ca. 30 Minuten
nach Wunsch auch mit VideocallBusiness Intelligence… what are we actually talking about!?
A Quick Guide in 6 episodes.
Today's topic – episode 4: Differentiate between various kinds of revenue and precisely define metrics like cost-turnover-ration, return rates or customer figures – this is what you'll learn in this blog post.
About this blog series
Business Intelligence. Is it baloney or a term that matters? Does it even concern me or will I have deleted the word from my vocabulary by tomorrow anyway? You’ve probably heard about it and read about it, but you may not have ever quite understood exactly what it means? Anyway – Business Intelligence surely is a term that leaves many people in a quandary. Maybe this is you? Then maybe we’ve got something for you: This Blog Series offers you some help by providing six successive blog articles about what we are actually talking about when we use the term Business Intelligence – based on our extensive BI-centered work in consulting and development for the online sector. For everyone who wants to know more, and in particular, for online retailers.
INDEX OF EPISODES
- Episode 1: What’s the use of Business Intelligence? Do I need that, too?
- Episode 2: BI Fundamentals – Metrics and KPIs
- Episode 3: Important Metrics for Successful eCommerce (I): Traffic, Average Session Duration, Conversion Rate, Average Order Value
- Episode 4: Important Metrics for Successful eCommerce (II): Sales Metrics, Cost-turnover Ratio, Return Rate, Customer Metrics, Repurchase Rate
- Episode 5: Important Metrics for Successful eCommerce (III): The Customer Lifetime Value Special
- Episode 6: 5 Golden Rules for your BI project – Drawing a Bottom line
Important Metrics for Successful eCommerce (II): Sales Metrics, Cost-turnover-Ratio, Return Rate, Customer Metrics, Repurchase Rate
…and on to our further explanations concerning the most important metrics for successful eCommerce:
5. Sales
- Data Source: ERP system/shop system
- Unit: Euros
- Calculation: Amount · achieved price
- Responsibility: Marketing
- Central Question: Which types of sales metrics are there and how exactly are they defined?
Ask five employees from different departments for their definition of sales and you will probably get five different definitions. And all of them are correct. But the problem is that there is no company-wide consensus or basis for comparison. This is especially important for holistic and cross-department analyses. Otherwise, your meetings won’t end with recommendations for action to the customer, but instead to the IT department. Accordingly, distinct definitions of the different types of sales metrics are a really central aspect of your data- and metrics-based work. To date, the definition workshops on our projects took two days alone to get to the moment when everybody could agree on the various sales metric’s definitions:
- Demand Sales
- Reversal Sales
- Gross Sales
- Return Sales
- Net Sales
The prefixes “Gross“ and “Net“ do not refer to the Value Added Tax (VAT) in this case. All metrics are, generally speaking, accounted for without VAT (as well as in the case of Gross Sales).
Here are probably the three most important sales definitions:
Demand sales: The Demand Sales include all sorts of sales before reversals, technical problems, stock difficulties, payment defaults and so forth. Accordingly, it refers to the overall possible sales that have been demanded in all channels.
Gross Sales (also: Delivery Sales): Which sales leave stock and are sent on their way to the customer? After discount of possible stock difficulties and system or customer mistakes, this amount is the so-called Gross Sales.
Net Sales: Having discounted the return sales from the Gross Sales, you get the Net Sales and thus the metric that shows you what really stays at the customer’s. To evaluate for example the success of marketing actions, all sales metrics that have been mentioned have to be calculated according to the order date.
The explanations above seem to be easy to implement. But building a data warehouse, you will quickly recognize that, for example, your commodities management software holds some tricky technical challenges for you concerning the proper calculation of sales: payment alternatives, states of order position, historicized pricing, discounts, coupons. Here, the several types of sales metrics have to be defined accurately with the whole team as well!
But finally, BI takes care of having it easier, clearer, more transparent at this point again: An eCommerce Data Warehouse can – once it is built – filter all kinds of sales metrics for all channels (chain store, catalogue, online, outbound) and display them according to the various data filters – by the order, by the invoice and by the delivery date.
6. Cost-turnover ratio
- Data Source: ERP system/shop system
- Unit: Percentage
- Calculation: Total Cost per Order (CPO) / Total Sales (before and after returns)
- Responsibility: Marketing
- Central Question: Which cost-turnover ratio (before and after returns) do I achieve per channel to deactivate – if necessary – malfunctioning channels immediately?
