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Blog, eCommerce

The hyper-competitiveness of the e-commerce industry has turned it into one of the biggest drivers of technology innovation. Today, the need for differentiation and the pressure to discount is driving e-commerce companies to look more closely at big data innovations such as predictive and prescriptive analytics, as well as artificial intelligence solutions, to remain competitive. The challenge lies in vast amounts of data. Big data is providing the opportunity for […]

The hyper-competitiveness of the e-commerce industry has turned it into one of the biggest drivers of technology innovation. Today, the need for differentiation and the pressure to discount is driving e-commerce companies to look more closely at big data innovations such as predictive and prescriptive analytics, as well as artificial intelligence solutions, to remain competitive.

The challenge lies in vast amounts of data. Big data is providing the opportunity for innovation, but its abundance and unstructured nature are inhibiting progress. To be data-driven, you first need to make sense of the data. For machines to read data, it first needs to be in a structured format. Human computing is unlocking opportunities hidden within a fog of data.

Big Data as a Driving Force in Innovation

Some of the most important innovations that have revolutionized the e-commerce industry have come from big data. eBay, a global leader in personalization, is a great example. Big data serves as a tool to drive innovation in customer experience with its ability to recommend similar items to a shopping guest. Also, big data powers eBay’s predictive machine learning algorithms that detect fraud based on purchase history and take action based on learnings within the data.

The eCommerce industry has access to substantially more data than many other industries. From CRM data and purchase history to social, website traffic and cookie data, eCommerce has an abundance of data to help them plan their next strategic move.

What Big Data Can Do For e-Commerce

The abundance of data that we now have access to gives retailers the opportunity to provide guests with a personalized experience and improve business operation efficiencies. According to Jeff Bezos, big data is powering the core functions of e-commerce businesses today.

“Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations and much more. Though less visible, much of the impact of machine learning will be core of this type – quietly but meaningfully improving core operations.” – Jeff Bezos, Amazon CEO

For example, when TeeSpring, a custom apparel company, was looking to make it easier for their guests to find the right t-shirt design for them, they knew that with better data, they could offer their customers a better experience. For a guest to find exactly what they are looking for (or the perfect things they didn’t know they needed), TeeSpring knew that each graphic would need to be tagged with descriptive terms to ensure it showed up in related searches. Enhancing the search functionality with better, more accurate data helps TeeSpring offer a better customer experience.

Let’s look at a few other examples of big data-driven innovations:

Personalized User Experience with Customer Profiling

By combining incoming data to build customer profiles, you can better understand what motivates a customer to purchase. Compiling multiple touch points from different channels in real-time allows you to create a holistic profile. After purchase, you can build a more robust profile with basket analysis and leverage data mining to understand more about who your shoppers are, including their age, income range, buying habits, and preferences. Customer profiles allow e-commerce companies to customize the shopping experience.

Dynamic Pricing

As we mentioned, the e-commerce market is hyper-competitive. Reduced customer loyalty means that shoppers will hunt for the best price. e-Commerce companies need dynamic pricing to remain competitive. Data needs to be gathered from multiple sources including competitor pricing, sales, regional preferences, and customer actions.

Customer Service

Exceeding expectations in customer service will build loyalty and generates word of mouth. With robust customer profiles that connect data sources, when a customer calls to complain, the customer services agent can see that what the customer has already said on contact forms and social media. This data equips the agent with more information to satisfy the guest.

Predictive Analytics

Know what is going to happen before it does. Big data powers machine learning algorithms that predict events. This feeds into supply chain visibility by understanding sales patterns to ensure product availability and avoid out-of-stock items.

Big data can also be a Big problem

All of this data and the need to remain competitive is driving technology innovation in e-commerce. While it is the data that enables innovation, the data also hampers progress. Leveraging big data is a big challenge. E-commerce companies are drowning in data. The amount of data, the variety of data, and the speed at which it is piling up are the biggest hurdles for e-commerce companies to overcome when looking to leverage their data.

Amount: Insights and product teams have access to so much data that they are overwhelmed.

Variety: Data comes from multiple channels and in different formats. It’s impossible for machines to make sense of this abundance of unstructured data.

