E-commerce leaders know that personalisation is key to increasing conversions, improving customer loyalty, and standing out in a crowded market. But despite heavy investment in personalisation technologies—AI-driven recommendations, behavioural targeting, and dynamic content—many retailers still struggle to deliver meaningful personalisation.

The reason? Bad data.

If your data is incomplete, inconsistent, or outdated, your personalisation efforts won’t just underperform—they’ll actively frustrate your customers. Instead of feeling understood, they’ll experience irrelevant recommendations, broken journeys, and missed opportunities.

Let’s explore how bad data hinders good personalisation—and what you can do to fix it.

Personalisation is only as good as your data

Every personalised experience—whether it’s a product recommendation, an email offer, or a homepage layout—depends on accurate, timely, and well-structured data. When that data is unreliable, the experiences you deliver won’t make sense to your customers.

Consider these common failures caused by bad data:

• A returning customer sees recommendations for products they’ve already purchased, making the site feel impersonal and irrelevant.
• A high-spending VIP customer is shown generic discounts instead of exclusive loyalty rewards.
• A long-time customer receives a “Welcome back” email as if they were a first-time shopper.
• A shopper adds an item to their cart but receives a promotion for a competing product, leading to confusion.

Each of these errors creates friction in the customer journey. Instead of reinforcing trust and engagement, they undermine the very purpose of personalisation.

The cost of bad data in personalisation

The impact of poor data quality isn’t just theoretical—it directly affects revenue, efficiency, and brand perception. Here’s how:

1. Wasted marketing spend

Personalisation depends on accurate customer segments, but bad data leads to irrelevant targeting:

• Customers receive discounts on products they don’t want.
• Email campaigns are based on outdated preferences.
• Retargeting ads chase customers who have already made a purchase.

This results in lower engagement, higher unsubscribe rates, and wasted budget.

2. Lost revenue opportunities

Good personalisation should drive higher average order value (AOV) and repeat purchases. But when data is unreliable:

• Cross-sell and upsell recommendations fail because the system doesn’t recognise a customer’s purchase history.
• Abandoned cart reminders go to customers who already checked out.
• Product recommendations are generic, making them easy to ignore.

Bad data means customers miss out on relevant products—and businesses miss out on revenue.

3. Operational inefficiencies

Duplicate, inconsistent, or inaccessible data slows down your teams and creates internal friction. Consider the challenges:

• Customer service teams can’t access accurate order histories, making issue resolution slower.
• Marketing teams waste time manually cleaning data before launching campaigns.
• Developers struggle with inconsistent data across platforms, leading to poor automation.

Instead of focusing on innovation and optimisation, teams are stuck fixing data issues.

4. Damaged customer trust

Customers expect seamless, relevant experiences. When they encounter:

• Conflicting messaging across different channels (e.g. different promotions in emails vs the website)
• Repeated requests for information they’ve already provided
• Misaligned recommendations that don’t reflect their behaviour

They lose trust in the brand. Frustration leads to lower engagement, fewer purchases, and, ultimately, churn.

How to fix your data for better personalisation

To unlock the full potential of personalisation, businesses must focus on data quality, accessibility, and real-time capabilities. Here’s how:

1. Unify data for a single customer view

One of the biggest barriers to personalisation is siloed data across platforms—marketing tools, CRM systems, analytics dashboards. When these systems don’t communicate, personalisation efforts become fragmented.

Solution: Implement a data platform that brings together purchase history, browsing behaviour, and customer interactions into a single, accessible profile. We recommend one a You Build It, You Run It (YBIYRI) platform.

2. Fix data quality at the source

Bad data is often the result of inconsistent data entry, duplicate records, or missing information. Instead of cleaning up later, prevent errors from happening in the first place.

Best practices:

• Standardise data collection across all systems.
• Use automation to deduplicate and validate customer records.
• Regularly audit your data for gaps and inconsistencies.

3. Enable real-time data access

Batch-processing delays mean personalisation often relies on stale data. If a customer makes a purchase but still sees ads for the same product, it’s a sign that personalisation is working with outdated information.

Solution: Implement real-time data streaming so that recommendations, promotions, and messaging reflect current customer behaviour.

4. Prioritise data governance & compliance

With the rise of GDPR and data privacy concerns, businesses must manage data responsibly. A lack of governance leads to security risks, compliance failures, and unreliable personalisation.

Best practices:

• Define clear data ownership across teams.
• Ensure compliance with privacy laws and customer consent.
• Regularly review data retention and accuracy policies.

Personalisation isn’t just about having the right tools—it’s about having the right data foundation. If your personalisation strategy isn’t working, the problem isn’t your AI or your CRM. It’s your data.

Fix that, and true, scalable, high-impact personalisation becomes possible.