In a short-term view, the cost-turnover ratio displays, if the applied budget is in due proportion to the amount of sales (before and after returns). The calculation of the cost-turnover ratio concerning for example a marketing campaign shouldn’t take place as late as 30 days after the campaign’s beginning. This way, you really make sure that every customer is appropriately taken into consideration. In a long-term view, there shouldn’t just be included the total CPO for the calculation of the cost-turnover ratio, but further indirect acquisition costs as well. If you for example keep an expensive SEO and Social Media team, this will certainly have an impact on the cost-turnover ratio, too. The resulting costs ‘just’ have to be calculated and to be integrated into your Data Warehouse.
7. Return rates (amount and value)
- Data Source: ERP system/shop system
- Unit: Percentage
- Calculation: (Amount of returns · achieved price) / (Amount of orders · achieved price)
- Responsibility: Marketing/Purchasing department
- Central Question: Which channels/customers/products provide the highest return rates and why?
The Devil Returns Prada. No matter what you sell online – it’s the customer’s right to send it back (groceries excluded). Returns are the cost driver where it pays off to work on optimization on a daily basis: From better pictures and text across virtual fitting rooms right up to online channel analyses that tell you which channel the multi-returners (important: define exactly what this means!) come from. Maybe after that the channel isn’t as attractive as you thought it was in the first place anymore, when you “only“ considered the demand sales.
To minimize return rates can easily have more positive impacts than, for example, increasing traffic: More satisfied customers, higher repurchase rates, growth of the contribution margin.
One of our customers was able to identify all 100%-returners, and with the help of our BI Solution, contacted them and enquired about what to improve from their point of view to minimize their return rates. Then the customer was able to evaluate the result of this unusual method (up to then, none of our customers had ever done this) with the help of our BI Solution again: 50% of the identified customers had either stopped ordering completely (so didn’t cause any return costs anymore) or had stopped returning their goods to such great extent.
8. Customer metrics
- Data Source: ERP system/shop system
- Unit: Depending on metric – often number of persons
- Calculation: Depending on metric
- Responsibility: CRM/Marketing
- Central Question: How many new customers from a specific customer segment could be acquired from which channel respectively from which online marketing action and be converted into existing customers? What characterizes the several segments of customers?
You can account for every metric separately, but you shouldn’t dissociate them. Again, a holistic analysis has the well-known advantage: How many initial purchasers really became new and then existing customers?
Number of initial purchasers: An initial purchaser is someone who orders something for the first time.
Number of new customers: New customers are initial purchasers who record positive net sales. So far, so simple.
Number of existing customers: Similar to the question for the exact definitions of sales, the situation looks concerning the quite simple question: “how is an existing customer defined?” To be more precise: How often, in what kind of time frame, with which order value does a customer have to purchase to be considered an existing customer?
As soon as the basics are defined and accessible in your Dashboard on a daily basis, you should think about further customer segmentation: Who are the top customers (for example those who never return anything), who are the flop customers (who – in contrast – return everything)? Which customers have to be reactivated and, first of all, how to do that (e.g., newsletter, telephone, coupon)? You find the answers in your Data Warehouse!
Additionally, further metrics that have already been introduced here can be drilled down to the dimension of a single customer respectively a single customer segment, of course. An AOV per customer segment, for example, can deliver important information regarding an efficient, differentiated customer management, as well as a return rate, an Average Session Duration or a Conversion Rate per customer segment can.
Another important customer metric is the Interpurchase Time. The Interpurchase Time shows between what length of time the customers buy something in my online shop – that is to say, the duration during which they are inactive between their purchases. Have I collected precious information concerning this by tracking or analysis? If I have, I am enabled to forecast future periods of my customers’ inactivity – and forecast values that I can, for example, build the development of new, optimized marketing strategies on.
9. Repurchase rate
- Data Source: ERP system/shop system
- Unit: Percentage
- Calculation: (Number of existing customers in period X + 1) / (Number of new customers + existing customers in period X)
- Central Question: How well does my business model work? Do I get “better” customers in my shop at all?
With the overwhelming amount of online shops today and the massive size of advertisement budgets it really is an achievement to acquire just one customer. In relation to the amount of money invested in the customer’s acquisition (depending e.g. on the acquisition channel), it may happen that a positive contribution margin can’t be achieved before the customer’s repeated ordering (repurchase).
Let's continue in Episode 5 of our BI blog series with the big Customer Lifetime Value (CLV) Special!
Especially for growth-oriented retail companies, we have compiled a KPI Guide with the top 5 KPIs. Download now for free and define your relevant key figures for an effective KPI strategy!
