re telling you exactly what they want to see, and they’re more likely to engage with what they asked for.

Build a preference centre that captures:

• Communication frequency preference (daily, weekly, monthly, special offers only)
• Product category interests
• Communication type preference (new arrivals, sales and offers, editorial content, events)
• Gender or persona (for fashion and lifestyle brands)

Map each preference to a contact data field in Dotdigital. Use those fields in your dynamic content conditions and segmentation. Prioritise sending to contacts who’ve indicated an interest in the specific category or type of campaign you’re sending — their engagement rates will be higher, and their unsubscribe rates lower, which improves your overall sender reputation.

Measuring Personalisation Impact

Track these metrics before and after implementing each personalisation layer to build a clear business case:

• Click-to-open rate (CTOR) — the clearest indicator of content relevance
• Revenue per email sent — the commercial impact measure
• Unsubscribe rate — personalisation should reduce this; if it doesn’t, the dynamic content isn’t actually more relevant
• Category revenue distribution — are personalised product recommendations actually driving purchases in the recommended categories?

Use Dotdigital’s reporting dashboard alongside your e-commerce analytics to connect email clicks to actual purchase data.

Working With Excelohunt

Implementing Dotdigital personalisation effectively — from integrating your product catalogue and configuring dynamic blocks to testing and measuring impact — requires both platform knowledge and e-commerce strategy. Excelohunt builds and optimises Dotdigital personalisation programmes for e-commerce brands, ensuring your data is flowing correctly, your templates are set up for dynamic content, and your strategy is delivering measurable revenue impact.

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