How do you handle data analysis for your online store?
Are you prioritizing your top-selling products for promotion, or are you discounting underperforming items to drive sales on a weekly or monthly basis? If you're concerned that these tactics aren't maximizing your sales, we have strategies backed by data analysis to help you boost revenue.
Before we dive into the main topic, please remember this.
Just because a product sells a lot or experiences a sudden increase in sales does not necessarily mean it's a good product.
The biggest factor contributing to an increase in product sales in ecommerce is exposure. According to our findings, there is a strong correlation between higher product impressions and increased sales across all ecommerce stores. This exposure also significantly impacts offline shops. A simple example is placing seasonal fruits or bestsellers at the center or front of a grocery store. On the other hand, products that don't fall into this category are usually stored in fridges or on the farther side of the store, affecting their sales due to varying levels of exposure. The same principle applies to clothing stores that display the most attractive outfit on a mannequin near the window. However, most businesses cannot guarantee the same level of exposure for all their products. As a result, the top 5-10% of products often account for 80% of total sales. If you're a marketer reading this, take a moment to confirm whether the best-selling products are performing well primarily because they receive the most exposure.
We will introduce you to online store product data analytics to identify potentially popular items you may have overlooked. This involves more than just pinpointing products with high sales due to extensive impressions. First, look for products with high click-through rates and also high purchase conversion rates. Such products typically exhibit the following characteristics:
Good product = High sales volume and revenue product = High impression x High click-through rate x High conversion rate x Low cancellation rate
Products brought in with confidence in their popularity often fail to reveal their true value when one of these four criteria is lacking. Identifying which aspect among the four criteria was lacking and taking action can turn it into a "well-selling and higher-revenue-generating good product."
: Two key metrics to look at during e-commerce data analysis
You should examine the criteria for your product to become a successful item. By considering these two metrics, you can determine the necessary actions to develop a good product.
A. Low Click-Through Rate (CTR) & High Purchase Conversion Rate (CVR)
Firstly, products belonging to Group A may have unattractive thumbnails or may not have been adequately exposed, which could be the cause. Conducting further checks on the exposure rate will identify which factor needs improvement: the location of exposure or the thumbnail. If the exposure rate is low, consider relocating the product to the top of the landing page for immediate visibility. If the exposure is moderate to high, consider changing the product's thumbnail.
Additionally, check the placement of your products within the ecommerce store. It's important to check if similar products are displayed in the same area of the platform. For instance, when a red sweater and another item of a similar color are shown simultaneously, both products may attract less attention. Instead, arrange products that don't overlap in images closely to increase visibility..Additionally, you can increase visibility through the use of emojis or highlighted text. Given that the product already has a high Purchase Conversion Rate (CVR), enhancing its exposure and click rates could further improve the CVR. However, increased exposure might also lead to a higher influx, potentially lowering the CVR. It's essential to monitor the data trend for 2 to 3 days; if the click rates increase without negatively impacting the Purchase Conversion Rate (CVR), then your sales are likely to rise. However, if the CVR starts to decline, consider the following recommendations.
In this scenario, the product's unique advantages are not being effectively highlighted. Visitors might click on an appealing thumbnail, but insufficient information on the details page, poor reviews, or high pricing can lead to them leaving without making a purchase. To tackle this issue, improve the product's details page and encourage customer reviews by organizing a review event or offering a discount on the product. Many customers who were initially disappointed may reconsider making a purchase after viewing the improved details page.
Additionally, enhance the credibility of this product by using an 'Social Proof Alert' on-site banner on your ecommerce store. Moreover, a prolonged shipping period can also reduce Purchase Conversion Rates (CVR), so ensure you have sufficient inventory.
C. Low Click-Thru Rates (CTR)· High Purchase Conversion Rates (CVR)This product is underperforming. If it has already received significant exposure in the ecommerce store yet continues to underperform, the issue may be multifaceted. Factors such as a high price, unattractive thumbnail, and insufficient information may all be contributing to its low sales. If the product already benefits from high exposure, consider categorizing it under 'Low Click Rates · High Purchase Conversion Rates (CVR)' or 'High Click Rates · High Purchase Conversion Rates.' Subsequently, enhance the details page and use on-site banners to improve the Purchase Conversion Rate, followed by an increase in exposure.
Try maximizing the use of popular products that already boast High CTR and High Purchase CVR. First, try to maximize the impressions or expose the product in the most efficient spot on the site. These products have a high potential for greater purchase rates compared to others. Display the top 5 products on the recommendation banners on-site or showcase them through performance marketing.
Some final words
We've shared strategies for analyzing ecommerce data and methods that can help you determine 'what contributes to a product's sales performance?'. For ambiguous questions like these, the answers typically lie within the data. Questions such as 'Is our growth satisfactory?' and 'How satisfied are our customers?' can often find resolution through data analysis. We hope this assists you in taking the first step towards finding solutions through data analysis.