How AI Can Take Personalization Beyond Addressed Emails to Offer Curated Shopper Journeys

Personalization is the latest buzzword in e-commerce, but applications of it vary depending where you shop — and have varying impact on an increasingly discerning consumer. Adding a customer’s name to a marketing email is no longer enough to foster a personal connection between brand and buyer. Fortunately, the technology is there to fuel a more individual relationship.

“Personalization itself isn’t a new concept,” said Christian Selchau-Hansen, co-founder and CEO at personalization platform Formation. “Companies can easily retarget users or recommend a product by utilizing user data — ‘I know you bought a sweater, so I’m going to show you more sweaters or boots or a coat.’ But this kind of personalization is really just the start of how companies can bring the utmost relevance to their consumers.”

One common approach to personalization is the use of segmentation: dividing a consumer base into segments of shared characteristics, such as geography, demographic or purchase behavior. While this can help sharpen the effectiveness of a marketing campaign, it does not guarantee that the messaging will resonate with an individual within that category.

At Formation, AI and machine learning are leveraged to fuel a hyper-tailored offer program. Instead of sharing a generic discount code or rewards points tied to a bestselling item, Formation creates an individual offer journey for each user. For example, a shopper might gain rewards for buying a specific style, if their purchase history suggests a frequent repeat purchase, or for spending above a certain threshold, if they consistently spend close to that amount.

These offers are then assembled into a journey that both gamifies the shopping experience and allows shoppers to engage in a naturally progressing process, rather than requiring a big investment upfront. By tying related offers together, retailers can ensure they are putting the most relevant items in front of their users and incentivising future participation.

“According to our recent Brand Loyalty 2020 report, 73% of consumers said the brands they engage with the most recognize them on a one-to-one level,” said Selchau-Hansen. “These personal journeys are far more relevant and valuable for each customer. They recognize a customer’s motivations and preferences, and then improve the experience and increase engagement over time.”

Sample journey from Formation for a shopper at a coffeeshop retailer
Formation generates offer journeys that both reward existing behaviours and promote new, relevant ways of engagement with a brand.
CREDIT: Courtesy of Formation

The use of AI ensures that the program automatically learns which offers are well-received and which fall flat, so that future offers can be optimized. By tying optimization to engagement, the technology rewards repeat customers intrinsically while also removing any need for employees to manually test, analyze and adjust the approach. This frees up staff to spend more time building creative opportunities for brand engagement, which can then be immediately implemented.

A 1:1 approach is a great way to generate loyalty, at a time when many consumers are shifting their brand allegiances. The Formation report found that while 58% of shoppers were more loyal than five years ago, 63% were enrolled in only one to three loyalty programs. This suggests there is a gap between the level of customer interest and the range of loyalty offerings currently available. And while loyalty is often associated with items that require frequent replacing, Selchau-Hansen believes this is a limiting perspective.

“Loyalty is not always about purchase frequency,” said Selchau-Hansen. “Even if these customers aren’t seeking constant engagement, once-a-year shoppers are extremely valuable in the long term. Of course, a big piece of this puzzle is knowing your customer and anticipating when they’ll start looking for that special pair of shoes.”

For footwear and apparel merchants, particular opportunities to look out for include seasonal changes — when many consumers look to replace last year’s boots or sandals — and also personal habits, such as when a runner needs to replace worn-out sneakers for a fresh pair. An AI-supported program can detect the impact of these on offer engagement and adjust accordingly, tailoring future offers even more closely to shopper preferences.

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