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Tech Guest Viewpoint: 2014 — The Year of Hyperlocal Mobile Advertising

BY CSA STAFF

By Jeremy Geiger, Retailigence

The headline is bold, but yes, 2014 is the year of hyperlocal mobile advertising, and there are three very important trends converging today to make this happen.

The first is smartphone proliferation; according to eMarketer, smartphone users worldwide will total nearly 2 billion in 2014.

The second trend is the current boom in consumer adoption (and increasing comfort level) of location-aware apps. A recent PuchTab survey of more than 1,000 consumers revealed 88% of participants said they would share their locations for coupons and offers; 72% would for shorter checkout times; and 69%, would for targeted alerts about sales and products they like. And finally, Ratko Vidakovic, VP of product and marketing at SiteScout, sums up the third trend, programmatic advertising, nicely:

“When you combine this [hyperlocal ads] with the ability to purchase ad impressions individually, through programmatic ad platforms that are powered by real-time bidding, marketers now have a cost-effective way to engage their audiences in a manner that matches ads to the context of their physical location.”

We are already starting to see the power of this huge opportunity. Here’s a brief example. As many of us experienced, severe winter weather had a huge impact on the economy this year. Freezing temperatures and mounting snow deterred shoppers from stepping outdoors. Many brick-and-mortar stores experienced steep traffic and sales declines.

But as soon as the snow hit the ground, one of our partners–a multinational department store chain–knew it needed a way to help guide its shoppers to items they needed during the storms.

Well acquainted with the happy convergence of the trends above and armed with a powerful database of local retail inventory and pricing data, the department store dynamically targeted mobile consumers in blanketed areas in real time with location-based product messages related to snow. The store promoted (1) snow tires, (2) car batteries, and (3) snow blowers, and allowed the shopper to instantly see which products were actually in stock in these three categories at their nearest store. For added convenience, the store also offered shoppers the ability to Buy Online and Pick-Up In Store (BOPIS), and had those items available for pickup within 15 minutes so shoppers could quickly head back indoors.

With hyperlocal ads and local inventory data, the store was not only able to increase foot traffic and drive in-store sales, but was also able to reach its customers at a challenging time with relevant information about the products they needed in that moment and where to find them nearby. Today, shoppers not only want to know where they can buy what they want, but also want product and store information readily available.

According to a book from technology experts Robert Scoble and Shel Israel, the “Age of Context” encompasses the convergence of five major technology trends: mobile, social, local, wearable, and Big Data. Together, these trends would create personalized, predictive and assistive products. As depicted in the snowstorm example above, one unquestionable element driving the “Age of Context” is hyperlocal advertising, and 2014 is the year brands and retailers are taking advantage of this massive opportunity to help them better engage with their customers and grow their bottom line.

Jeremy Geiger is CEO of Retailigence.

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TechBytes: CurrentC — Two Pros and Three Cons

BY Dan Berthiaume

CurrentC, the mobile payment service under development from a retailer consortium known as Merchant Customer Exchange, or MCX, has been getting a lot of attention in the past week. First MCX members CVS and Rite Aid disabled their NFC payment systems to block the rival ApplePay mobile payment service, and then hackers stole the emails of CurrentC users. And despite all this publicity, CurrentC is still in pilot mode.

Nevertheless, now that CurrentC is a topic of mainstream news discussion, it’s time to look at the pros and cons of CurrentC as a viable mobile payment option for retailers. As of now, the cons outnumber the pros, but let’s start on the positive side of things.

Pro #1: Major retailers are involved

MCX is hardly some ragtag bunch of mom-and-pop stores. In addition to CVS and Rite Aid, other members include Wal-Mart, Best Buy and Target. As exemplified by CVS and Rite Aid shutting down NFC entirely, many of them are purposely not accepting ApplePay (more on Apple shortly), leaving a sizable number of highly trafficked stores open for an alternative.

Pro #2: Retailers are more involved in technology development

Retailers are much more actively involved in the development of technology than they used to be. MCX members like Wal-Mart and Target operate their own Silicon Valley development labs, and whole IT companies like Springboard Retail have evolved from proprietary in-house efforts of different retailers. Retailers are no longer strictly end users in the IT game, but are turning into sophisticated providers as well. Thus a retailer-driven IT effort like CurrentC has more plausible chances of success today than it would have even a few years ago.

Con #1: Apple

There’s no getting around it–even Wal-Mart cannot take the prospect of directly competing with Apple lightly. ApplePay has a huge built-in user base and benefits from Apple’s design and development expertise, which still surpasses that of any retailer (or arguably any IT provider). Offering an alternative to a service provided by Apple, which is also supported by the major payment card providers, is a big step.

