There are different types of e-commerce data, and although all of them are important, product data is both undervalued and highly underused by e-commerce marketers. To effectively analyse and act on product data, companies need a PMP (Product Marketing Platform).
This article will give you an overview of e-commerce data and data analysis, offer insight on how you can use quality product data to improve your business, and introduce the PMP (Product Marketing Platform).
While there are numerous types of e-commerce data sources, there are three primary sources that you should be paying attention to.
The first e-commerce data source is customer or consumer data. This refers to the behavioural, personal, and demographic information about customers. The goal of gathering customer data is to improve the customer experience, understand customers’ behaviour and buying decisions, and to maximise your e-commerce profits.
Customer data is also essential for customer segmentation. Customer segmentation, or market segmentation, is the division of potential customers into different groups based on customers’ demographics, needs, and buying characteristics, in order to better optimise your ads.
The second e-commerce data source is campaign data. Campaign data is simply the information you’re able to see about the campaigns you’re running in platforms such as Google Ads, Facebook, and Google Analytics. Examples of campaign data include impressions, ad spend, ROAS (return on ad spend), and CTR (click-through rate).
The third source of e-commerce data is product data, and it’s highly underused. While campaign data refers to how your campaigns are performing, it doesn’t grant you insight into how each of your products are performing. That's where product data comes in. By understanding the performance of each product through metrics like number of transactions, revenue, and margin, you can determine your best-selling products, your low-performing products, and much more.
While most e-commerce retailers understand the importance of customer data, and the majority of sellers already have the tools to analyse campaign data, product data remains undervalued and unseen across departments. As marketers continue their search for more data (87% think data is the most underused resource in their company), product data continues to prove its value as the cherry on top of the e-commerce data sundae.
It’s critical to understand how to properly analyse e-commerce data. E-commerce analytics is the process of compiling data from all of the areas that affect your online store and using this data to understand the trends and changes in consumers’ behaviour. This enables you to make data-driven decisions to fuel more online sales.
E-commerce data analysis works to answer quantitative questions about your business and, depending on the tool(s) that you use, you can analyse performance by tracking different types of data, such as conversion rates, traffic sources, AOV (average order value), checkout flow, and session duration.
While this kind of analysis offers quantitative data about what happens on your site, it doesn’t tell you why it happens, nor does it offer insight to your product performance.
This is problematic, because when it comes time to form a hypothesis for product improvement or to pinpoint how to fix particular pain points, your overall perspective will be insufficient, leaving you guessing rather than knowing what will work for your business.
That’s where product marketing systems come in handy, and the best way to extract holistic granular e-commerce data, including product data, is through a PMP (Product Marketing Platform). Skip ahead for more on the PMP here.
Here are a few examples of how product data can improve your business:
One of the biggest problems e-commerce retailers face is that company data is siloed, meaning different teams don’t have access to the same information. For example, the marketing team doesn't know the purchase price of each product, and the purchasing team doesn’t know the ad spend of each sale. This makes it difficult for departments to harmonise.
With a PMP, each team has access to the same source of integrated, cross-channel data. Hurrah to all being on the (literal) same page!
I’m going to let you in on a shocking secret-- it’s likely that 50% of your impressions are going to just 1% of your catalog. Yikes. Beyond that, you don’t have control over which 1% of your catalog those impressions go to. Double yikes.
Why is this happening? While Facebook and Google care about their advertisers’ goals (e.g. profitability), they also have their own goals, like offering users a superb experience.
The more data Google and Facebook have about each product, the better equipped they are to predict user experience, which benefits them. This means the more they promote one product, the more data they have on it, leading them to continue to promote that product and leave your other items out.
This is a textbook example of the Principal Agent Problem. In this case, the agent (Facebook and Google) makes decisions on behalf of the principal (you). Conflict occurs if one decision is best for the principal, but a different decision is best for the agent. The platforms favor the user experience, because they need to have users to have advertisers, but it doesn’t work the other way around.
To enhance the algorithms, you can use your product data to create separate product groups with their own bids and budgets, based around your specific goals. The social algorithms will pick from the selected groups rather than from the entire catalog, ensuring that all of the products you want promoted actually get promoted.
This goes hand in hand with enhancing your dynamic ads through product data. Facebook Discovery Commerce is an online sales system that places the customer in an “always-shopping-mode.” It is powered by data and machine learning that connects people with the perfect products based on their online behaviour before the customer starts searching for them.
If you are already capitalising on Facebook Discovery Commerce, Product Insights can take your business to the next level by providing insight into which products you should promote, which products you should stop promoting, and what your most popular channel for each product is.
ROI Hunter, a PMP (Product Marketing Platform), was designed to connect departments to product performance data and enable them to use it effectively.
With the PMP’s unique tool, Product Insights, retailers can gather product-level performance data (margin, chance of return, stock levels, etc.) from Facebook, Google Analytics, Google Ads, and custom sources, and combine it with their product catalog. From there, they can use the performance metrics to filter their catalog, and create highly specific product sets for their goals.
There are additional benefits to a PMP, such as helping e-commerce retailers to:
If you’re interested in learning more about real-world PMP success stories, take a look here.
The e-commerce industry has become a goliath, and it’s only going to get bigger. Though all data sources are important, product data is the cherry on top of the e-commerce data sundae.
By using a PMP to harness the power of product data, you can gain actionable insight into your performance, align your departments, enhance your dynamic ads, and improve Discovery Commerce.