The Five Key Ways Big Data Can Help Online Retailers

Playing in the online retail industry is increasingly difficult. Margins are becoming tighter, while the massive influx of newcomers further saturates a market that is already filled to the brim with established competitors.

In an age in which data has evolved into a hyper-valuable commodity, the so-called “new oil,” businesses are turning to Big Data to keep pushing forward. Many companies are finding more and more value in data. How? By analyzing it in myriad new ways, by visualizing it for patterns and trends, and by drilling down at the granular level in order to discover hidden opportunities.

Big Data has had a positive, documented impact on many industries. This includes retail. With Big Data, retailers generate more sales, improve forecasting, optimize supply chain processes, and enhance inventory management. On top of that, Big Data helps streamline even the most complex processes, while drastically cutting down on operational costs. A number of retail leaders are leveraging Big Data to great success.

If you’re in retail, finding the right software and platforms to make Big Data work for you is worth it. Here are the top five ways that Big Data can help online retailers.

1. Predict the Market

The retail sector is hypercompetitive. While there are millions of consumers that flock to retails stores on a regular basis, both physical brick-and-mortar shops and online stores, retailers have a constant need to stand out from the rest of the competition. Big Data provides retailers the leverage they need to elevate themselves from the rest.

How? Big data enables businesses to make more accurate forecasts of market trends, consumer behavior, and shopping patterns. This helps them make data-driven decisions, implement more effective marketing promotions and send relevant branded messages as well as personalized content in an effort to reach, engage, and convert both new and existing customers.

Big Data gives retailers an understanding of what their consumers desire, based on their purchase history, previous communications, and interactions, products viewed, and more. Even non-common business variables such as the weather, season, and birthdays come into play when it comes to predicting the market overall and the customer in particular.

When American retailer Target utilized Data Analytics to tailor its Marketing campaign to potentially pregnant women, it displayed how effective Big Data can be when crafting and executing focused, highly targeted Marketing efforts. However, it also revealed that using Big Data can cross several lines, particularly the privacy of customers. Retailers must find that delicate balance where they can utilize Big Data, while at the same time safeguarding the security of their customers’ information and privacy.

2. Recognize, Attract, and Retain Customers with Big CLTV

Every business organization needs to reach out to more people and add more customers to its base. But Sales and Marketing campaigns intended to attract and acquire new customers can be costly if they fail to entice consumers that offer big customer lifetime value (CLTV).

The Cost of Customer Acquisition (CAC) – which includes Advertising, Sales, Marketing, Promotions, and other endeavors to initiate, engage, and convert people into paying customers – should be significantly lower than the customer lifetime value (CLTV). Simply put, you want the bigger spenders to be the majority of your consumer base, without spending too much on the acquisition and retention process.

The problem for most retailers lies in finding, acquiring, and retaining these valuable customers. There are many pain points in determining customer lifetime value. But with Big Data, retailers can address many of these issues and ultimately find and keep customers who add huge value to their organization.

By combining their own data and information from other sources, retailers can leverage big data to identify if a customer is going to be profitable or not and for how long. They can also estimate how much they should spend on CAC. Starbucks and Netflix are some of the major brands that utilize Big Data to estimate CLTV and use the insights to add more customers to their organizations.

Once customers with a potentially large CLTV are recognized, Big Data can be used to derive actionable insights to further dig deep into their customers’ patterns, behavior, motivation, etc. Retailers can then use the insights to develop and execute highly targeted and personalized communication, content, promotions, and other Sales and Marketing initiatives to drive more profits, enhance retention, and keep a couple of steps ahead of the competition.

3. Improve Supply Chain Efficiency

Big Data is considered an essential technology in supply chain management, according to 64% of supply chain executives surveyed by SCM World. This preps the foundation for long term changes in supply chain processes and practices among organizations across industries.

This data coincides with a separate report by Accenture wherein approximately 80% of business leaders invest in data analytics as part of their initiative to integrate big data into their supply chain management strategies. According to Accenture, data analytics accelerate the supply chain onboarding process.

Big Data provides supply chain organizations with the platform and tools to create effective models from large volumes of unstructured and structured data from different sources. From a supply chain management perspective, this gives them a clearer and bigger picture of their operations and allows them to dive deep to gain insights into various aspects of their supply chain operations.

With big data, supply chain organizations have better visibility, enabling them to discover and address issues that hamper the flow of the supply chain such as bottlenecks, underperforming/broken assets, unresponsive clients and partners, and more. With the ability to spot and resolve volatile issues long before they become full-blown problems, users that leverage big data are able to keep the supply chain moving or even improve the efficiencies of multiple facets that were deemed impossible in the past.

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4. Perform Sentiment Analysis

What people say about your brand, products, and/or services matters greatly. Positive reviews from authentic customers can do wonders for your sales. On the other hand, a negative comment can wreak havoc on your brand’s reputation and adversely impact sales if left unchecked.

Combining Big Data with AI, Machine Learning, and Natural Language Processing, Sentiment Analysis becomes more accurate. Therefore, the quality of insights improve immensely and become more actionable. Prevailing emotions and sentiments are identified with precision.

Sentiments and emotions are now key to delivering personalized customer experiences and gaining customer satisfaction and trust. Businesses must quickly identify these sentiments and gain insights from it if they are to get great value.

5. Detect Fraud

Businesses all over the world lose $4 trillion a year to fraud, according to the Association of Certified Fraud Examiners’ (ACFE) 2018 report. While technologies advancing at a rapid pace have positively impacted various industries, it also provided fraudsters and cybercriminals the platforms and tools to evolve their attacks, avoid detection, and succeed in their plans to commit fraud.

Traditional fraud prevention systems are struggling. The good news is that Big Data, combined with modern analytics technology and fraud analytics techniques, can help in the detection and prevention of fraudulent activities before, during, and right after it happens.

By accessing and analyzing data from multiple, disparate sources, anti-fraud systems can quickly recognize scenarios, trends, and patterns that relate or are closely associated with fraudulent acts. Creating and deploying anti-fraud models based on recent data is simplified and streamlined.

Every organization has unique security requirements, processes, and systems. By utilizing big data, they can create custom fraud detection and prevention solutions that are specifically tailored to address their needs.

Leveraging big data isn’t always easy – particularly for businesses that remain rooted in old-school frameworks and depend largely on antiquated legacy systems to run their operations. Big data is a dynamic, evolving field. It can take time to get to grips with and fully exploit. 

However, organizations that fail to integrate Big Data into their core initiatives for growth are apt to be left behind. Finding the right software and the right partners is well worth it. Big Data can be an enormous boost to almost every aspect of the world of online retailing. 

Read more: Big Data: The Role of Predictive Analytics in Sales Growth