Practical Ecommerce

6 Uses of Big Data for Online Retailers

In “Understanding Big Data for Ecommerce,” we provided a primer on the growth of data and its implications for ecommerce merchants. This article will add to that post by explaining Big Data in more detail and presenting its most common uses for ecommerce sites.

There are many definitions of Big Data. My favorite is: “Data that is difficult to process and analyze using traditional database and software techniques.”

The 4 V’s of Big Data

The challenges associated with Big Data are the “4 V’s”: Volume, Velocity, Variety, and Value.

<img src="/files/images/0005/7498/bigdatachallenges_lightbox.jpg" width="567" height="228" align="aligncenter" alt="The "4 V's" of Big Data: Volume, Velocity, Variety, and Value. Source: Oracle.“/>The "4 V’s" of Big Data: Volume, Velocity, Variety, and Value. Source: Oracle.

  • The Volume challenge exists because most businesses generate much more data than what their systems were designed to handle.
  • The Velocity challenge exists if a company’s data analysis or data storage runs slower than its data generation. This could be because of customer clicks on your website or thousands of sales transactions every second — a good problem to have.
  • The Variety challenge exists because of the need to process different types of data to produce the desired insights. This could include, for example, analyzing data from social networks, databases and customer service call records at the same time.
  • The Value challenge applies to deriving valuable insights from data, which is the most important of all V’s in my view. A company can usually collect all the data but the challenge is to ask the right questions to get value from it.

6 Uses of Big Data for Online Retailers

Most small merchants think that Big Data analysis is for larger companies. In fact, it is important for small businesses, too, as they attempt to compete with the larger ones. This becomes even more important as online retailers interact with their customers in real time. Note, however, that handling large sets of data can increase a site’s load time. A slow site harms every aspect of the shopping process.

Here are six uses of Big Data for online retailers.

  1. Personalization. Consumers shop with the same retailer in different ways. Data from these multiple touch points should be processed in real-time to offer the shopper a personalized experience, including content and promotions.

    For example, do not treat loyal customers the same as new ones. The experience needs to be personalized to reward loyal customers. It should look attractive and “sticky” for new customers.

  2. Dynamic pricing. You need dynamic pricing if your products compete on price with other sites. This requires taking data from multiple sources, such as competitor pricing, product sales, regional preferences, and customer actions to determine the right price to close the sale. Large merchants like Amazon already support this functionality. Overcoming this challenge will give your business a huge competitive advantage.

  3. Customer service. Excellent customer service is critical to the success of an ecommerce site. Zappos and Netflix are examples of terrific customer service. But Big Data has made customer service a challenge by requiring seemingly every interaction with a shopper to be used for serving that shopper. To continue to excel at customer service, online retailers need to overcome this challenge.

    For example, if a customer has complained via the contact form on your online store and also tweeted about it, it will be good to have this background when he calls customer service. This will result in the customer feeling valued, creating a quicker resolution.

  4. Managing fraud. Larger data sets help increase fraud detection. But it requires the right infrastructure, to detect fraud in real-time. This will lead to a safer environment to run your business and improved profitability.

    Most online retailers need to process their sales transactions against defined fraud patterns, for detection. If it’s not done in near real-time, it could be too late to catch the fraudsters.

  5. Supply chain visibility. Customers expect to know the exact availability, status, and location of their orders. This can get complicated for retailers if multiple third parties are involved in the supply chain. But, it is a challenge that needs to be overcome to keep customers happy.

    A customer who has purchased a backordered product would want to know the status. This will require your commerce, warehousing, and transportation functions to communicate with each other and with any third-party systems in your supply chain. This functionality is best implemented by making small changes gradually.

  6. Predictive analytics. Analytics is crucial for all online retails, regardless of size. Without analytics it is difficult to sustain your business. Big Data has helped businesses identify events before they occur. This is called “predictive analytics.” Predictive analytics is becoming an important tool for many businesses.

    A good example of this is predicting the revenue from a certain product in the next quarter. Knowing this, a merchant can better manage its inventory costs and avoid key out-of-stock products.

