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GUEST COMMENT How to avoid four common personalisation mistakes

Just mentioning a shopper’s name isn’t enough to inspire engagement or brand loyalty anymore. With customers expecting to be recognised as an individual, marketing automation plays an important role in ensuring customer relationships are nurtured at scale. By automatically delivering relevant content the moment a shopper is most engaged, personalisation platforms help freeing up time for marketers to focus on the more creative and strategic aspects of a campaign.

However, you only have to type into Google ‘marketing personalisation fails’ to remind yourself of common pitfalls. Adidas was the latest brand to have an automation blunder with its #DareToCreate campaign. The campaign invited users to input their Twitter handles onto images of Arsenal’s new kit, and share a tweet showcasing the personalised jersey. But the system was unable to identify and filter offensive words or names. Instead, it simply followed the programmed rules, resulting in a social media disaster for both Adidas and Arsenal.

This example highlights the significance of human oversight when running automated personalisation campaigns. Being aware of the scenarios where automation could go wrong if not monitored properly is important to every marketer. As such, we’ve outlined four key areas marketers should pay attention to.  



If campaigns are based on demographic information rather than customer behaviour, dynamic emails could be less effective than expected. For example, including a promotion for high-end beauty products into emails being sent to female shoppers could alienate customers with low spending power who would never spend £30 on a lipstick. On the other hand, promoting an exclusive in-store event to subscribers based on their postcode might mean missing out on those shoppers who live further away but buy there regularly due to its proximity to their workplace or convenient parking.

Therefore, brands should be targeting their customers based on purchase history, recent behaviour and current geolocation, rather than demographic information like shipping address or gender. What’s more, brands need to ensure they’re collecting as much detail about their customers as possible, to ensure they’re delivering the types of messages people are most interested in. This not only increases the chances of a conversion but also helps build brand loyalty.


Data capture pop-ups are a hugely effective tool for email acquisition and unlocking customer data. They can help brands build a more comprehensive profile of the customer and nurture them with tailored email marketing.

However, for those shoppers who have already shared their details with a brand, such as current customers, an automated pop-up offering 10% off their first purchase in exchange for their email address will likely be irritating. Pop-ups can still play a vital role in website marketing to the existing customer base, but should reflect what stage the customer is at in the funnel to make them really effective. For example, the pop-up could recognise a loyal customer and invite them to complete a short survey about their recent purchase experience.

Product recommendations


Brands need to ensure they take gifting seasons into consideration in their product recommendations strategy. Browse and purchase behaviour related to gifting can cause anomalies in the data profile of a shopper, causing irrelevant product suggestions long after the customer has bought the gift and returned to shopping for themselves.

Around gift giving occasions, brands should tweak the recommendation strategies to focus less on behaviour and more on providing crowd-sourced gifting inspiration, such as suggesting popular products and bestsellers. It’s important that brands don’t forget to switch recommendation rules back to normal after the events have passed, so the customer receives targeted communications based on their everyday behaviour.   





Returns can be a huge drain on margins, but they can also impact a brand’s marketing efforts. Very often, returns are not fed into marketing data which can result in poor personalisation and offers based on products the shopper didn’t like and send back.

What’s more, for brands with high levels of serial returners – like ASOS for example – it becomes difficult to identify VIP customers as CLTV and AOV will be skewed. Someone may look like a high-value customer because of the volume of purchases they are making and be rewarded with exclusive offers or services. But they are actually returning nine out of ten purchases. This can be avoided by updating the customer profile with the products that were sent back.

Personalisation platforms provide a great way to deliver personalisation driven by real-time data at every stage of the customer journey. However, to be truly able to recognise the individual customer and deliver an experience that meets and exceeds expectations, brands need to consider all potential scenarios and pre-empt what could go wrong.

Mike Austin is co-founder and chief executive of Fresh Relevance

Image: Fotolia

Author image courtesy of Mike Austin/Fresh Relevance

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