People love talking about their shopping and service experiences in places like Twitter, Facebook, and even Pinterest.  Still, many people are inclined to talk about those negative experiences than positive ones, that’s because there aren’t many smooth ways to capture those great experiences.  [24]7 has a new social solution that will change that, and is designed to help businesses turn brand advocates into a direct sales force online.  The [24]7 platform leverages prediction, real-time decisioning, and intuitive design frameworks to make this social experience click easily for customers and shoppers.  This social shopping experience is the latest in the company’s building the ultimate omnichannel experience – intelligently building and connecting customer experiences on the web, in mobile, chat, phone and social environments.  The technology behind the suite of applications is as cutting edge as it gets, with big data, predictive analytics, and cloud technology.

Shop and Share

Forrester Research has projected that by 2016, US consumers will spend $327 billion shopping online.  It’s a hyper-competitive market that’s exploding for a lot of reasons, convenience, pricing, and according to that same report, web sites and services experiences are improving.  Capturing that company’s satisfied customer and converting them into a brand advocate means giving them the means to get that information to their friends and network socially.  People want to talk about a great sale, a great buy, or even research about a product they want.  That’s where [24]7 integrates predictive analysis, to help identify which customers are likely to share product and ultimately brand information, and then incentivize these same users to do so.

In a briefing with [24]7 VP of Product Marketing Brooks Crichlow, we reviewed the opportunity that social shopping creates.  There’s a number of interesting observations about social shopping.  Where brand recognition and product discovery are a desired product, conversions and engagement trend along a number of statistics about online consumers:

• Share – 15% currently share promotions with friends through social channels.
• Discovery – 40% read product and brand reviews posted by their friends.
• Convert – 5% click through shared links end up making that purchase.

The experience is designed to integrate, not take consumers away from the shopping environment.  Layers are integrated at these various points, from product pages, at checkout, or on a promotional page – it’s flexible.  [24]7 Social integrates in a number of ways into the shopping experience.

• Shoppers have the ability to share their purchase after checkout
• Shoppers can share products while browsing products
• Shoppers can share promotions
• Shoppers can share their comments about a positive customer service experience

Now that social sharing is a natural extension of the customer experience, businesses can leverage and maximize the application of this technology.  This is where analytic prediction helps companies identify the kind of customers they want to engage with and convert into brand advocates.  When and where to do that is a special formula that is individual to each business because of their clients, because of their design, because of promotions and other factors.  The system makes that specialization possible, and it also allows it so customers who are not targeted as those likely to share are not spammed.

Predicting which users are most likely to share their shopping experience is where real-time analysis gets into motion.  Shoppers can come from anywhere, sometimes they’re regular shoppers, sometimes they’ve wandered in from links, sometimes they come from search engines – the key is that decisions must be made instantly.

How to find those social sharers?  Well that’s based on a ‘Klout score’ – a score the system formulates instantly is meant to represent how likely you are to share something about your shopping experience.  Based on a number of factors such as real-time behavior, past sharing behaviors, and your Klout score, whether you’re registered or anonymously in the system, a rank is produced.  At that time, based on a company’s promotional programs at the time, they will then receive the social program designed for that group.

So imagine a scenario where a shopper has been identified as likely to share a promotional discount, and you’ve planned future financial incentives for shoppers that share.   You can actually drive increased sharing, gaining more and more social customers along the way, gaining brand and product awareness.