Online stores are facing an ever-increasing emphasis on data analytics. Data-driven decisions are becoming more and more pertinent to solving business problems and gaining a competitive advantage in an overcrowded virtual market.
It’s not happening without a reason. There are multiple factors driving data analytics trend including cheaper digital power, more sophisticated and faster analytics, greater amount and types of data, better math and modeling behind analytics software solutions, as well as significantly cheaper data storage.
In fact, what used to be an option is now a necessity. Companies that fail to leverage data analytics are forgoing better decision-making, better products, and better customer engagement.
Why is Data Analytics a Key to Business Strategy?
You could argue, that it’s nothing new, research has always been a key to more informed decision making. However, data analytics and research are two very different things. Research tends to look backward, is aggregated and more rigid in structure. Analytics, on the other hand, are real-time, more granular and predictive.
Classic research tends to ask and answer questions such as “How did product or an add A perform as compared to product B?” However, in the fast-changing digital business landscape, such questions and answers are irrelevant and often outdated by the time they can be applied. Data analytics ask and answer questions “If I had an extra budget should it be spent on customer service, brand building, digital or retail sales?”
The application of data analytics in decision making makes it an essential part of any business strategy.
Data vs. Big Data Analytics
As everything in the realm of the digital is getting faster and smarter, data analytics is not being left behind. Thanks to the rise of Industry 4.0 and the Internet of Things (IoT), the amount of data and the complexity of insights that can be derived from them has revolutionized analytics.
Today, it’s the big data analytics that is disturbing business decision making and strategy planning, and not just in e-commerce! Four key elements set Big Data apart from the traditional data.
- Volume. As the name suggests, Big Data entails collecting enormous amounts of data from a diverse variety of sources regarding demographics, social media, interests, behaviors, and much more. The most significant challenge is the ability to process and analyze the volume of data in different formats and leverage them appropriately.
- Diversity. As mentioned above, Big Data comes in many different formats from the traditional databases and documents to emails, videos, and social media actions. E-commerce businesses that want to succeed need to learn to process and interpret this variety of data correctly.
- Speed. The speed refers to the frequency of data generation and delivery. Big Data never stops that is why, to leverage it correctly, it needs to be closely synced with business strategy and decision making. It’s the speed and immediate application that gives it power. E.g., if data is processed immediately, you can predict out-of-stock situations before they happen and communicate with your suppliers to prevent it.
- Precision. This particular characteristic of Big Data refers to the uncertainty associated with some types of data which demand meticulous verification and high level of security. Some data remains unpredictable as per technology failure, human lack of honesty, or some economic factors.
What Types of Data is Relevant to E-commerce Businesses?
The pool of data and data types is virtually never-ending. Some types are more relevant to online businesses than others. E-commerce businesses such as Amazon or Netflix capture and use a variety of data such as orders, visits, referring links, keywords, browsing, social media engagement, etc. This data can be organized into five broad categories:
1. Business Activity (Transaction) Data
Business activity data are a collection of all exchanges between the customers and your company over time. These data are easily organized as they are structured in nature. Still, they come from a variety of sources such as transactions, customer profiles, loyalty programs, distribution frequency, customer complaints, or service usage. There is no doubt transaction data can be leveraged in w wide number of ways. For example, Amazon uses them, through collaborative modeling, to generate “you might also want” prompts for each of their products. The company revealed that this recommendation technique generated 30% of their sales.
2. Operational Data
Operational data include all the technical and logistic aspects of your supply chain. These data are also reasonably structured and organized, and if assessed and analyzed on a regular basis they can have a positive influence on your operational budget.
For instance, by tracking and analyzing data of your shipping and inventory lifecycle, you can predict with a comfortable foresight any inventory leftovers or shipping difficulties and thus, reduce costs related to product storage or returns.
3. Click-stream Data
Click-stream data is collected across the web including websites, social media posts, and ads. They are most useful in predicting customer preferences, interests, and tastes. For instance, Netflix analyzes data from billions of reviews of films and TV series that are liked, loved or hated to optimize their offering to customer tastes, influencing which movies or shows will be acquired or made.
