You’ve probably heard the saying, “data is king.” Well, that’s especially true in the eCommerce world. If you want to be successful, you need to understand your customers and their buying habits. Unfortunately, many businesses are unknowingly losing money because they’re letting the value of their data go untapped.
This blog post will discuss the dangers of data dumping and how modern eCommerce analytics can help you avoid them. We will also cover some of the benefits of using customer feedback to foster organization-wide customer-centric eCommerce strategies.
eCommerce Analytics: What Is It and Why You Need It?
Enterprise eCommerce organizations collect massive amounts of data on a daily basis. Enterprise eCommerce analytics analyzes this data to gain insights that can help improve strategies to directly impact KPIs. This data includes:
- Website traffic
- Customer demographics
- Customer feedback
- Purchase history
- Conversion rates
By understanding eCommerce analytics and how it can be used, organizations can make better-informed decisions about where to invest resources.
There are a number of different types of eCommerce analytics, but some of the most important include:
- Web analytics
- Customer segmentation
- Customer sentiment
- A/B testing
Web analytics provide an overview of how people interact with a website, including which pages they visit most often and how long they spend on each page. Customer segmentation helps identify groups of customers with similar characteristics, so brands can tailor their marketing efforts to appeal to them more effectively. Customer sentiment provides information about how consumers think and feel about every aspect of a brand’s product and customer experience. And A/B testing allows brands to compare two versions of a web page or product to see which one performs better.
Different Methods of Customer Data Collection
There are many different methods of data collection for eCommerce organizations. The most common method is web scraping, which involves using software to extract data from websites.
Another popular method is data mining, which uses algorithms to identify and extract patterns from large data sets. Other data collection methods include:
- Customer surveys
- Focus groups
- User testing
- Organic and promotional customer feedback
eCommerce analytics play a vital role in understanding how customers interact with an online store. By tracking data such as:
- Page views
- Add-to-cart rates
- Conversion rates
eCommerce businesses can gain insights into which areas of their site are performing well and which need improvement.
Manual analysis involves going through each page of the site and recording data points such as the number of views, the number of add-to-carts, and the number of conversions. This data can then be analyzed to identify patterns and trends. Manual analysis is time-consuming, but it provides invaluable insights that can be used to improve the performance of an online store.
On the other hand, AI analysis is a method of eCommerce analytics that uses artificial intelligence algorithms to identify patterns and trends in data. AI analysis is much faster than manual analysis and can be used to process large data sets quickly as well as perform contextual analysis of qualitative text-based feedback such as reviews and ratings.
The Problem with Manual Analysis
For enterprise CPG organizations, eCommerce analytics are essential for understanding and optimizing their online reputation and sales. However, manually analyzing eCommerce data can be time-consuming and error-prone.
Moreover, it can be difficult to draw insights from large data sets without the help of specialized software. As a result, many CPG organizations choose to invest in eCommerce analytics tools that can automate the analysis process. These tools can help speed up decision-making and improve accuracy, ultimately leading to higher sales and profits.
Two main types of bias can skew eCommerce analytics: self-selection bias and data interpretation bias.
Self-selection bias occurs when people consciously or unconsciously select products that fit their preferences. For example, someone who likes a certain type of food is more likely to buy it online than someone who doesn’t like it.
Data interpretation bias, on the other hand, happens when people misinterpret data or use it to support their own preconceptions. For example, a person might look at a chart and see patterns that don’t actually exist.
Both self-selection bias and data interpretation bias can lead to inaccurate conclusions about customer behavior. This can have serious consequences for CPG organizations, who may make wrong decisions about:
- Pricing and value
- Messaging claims
- Product development
To avoid this problem, companies need to be aware of the potential for bias and take steps to mitigate it.
Small Sample Sizes
One of the main issues with manual analysis for CPG companies is that it can be difficult to get a representative sample of the data. For example, if a company only has data from a few hundred customers, it may not be able to accurately identify trends or understand the behavior of its customer base.
Additionally, manual analysis can be time-consuming and expensive, limiting the ability of CPG companies to keep up with their competitors.
Fortunately, tools are now available to help CPG companies overcome these challenges. One such tool is eCommerce analytics software, which automates the process of collecting and analyzing data. This type of software can quickly and accurately identify trends and patterns in customer behavior, making it an invaluable tool for CPG companies.
Additionally, eCommerce analytics software is usually much more affordable than traditional manual analysis methods. As a result, it is becoming increasingly popular among CPG companies looking to improve their eCommerce operations.
For CPG organizations, eCommerce analytics is essential for understanding how customers interact with their products online. However, manually analyzing eCommerce data can be a time-consuming and tedious process, particularly for companies with large and complex data sets.
This is often due to a lack of bandwidth within the organization – that is, there are not enough staff members with the necessary skill sets to effectively analyze the data.
As a result, many CPG companies are turning to eCommerce analytic tools to automate the data analysis process and improve decision-making. These tools use artificial intelligence (AI) to quickly identify patterns and insights in eCommerce data, freeing up valuable time and resources that can be spent on other areas of the business.
The Cost of Manual Analysis
In any business, but particularly in the enterprise CPG industry, data is critical to understanding Voice of Customer, and where best to allocate resources.
In the past, this data was gathered manually through surveys and interviews. However, with the advent of social media and other online platforms, gathering huge amounts of data are now possible. This has led to a debate about whether a manual analysis is still necessary.
There are persuasive arguments on both sides. Those who advocate for manual analysis point to its accuracy and detail-oriented approach. They argue that social media data can be misleading and that it is important to have a human element to understand customer behavior’s nuances.
On the other hand, those who advocate for automatic data gathering argue that it is more cost-effective and efficient. They also point out that manual analysis can be biased and that it is often based on small sample sizes.
An emerging and largely untapped resource for AI based analysis is consumer sentiment and Voice of Customer. With the growing presence of eCommerce, online reviews & ratings have become and increasingly reliable and data rich source for shopper sentiment analysis that forward looking mid to enterprise level CPG’s are increasingly relying on as their source of truth.
The best approach likely depends on the specific KPIs a company is trying to track, its sales goals, and available resources. However, one thing is certain: in today’s data-driven world, companies must carefully consider how they collect and analyze information if they want to stay ahead of the competition.
Implementing an eCommerce Analytics Strategy That Converts
It’s no secret that data is becoming increasingly important in today’s business world. Companies are collecting more data than ever before, and competitive organizations utilize their customer data to gain valuable insights across various departments.
In this article, we have discussed the pitfalls of maintaining outdated data collection and interpretation practices, as well as a variety of options that are available to competitive and forward looking organizations.
As manual analysis becomes more inefficient and obsolete, organizations are turning to AI based solutions to gain insights that lead to more efficient eCommerce solutions. Customer sentiment and Voice of Customer are emergent as key resources for agile decision making and customer-centric strategies.
Yogi offers a cutting edge AI based solution to make customer sentiment analysis quicker and easier than ever while saving time and resources for CPG’s. By contextually analyzing customer feedback such as reviews & ratings, survey responses, and more, Yogi enables organizations to understand their customers at the deepest level possible in real-time.
Looking for a deeper look into customer sentiment for your products and your competitor's products?
Yogi’s K-means-based AI platform analyses Reviews & Ratings at the deepest level possible across all competitors and retailers. Save time searching for insights so you can move the needle for your brand today.