If you’re an online retailer, you might be struggling to increase conversions because customers just can’t find what they are looking for easily enough. With product catalogues growing, customers are relying more and more on shopping engines and marketplaces as retailers have been slow to implement more user-friendly product search and navigation technology.
Product recommendations can also help potential customers find what they are looking for, easily and quicker. A scientific study by Häubl and Murray in 2003 found that websites that use personalized product recommendations are a third easier for potential purchasers to find products that they are interested in and becomes increasingly easier on subsequent returning visits.
This is because personalization technology tries to learn and understand each visitor’s interests. As the visitor moves around the website, through different categories and browsing various products, their unique preferences become apparent, and marketing personalization/recommendation engine technologies can start to predict products that may be relevant.
Personalization systems store the visitors preferences against the standard product attributes that a retailer already has (for example category, brand, colour, size and so on), gleaned from their behaviour as they browse and purchase products.
These preferences are used in two ways:
- Firstly, when a visitor returns, these preferences c an be recalled in order to “filter” product recommendations that are sourced by an algorithm. For example, if a visitor interested in “red” “t-shirts” returns, a “best sellers” algorithm will be used to source a list of relevant products – using these preferences to filter 2 or 3 product recommendations will result in the most popular red t-shirts being presented in the first few recommendations.
- Secondly, these preferences can be used to identify more targeted “people like me” recommendations. By using each preference to identify other similar visitors, recommendations will be more aligned to common interests across all categories.
This powerful, valuable preference data can also be used to retarget customers with personal content through email marketing – whether the data is used to create relevant communications to specific segments, or if it is used to inject personalized product recommendations directly into emails.
To find out more on why understanding individual customer preferences are so valuable, take a look at my previous post on “The Power of Preferences”.
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17 Jun 2013