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Home Social Media Matching offers with brands and products that consumers are already engaged with

Screenshot of iLikeOffers Homepage

iLikeOffers recently launched in the UK, putting a new twist on the voucher codes and discount phenomenon which has gripped the UK over the last 5 years. With the abundance of sites already in the market, we set out to create something truly unique and new technology had to underpin it.

The idea is simple – to help users discover offers from the brands they love, rather than sifting through hundreds of deals. To make this work, the offers have to be personalised for each user, helping to improve relevance. So how do you get an idea of brands and products users like? The answer in this case was Facebook, together with a user’s interest in a particular category such as travel or fashion.  As Facebook is the most popular social network with 60% using it (SocialBakers 2012) it was the obvious choice as it could be used to help a great proportion of users.

The technology is incredibly complex so I’ll try to keep it simple. Essentially after a user logs into iLikeOffers via the Facebook login button, recent ‘likes’ and other activity is reviewed against a keyword set, which then matches this to any offers in the database. Other factors are then assessed as part of the algorithm in real-time, before presenting the user with a list of ‘Personalised Offers’. An example would be a user who ‘likes’ a fashion brand would receive available offers from that brand in their offers feed. Alternatively if a user were engaged with the new iPhone 5, they would then receive product based offers for the iPhone in their feed.

The next question is how do you then identify the popularity of discounts from the thousands available. Again the answer was Facebook, but also other social networks including Twitter and Pinterest.  For each offer and brand listed on the site, the total number of social shares is aggregated to provide a total score – this metric is then used for ranking popularity. The sharing mechanism also enables users to provide recommendations to friends, which has become increasingly important as identified in a study by Tamba (June 2012), which found that 76% of consumers relied on recommendations from friends to inform their purchase decisions.

When you’re dealing with large data sets, there are always tweaks to the technology that need to be made on an ongoing basis, results won’t always be perfect, but it’s only just the start. Is this the future of vouchers and discounts in the UK? That’s up for you to decide, as the site continues to develop over the coming years.



Shane Atkins
Hello! I want to show you how to get more traffic from blogging, and social media by helping you grow your subscribers and social influence by using a mix of best practices with trail and error. I'm going to be talking about blogging and social media a lot as I love them both 🙂 Want to know more? Check out my about page.
Shane Atkins

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