Data-driven decision-making: 3 ways to drive retail resilience
Retailers can improve CX and strengthen the bottom line by taking a fresh approach to data.
As data volumes grow, businesses are using cutting-edge technologies to tap into the power of data science. With the volume of data projected to reach 180 zettabytes by the year 2025, e-commerce leaders are looking to big data to drive future innovation.
Big data e-commerce refers to an approach that leverages data and analytics to increase customer engagement, boost sales, and tailor the shopping experience.
But what is big data exactly? More importantly, how is it influencing one of the fastest-growing industries of our times?
Big data definition + examples
Big data refers to a mix of structured, semi-structured, and unstructured data gathered by organizations, which can be harnessed for insights and used in advanced analytics like predictive modeling and machine learning.
Big data systems, along with other analytics tools, have become vital due to the three key characteristics of big data: the volume of data across various sources, the diverse types of data it encompasses, and the high speed at which this data is generated, collected, and processed.
Big data comes from external sources, such as financial market data, user data, weather updates, traffic conditions, geographic data, and scientific research findings in addition to data generated within a company. Big data isn’t limited to just text or numbers; it includes videos, images, or audio files. Today, we have big data applications for the continuous processing and collection of streaming data.
Here are some examples of how companies use big data:
- Financial services companies employ big data systems for tasks like risk management and real-time analysis of market data.
- In the energy sector, big data helps oil and gas companies to discover promising drilling sites and monitor pipeline operations. Similarly, utilities utilize it to oversee electrical grid systems.
- Manufacturers and transportation companies depend on big data to streamline supply chain management and improve the efficiency of delivery routes.
Why big data is important for e-commerce
Big data can be compared to a massive, swiftly moving, and incredibly varied ocean. A sea of data, collected from countless sources, surging forward every second. The challenge isn’t collecting this data; it’s figuring out what to do with it all.
Companies with an online retail presence are seizing the opportunity to use data to gain valuable insights into customer behavior, which, in turn, helps them improve the overall customer experience.
As Zippia’s research shows, 97.2% of businesses are investing in big data and artificial intelligence. Each customer interaction, click, purchase, or review contributes to this treasure trove of data.
Big data helps companies like Amazon provide personalized product recommendations based on a customer’s browsing and purchase history, increasing sales. Additionally, it enables e-commerce platforms to track and analyze customer behavior to optimize online stores, leading to higher conversion rates and profits.
Big data’s big impact: Personalized shopping experiences
Personalization is no longer a tool of big brand journeys. With big data all retailers can offer personalized shopping experiences.
4 big data e-commerce benefits
In the realm of e-commerce, the benefit of big data is the speed at which you can make decisions and determine whether you’re leading the pack or falling behind your competitors.
Here are four examples of how big data improves e-commerce:
1. Personalized customer experience
By analyzing your online behavior, including browsing and purchasing history, along with social media interactions, businesses can provide a shopping experience that feels custom-made.
Through big data analytics, e-commerce companies can build a full view of their customers. This helps them categorize customers by factors like gender, location, and social media activity to craft personalized emails, develop marketing strategies for various customer segments, and release products tailored to different groups of consumers.
2. Improved operations and data management
Big data can help companies improve both their back end and front end e-commerce operations. For example, through past sales data analysis, companies can anticipate future buying trends in order to manage their stockpile more efficiently. This insight can even help reduce inventory costs.
Companies also can use predictive analytics, fueled by big data, to estimate the average checkout waiting time and implement improvements to streamline checkout for better CX.
Meanwhile, big data is improving supply chain management and delivery optimization by supporting real-time tracking and management of shipments, ensuring packages arrive promptly for increased customer satisfaction. Data analytics can automate return and refund management systems, ensuring a smooth, hassle-free process.
3. Accurate predictions = more revenue
By understanding buying behavior and preferences, businesses can refine their marketing efforts to target the right customers. For example, there’s a much better chance of someone opening an email if it’s tailored for them rather than generic messaging.
AI algorithms use big data to forecast customers’ future purchases and timing. Brands such as Sephora and Netflix use big data to monitor user actions and track customer preferences. With big data, a brand can predict a customer’s lifetime value by studying their purchasing history.
Plus, through competitive analysis, businesses can continually adjust their offerings and pricing, increasing the chances someone will buy.
4. Better fraud prevention & risk management
By identifying patterns and trends in customer data, businesses can detect anomalies that might signify fraudulent activities. For example, if a customer typically makes small purchases in their country, but suddenly attempts a large transaction from a foreign location, the system can flag it as suspicious.
This timely detection helps companies reduce the risk of money laundering, thereby protecting both themselves and their customers.
AI-driven facial recognition and identity verification systems offer added e-commerce security for their ability to detect fake customers. These systems use ML models trained on big data sets of facial features and biometric data. Customers verify their identities by taking a selfie or using their fingerprints. AI algorithms analyze these biometric data points and compare them against internal databases. This provides a seamless and secure user experience, and reduces the risk of identity theft.
Customer trust: Definition, value, and 6 powerful ways to earn it
As business processes become increasingly automated, companies are even more reliant on customer trust and emotional intelligence.
Big data e-commerce security risks
Big data, while transformative, comes with security challenges. E-commerce businesses need to ensure they’re responsibly handling customer data and implementing comprehensive security measures to protect against these risks:
- Data breaches: E-commerce platforms store a treasure trove of customer data, making them a lucrative targets for cyberattacks. Maintaining robust security to protect against data breaches is a constant challenge. Even a single breach can lead to the exposure of sensitive customer information and erode trust.
- Scalability: As e-commerce companies collect more data over time, they must ensure their security infrastructure can scale accordingly. Handling large volumes of data securely is no small feat.
- Third-party risks: E-commerce platforms often rely on third-party vendors for various services, such as hosting, data analytics, and payment processing. These partnerships can introduce security risks if not carefully managed.
- Compliance: Brands risk penalties and fines if they run afoul of regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
That said, the future of big data in e-commerce appears bright. Data scientists are working to more closely integrate advanced predictive analytics with AI and machine learning. This suggests that the impact of big data on e-commerce is only set to grow.
