The Future is Here: 5 Ways Virtual Reality is Revolutionizing Online Shopping

The Future is Here: 5 Ways Virtual Reality is Revolutionizing Online Shopping

Facebook made headlines when it made a $2 billion purchase of VR pioneer Oculus in 2014. The social media giant’s move signified a huge transformation for gaming and mobile devices. Its Focus 360 technology (thanks to in-house, proprietary Oculus tech) enables users to enjoy a 360-degree panoramic experience without using a headset. Now that we’re closer to VR experiences becoming a day-to-day reality, the question lingers about how virtual reality will improve the online shopping experience.

1. Hardware is Getting Cheaper

While not everyone is clamoring to buy a Samsung Gear VR headset, Google Cardboard is proving there is an appetite for a cheap, essentially disposable viewer. Amazon has been in the hardware business for some time, and it’s not unlikely that they could develop a proprietary headset that works exclusively with their store. Alibaba, the e-commerce giant, successfully launched a VR shopping headset in 2016 for Singles Day in China, the country’s largest online shopping day of the year. Other major e-tailers could easily rush to compete to give shoppers the most in-person like shopping experience from the convenience of their mobile devices.

2. Try it On

If you’ve never purchased clothing from a brand, and aren’t familiar with how its sizing fits you, buying clothing online presents some risk. Major retailers like Zappos and Nordstrom make their online shopping experiences more appealing by offering free shipping for returns, mitigating the risk that a new pair of boots or a coat doesn’t fit quite like you thought it would. Imagine, though, slipping on a pair of goggles and actually trying on a new garment or accessory.

It’s not out of the question to think that consumers would be motivated to use a similar easy-to-deploy tech for trying on everything from lingerie to a pair of jeans. That future isn’t as far away as you may think. In 2015, Underside, a Belgian app developer, created an app that enabled you to try on the Apple Watch. The technology wasn’t difficult to implement: cut out a piece of paper, affix it to your wrist and use the iPhone’s camera (and the AR Watch app) to view the watch on your wrist. The Gap is already letting shoppers play with AR dressing rooms.

3. Looking Around

Physical expansion is costly, and retailers can’t possibly open a location in small markets where a limited customer base doesn’t guarantee the volume they need. So, before Zara or luxury brands expand into a new city, they’re more likely to invest in virtual shopping experiences. Imagine touring a store, touching the merchandise, and enjoying the relaxing and fun experience of looking around. Virtual reality shopping won’t always replace the excitement of the in-person experience, but if the closest Chanel store is hundreds of miles away, this may be your only way to examine a $3,000 purse.

4. Using Virtual Reality Online for Large Purchases

It’s typically very difficult to experience a new sofa, dining table set, or other household goods when you’re looking online. It’s equally challenging to picture a sofa that you do see in person in your house. A future where you can virtually place that large piece of furniture in your home is not far away. VR and AR developers are working hard and the race is on between companies like Oculus and Magic Leap to solve these big problems for big purchases. For instance, the Ikea app already has AR features that shoppers can use on Android and iOS.

5. Booking a Vacation

Photos and videos already do a pretty good job of capturing the essence of sitting beach-side at a five-star resort. Travel agencies, hotels, and travel writers will soon deploy AR and VR to make it even easier for you to book your next vacation. This landscape is shifting constantly, but it’s probably only a matter of time before you can click onto a hotel website or the visitor’s bureau for a country or landmark and feel like you’re literally standing next to a volcano or sipping cocktails on the sand. There are a host of apps right now that are pushing the technology along, albeit with mixed results, but as the hardware improves and gets cheaper, we anticipate that the tourism industry will exploit augmented and virtual reality to improve online browsing for potential guests.

Is your content future proof? Is your team prepared to use virtual reality in your user’s online shopping experience? We’re here to help you plug up any holes in your content quality so you’re ready for anything.

5 Steps to Build a Successful Customer Behavior Analytics Model

5 Steps to Build a Successful Customer Behavior Analytics Model

There isn’t a marketer on earth who would turn down the ability to look into the future. What brand manager wouldn’t want to know exactly what and how much of a new technology or a hot new gadget customer will buy in droves next holiday season? Predictive analytics is a relatively new tactic and has been met with mixed reviews and success. Executing it properly requires a hefty amount of dedicated resources and a fair amount of experimentation. Going through these exercises, though, can certainly give you some interesting insights into how well you can leverage customer behavior analytics to facilitate business growth.

What is Predictive Analytics?

Predictive analytics has been around for a long time in several industries, including insurance. In that model, actuaries use historical data to predict certain outcomes and apply a cost to risk assessment. So, for automotive insurance, a driver’s history factors into the likely cost of potential accidents, driving individual policy premiums up. A policyholder with safe driving record typically receives an incentive (or discount) as a reward for good behavior.

Using customer behavior analytics to predict buying patterns, prevent fraud and make other business decisions is a relatively new development. With advancements in machine learning and more insightful data, companies are starting to develop methodologies to make educated, scientific guesses as to what their customers are likely to do in the next buying season. Developing your own analytical framework is resource-intensive and will garner mixed results, but the process, if deployed correctly, is informative and educational.

1. Use Regression Models

You can’t guess what someone might do if you don’t study what they’ve already done. There is a lot of experimentation that goes into content marketing. Start with buying history. Look at responses from your previous campaigns and organize them. Some items you should study include:

  • Number of items purchased
  • Total purchase amounts
  • Discount codes
  • Purchase dates

If you’re looking for more information about the general use of regression analysis, which is used across the spectrum from science to education, check out this piece from the Harvard Business Journal.

