Shopify stores sit on a substantial amount of customer data — purchase history, browsing behaviour, location, average order value, products viewed, segments by spend level. Most Shopify brands send the same campaign to their entire list and leave that data unused. Personalisation closes that gap: using what you know about your customers to send them more relevant emails that convert at higher rates. Here is how to apply it practically, at different levels of complexity.
Why Personalisation Increases Performance
The mechanism is simple. An email that addresses a specific customer's actual situation — the products they have bought, the category they browse most, their purchase frequency — is more relevant than a generic campaign sent to everyone. More relevant emails generate higher open rates, higher click rates, and higher conversion rates. Segmented campaigns generate 30–50% more revenue per email than unsegmented ones, per Mailchimp benchmark data.
Personalisation does not require complex technology at the basic level. It starts with segmentation — deciding who gets what — and graduates through dynamic content, to fully personalised product recommendations.
Level 1: Basic Segmentation
The simplest form of personalisation is sending different campaigns to different segments of your list based on purchase history.
- New subscribers (no purchase): Welcome series, educational content, social proof. They need to be sold on the brand before you can sell them a product.
- First-time buyers: Post-purchase flow, cross-sell relevant to what they bought, loyalty programme introduction.
- Repeat buyers: VIP treatment, early access to launches, higher-value offers. These are your most valuable customers.
- Lapsed customers (90+ days since purchase): Win-back sequence with an incentive to return.
Setting up these four segments in Klaviyo takes about 30 minutes and immediately changes the relevance of every email you send.
Level 2: Purchase-Based Product Recommendations
If someone bought your face serum, they are a higher-probability buyer of your moisturiser than someone who bought your body lotion. If someone bought running shorts, they are more likely to respond to an email about your new running shoe collaboration than your lifestyle range.
Use purchase data to trigger product recommendation emails that are specific to what the customer already owns. Klaviyo's predictive analytics can automate this based on your catalogue data. At a simpler level, you can create manual segments by product purchased and build dedicated cross-sell campaigns for each.
Level 3: Dynamic Content Blocks
Dynamic content lets you send one email with sections that change based on subscriber attributes. A product recommendation block that shows different products to skincare buyers versus haircare buyers. A hero image that shows different seasonal products based on the subscriber's country. A promo code block that only appears for first-time buyers.
Klaviyo supports dynamic content blocks natively. The setup time is higher than basic segmentation but the output is a single campaign with multiple personalised versions — which means less production overhead per segment.
Level 4: Predictive Personalisation
Klaviyo and some other ESPs offer predictive analytics that forecast individual customer behaviour: predicted next purchase date, predicted lifetime value, predicted churn risk. Sending a campaign to customers predicted to churn before they actually lapse is more effective than a win-back sequence after they already have.
This level requires a list large enough for the predictions to be statistically meaningful — typically 1,000+ customers with purchase history — and an ESP that supports it. It is the right approach for brands scaling past $500k/year in email revenue; it is overkill for brands just getting started.
Brand-Level Personalisation vs Individual-Level Personalisation
There is a type of personalisation that most email tools ignore: making the email feel like it was written specifically for your brand's relationship with its customer, not just their demographic or purchase data.
A fashion brand that sends emails that sound like its Instagram captions is personalising at the brand level — the subscriber feels addressed as part of this brand's community, not as a generic DTC email recipient. This is harder to achieve with standard templates but is what drives long-term engagement and loyalty.
SendKite approaches personalisation at the brand level: by learning your specific brand voice and visual identity from your Instagram and Shopify store, the emails it generates feel more like your brand talking to your customers than a generic campaign from a shared template. This works regardless of list size or customer data depth. See what it generates for your store before deciding whether to build the personalisation stack manually.
For the broader email strategy context, Ecommerce Email Marketing Strategy: What Top DTC Brands Do Differently covers segmentation and personalisation alongside the full programme structure.