UNIVERSITY PARK, Pa. — In smart cities of the future, sensors distributed throughout buildings and bridges could monitor infrastructure health. Cloud-based computing could decrease traffic with real-time analysis available to commuters. Windows could tint themselves darker on sunny days or lighten to brighten a room on cloudy ones. 

None of these innovations, however, can materialize without managing the enormous amounts of data generated by robust sensing networks, according to an interdisciplinary team of Penn State researchers. They published a perspective article on March 19 in Science, highlighting smart materials that can sense environmental changes and respond accordingly — without externally transferring data — as one avenue to avoid this data overload.

Science also released a podcast featuring the work on March 18, outlining the benefits of implementing smart materials in tomorrow’s cities, ranging from self-healing concrete structures to building materials that can solve complex equations. 

“Since this problem sits at the intersection of materials science, structural health monitoring and computation, collaboration was important from the get-go,” said Rebecca Napolitano, assistant professor of architectural engineering, who co-wrote the article. 

Napolitano collaborated with Wesley Reinhart, assistant professor of materials science and engineering in the College of Earth and Mineral Sciences, and Juan Pablo Gevaudan, affiliate professor of architectural engineering and Marie Sklodowska-Curie Research Fellow at the University of Leeds. 

Napolitano’s interest in smart cities began during her graduate studies, when she wondered how historic buildings would be accommodated in cities of the future. Her current research, focused on supporting merging infrastructures via computational methods, led her to collaborate with Gevaudan. He investigates how modern concrete materials degrade to better engineer their response and ability to adapt to certain conditions in new and existing buildings. The two researchers lead the adaptive architecture research area in the Convergence Center for Living Multifunctional Material Systems at Penn State.

Reinhart and Napolitano previously collaborated to investigate building damage at the micro- and macroscales through computation. Reinhart works on metamaterials, which are materials engineered to gain unique properties from their structure, and their potential applications in infrastructure. For example, such a metamaterial could sense the path of the sun and perform a calculation to adjust a solar panel accordingly and optimize the energy stored. 

The researchers plan to continue exploring avenues for developing these materials and robust computational methods to optimize them. With further research, according to the researchers, the implementation of smart materials could increase the lifetime of buildings and civil structures, reduce energy consumption and reduce waste from production and use of electronic sensors — all on a citywide scale.

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It’s pretty safe to say that as the world of e-commerce grows and expands with the constantly changing behaviors of consumers, so does the data that is stored.

Have you ever noticed how you may do a Google search of a particular type of product and then the next time you log into your social media account, you start to see that same type of product in ads from different businesses? Conspiracy theorists like to say that “we’re being watched,” and in a sense, we kind of are being watched, but not in a “G-14 classified” kind of way.

This Deja Vu type of shopping experience is no coincidence but more so one of the results of big data in e-commerce marketing. And big data isn’t something that’s just pulled from your web browser histories either. Big data is collected from things like

• Abandoned shopping carts
• Road cameras
• Geolocation services
• Social media activity
• Transaction receipts

Typically, big data is divided into three categories: transactional, social, and machine data.

Transactional Data is exactly what it says it is. Any types of online transactions you’ve made, big data analyzes the behaviors you have before making that transaction, leaving companies to use that information to improve your buying experience.

Social Data stems from your online social activity. “Likes,” “Loves,” “Comments,” and “Tweets” are actions that provide e-commerce companies with insight into certain interests and behaviors of their ideal customers. Even video uploads and “number of views” deeply influence e-commerce marketing analytics.

Machine Data is information that is collected from various types of equipment and sensors that keep track of various patterns and behaviors of users.

E-commerce businesses are leveraging off of big data analytics as a way to have a better understanding of their customers and their shopping behaviors. The data that they collect and analyze allows them to “custom-fit” all their marketing efforts to the specific preferences of their customers, train staff on tailored customer service tactics to meet their customers’ needs, and the creation of products their customers actually want to buy.

To be an e-commerce business owner trying to keep up with the ever-changing trends and behaviors of their customers is a job all in itself. That reason alone is why so many e-commerce business owners turn to sites like Shopify where they can go to the marketplace to hire industry experts that have expertise specifically for e-commerce marketing.

So, to answer the question of how is big data impacting e-commerce marketing in 2020? The answer would be that it’s impacting e-commerce marketing in a major way. According to businesswire.com, global big data analytics in retail was reported at $3.45 billion in 2018… By the end of 2024, the market is expected to reach 10.94 billion.

Because of the boom in e-commerce and the increase in online shopping, retailers are using big data as effective efforts to stay competitive and relevant in their market.

Let’s dive deeper into just how big data is fostering a major impact on e-commerce marketing.

Big Data’s Impact on E-Commerce Marketing in 2020

A More Personalized Shopping Experience

E-commerce businesses are leaps and bounds above brick-and-mortar stores when it comes to creating a more personalized shopping experience for customers. Sure, having a greeter at the front of a store is nice, but all too often, they can be a bit of a nuisance at times.

