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Annual Report for FY23

Eagle Eye Annual Report FY23

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3 min read

The Unfulfilled Promise of Personalised Offers

The Unfulfilled Promise of Personalised Offers

Personalised offers have the potential to transform the way retailers engage with customers, making it a game-changer for retail marketers. In fact, reallocating just 25% of the budget from mass promotions to personalised offers could increase retailers' return on investment by an astounding 200%, according to BCG. In today's challenging economic environment, cost savings from personalised offers could help marketing teams shine and become a hero department.

Despite these potential benefits, only a small percentage of omnichannel retailers worldwide have adopted personalised offers at scale. This article delves into the reasons why retailers have been slow to adopt this approach and provides insights on how to overcome the challenges.

A personalised offer is one where both the qualification criteria and the reward are tailored to the individual, as illustrated by this personalised Tropicana challenge from Eagle Eye company Untie Nots:

AI-driven, hyper-personalized "challenges" reward incremental behavior

Executing this level of personalisation at scale is a challenge. To illustrate, consider a retailer with ten million customers on its database that wants to send ten personalised offers a week to each customer through digital channels such as a loyalty app or email.

To deliver a weekly personalisation program comprising 100 million unique offers, the retailer must follow these steps:

  1. Build the total pool of available offers (which may involve tens or even hundreds of millions of offer permutations).
  2. Determine the optimal mix of ten personalised offers for each customer and create a coupon for each personalised offer.
  3. Allocate the 100 million offer coupons to the ten million customers, making them accessible across all customer touchpoints at any time.
  4. Verify the customer's identity after they have finished shopping, typically at the point of payment.
  5. Check the products in the customer's basket against their personalised coupons.
  6. Apply the appropriate points or discounts to the transaction.
  7. Update the transaction total, including any discounts, as well as the customer's receipt (and loyalty program ledger, if necessary).

Most retailers find it extremely challenging to deliver personalised offers in this way with the systems available. The sheer scale of processing required to execute this volume of offers is huge, and to deliver this in a physical store, steps 3-7 must be executed in under half a second to prevent delays at the checkout. Most systems simply can’t operate at this speed.

The “Personalised Offer Maturity Matrix” illustrates the situation facing retailers.

Personalised Offer Maturity Matrix

Personalised offer maturity matrix

In the bottom left, #1 quadrant sit most current offer programs. Analysis of customer behaviour is done at a customer segment level and offers are also allocated and executed to these segments. The downside of this approach is that the available offer pool for each customer is restricted which means that most customers do not perceive the offers to be relevant to them.

Although many of the world's top retailers have invested in advanced analytics capabilities to offer individualised customer recommendations, they often encounter a bottleneck with legacy systems when it comes to delivering personalised offers. As a result, these retailers are often stuck in quadrant #2 of the Personalised Offer Maturity Matrix. A helpful analogy to understand this situation is that of someone who has written a letter that a friend would love to receive, but they cannot deliver it to their friend's house. Essentially, these retailers lack a digital "postal service" to facilitate the delivery of personalised offers to their customers.

However, some retailers have overcome these challenges and are now delivering fully personalised offer programs, issuing tens or even hundreds of millions of personalised offers each week. These retailers are experimenting with different approaches to reach quadrant #4 of the Personalised Offer Maturity Matrix. The most popular is to execute the steps via a cloud-based offers engine that communicates in real-time with customers and the retailer's systems, including in-store POS.

You can see a few examples below from around the world.

Personalised Offer Examples From Around the World

Personalised offer examples from around the World

Once a retailer has the systems in place to deliver personalisation at scale, our experience is that tremendous benefits come from increasing the share of personalised offers in the pool, while also reducing the money spent on less effective and less measurable above-the-line campaigns. At Eagle Eye, we've witnessed a 7-10% uplift in revenue from participating customers through personalised offer programs.

As the retail industry continues to evolve, it's intriguing to consider how retailers can fully harness the power of personalised offers. Given the growing emphasis on the retail media space, it's likely that quadrant #4 retailers will soon be offering real-time personalised ads to both in-store and online customers. These ads, which will blend personalised offers with AI-generated content tailored to individual shoppers, hold the potential to capture customers' attention and influence their purchasing decisions at the crucial point of the sale. This development has the potential to propel retail media businesses to new heights and transform the way customers interact with brands, especially in physical stores.

Although implementing personalised offers presents both technical and people-related challenges, retailers cannot afford to overlook their undeniable potential, particularly as AI transforms retail into an increasingly "real-time" industry. Retailers that can successfully navigate these challenges will unlock the full benefits of personalised marketing, gaining a significant edge over competitors who remain focused on generic mass promotions.

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