Home Ecommerce analytics dashboard playbook Product insights
Product ratings influence E-commerce purchases. Understanding what products customers like, and what they are likely to purchase helps in planning future strategies. Identifying top profitable products, top profitable bundles, top non-performing products help in realizing what works and what does not. The dashboard answers the below questions:
Product Performance Dashboard for E-commerce Businesses is a one-stop solution that focuses on combining & analyzing Product Catalogue, Sales Performance, Inventory and User interaction data points to enable accessing all granular level insights about everything that is happening with your products. Which in turn helps Merchandisers & Marketers in eliminating the guesswork, taking actions towards higher profits, better customer satisfaction, efficient & optimized product catalogue and inventory management.
The quick indicator view boxes at the top of the report make it easy for businesses to track the numbers they need. It can even be compared with the previous year to see if the situation is better or worse.
This report includes the basic KPIs important from a sales perspective – total orders and gross revenue. The report has a New/Repeating Order Type filter which gives the users three choices – to view the data related to new orders alone, repeating orders alone, or both. In addition, it also helps in monitoring the performance of a single product, users can select the appropriate product type they want to view using the product slicers: Category, Style, Name. It identifies opportunities to achieve the below goals:
Goals
Questions to ask
Dashboard views
Track the current sales situation
What is the total order number?
Total Orders
Track the top-selling items
Which are the top-revenue generating products?
% Order w/Products
Track Revenue growth
How is revenue trend?
Gross Revenue
Compare New and Repeating Orders
What is difference between New and Repeating Orders?
First-time Orders, Repeat Orders
First-time orders:
Total of the first order of each customer
Repeat orders:
Total of the repeat orders of each customer
% orders w/Products:
Out of total orders, how many % of orders contain the selected product?
Product analysis provides all of the information necessary to determine if a product meets the company’s expectation and customer preferences, it does well or not compared to other products. Businesses can decide if they want to boost or eliminate the item from their inventory based on product performance.
This report displays the statistics regarding the most popular and highly purchased products by new customers organized in visuals which can be filtered by a specific period of Time, Category, Style, Name, etc. We can see which products are doing well, so we can run advertising programs and manage imports better. The business could achieve the below goals:
Goals
Questions to ask
Dashboard views
Track top-selling items on the first order
What are top-selling items?
Top 5 Products by Customers Contribution w/Product
Track customers contribution on their first orders
How much customers contribute to business on their first orders?
Customers Contribution w/Product
See the relationship between New Customers and Average New Customers Contribution
Do New Customers and Average New Customers Contribution have the relationship?
New Customers vs Avg New Customers Contribution Plot
New customers:
Customers purchasing for the first time
Customers contribution w/Product:
Total customers contribution on their first orders
Average new customers contribution = (customers contribution w/product)/(new customers):
Average amount of money customers spend on the first order
Understanding your new customers and hero products is a key aspect of being able to effectively market to customers and build relationships with them.
This report gives a visual representation of your product return data and displays it in an easy-to-understand form. With the product returns dashboard, you can gain valuable insights into return patterns and customer behavior, helping you make better decisions about pricing, products, the customer experience, and more. The business could achieve the below goals:
Goals
Questions to ask
Dashboard views
Track returned product rate
Is returned product rate high or low?
Returned Quantity/ Revenue Rate
Frequently returned products
What are frequently returned products?
Returned Quantity/ Revenue by Category, Style
Common return reasons
What is the most common reason? Can it be improved?
Return Reason
Preferred methods for returning products
Customers prefer Exchange or Refund method?
Total Returns by Return Type
Exchange:
Customers return goods and exchange for other products, partial refund is possible
Refund:
Customers return goods and business refund the customers
Returned Quantity/Revenue Rate:
Ratio of quantity/revenue that customers returned to total quantity/total revenue
When a customer returns your product, your reputation is at stake. It is critical that you understand why products are returned as well as how to prevent future returns. Implementing a Product Returns Analysis dashboard will help you uncover product defects and/or quality flaws that otherwise may not have been captured during the manufacturing process.
