Massive Growth in Social Networking Features on Online Stores Expected
Sunday, February 22nd, 2009Adobe have released their “Scene 7 2009 Online Customer Experience - The Next Generation” survey.
Amongst its detailed results it shows a growing trend within owners of online stores who are looking to add social media type functionality to their site in 2009.
Over 90 percent of our survey’s respondents are planning to invest in new rich media and social networking technologies to ensure their brands and products stand out on the web.
The survey aims to identify what businesses plan to do in 2009 to enhance online customer experience and increase sales conversion rates. Leading companies from all industries worldwide were asked what rich media merchandising, social networking, mobile and personalisation features they use or plan to use, along with their expected or actual effectiveness and timeframes to deploy these features.
This year’s survey also asked for the first time in-depth questions in the areas of personalisation, social networking and mobile marketing.
Below is a selection of the highlights from the survey which include the most popular deployed features and their effectiveness, as well as business investments planned over the next 12 months:
- 92% of all respondents planned to conduct customer experience projects within the coming year. This is up from 53% last year as, perhaps because of the economic climate, these projects were delayed until the half of 2009.
- The top four rich media features implemented were, in order of popularity, lifestyle imagery, alternative views, audio/animation and zoom.
- The top four most effective features were zoom, visual filtering & advanced search, lifestyle imagery/photos and search landing pages.
- Globally, the highest ranking planned rich media features for 2009 were 360-degree spin, online catalogues, podcasts, product comparisons and videos. The greatest adoption of planned versus already deployed rich media features are in the areas of product comparisons, podcasts, 360-degree spin, mix and match and 3-D visualisation.
- When looking across all features surveyed and comparing 2008 responses to 2009, social networking features moved up significantly across all questions with respect to deployed, planned and effectiveness.
- Approximately one-fifth of all global respondents have deployed blogs, RSS feeds, user ratings/rankings/comments and syndicated content with over 30% listing user ratings and blogs as their highest planned features for 2009.
- User ratings/rankings/comments, URL sharing, user generated content and blogs were ranked as the most effective features, while syndicated content ranked lower in effectiveness.
- Although personalisation features had a low adoption rate, all falling below 15%, the trend remains flat or down for these features year over year. The top two features used are personalised stores and web-to-print applications. It is interesting that most personalisation features continued to earn the highest effective or very effective rates over 80%.
- Similar to personalisation features, there remains lower adoption of mobile features, which all fall below 11%. However, this is an increase over last year’s results of 5% usage rate. Mobile promotions used to drive to store or web was the highest deployed tactic.
These results have confirmed the importance of adding content to your site regularly, using blogs and RSS feeds to push new content regularly to the search engines. URL sharing and social bookmarking remain an excellent way to build links to your site from the social networks and interactive features like user ranking, comments and reviews build trust and confidence in both your products and stores. These are all effective tools to drive search engine optimisation, the majority of which can be easily managed by store owners themselves.
Contact Vanilla Storm for more information on how you can ensure your site utilises these proven techniques to grow your online business.
To read the full report from Adobe, you can register to receive your copy here.
