How do you make decisions in your business?
Listening to advice from others?
Going off of your “gut feeling”?
Googling (and finding articles like this one)?
These are fine methods to use in the short-term, on a time crunch, or in low-stakes situations but they won’t cut it if you’re trying to make major decisions that will affect your business for the long-term.
Decisions like that require one thing:
Analytics.
For sales management, that would be sales analytics.
We’ll show you why sales analytics are so critical to the steady growth and effectiveness of your organization and the different types of sales analytics available to you.
But before we dive into all of that, let’s define sales analytics.
What Is Sales Analytics?
Sales analytics is the process for identifying, modeling, understanding, and prdicting sales trends while improving your ability to capitalize on these trends and your sales results.
In that sense, sales analytics is a process for comprehending and reconciling with past sales results and determining the best course of action for increased sales results in the future.
Sales analytics is usually performed by sales operations.
It should be noted that sales analytics isn’t just about sales.
It involves nearly all forms of business transactions and interactions, including those that don’t directly lead to sales, like the number of people who attended a major conference in your industry.
You can compare that number to the number of people that visited your booth or saw your ads, which helps you understand your current level of brand recognition.
Sales analytics have become so popular and powerful that many organizations have a dedicated group of data miners and analysts. These companies recognize the enormous value in collecting and intelligently using sales data.
3 Benefits of Sales Analytics
Sales analytics offers many advantages to companies willing to put in the work to get the most out of the data gathered.
Here are just a few benefits of sales analytics:
Identification of Missed Opportunities
Analyzing sales data helps companies pinpoint where they may have missed opportunities to capture more market share and pull in more customers. This is especially informative when juxtaposed with market and competitor research.
You should analyze your own products and the products your competition pushes out to understand what’s working for them, what’s not working for you, and how you can close the gap.
Clarification of Future Decisions
Clarifying big decisions for your business and brand that will have far-reaching effects is one of the key benefits of sales analytics.
From inventory management to marketing campaigns to exciting new offers, sales analytics helps you make much smarter decisions about the future.
You may realize you have to discontinue a product you thought was going to be a big hit but after a major change in the market – like, for example, cell phones with a touchscreen instead of buttons – you may have to pivot to something completely different to keep up with market demand. Product analytics can help guide you through those kinds of decisions.
Recognition of Market Trends
Market trends are a constant changing force that you need to be hyper aware of in order to serve your customers exactly what they want.
Analyzing last year’s sales data can show you when sales spiked, when they dipped, and what you need to do to ride the wave.
Maybe you need to have better offerings or deals around specific holidays like Halloween or Christmas. Or perhaps you should time your next marketing campaign with a major cultural event, like the Olympics.
Analyzing your sales data will reveal your best next steps.
12 Types of Sales Data Analysis
There are many, many ways to analyze sales data. Countless equations and formulas, and endless ways to parse through raw information.
Here are 12 types of sales data analysis that almost any business will benefit from using:
1. SKUs
One of the most common and most basic types of sales analysis is examining your products’ stock keeping units (SKUs) – the size and variation of each product by color, geography, price, brand, etc.
This allows you to see the trend in sales over several years in different states and metropolitan areas. It also helps you identify your best sellers. Maybe your women’s tennis shoes sell better in Minneapolis than men’s, but men’s athletic pants sell better than women’s in the same area.
Once you have this knowledge, then you can search for the answer to the question “why?”
2. Channel of Distribution
Analyzing your sales data by channel of distribution is very important if you’re selling your product in many different places.
Individual retailers should be looked at separately, but they should also be grouped by type. For instance, supermarkets, vending machines, online ecommerce platforms, those are all unique channels of distribution that need to be analyzed separately to get a full picture of the performance of your products across time and space.
You can aggregate all of the sales of your products to study the distribution of total product sales by different channels. You’ll be able to see changes from year to year, month to month, channel to channel.
3. Per Capita
Per capita sales analysis is a “relative” technique for gaining new understanding of your data, allowing it to take on new meaning.
For example, you may take the annual sales, or unit sales, of one brand state by state and divide it by the total population of each state. This will give you annual sales per person by state.
You may discover surprising trends, such as getting higher sales in states closer to your corporate headquarters and lower per capita sales in states further away.
