Turning Big Data Challenges in eCommerce into Opportunities

June 13, 2017

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.