Foursquare, Gowalla, Facebook Places and Yelp – among others – have popularized the concept of “checking in” at a particular location on a smartphone – basically broadcasting one’s location to friends, followers and/or business owners over a particular application or multiple social networks. Multiple variations on the theme exist, most notably with game mechanics, such as leaderboards, levels, badges, etc. that incentivize user engagement and social emulation.
The check in concept for consumer engagement has caught on like fire and start-ups are now racing to bring it to everything from websites – e.g. OneTrueFan and BadgeVille – to entertainment – e.g. Philo and Miso – to social shopping.
Social shopping experiments with check in break down in three categories:
- augmenting the shopping experience, such as Stickybits – that creates online forums for each scanned product where consumers can exchange.
Results from early pilots and studies in this field indicate that rewarding users for taking specific actions has been most successful at stimulating user engagement with the app and their partner brands and retailers. As a result, retailers are expected to shift an increasing share of their direct marketing budgets to these platforms, to the tune of $1.8 billion by 2015.
The lack of distribution arrangement with a tier-one network – in terms of subsidies, network compatibility and cross-channel distribution – kind of cornered the Nexus One into a rather small niche – early-adopter consumers, willing to buy the handset online, (most likely) at full price, and (initially at least) without the benefit of 3G for AT&T customers.
The web store sports a minimalistic design and look and feel, which does a great job at conveying sophistication and simplicity. However, it doesn’t provide the type of information that consumers are now expecting from any serious retailers, including customer testimonials and reviews. The Nexus One Youtube channel is equally disappointing, because Google could have use consumer generated videos to address the lack of physical retail presence. Finally, the limited set of accessories might have also curbed the Nexus One’s appeal, especially compared to the well-endowed iPhone.
Even though Google hyped the web-only distribution model as equally revolutionnary as the Nexus One handset, it now feels like Google was above all trying to showcase a compelling Android handset to accelerate Android’s trajectory rather than disrupt mobile distribution models.
personalized recommendations to help users discover new shows
Ad Tailor – a “like” button for ads
Finding shows on hulu was notoriously difficult – mostly parsing through an endless list with limited sorting options – thereby limiting cross-pollination and discovery. Any form of filtering – apparently based on previously watched shows – is indeed an improvement, even though quite a few shows I’ve already watched showed up.
Recommendations might feel like an afterthought to hulu, since exclusive content will always be a primary draw for users. Moreover, Netflix set a pretty high bar in terms of efforts and money spent developing their own recommendation engine. Nevertheless, hulu could step up its game a bit in this field and improve their chances at cross-pollinating audiences and fan bases. For instance, they could/ should encourage users to share their Netflix history and ratings. They could also add a Facebook “like” button to show pages and actual shows.
The Ad Tailor is an intriguing concept – akin to the Digg ads. There’s immediate value for hulu in increasing their ads targeting, by adding a direct feedback loop. There will be even more value for hulu if/when it starts tie user preferences about shows and movies with ads.
The barcode scanning app RedLaser has just announced that their iPhone/Android app was downloaded over 2 million times, with half of their users being active over the last month.
Overall, this success is an early confirmation of the potential for blending always-on Internet access with the brick-and-mortar shopping experience.
With twice as many users as Foursquare, the potential seems limitless from social shopping to real-time coupons, even though most retailers have yet to make the plunge.
Forrester’s Sucharita Mulpuru (@smulpuru) has a new research report out with interesting findings about emerging trends in mobile and multi-channel retail. Overall, the report confirms that the mobile web is on the cusp of becoming a game-changer for both web and brick-and-mortar retailers. Smartphone users are becoming as likely to make an online purchase on the go as they are to use their smartphones in the context of a brick-and-mortar purchase.
Competition is moving inside stores. A case in point is the Amazon App – an amazing iPhone app that enables users to take pictures of products they like to see if they could find them through Amazon and add them to their wishlist. With this app, Amazon will not only capture demand from consumers’ casual encounters with products they like, but also prey on shopping malls and department stores.
Price arbitrage is becoming much easier. Applications like ShopSavvy enables users to scan product bar codes to compare prices online and in nearby stores. Even without these apps, customers use their smartphones to make online purchases with in-store delivery if they find it more advantageous, only heightening cannibalizing within the same franchise.
Product and service information is becoming more accessible and more transparent. Two popular examples show that retailers’ traditional information advantage is further eroded, with relevant information being delivered in the hands of consumers while they shop. The GoodGuide iPhone app, for instance, delivers ratings about green, healthy and organic products right to your pocket and while you shop. The Yelp iPhone app delivers location-based user reviews and pictures of shops and services.
The advent of the mobile web ushers new challenges and opportunities for retailers. Empowering their sales force with loyalty program offerings and/or some form of price-matching latitude could help retailers tactically meet these challenges in the short run. Retailers should also consider the upside in mobile interactions, from delivering personalized, social or value-added information to sending location-based time-bound coupons to mobile users.
Amazon’s results this week were in sharp contrast with the overall health of the retail sector. As highlighted during the holiday seasons, Amazon ruthlessly grabbed market share in an overall down retail market and turned a respectable profit on its very solid execution.
Amazon’s success builds on
- a strong brand that keeps customer acquisition costs low;
- aggressive pricing that kept the brand at the forefront of many shopping comparison engines; and
- an experience that nurtures superior customer loyalty throughout the entire shopping cycle, from personalized recommendations to customer reviews.
Amazon’s success puts increased pressure on competitors to step up, at a time when most online retailers are in survival rather than investment mode. Retailers actively should be looking at three crucial areas to grow their top line in this adverse environment.
