subject: Personalised Recommendations Score Big Results [print this page] Personalised recommendations on demand are a growing category of ecommerce optimisation that increases conversion rates by customising the online experience for each and every visitor in real time.
This type of personalisation goes beyond the scenario based strategies that are already becoming more common. In a scenario based feature, additional products are shown based on scenarios, e.g., the customer purchases a sleeping bag and a recommendation for a camp stove pops up. On demand optimisation services go a step further by microtargeting the online experience based on the specific clicks and behavior of each visitor. In the camping example, the suggested products might be tailored to extreme camping in response to the visitor clicking on products for subzero camping.
Up until now, it was difficult for smaller retailers to be able to afford these sophisticated strategies. But now, as SaaS (software as a service) has come into its own, these solutions are more readily available to everyone. However, not everyone has figured it out yet. Only 27.6% of online retailers offer personalised product recommendations, according to an August 2008 study from Internet Retailer.
Ecommerce companies that have taken the leap and implemented on demand personalisation said sales have increased dramatically because more leads are converted into transactions. In fact:
- Twenty one percent reported higher total sales
- Twenty one percent reported improved conversion rate
- Nineteen percent have seen better cross selling and upselling opportunities based on purchasing history and product preferences of individuals
- Fifteen percent are selling bigger ticket items
- Eleven percent credit personalisation with reduced returns and reduced shopping cart abandonment
- Ten Percent are experiencing improved open and click through rates of segmented email campaigns
Solutions Vary
Companies are selecting a variety of different methods to implement their personalisation strategies. The most typical are those that recommend products or services similar to those being purchased or evaluated. Some features remember shoppers preferences or previous purchases or list the sites top sellers. More sophisticated features evaluate the content of the viewers activity, and interpret layers of information to develop more targeted choices.
For example, a toy seller's site might recognise that the visitor is looking at baby toys, note that eco friendly and ethnic toys are more popular, and identify that the toys seem to be for a female aged six to twelve months. This information suggests that when the customer purchases a tactile blanket, the next suggested items should be items such as cloth dolls dressed in international clothes, and board books that show children treating the earth's resources wisely.
Another solution uses behavior such as time spent and day of week to program the sales funnel. Recognising that some people who shop during the week are in a hurry (because they are probably at work) and clicking rapidly, the personalisation can take them rapidly through the sales process. When the site recognises a leisurely browser, possibly on a weekend day or late at night, the feature leads them on a leisurely stroll through products and options.
It appears that any kind of personalisation improves conversion, so it makes sense to figure out what you can afford.
