What is revenue analytics?
Revenue analytics is a broad term to define data analysis that eventually leads to a revenue-related decision. This includes analysis on leads, customers, product/services, and (sometimes) operations.
From a business standpoint, founders and executives need to know:
- Where their sales are coming from.
- Who is buying (popularly called an Ideal Customer Persona).
- How much are they willing to pay.
- How can we reach more of these folks to buy.
All data and metrics answering these questions feed into a revenue generation strategy. The data and metrics used to build this strategy is called revenue analytics.
Revenue analytics means slightly different things for different companies depending on whether you sell to businesses or endusers. At least one of these techniques should be relevant to you:
Revenue analytics for B2B businesses.
Companies that sell services or products to other businesses typically follow a sales funnel to get paying customers:
- Prospects are at the top of the funnel: they’re potential users of your product or service. For example, “all marketing people” could be the universe of people who may benefit from your product (i.e. prospects).
- Qualified leads are a shorter list of prospects that you prioritize based on certain (qualifying) criteria. For instance, “marketing managers in D2C brands doing less than $10m in sales” could be your qualified list of leads.
- Interested leads are a subset of your qualified leads who may have shown interest in your product or service. For instance, people who have received an email from you and downloaded a whitepaper, or those who have seen an ad and signed up for an account.
- Closed or won leads are generally leads who have converted into customers.
There are many metrics that you would use in this scenario, but let’s look at the key ones:
- Funnel metrics are simply the number of people who are at each stage of your funnel. How many prospects, qualified leads, interested leads, and closed/won leads do you have at each stage? You could break it down further into: how many people signed up for a whitepaper or event, how many of those started a trial of your product, how many became paying customers, etc.
- Conversion rates are the percentage of people who move from one stage to the next. E.g., if 100 leads sign up for a webinar and then 10 of them start a trial of your product, that means your conversion rate is 10%.
- Channel analysis includes the metrics and data segregated by different channels (e.g. ad platforms, content pieces, etc). For each channel you use, you should track the following metrics:
- Click-through rate
- Conversion rate
Revenue analytics for B2C businesses.
B2C businesses typically have a lot more variables to keep in mind. Unlike B2B businesses where you have a finite number of products or services to sell, B2C businesses typically have a wide array of products to sell. If you are a direct-to-consumer (D2C) brand, then you may need to consider data from operations and fulfilment as well. At the very least, these are the metrics you should track:
- Customer analytics refers to using data to understand your customers. You should look at answering the following questions (and conduct the analysis in parenthesis):
- Where do my customers come from? (channel analysis)
- What products do they buy? (sales and returns by product SKU)
- Where in the purchase funnel do they drop off? (conversion rates)
- Who are my most valuable customers? (customer cohort analysis)
- Product analytics refers to the metrics you need to assess the right price, inventory levels, suppliers, and product variants to focus on. Look at these questions:
- Which products have a faster sell-through rate? (sell-through rate)
- Which products do my most loyal customers purchase? (sell-through rate of products within specific customer cohorts)
- What price-points move the fastest? (sell-through rate or speed of sale within specific price points)
- Which hot-selling products am I running out of? (inventory levels by product SKUs)
- Operations analytics refers to metrics that measure your effectiveness with warehousing, fulfillment, and deliveries. Look at these questions:
- Which warehouse should I stock which products in? (sales by region)
- How long is it taking me to get orders out the door? (ship out times)
- Which locations am I getting more returns from? (returns by region)
- Which logistics partners are performing well? (delivery timelines by logistics partners)
- Marketing analytics refers to marketing metrics to measure the effectiveness of your marketing efforts. Look at these questions:
- Which marketing channels are leading to sales? (sales by channel)
- Which campaigns and channels give me the best bang for my buck? (Return on Ad Spend)
- Can I trust the data my marketing channels provide? (conversion rate comparison by different attribution models)
This may seem like a lot of work (…and it is) but it is essential to use this data to make your decisions around:
- Which customer profile to deep dive on.
- Which ad channels to prioritize.
- Which products and services to focus on.
Collecting and measuring data for analysis.
Typically conducting this analysis means importing data from a lot of different sources (Shopify, Google Ads, your e-mailing software etc) into a spreadsheet to really understand what the data is telling you. You can also use platforms like Airboxr to combine the data for your analysis and automate the creation of cohort analysis, shipping partner analysis, and marketing KPI reports (see more).
In addition, you should consider tracking how users behave on your website and use that information to send them just-in-time nudges (or feed into your analysis above). Tools such as segment.com help you track user behavior and feed that into your website or e-mailing software.
Whichever tool you use, you must begin with the end in mind—ask yourself the question:
What decision am I going to make based on this data?
Knowing what decision you need to make is more important than just collecting data aimlessly. It is easy to get overwhelmed with data looking for interesting insights. The good folks at Dropbox found that decision-fatigue is a real thing—when faced with too much data, businesses tend to become less, not more, data oriented: making more gut-based decisions while looking at less data.
Whether you’re running a B2B or B2C business, you need clear revenue goals. Start with your revenue goals, then go to questions that need to be answered to help with those goals, then decide how to use data to support those goals.