Build an online shopping experience that fosters discovery
Retailers have to better capture consumers mind before they have actually made their mind on a particular product. Google delivers very precise results when users search for an exact product – bringing competitors one click away from each others, with price becoming the only differentiator. Google is however inefficient at helping users shop a product category, such as a camera or clothes.
Retailers have to rethink how they can leverage their expertise to build a strong funnel from discovery to conversion. Zappos has just released a new way of exploring their catalog that enables customers to intuitively compare and explore shoes and accessories based on colors and shape. Distilled Clothing showcases its collection with an arresting use of videos that keeps the viewer engaged and wanting more.
Expand your reach online and offline
BestBuy has been experimenting for quite some time to expand its reach online and offline. Last year, it started supporting microformats on its website to help build a critical mass of products to tag with Giftag – their social wishlist browser plugin and service. Giftag follows Amazon’s path of helping users share their wish list of products they want to buy, while keeping its finger on the pulse of consumer trends.
With mobile internet becoming pervasive, offline and online shopping are converging. Users are increasingly using their mobile devices to arbitrage between online and offline channels. Multi-channel retailers should however seize on this opportunity to compete with Amazon. Remix – BestBuy’s combined API and affiliate program – does just that. It enables developers to build applications on top of both product catalog, as well as online and offline store inventory.
Use recommendation to build long-lasting customer loyalty
We’ve discussed the opportunities and challenges of online recommendation in numerous earlier posts. The current economic climate only heightens the need for retailers to nurture their customer relationship and improve on customer retention.
As previously highlighted, delivering a personalized online experience remains particularly difficult to achieve, because it requires mastering multiple technologies – user segmentation, content targeting, recommendation, website optimization – as well as aggregating data across online marketing channels and overcoming internal resistance. Those are reasons however why personalization remains a key source of competitive advantage, as it keeps customers engaged and helps strengthen customer loyalty.
Apple has quietly released a few changes to its iPhone App Store on iTunes, in an attempt to alleviate some of the growing pains around its app ecosystem. The App Store has recently hit 10,000 apps and is expected to accept many more.
“Most popular” lists and 19 high-level categories are hardly up to the task of helping consumers find new apps. Limited discovery is hampering the App Store’s growth and monetization and could have a depressing effect on the ecosystem.
There are however quite a few opportunities for Apple to improve discovery and bring the overall shopping experience on the App Store away from simply being a “port” of the iTunes Musicstore:
- Improve discovery through experience
- Enable free trials – free trials are critical to bring feature-rich and higher priced apps to the ecosystem.
- Add videos and user video reviews – text and screenshots do little justice to an app’s capabilities, design, and overall user experience;
- Improve discovery through richer feedback and needs analysis
- Categories and search are limited tools for demand generation;
- Amazon-style recommendations and discovery needs to be more ubiquitous and prominent on the App Store to enable genuine comparison between apps;
- Blending the feedback process about apps much with the user experience on the iPhone could open up a much more personalized experience
- Improve discovery through finer segmentation and filtering – the free vs. paid segmentation is dragging all paid iPhone apps down in the ringtones pricing range; there are only 14 apps over $100 (out of 10,000) and there are still relatively few games at a price comparable to that of a typical game console.
For the second year, Netflix has awarded a $50K progress prize. The winning team – BellKor in BigChaos – improved on Netflix’s recommendation algorithm by 9.44%. Although the 10% improvement seems to be getting closer in absolute terms, the last stretch might prove elusive for a little while longer.
Is Netflix’s ground-breaking crowdsourcing approach reaching a limit? Netflix has undeniably got a lot of value out of their contest – 10,000s people working and an algorithmic improvement very close to 10% – for only $100K so far. However, the winning method this year – and the likely research directions for next year – have exponential data and computing power requirements – possibly putting the contest out of reach of most casual researchers.
Are “curated” recommendations superior to pure statistics? Curated recommendations have received some hype lately – from Techmeme’s hiring of an editor to the coincidental launch this week of ClerkDogs – a “clerks-in-a-box” online movie recommendation service? A human touch can certainly rebalance or add a forward-looking perspective to recommendations where data sets are ambiguous (such as clickstream), too small, or lack enough historical data.
Gabe Rivera of Techmeme fame has sparked a lively debate as he announced he had hired an editor to improve interestingness, reactivity and relevance of his news aggregation website (see VentureBeat, TechCrunch, and ReadWriteWeb). Rivera’s plight echoes Netflix’s challenges at boosting its movie recommendation engine, as algorithmic improvements gradually near their natural asymptote.
Techmeme is good at aggregating news overtime but not at breaking them, pointing to several common datamining and recommendation challenges:
- “Cold start” – interestingly digg’s social voting approach hasn’t been able to overcome that challenge either – in both cases, the services cannot anticipate news’ propagation and velocity;
- Context – Techmeme sometimes mixes up headlines and for instance ended up featuring news about Anna Nicole Smith’s hospitalization after she’s already been declared dead;
- Outliers or the “Napoleon Dynamite” problem, as the New York Times dubs it – identifying newsworthy pieces from uncommon sources before they make it into the mainstream is also an issue.
Interestingness and relevance are Techmeme’s other key reasons to bring in a human eye. Techmeme bets that an expert hand can be a better judge than crowdsourced implicit feedback based on clickstream or explicit feedback such as social voting. This approach seems to contradict much of the crowdsourcing mantra, although Techmeme’s case is more about rebalancing than shunning crowdsourcing.
For online retailers, Techmeme’s move to “curated news aggregation” highlights opportunities to blend human input, datamining, and recommendation:
- to add context to a product recommendation – based on usage, audience background, as well as internal needs through promotions;
- to identify new and unexplored relationships between products – for product discovery, up-sell, and outliers.< >< ><-->