Gagan Mehra
Gagan Mehra
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Comments ( 8 )

  1. Doug Laney March 28, 2013 Reply

    Great piece Gagan. Cool to see the industry adopting Gartner’s "Vs" of Big Data, albeit 12 years after we first published them in a piece I authored about Three-Dimensional Data Management Challenges (ref: http://goo.gl/wH3qG). Note that however important "value" is to information overall, it is *not* at all a defining characteristic of Big Data as are the other (original) 3Vs. In fact, as data volumes, velocities and variety grow, it has been shown that the incremental value of each informational unit actually declines.

    This is a challenge to many orgs and why I have developed info valuation models for quantifying information’s economic benefits. Regardless, nice piece and great examples.

    Doug Laney, VP Research, Gartner, @doug_laney

  2. Valerie Holstein March 28, 2013 Reply

    I am absolutely against practicing dynamic pricing. That just leads to cannibalism and why Amazon is dominating the world and eradicating small businesses in its path. We need to focus on customer service and experience. If the two are excelled at, there should be no reason to lower pricing.

  3. Gagan Mehra March 28, 2013 Reply

    Thanks a lot, Doug. Appreciate your comment. I have read a lot of your research and really enjoy it. You were way ahead of the game when you wrote the original piece in 2001.

    I have seen most companies struggling more with the value part of big data as they attempt to justify the ROI. This makes it really important to set clear objectives before launching a big data effort. This is where your work in the Infonomics space and the associated valuation models can be really useful.

  4. Gagan Mehra March 28, 2013 Reply

    Thanks for your comment, Valerie. Dynamic pricing is the unfortunate truth of a competitive online market. If your product is also offered by your competitors then dynamic pricing is important unless you are certain that your customers will buy from you regardless of price. I have seen this firsthand with loyal Amazon customers who decide to shop somewhere else if the price on Amazon is not the best. This trend is one of the big reasons price monitoring sites like camelcamelcamel.com are so successful.

  5. rwang0 March 29, 2013 Reply

    Great post. we see the intersection of omnichannels, with payment technologies, demand signals (social, mobile, intent), supply chains, frictionless enablers, and big data creating a world of matrix commerce. here’s a quick primer:

    http://www.constellationrg.com/about/press-releases/20130116/constellation-publishes-big-idea-report-introducing-matrix-commerce

  6. Sri Velamoor April 11, 2013 Reply

    Good article Gagan and I appreciate the simplicity and readibility.

    On the topic of Dynamic Pricing, I completely agree that it is being used by the likes of Amazon to influence price-based decision making in their favor. However, I dont think that means that etailers should all subscribe to dynamic pricing practices. What needs to happen as a first step is for these retailers to get and stay informed about how their prices and assortment compare to others in the marketplace. They need to know how they appear in the eyes of the price comparing consumers. That capability can then be followed by a pricing action that is data and insight driven. The challenge here is that there havent been many options for small businesses to develop a price and assortment intelligence capability economically..until now.

  7. dasenator June 26, 2013 Reply

    I like the article, but I think velocity (or speed) should also be one of your uses for big data. Nothing impacts online sales more directly than speed. Even if you get all the other 6 correct, if your site is sluggish, you lose. Rule of thumb: Nobody waits on line online.

    Why not use big data to get end users to your site faster? Performance based traffic management is critical to a positive e-commerce experience. Starbucks figured out a long time ago that they need to make stores convenient.

  8. Doug Laney August 14, 2013 Reply

    Gagan, Thank you kindly. Yes, as you mention companies struggle with the value of data. True, but that doesn’t make it one of the qualities of Big Data, rather more an objective. Conflating "value" with an actual characteristic of Big Data confounds data management strategies, because if you have Big Data then you assume you automatically have value. Not so. As an objective of big data, "value" is something that needs to be measured.

    At Gartner we have introduced a framework and methods for information valuation, as part of our infonomics research. Quantifying the potential and actual economic value of information assets is even more critical for Big Data investments–as they tend to be more speculative and expensive.

    Doug Laney, VP Research
    Gartner, @doug_laney

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