The dashboard shows product performance in the first 10 orders each customer purchased. We can see the customer’s behavior towards the product through the orders. The dashboard answers the below questions:
Customers first have a tendency to purchase underwear items while accessing the company’s products, which is its primary product category. Up to 56% of the initial orders included Underwear, and 33% of them included Bras; these percentages also marginally increased on the second order. Customers did, however, eventually start trying other items including Tops, Bottoms, and Dresses in their subsequent orders. Hence, after recognizing trends, we can provide discounts and coupons for Tops, Bottoms, and Dresses products based on the quantity of prior purchases made by customers and the items in their cart. Businesses can boost sales and profitability by identifying the ideal customer behavior.
Which items will predominate purchase orders and the general movement of the categories were shown in the ‘Product Presence by Order frequency’ matrix above. What if we would like to see more specifics about each category? This is what the matrix “Order Share% by Category” will enable us to do. If customers purchase Underwear on their first order, 76% of them will do so again on their subsequent orders, according to the matrix. Dark diagonal line also shows that customers are more likely to acquire goods they have already purchased, whether as a result of a positive experience with a used item or a hesitation to try a brand-new one. Due to the fact that we are now deeper in the customer’s shopping basket, business can then provide even better deals than the standard offer given before.
Customer behavior analytics allows any business to accurately answer all these questions and increase the probability of making a sale. By analyzing customer behaviors, you can predict what products they want, determine purchasing ability, set up your marketing campaign.
This dashboard monitors the current performance of product pairs and identifies which pair of products is doing well. Thereby the marketing team can build a campaign to promote these two products together, which helps in increasing revenues and profits. The dashboard answers the below questions:
The Basket analysis pattern builds on a specific application of the Survey pattern. The goal of Basket analysis is to analyze relationships between events. A typical example is to analyze which products are frequently purchased together. This means they are in the same “basket”, hence the name of this pattern. Two products are related when they are present in the same basket. In other words, the event granularity is the purchase of a product. The basket can be the most intuitive, like a sales order; but the basket can also be a customer; In that case, products are related if they are purchased by the same customer, albeit across different orders.
Because the pattern is about checking when there is a relationship between two products, we will call Product (Category/Style/SKU) the original viewed product and And Product (And Category/And Style/And SKU) the co-viewed product. Click on any category in the Category slicer, for example we choose “Underwear”:
The # Orders
The # Orders measure returns the number of unique baskets in the current filter context. It shows you how many orders contain at least one product of the selected Category. In this case, there are 9246 orders containing at least one product from the “Underwear” category.
# Orders And
The # Orders And returns the number of unique baskets containing products of the And Product selection, ignore the Product selection in the current filter context. There are 5553 orders containing at least one product from the “Bras” category, 5456 orders containing at least one product from the “Tops” category and so on.
# Orders Total
# Orders Total returns the total number of baskets and ignore any filter over Product and And Product. That means we have a total of 16928 unique orders.
# Orders Both
# Orders Both returns the number of unique baskets containing products from both the categories selected with the slicers. It shows that 3173 orders contain products from both categories: “Underwear” and “Bras”.
% Orders Support
% Orders Support returns the support of the association rule. Support is the ratio between # Orders Both and # Orders Total. It shows that 18.74% of the orders contain products from both categories: “Underwear” and “Bras”.
% Orders Confidence
% Orders Confidence returns the confidence of the association rule. Confidence is the ratio between # Orders Both and # Orders. It shows that out of all the orders containing “Underwear”, 34.32% also contain “Bras” products.
Orders Lift
Orders Lift returns the ratio of confidence to the probability of the selection in And Product.
A lift greater than 1 indicates an association rule which is good enough to predict events. The greater the lift, the stronger the association. It reports that the association rule between “Underwear” and “Bras” is 1.05, obtained by dividing the % Orders Confidence (34.32%) by the probability of # Orders And over # Orders Total (5553/16928 = 32.8%).
Looking at the % Orders Confidence matrix, we can see that customers buying “Bras” are likely to buy “Underwear” too (confidence is 57.14%), whereas only 34.32% of customers buying “Underwear” also buy “Bras”.
After identifying the relationship between these two categories, we can choose the Style, And Style, Name, And Name (color/size) to see the metrics on specific products. Based on that, a Marketing campaign to promote those two products together will be conducted.
The dashboard gives a detailed view of these measures from product pairs and make it easy for the Sales – Marketing team to take decisions.
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