2. Segment Customers into Groups

Now that you’re on your way to building up your historical data, it’s time to merge that with some customer segmentation. The more refined you can make these groups, the more you can stop guessing what customers want and simply observe what they do. Customer observation is at the heart of all good customer experience marketing. If you have a strong statistics team, use them to integrate one of the many accepted statistical models for segmentation. If not, building those groups manually may take time, but it could also still be worthwhile especially if you segment by:

  • Demographics (age, region, income)
  • Buying history (x purchases within the last x months)
  • Responsive behavior (referrals from social media, newsletters, email campaigns, etc.)

Amazon Prime Customer Segmentation

Before you begin, understand that statistical modeling is complex and requires active maintenance. However, the better you and your team get at it, the more sophisticated your marketing becomes down the line.

3. Deploy a Smart Suggestion Engine

Amazon mastered the suggestion engine early on and owes a huge amount of its success to this mastery. If you don’t have the resources in-house to build your own, there are plenty of enterprise solutions that use actual machine learning to make smarter suggestions to your buyers using customer behavior analytics and buying history. The advantage of a third party engine is that you don’t have to maintain or update it. The disadvantage is that you don’t own it outright; you may stumble into some interesting and useful IP if you DIY an in-house engine. In any case, if you’re selling goods online, you need a recommendation driver. So do your homework and figure out which solution works best for you and your audience.

4. Build Slowly

The good news about customer behavior analysis is that the industry is getting better and better at data collection. The downside: there is an almost unlimited amount of data to sort through. So before you throw everything into one pot and overwhelm your marketing team, try adding one to two factors at a time, experiment, and then add another factor. Predictive analytics for marketing purposes is still a developing field. You’ll need plenty of flexibility to integrate real-time data as you go. Doing so will make the process more exciting and engaging for your team.

5. Stop Making Assumptions

Let this process educate you. If (or more likely, when) you learn the data you used to collect isn’t proving to be as useful as you assumed it was, stop collecting it. For example, data researchers used to bank all of their messaging predictions on bounce rates. Today, most marketers tend to ignore that statistic. The Predictive analysis should stop you from guessing, not encourage you to guess more. As soon as you start to see new patterns emerge, respect the data and let it push you into areas where you never thought you’d go.

The most important takeaway in all of the marketing is this: data never lies. Data and customer behavior analytics paradigms should become the drivers of all of your product development and marketing tactics. It’s never a perfect science, but it is a science. How well you use it can determine the difference between repeated successes or disappointment.

Toys R Us Failed. Here Are the Main Reasons Why

Toys R Us Failed. Here Are the Main Reasons Why

When Toys R Us announced plans to shutter its U.K. and U.S. locations, the retail industry was buzzing with speculation over why the one-time toy heavyweight had failed after nearly seven decades in operation. While years of declining sales and mounting debt tell much of the story, a series of retail follies and missteps paint a clearer picture of what happened to the former retail giant. Here we outline how Toys R Us’ failure to adapt to changing consumer behavior, innovate its business model, and incorporate technology into the user experience ultimately led to its demise.

Toys R Us ceased to be the “experience” it was once known for.

At eTail West and ShopTalk, two of retail’s recent industry gatherings, experts highlighted how consumers seek shopping trips that are experiential. For example, when you enter a car dealership, you want to test drive a car – it’s part of the car buying experience. Similarly, Toys R Us was known for providing that special you-had-to-be-there shopping experience. “It was ceiling-to-floor toys. It was a destination,” retail analyst Kate Hardcastle said in a February interview with BBC. Recent years, however, have been defined by a surplus of inventory, sloppy shelves, fewer special events, and near-nonexistent customer service. “Today, a trip to Toys R Us has been characterized as lacking in inspiration,” Hardcastle said. Basically, Toys R Us became old and nostalgic rather than the cool place to go. Greg Portell of retail consultancy A.T. Kearney added that a breadth of inventory means nothing if you don’t have someone to help you experience it. “It’s hard to sell toys in a cold, warehouse environment,” he said.

Failure to innovate allowed competitors to step up.

Now that its stores no longer put the customer experience front and center, Toys R Us was left to compete on price alone. This didn’t mesh well with the business model that had made the company a “category killer,” meaning it specialized in one type of merchandise, making it the dominant retailer in that category. The fact is, relying exclusively on toys for profit allowed large competitors like WalMart and Target to offer the same products at a better price. In the toy business, brand loyalty is to the manufacturer, not the supplier, so when competitors priced toys at low-margins or as loss-leaders during the Holiday shopping season and offered aggressive online shipping options, Toys R Us was left unable to compete. The inability to adjust to a big market shift to ecommerce also left Toys R Us vulnerable to Amazon’s growth. While all retailers felt the impact of Amazon’s presence, Toys R Us took the brunt of it, lacking the resources to fight the traditional discount and dollar brick-and-mortar retailers. Without a major online presence, they were squeezed out of the market.

Too little, too late to introduce new technologies.

Toys R Us’ inability to innovate also spilled over to new technologies. In a world where kids can use a mobile app to distort their face or make them a superhero, Toys R Us’ response was to create one new aisle. Basically, it didn’t adapt to new technologies, it just included them as part of the regular store. Denise Dahlhoff, research director at Wharton’s Jay H. Baker Retailing Center, went further to point out competitors like Build-A-Bear that were able to adapt to the changing times. The company offered the ability to take a bear that you built online and bathe it in a virtual tub, Dahlhoff said. “It was just more interactive. You could pick your own customized sound for the bear.” In the end, a series of organizational gaffes and failures led to the collapse of a company once synonymous with the concept of “play.” It’s important to note that no one factor is to blame, but rather a cascade of causes from not adjusting to the constantly changing retail market to not incorporating key technological advancements into the user experience. In a recent piece for Forbes, retail guru Steven Dennis countered the commonly held notion that “physical retail is dead” by stating that no, in fact “boring retail is dead.” So was the fate of Toys R Us.