Con #2: Complex Transactions

Making a purchase with CurrentC is much more complex than the tap and pay procedure offered by ApplePay. CurrentC users must open the CurrentC app, scan a QR code, and display a paycode to the cashier for approval. CurrentC does offer some convenient features, such as redeeming exclusive offers, programs and coupons automatically, earning instant loyalty rewards and points, and having the option to pay with checking accounts and store gift cards as well as select debit and credit cards. However, perpetually rushed consumers want a checkout process that is faster than a traditional POS transaction, not slower.

Con #3: Security

CurrentC has already experienced a security breach, with hackers reportedly gaining access to user email addresses. CurrentC requires users to provide personal data including driver's license number, Social Security number, and date of birth. While security protocols include a four-digit passcode, a secure unique paycode attached to every purchase, and storage of information on an encrypted cloud rather than on the phone, having a breach while still in pilot mode is not the best way to gain widespread media exposure. MCX also says it will share some user data for marketing purposes, which may give consumers pause.

CurrentC is scheduled to go into full rollout mode early next year. There are no guarantees of its long-term success, but it should hardly be counted out, either. ApplePay is clearly here to stay, but there may be room for more than one player on the automated mobile payment court.

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Tech Guest Viewpoint: How to Counterbalance Instinct with Data

BY CSA STAFF

By John Bible, Oracle

When we began applying optimization technology to retail, the industry had lots of data, but only a little science. Retailers collected massive amounts of data, about products, customers, prices, promotions and a host of other measurable things and events. But as a rule, retailers are still in the early stages of using their data in truly scientific ways – ways that will make pricing more effective and assortments more appealing. There remains a significant opportunity to turn rich troves of data into dollars by better demand measurement and management.

Why is the need for applying science to retail decision-making so urgent? Because traditional brick-and-mortar multi-channel retailers have online competitors ruled by data scientists who define retail as a data mining and optimization problem. Internet-only companies are data-driven in ways that traditional retailers simply haven’t experienced.

The Science of Pricing

In the area of pricing, for example, the sheer number and range of price changes performed every day by Amazon.com are clearly automated responses to predicted customer demand patterns. For example, if more people are shopping during their lunch hours, it can make sense to raise prices for just those middle two hours of the day.

Today’s optimization solutions aim to address this imbalance by injecting innovation and true productized science into the array of mission-critical retail solutions. Key tools delivering proven value include:

• Customer analytics and market basket analysis

• Price optimization

• Enterprise-level clustering

• Demand forecasting

• Category planning and assortment optimization

• Size profile and pre-pack optimization

• Promotion management and optimization, supported by forecasting, customer segmentation, price and promotion effects

Removing Emotional Bias from Pricing Decisions

One of the clearest illustrations of the value that applying science brings to retail is in the area of pricing. Many retailers have already made important strides in this area, deploying sophisticated markdown optimization and pricing solutions designed to maximize margins throughout a product’s entire lifecycle. In fact, lifecycle pricing, which takes a pre-planned, holistic approach to products with limited lifecycles, such as fashion apparel, has proven far more valuable than a more piecemeal approach.

How the Science Works

Price optimization tools are also useful in removing the emotional component of pricing decisions and revealing the biases of those making those decisions. If a buyer has “bet” on the sales prospects of a sweater in a particular style, and those prospects remain unfulfilled at the end of the sales season, that buyer might be embarrassed for having advocated for a product that didn’t perform. The buyer would be likely to resist marking down the item’s price, even though that is clearly the optimal decision to make.

The need for art and a “gut feel” will never go away in retail, but science-based solutions provide a ruthless (but necessary) counterbalance. And because these solutions’ algorithms are based on data mining multiple years of the retailer’s own sales and customer history, they can confidently recommend that a 50% price cut will be needed, given the current inventory position and projected demand derived from sophisticated causal models.

The granular data mining capabilities of these solutions allows them to explore every facet of price elasticity, taking into account every aspect of the merchandise hierarchy, the product’s attributes, along with regional and geographic variations. Even with new products that have no history, the algorithms can examine the performance of similar products to discover the patterns needed to support initial pricing decisions and to chart a likely model for lifecycle pricing.

Price is just one critical lever for stimulating demand that retailers can use in today’s hyper-competitive marketplace. By embedding optimization technology in retail processes, retailers can use levers to turn the data they already gather into the demand they desperately need.

John Bible is senior director of retail data science and insight for Oracle.

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