4. Video Data
Video data are inherently unstructured but have proven their value over the years. Video data analyze viewing habits using image analysis. They allow you to determine what part of the video and image has the most effect on the audience or when the audience is likely to lose interest.
5. Audio Data
Audio, like video data, is unstructured and harder to process but can be invaluable. Audio data are data collected from phone interactions, call centers, and customer service. They are particularly useful in analyzing customer-buying behavior, finding the right communication style as well as targeting new customers. Finance and credit card companies track the data and activities from call centers to offer customized offers in milliseconds.
What Strategic Values can Online Stores Gain from Big Data Analytics?
Data analytics provide value to ecommerce businesses on three different levels:
- transactional
- informational
- strategic
Transactional benefits are a result of improved efficiency and cut costs, informational value gives you better insight into decision making, and the strategic value is all about gaining a strategic advantage.
Personalization
A first noteworthy application of analytics is in ecommerce is personalized service and products. Personalization can come in form of special offers, promotions, product suggestions or content. Thanks to real-time analytics, you can diversify communication for new, loyal, dormant, or returning customers. Studies show that personalization can increase sales by more than 10% or provide eight times ROI of marketing spendings.
Dynamic Pricing
In an era when customer is the king, one needs to grasp whatever advantage there is to attract customers. The best thing about e-commerce was its ability to offer better pricing than brick-and-mortar stores. However, with the virtual market getting ever more crowded it’s no longer about the having the lowest price at all costs, it’s about finding the best price for a particular time and place.
Dynamic pricing systems monitor competitors’ prices based on a variety of factors. For instance, before peak periods such as Black Friday or Christmas, Amazon processes big data based on competitor’s pricing, customer actions, product sales as well as regional and geographical preferences. Offering the lowest prices on the most popular products helps Amazon be perceived as the cheapest option on overall. By doing this, Amazon attracts the necessary clientele and earns money mainly on the less popular products.
Customer Service
Another area where Big Data can rock your business strategy is customer service. Centralizing your customer service channels and information on one dashboard provides you with a 360-degree view of your clients. By compiling and analyzing data from all offline and online interactions, social media history and purchases you can give a more personalized service and even handle complaints before they occur. E.g., many software companies offer innovative post-sale customer service thanks to data obtained from intelligent sensors rooted in their products.
Supply Chain Visibility
Expectation to track the delivery of online orders has transcended from being a luxury to an expected service. Customers expect to have information about exact availability, real-time status, and location of their orders. Digitalizing your supply chain and shipping not only provides information to shoppers but also your business. Thanks to collected data you can review the efficiency of the process, identify frictions and prevents errors. Big Data plays a crucial role in this context as they collect various information from multiple parties and stakeholders and thus can provide you with precise and current information.
Security and Fraud Detection
Digital fraud-related losses are not small, not even speaking of the damage on company image. Data Analytics can help you identify relevant insights and prevent fraud before it happens. With the use of appropriate analytics, ecommerce businesses can recognize fraud in real time by cross-referencing transaction data with client’s purchase history, social feed, weblogs, and location. For instance, Visa uses Big Data analytics to inspect each transaction from 500 different aspects thus saving the company over billion in loss value.
Prediction
The ability to predict the development of trends, transactions, demands, customer behavior, process failures, and so forth strictly depends on your ability to collect and analyze data. Data-driven strategies are all about gathering as much accurate information as possible, examining it as quickly as possible and creating predictions that underline a well-throughout business strategy. Big Data analytics allow you to prepare and allocate your budget minimizing waste and maximizing profits based on a prediction of future sales patterns, stockouts, customer gains, and losses.
Data Analytics at the Core of Business Strategy
The shift to incorporating analytics into your strategic planning as well as execution has its reasons. Keeping an eye on real-time analytics allows your ecommerce to react to the rising trends and threats faster and with more flexibility.
If you are not turning your data into analytics and the analytics into actions, it’s time to make a few changes. If you only review analytics on an annual or quarterly basis, you are risking losing competitive advantage and market share. Online market moves fast and old business practices are no longer able to save the day. Explore new agile ecommerce business strategies built on real-time analytics before it’s too late.
