MARKETING/SOCIAL MEDIA

Olapic becomes marketing partner of major social network

BY Dan Berthiaume

Visual marketing platform provider Olapic, which turns consumer-generated photos and videos into brand assets for use in marketing, is teaming up with a major social network.

Olapic is now part of the Facebook Marketing Partner Program.

As a partner, Olapic will help brands globally collect, curate and manage original consumer imagery, or earned content, in advertising and marketing efforts on the social platform. Olapic is able to service the needs of global enterprise brands that are looking to advertise on Facebook by using earned content and decreasing the costs associated with generating fresh creative assets.

Olapic is also working with brands and retailers on using earned content in campaigns for the new Instagram Ads API. Both Instagram and Facebook ads can be launched directly from the Olapic dashboard to streamline the process.

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Tech Guest Viewpoint: Avoiding Time-Series Demand Forecasting

BY Ron Menich

Silicon Valley’s Winchester House confounds visitors from around the world. An eccentric heiress spent decades adding endless rooms and hallways, doors that lead to nowhere, and random structural additions — turning a home into an inscrutable, imposing and meandering oddity.

It’s a prime example of add-ons gone wild — and a good analogy for the state of retail forecasting today. Many of today’s retail forecasting systems have been added to and tweaked over decades until they have become imposing and complex, leading to a burdensome, difficult to maintain and a vastly different expression of their original intent — to provide retailers with accurate forecasts that lead to greater insight on the drivers of consumer demand, improved sell-through, successful promotions, and greater profitability.

The time-series forecasting methods still used by most retailers today date back to the early 1960’s. Over time, generations of developers added here and tweaked there, and today the science that data analysts rely on is often older than they are. Despite decades of effort, the cost exacted on retailers is sobering: IHL Group estimates that retailers worldwide lose $220 billion annually due to inaccurate forecasts.

The time-series model hinders accurate forecasting in several ways:

• Time-series forecasts degrade over time. As scientists add on and manipulate these traditional forecasts, the models can exponentially degrade and become increasingly inaccurate and bulky. As a result, it becomes difficult to harvest high-performance output without requiring a great deal of data scientist input, turning an automated process into one that’s highly manual.

• Traditional forecasting methods are unsuited for new products or stores. Time-series forecasting looks into the past and projects into the future, so what do retailers do with new products and stores? Because so many retailers are centered on a time-series approach, they’ve adopted a “like SKU” or “like store” tactic in an attempt to identify the history of a similar past SKU or for other markets, but what happens when you introduce a completely new category? or a new set of attributes? or enter a new market? or when (inevitably) fickle consumers change? Time-series methods just don’t cut it, while machine learning-based forecasters that ask for a forecast on a new product, get it — naturally and with no need for specific algorithms or user input.

• Outdated time-series models constrict retailers’ abilities to forecast at a granular level. Situations such as new stores or SKUs, complex promotions and regional events are highly relevant, frequent in general yet infrequent in terms of specific scenarios, and are beyond the abilities of a time-series based model. Retailers that want to shape demand and accurately forecast promotional activity or do so at a local market level can expect a cumbersome output that requires their data scientists to spend time manipulating the inputs and outputs, with consistently poor results.

Clearly, it’s time to tear down time-series forecasting, and make room for a new paradigm to take its place. Machine learning technology, the modern underpinning of the innovations used by Netflix, Amazon and Google to understand their customers at great depths — has the power to transform demand forecasting in retail by setting better prices and creating better promotions.

Machine learning uses causal factors such as product descriptions, competitors, promotions, regional events, and trending styles and colors to determine exact demand drivers based on the many attributes involved, for every possible SKU-store combination. And if an attribute provides no value or inherent impact on demand, the machine learning forecast models are smart enough to discard it where it is irrelevant, and to use it when it offers value in a different situation. There is no such thing as too many factors with machine learning models.

Don’t let your retail enterprise get lost in its own Winchester House maze. Machine learning-based forecasting delivers a new and better “house” — where they can understand and act on the true drivers of demand and enter the 21st century of forecasts with accurate context that drives specific and actionable insights.



Ron Menich is Ph.D., EVP and Chief Scientist, Predictix.

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Consumers Want it Now — But When Will Retailers Deliver?

BY Farla Efros

Today’s consumer is driven by an “I want it now” mentality, yet retailers are still not prepared to deliver. Reducing the time it takes an order to arrive at a customer’s home is every retailer’s objective, but while quicker fulfillment makes customers happy, it comes at a cost.

Although retailers are working hard to transform their supply chains to provide consumers the same ease, convenience and value regardless of channel (mobile, online, or in-store), 80% of merchants are not prepared for the changes required to implement a customer-centric, omnichannel model. This finding was revealed in the HRC Advisory’s 2015 Supply Chain Transformation study. The recent report also identified the challenges presented to retailers by their pure-play e-commerce counterparts, expensive online returns, and cannibalization of in-store sales.