E-commerce businesses have a plethora of predictive analytic tools to give them a better idea of current and future behaviors of customers. Everything from tracking clicks per page to tracking the length of time between a visitor’s journey from your home page and checkout, big data is providing several different sources of data.

Data scientists are doing even more research now to find new sources of data to provide customers with a more personalized shopping experience. New on the horizon, text analytics is one of the newest sources of data being utilized in lots of businesses.

Everything from analyzing the texts in comments on business websites to reviews on third-party sites, this is a source of data that can take information that customers are saying and apply it as an improvement measure to give customers what they really want.

Increase in Cross-Border Sales

People all over the world love to shop, and big data has made it possible for e-commerce businesses to be more accommodating to neighboring countries. Based on the products you’re selling, big data can tell you where your products are competitive or unique, and you can start selling in that market.

Just make sure that you localize the products you’re selling for those geographic locations. That means having language options, making sure your prices and sizes are converted, and that you’ve set the shipping costs properly.

Impeccable Customer Service

Happy customers are your key source in turning customers into loyal customers, and the way to keep customers happy is through impeccable customer service. Big data helps e-commerce businesses provide great customer service in detecting your brand’s perception on social media and customer satisfaction levels (if you offer customers to rate you). In fact, big data can tell you exactly when a customers’ perception of your business shifted!

E-commerce businesses can take this information and use it to make appropriate changes to their site and even use it to train employees on how to interact with customers, whether it’s through chat box interactions or simply making the checkout process more convenient.

Personalized Marketing

Long gone are the days where you can get away with emails that say things like “Dear Valued Customer”… especially when people are loyal customers to a particular online store. We are in the era of sophisticated shoppers with high expectations, and when your customers are sophisticated, that means that your marketing efforts need to get sophisticated as well.

Big data has transformed digital marketing in ways to make the customer feel like a particular store really cares about their taste, interests, and preferences. For example, if you have a customer that only buys purses from your site, you may want to send her emails when new inventories or collections of purses arrive. Sending her an email about shoes might make her delete the email altogether.

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But there’s a balance between not enough data and too much. What’s the right amount of data to work with as demand planner or data scientist?

There is a debate about how much data is enough and how much data is too much. According to some, the rule of thumb is to think smaller and focus on quality over quantity. On the other hand, Viktor Mayer-Schönberger and Kenneth Cukier explained in their book Big Data: A Revolution That Will Transform How We Live, Work, and Think, that “When data was sparse, every data point was critical, and thus great care was taken to avoid letting any point bias the analysis. However, in many new situations that are cropping up today, allowing for imprecision—for messiness—may be a positive feature, not a shortcoming.”

Of course, larger datasets are more likely to have errors, and analysts don’t always have time to carefully clean each and every data point. Mayer-Schönberger and Cukier have an intriguing response to this problem, saying that “moving into a world of big data will require us to change our thinking about the merits of exactitude. The obsession with exactness is an artifact of the information-deprived analog era.”

Supporting this idea, some studies in data science have found that even massive, error-prone datasets can be more reliable than simple and smaller samples. The question is, therefore, are we willing to sacrifice some accuracy in return for learning more?

Like so many things in demand planning and predictive analytics, one size does not always fit all. You need to understand your business problem, understand your resources, and understand the trade-offs. There is no rule about how much data you need for your predictive modeling problem.

The amount of data you need ultimately depends on a variety of factors:

The Complexity Of The Business Problem You’re Solving

Not necessarily the computational complexity, (although this an important consideration). How important is precision verses information? You should define this business problem and then select the closest possible data to achieve that goal. For example, if you want to forecast the future sales of a particular item, the historical sales of that item may be the closest to that goal. From there, other drivers that may contribute to future sales or understanding past sales should be next. Attributes that have no correlation to the problem are not needed.

The Complexity Of The Algorithm

How many samples are needed to demonstrate performance or to train the model? For some linear algorithms, you may find you can achieve good performance with a hundred or few dozen examples per class. For some machine learning algorithms, you may need hundreds or even thousands of examples per class. This is true of nonlinear algorithms like random forest or an artificial neural network. In fact, some algorithms like deep learning methods can continue to improve in skill as you give them more data.

How Much Data Is Available

Are the data’s volume, velocity, or variety beyond your company’s ability to store, or process, or use it? A great starting point is working with what is available and manageable. What kind of data do you already have? In Business-to-Business, most companies are in possession of customer records or sales transactions. These datasets usually come from CRM and ERP systems. A lot of companies are already collecting or beginning to collect third party data in the form of POS data. From here, consider other sources, both internal and external, that can add value or insights.

Summary

This does not solve the debate and the right amount of data is still unknowable. Your goal should be to continue to think big and work with what you have, gather the data you need for the problem and algorithm you have.

When it comes to gathering data, it is like the best time to plant a tree was ten years ago.  Focus on the data available and the insights you have today while building the roadmap and capabilities you want to achieve in the future. Even though you may not use it now, don’t wait until tomorrow to start collecting what you may need for tomorrow.

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