And rightfully so!
The surfacing of the Facebook debacle involving massive amounts of data collected from 87 million users – which was then used to influence the 2016 U.S. Presidential election – revealed Big Data as the powerful beast it can be.
It’s no wonder that most business owners know and understand the importance of data. Recognizing this potential for their businesses, enterprise organizations surveyed for Adobe’s 2019 Digital Trends research regard ‘data-driven marketing that focuses on the individual’ as the single most exciting opportunity in 2019. However, results show that only a small number of enterprises actually know how to leverage and use data, with percentage of data used clocking in at as low as 0.5%.
That’s why, in this post, we’ll take a deeper dive into what exactly data analysis is – and how understanding data can serve as an essential tool in your arsenal as you grow your e-Commerce channel.
For starters, our own Data Analyst, Maiara Candido, defines data analysis – turning numbers into information, giving them a meaning, in order to solve problems – and continues to explain its importance. “Data is the new everything. It can be applied to many kinds of businesses and scientific purposes.”
“For instance, there is descriptive analysis, which is performed to understand a current situation, using real time information. Let's assume that a clothing company is looking to determine how a specific product or style is performing, for example. In this way, this analysis and interpretation of data will capture sales volume, customer opinions and feedback; monitor social media and analyze how it performs in comparison with competitors. There’s also predictive analysis. This analysis helps to stipulate which path the market is likely to take, forecasting its direction or trends. Let’s say a summer wear retailer, in preparation for the next season, wants to know what customers’ demands will be, which will be the trending colors, styles, etc. Using predictive data analysis, it’s possible to narrow down those expectations, so you can target your efforts at what will serve your customers. And then there’s prescriptive analysis. Prescriptive analysis uses techniques from both descriptive and predictive analysis to go even further – and suggest business decisions or predict how an action will perform in the market. For instance, maybe a retailer wants to set up a promotion and would like to know the discount applied to boost sales and increase revenue. Perspective analysis will assess the current performance, analyze the market trends and suggest the best approach to the business owner. Finally, there’s diagnostic analysis. Diagnostic analysis determines the parameters of a certain behavior. In a business scenario, for instance, it points out where the sales occurred, how it happened, when it happened, or why a customer did not make the purchase. By that, it becomes clear what should be changed and maintained in order to redirect or maintain a strategy or action.”
It is only obvious that companies which treat their data as a valuable and critical competitive asset – while taking care to respect consumer privacy – can use it to help drive their commercial strategies through better customer experiences.
“Data analysis makes it possible to understand how your business stands in the market, study your target audience, identify customer preferences, for example, and use them in marketing strategies, purchasing management, inventory management, etc.”
Understanding the customer is an important ingredient in the success of any business. The product or the service offered must have a reason to exist and an audience that is interested in acquiring it. Data analysis in e-Commerce allows the owner of a virtual store to understand which points are working or not, according to the habits and profile of store visitors and customers.
And while you may think that access to Big Data is confined to big retailers that can afford an in-house team or who can afford to buy data from data brokers, you’ll be happy to discover that this logic is flawed. Even the smallest businesses have the means to access and analyze e-Commerce Big Data.
Maiara explains what should serve as your starting point – and difference exists between knowing your data analyzing tools, and more importantly, knowing your business:
“More than tools, a person should know the key elements that make their businesses. For eCommerce, there are main metrics that should be closely watched and analyzed. These metrics include Bounce Rate, which is defined as the percentage of visitors to a website who leave the site without browsing; Conversion Rate (CR), which is a ratio between the number of conversions and total number of visits; Revenue Per Visitor (RPV), meaning the average amount across all purchases and visitors, and as such representing a direct correlation between the number of visitors and site revenue; Average Order Value (AOV), meaning the average amount customers spend each time they complete a purchase order; and Cart Rate, which is the ratio between the number of visits with carts and total number of visits.”
All of this leads to leveraging opportunities which arise from understanding data.
According to the fascinating and insightful Adobe 2019 Digital Trends Report, the top three opportunities for business according to marketers are: Optimizing customer experience; Creating compelling content for digital experiences; and Data-driven marketing that focuses on the individual. Data analysis plays a major role in all three of these topics. It implements all the types of analyses mentioned before. They give a thorough business and customer view that will support business tactics.
“Personally, I believe that the most important trend in e-Commerce today is optimizing customer experience. As much as it is important to attract a new audience to your website, customer experience is going to be absolutely crucial in sales conversion. It plays a primary role.”
According to the same Adobe report, a key requirement for delivering better data-driven customer experiences is understanding the journeys taken by each user and how to improve them. According to Ivan Pollard, Global CMO at U.S.-based multinational food company General Mills, understanding the complexities of the customer journey is the second most important priority after producing high-quality and affordable food.
“Data is going to unlock the complexity of any number of customer journeys and we can understand and connect with them at the right time. And every one of them will be slightly different. Understanding the quantum world of customer journeys is priority number two. It’s where data meets products meets customer experience.”
Maiara continues to explain how any company or organization can leverage data: “The organization should have well-defined objectives so that it can perform a good analysis of their data – based on the goals to be met and the metrics established to evaluate them. Without this planning, it’s difficult to be able to reach them.”
The success of any organization goes through data analysis. Companies are influenced by both external and internal factors. Organizing, processing and deciphering relevant information can deliver them insights that make it possible to understand what is working – and identify what needs to be improved. With a better understanding of your target audience and the behavior of you customers in your website, it’s possible to build data-driven strategies focused on customers.
While data-fueled customer journey management and personalization are becoming increasingly vital for successful marketing and customer experience programs, companies are setting themselves up for failure if they think excellence in data can compensate for sub-standard creativity and design.
Creatives regard ‘standout content and campaigns’ as the number one priority for 2019, highlighting the need for cut-through and stopping power in a world where the attention of consumers is more elusive than ever.
“Data-driven strategies are based on statistics and can be comprehended and applied by anyone interested in implementing an analytical approach into their businesses. But, in my opinion, what makes a good data analyst is someone with a deep knowledge of their business – who is able to combine both analytical and intuition. Why? Because sometimes, data may not provide the best decision possible. Data, in specific situations, may be biased. For instance, data collection is insufficient for the analysis and does not reflect the business, or the algorithm applied is not the best to achieve the goal. In that situation, a person with deep knowledge of the business will know how to balance and weigh all the information – and then make the best decisions for the business.”
To wrap it up, Maiara highlights one thing that should go before any analyzing:
“The key point is to first understand your business and the metrics involved, and how it is performing right now – then planning what you want to achieve.”