Velocity: The heap of unstructured data is growing rapidly every single day.

e-Commerce companies can’t be data-driven without first understanding what the data is saying. For technology innovations to be leveraged, this abundance of data must first be structured. Machines aren’t currently capable of doing this; humans must intervene. From the product side, data labeling takes up the majority of a data scientist’s time. This time would be better spent focusing on core projects that will provide e-commerce companies with the competitive advantages needed today.

Human-in-the-loop computing unlocks the value hidden within data at scale

Product teams are hindered by the amount of data that requires labeling. Recent innovations such as increased bandwidth and cloud computing have created a newly digitized economy across the globe providing opportunity in places where they previously did not exist. At iMerit, we constantly encounter unexpected use cases where customers are exploring their data and call upon us to unlock parts of the trove to improve the customer experience.

By handing off data labeling requirements to data specialists, product teams can focus on the innovations that will provide the competitive edge required today. Our scalable, on-demand workforce provides e-commerce companies with data services that promote:

  • Increased search relevance providing a better customer experience
  • Conversion rate optimization through up-selling and dynamic pricing opportunities
  • Enhanced customer service with converged data

To remain competitive, e-commerce companies need to be focused on what their customers want. On-demand workforces allow you to outsource the data work while remaining focused on what’s next.

Do you have data needs? Launch a custom workforce to handle your data work today.

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Blog, eCommerce

Machine learning seems to be at the forefront of everyone’s mind. The buzz at NRF Retail’s BIG Show 2017 was no different. See what we learned about the intersection of retail and machine learning. Machine learning, combined with strong datasets and shoppers and trends, is already helping retailers create a customized, seamless, and predictive experience for their shoppers. As the predictive capabilities of computers improve, so will both online and […]

Machine learning seems to be at the forefront of everyone’s mind. The buzz at NRF Retail’s BIG Show 2017 was no different. See what we learned about the intersection of retail and machine learning.

Machine learning, combined with strong datasets and shoppers and trends, is already helping retailers create a customized, seamless, and predictive experience for their shoppers. As the predictive capabilities of computers improve, so will both online and offline shopping user experiences. With this advancement come opportunities for data scientists and more.

Data science: the key to personalization

One of the many machine learning technologies that we saw at NRF 2017 revolves around the idea of detailed personalization. As the amount of data on each individual shopper increases, so do the opportunities for data scientists to crunch the data. Algorithms that predict what users might like best, or what they might not yet know they like, are already in use and will continue to improve.

In another application, facial-recognition technology can be used on shoppers in brick-and-mortar stores to identify them, pull up the existing data on them, and guide them to the items that they are more likely to like and ultimately purchase. This kind of facial recognition technology can be used to reward loyal shoppers coming back for another visit. It is also bridging online and offline shopping by matching faces to online shopper personas and search or purchase history.


Testing out facial recognition technology that can tell gender, mood, and age range

New realities and interactive possibilities

Machine learning has interactive applications, too. Imagine being able to test-drive a new sofa, seeing exactly how it will look in your living room and debating whether it matches the color of your walls or rug. Retailers are eager to apply both virtual reality (VR) and augmented reality (AR). With VR experiences, virtual stores are places you can “visit” from the comfort of your home with the use of a headset. Imagine virtually browsing aisles of a grocery store without having to navigate through crowds with a shopping cart that inevitably has that one wobbly wheel.

On the other end of the [x]-reality spectrum is AR, which would help shoppers in the earlier sofa example. With AR, one can see both real “reality” and an augmented portion of it – in this case the potential sofa. This kind of technology would also allow shoppers to “try-on” clothes without working up a sweat shimmying into and out of too many pairs of jeans. At NRF we saw retailers using these new technologies to make shopping experiences more seamless and enjoyable for shoppers.

Virtual Reality
Using VR to design a new living room

On top of these, artificial intelligence can add yet another interactive layer. Chatbots – computer programs powered by artificial intelligence that are able to improvise and converse like humans – are being deployed online to chat with shoppers about what they’re looking for and help them find it. Though still in their infancy, and with examples to show that there is room for improvement, chatbots could be the future of everything from personalized shopping to customer service.

Taken together, machine learning’s many applications have a lot to offer to retail companies both online and offline.

But the implications of this growth go further still.

As dealers in data, we look at these trends and see a parallel rise in the need for massive data storage, processing, and analysis. Everyone from cloud technology architects to data scientists to data security experts should keep their eyes on the rapidly growing application of ML to retail and ecommerce.

The opportunities will be BIG!

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