So how can retailers be better prepared to enable the transformations required?

Strengthen Supply Chain & Fulfillment Capabilities
Retailers currently lack the capabilities necessary to compete with their pure-play e-commerce counterparts, as only 35% of those surveyed had online capabilities such as vendor drop-ship, or order in-store and deliver to the customer. Meanwhile, their e-commerce competitors have spent years investing heavily in these supply-chain capabilities. Recent announcements of same-day, and even one-hour shipping, in addition to free shipping (e.g., Amazon Prime), are applying further pressure to conventional retailers. These competitive shipping offers, are forcing traditional retailers not only to set up fulfillment centers in order to compete, but also to re-configure their supply chains in order to service this new model. Wal-Mart’s announcement of plans to spend between $1.2 billion and $1.5 billion this year on global e-commerce efforts, amid ramped up competition from Amazon, is indicative of the urgent challenge brick-and-mortar retailers are facing to catch-up.

Reduce Returns of Online Purchases with Ship to Store
Ninety-five percent of retailers acknowledge that their biggest hurdle in transforming the supply chain is how to mitigate online returns, which can run as high as 30%. Even returns to a fulfillment center or direct to a supplier incur incremental freight costs, the risk of shipping-related product damage, and a lost opportunity for a replacement sale in-store.

One solution, which can help to drastically reduce the rate of returns overall, is to have online purchases shipped to an actual store for pickup. When a consumer comes into the store to pick-up their product, they are likely to touch and feel the fabric, try the garment on to test size and fit, or examine how the product actually works, all of which can reduce return rates to a more acceptable 15%.

Shipping to store can be advantageous for both the retailer and consumer. Consumers save on shipping costs they may incur by having the product delivered to home (plus additional costs of return shipping, should they decide against keeping the item). Retailers enjoy the benefit of increased store traffic, and the likelihood that once captured in-store, a shopper is more likely to make additional purchases. A win-win for all.

Leverage E-Commerce Sales In-Store.
Seventy-five percent of the retailers surveyed indicated that their e-commerce sales are cannibalizing sales that would have otherwise been made in stores. And while e-commerce sales growth rates are often 10%-15% greater than physical store growth rates, nearly three-quarters (70%) of retailers surveyed said they are still struggling to develop a profitable economic business model for e-commerce while simultaneously maintaining acceptable store profitability.

While there is no simple remedy, retailers can begin by leveraging e-commerce sales in the physical store. We’re already seeing several smart retailers using kiosks as a way to sell merchandise not offered or available in the store. This strategy allows the retailer to free up cash flow and space by maintaining less inventory in the store, while still offering the full online assortment to their brick-and-mortar shoppers.

Update Integration Between Physical Stores and E-Commerce Fulfillment.
In order to successfully accomplish any of the above, retailers need better integration between the physical store network and e-commerce fulfillment. This integration has proven to be the best way of providing better inventory availability, increased gross margin, reduced shipping/fulfillment costs, lower return rates and increased foot traffic in stores However, more than half (52%) of the retailers surveyed admitted that they do not have the systems or processes in place to provide the required visibility to accurate inventory on hand in each store.

Eighty percent of the retailers surveyed identified inventory visibility and accurate assortment planning between online and physical channels to be the top-two challenges in enabling fulfillment capabilities. In fact, only half of the retailers surveyed (53%) are currently able to present customers with accurate inventory information and to fulfill the entire order at the time of online purchase. Further, only half of the retailers are able to ensure fulfillment from the closest location, when an item is available in multiple locations and distribution centers. To be most cost-effective and efficient, retailers need to be able to identify and present all inventory from store distribution centers, e-commerce fulfillment centers and physical stores as a single shared inventory pool.

Despite the working capital and operating cost challenges of funding, storing and distributing inventory for each channel in separate distribution facilities, 55% of the retailers continue to have dedicated fulfillment facilities for each channel, and only 25% of these retailers are launching initiatives to combine these facilities in order to serve both channels more cost-effectively and optimize their working capital investments in inventory.

With online and omnichannel customer demand growing rapidly, extremely high shipping costs and high return rates, retailers are working hard to transform inventory processes from the traditional inventory push model to a responsive, customer-centric model that provides flexible purchase and return options to the customer, and allows full access to all inventory across the system.

The primary challenge for retailers is to ensure they maintain a consistent, high service levels for their customers regardless of the channel in which they shop, pick up orders or return goods, while maintaining profitability during this time of major transition.



Farla Efros is president of HRC Advisory, a leading strategic retail advisory firm focused on increasing retail profitability.

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