Business, Marketing, Strategy

Is Social Commerce a Bit of an Oversell?

Social Commerce has been a great talking point in the past couple of years. It’s based on a simple insight that marketers have known since kingdom come but haven’t been able to exploit – consumers don’t trust marketers, they trust friends and family. According to consumer research, 92% of consumers trust recommendations from people they know1 and 70% of consumers trust opinions of unknown users2. And more importantly, consumers today need relevant and timely recommendations. Social networks like Facebook and Twitter got the “social” in place by helping us connect but how could marketers induce us to spend by simply “being social”? It so happens that our conversations with friends often lead to an exchange of information and recommendations on brands and goods that can spur purchases.

The First Wave

Notice the increasing number of social sites designed around interests – fashion, DIY, crafts, photography, travel, dining, et al.? Pinterest, Polyvore, Tumblr and Foursquare are a few that come to mind. Twitter and Facebook also have some elements of social commerce. Several of these sites have a visual interface like the mood-board on Pinterest that users can pin images onto along with the Twitter-like Followers concept to “like” or endorse other users’ boards. The idea is that we as users express our interest in ideas, themes, or even consumer goods because we enjoy personalizing and sharing our interests as an extension of our selves. In short, sharing interests gets people talking and talking leads to purchases. Sure it all sounds dandy, but it’s not without a few hiccups. Allow me to explain

1. Interest does not necessarily equal Purchase Intent

Monetate-convOne interesting factoid about social commerce sites like Pinterest, Polyvore and the like is that they have a strong female user base (nearly 70-80%). Categories like apparel, fashion, crafts, cooking, etc. dominate interactions. Women spend a great deal of time online browsing e-commerce sites for apparel, shoes, travel, etc. but may purchase very infrequently. They might like to create inspiration boards filled with items they love but they might not be able to afford a lot of those things or might not even think they need to buy them.


This explains the data from Monetate’s Ecommerce Quarterly 2013 which compares conversion rates by referrer (source site where purchase process originated). Of course, some experts have suggested that most users may be loath to leave their interest-driven activities and get down to the drudgery of online shopping. Instead, they may use a search engine at a later date to purchase the item directly from the e-commerce site. Hence, these rates may be understated.

Though what’s exciting about social commerce is that average order values are fairly large, which means if the conversion rates could be improved, it could be highly effective

2. It won’t work equally well across all categories

There are two aspects of converting a social commerce interaction to a purchase: 1) The category of the good should be such that it stimulates social conversations and 2) The category of the good should be such that a recommendation is valued. In the offline channel, consumer research from a few years ago in the US stated the efficacy of word-of-mouth recommendations for various categories of goods as:
BIGResearchEating out and Apparel are two categories that have been explored in social commerce today because they satisfy the two conditions. But electronics, a high-involvement and expensive purchase which should rely heavily on recommendations, hardly figures on any social commerce site because it simply isn’t a topic that people discuss on a regular basis.

3. We are habituated to offline interactions

76% of Word-of-Mouth conversations take place in person3. The fact is that social commerce advocates expect that people will carry out social interactions online instead of or in addition to their offline interactions. That’s a big ask. Sure, some folks may give it a whirl for the sheer novelty of the online platform but the lack of sustained interest has felled many an exciting platform or app. Changing habits is not easy but not impossible either. Social commerce platforms need to cleverly convince users that it is worthwhile spending their time on online social interactions. The need gaps that social and e-commerce fill are networks and convenience – these should be unified to bring a truly valuable proposition for users.

4. Offer/coupon model does not ensure patronage

In a bid to build traction (in Groupon’s case, it’s the entire business model), most social commerce platforms tie up with merchants to offer deals and coupons, hoping that once users get a taste of the platform’s features, they’ll stay. It’s a problem similar to Facebook’s “Like” button on brand pages which research has shown is mostly clicked to avail of offers/deals. Once the offers dry up, so does the user base. Similarly, gamification strategies like those on Foursquare rarely build up any social incentives to sustain interest.

There’s no doubt that social commerce is here to stay. In theory, it’s brilliant. In its current format, however, the implementation is slightly lacking. Innovation in terms of balancing the social and commerce elements and clarifying the value propositions for consumers and businesses is much needed.

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The Predicament of Predictive Analytics

( wishes it readers a very Happy and Prosperous Diwali! May this Diwali bring in success, happiness and prosperity)

The news broke earlier this year. Target, the American discount retailer, was in the eye of a storm – a controversy involving teenage pregnancy, discount coupons and oddly enough, predictive analytics. Predictive Analytics is a burgeoning field in almost all businesses but lends itself especially well to retail. This is because it relies on Big Data to predict consumer buying patterns and electronic point-of-sales (POS) systems and loyalty programmes in retail stores have simplified data collection to a great extent. What Target did was apply the power of predictive analytics to unearth a potential gold-mine.

Bang On Target

Andrew Pole, a statistician employed by Target, hit upon the idea of identifying pregnant women among Target’s shoppers and sending them coupons and other promotional materials related to maternity.  He did this by first identifying pregnant customers from the baby shower gift registry – the American habit of letting guests known what to gift you on is a marketer’s manna from heaven. But this would only let Target get in at the third trimester – they wanted in early. So, they started running statistical tests on POS data and identified items that pregnant women would typically buy a lot of – iron, calcium, vitamins, minerals, shea butter and body creams, etc. Their program would come up with a pregnancy score for each customer based on the quantity of these purchases and Target would have a target (pun unintended) whom they could inundate with diaper discounts, promotions on nutritional supplements, etc. And it worked! Target sent coupons for baby items to a teenage girl whose parents figured out she was pregnant, all thanks to Target’s coupons. Since then, Target has wised up. Their promotions are much subtler; diaper coupons alongside cutlery or apparel promotions. Stealth is the new mantra.

What customers want

Clearly, there are benefits to knowing what your customers need before they know it themselves. It’s the Holy Grail for marketers. But how ethical is this practice? Customers don’t want to be mere puppets to a marketer’s actions, stealthy or otherwise. We like to believe that every small decision we make is our own and not influenced by others. Funnily enough, we have been prey to the subtle manipulations of marketers for years. Store layouts, shelf placements, print promotions – all goad us into making impulsive purchases. Predictive analytics, at least, point us in the right direction. We get what we need and in Target’s case, at good discounts. It may not be ethical in the true sense but it is mutually beneficial as long as consumers are aware of the practice.

View: Derren Brown turns the tables on Advertisers – Subliminal Advertising

An interesting aspect of predictive analytics is that it can be applied to existing customers whose data the store has access to, but it can also employ a self-learning algorithm which can detect new patterns. Customer buying patterns and the resulting segmentation can then be generalized to the larger population using discriminant demographics allowing retailers to better position products and target customers.

Should we be concerned?

The really worrisome part about predictive analytics is security. Personal information about one’s habits, purchases, demographic and financial information should not be stored away in a remote datacenter opening possibilities for a virtual attack. Certainly, some rules and regulations are necessary to ensure that the consumer’s right to privacy and security are protected at the highest level. As the field of predictive analytics grows with improved data storage and computing technology, expect to see new laws dictating how marketers can use consumer data.

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