4. Per Comparable Economic Data
Another way to analyze sales data on a relative basis is by comparing your sales to different sets of economic data.
One way to do this is comparing annual unit sales to the GDP of a specific state, or other economic variables like total gasoline consumption, total prescription pill consumption, etc.
This helps you understand how your brand and products are doing relative to other measures of economic potential and growth.
5. Category Development Index
The category development index (CDI) measures the sales performance of a category of goods or services in a specific group. It’s widely used and can be calculated for cities, counties, states, or even groups of states.
You could take your total annual sales for one product category by state, calculate the per capita sales number for each state, calculate the overall average per capita sales for the US, divide each state’s per capita sales by average per capita sales and multiply by 100.
Then you’ll 100 as the average index score and any state with a CDI above 100 would be considered above average market while those with CDIs below 100 are below average markets.
6. Brand Development Index
Brand development index (BDI) is similar to CDI, but focuses on individual brands within a product category to help you allocate advertising and promotional expenditures across different geographical areas.
The way to calculate BDI is very similar to CDI.
But BDIs don’t need to be based on per capita sales for the whole population. They could be based on adults only or female teenagers only, for example, as long as per capita sales data are available for these groups.
7. Competitive Trends
Competitive trends are one of the most useful types of sales analytics you can depend on.
Think about it this way:
If you know your brand of ranch dressing has a 5% market share in Illinois and annual sales in Illinois are $1.5 million, then you can easily calculate the total size of the ranch dressing market in Illinois.
100 divided by 5 multiplied by 1.5 million.
Total market share in Illinois is $30,000,000.
This allows you to understand how much ranch dressing each of your competitors are selling in Illinois each year – letting you know how well you’re doing in relation to your competition.
8. Analytical Database
Forming an analytical database is a huge step toward ever more sophisticated forms of sales analysis.
It would contain all of your historical sales data along with competitive data, demographic data, economic data, and so much more.
You can also continuously add marketing variables to the database like ad spends by media, distribution level, and promotions.
9. Cross Tabulations
After you have your analytical database in place, you can accomplish more powerful analytic work by creating cross tabulations.
One way perform cross tabulations is through the contrast of extremes. For example, contrasting geographic areas with the highest BDIs to those with the lowest and identifying the reasons for differences you find.
You could also compare areas with high unemployment to those with low unemployment, or high growth and low growth, to determine how well your product may perform.
10. Multivariate Analysis
Multivariate analysis is a method that applies various advanced techniques for examining relationships among multiple variables at the same time.
It allows you to perform scientific econometric analysis to understand the true cause and effect driving your business’s growth or decline.
To perform this type of analysis, you’ll have to gather up all of the demographic and economic data you believe played a role in increasing or decreasing your sales and put it all into your analytical database.And if your data is backed up to a hard drive, consider investing in hard drive recovery software to ensure that you can retain your data.
11. Loyalty Programs
If you’ve run loyalty programs, you probably have tons of data available about your customers. You can see what an individual customer buys and how their buying patterns may have changed over time.
To do this right, choose a representative sample from your loyalty program records and examine patterns, trends, and changes in sales by different variables such as demographics, time, season of the year, and so on.
12. Marketing Research
Marketing research helps define opportunities and problems, refine marketing actions, and monitor marketing performance.
This helps you make smarter marketing decisions and to better understand the market you’re selling to.
While many of the other types of sales analytics on this list are concerned with what happened in the past, marketing research helps you look far into the future to determine the best course of action.
An Overlooked Way of Communicating Sales Analytics Data to Your Team (You’ll Want to Try at Least Once)
Data is not sexy.
While analysts may enjoy pouring over facts, figures, and formulas, most other executives may not. And other departments, like marketing and sales, may glaze over when you start sharing this data.
To help you communicate this information and show why it’s relevant and valuable, we recommend you opt for visual communication over text-based.
Like annotating screenshots of spreadsheets, recording your screen as you walk through difficult data, and hosting a webcam meeting where you can field questions and answer any concerns.
You can get all of this done and more with a tool like Zight (formerly CloudApp), voted by G2 Crowd as one of the top sales enablement tools.
Make your data easier to understand, consume, and share by discovering why Zight (formerly CloudApp) is an essential sales analytics tool today!
