Top Sales Data Analysis Techniques For Businesses

Any business that denies the importance of sales and marketing is a business that’s driving itself to the ground. Without proper sales and marketing strategies, a business will find it hard to grow at best and perform very poorly in not-so-good scenarios. 

We’re coming into a time, however, when sales is changing drastically. Gone are the “good ol’ days” of door-to-door sales being the only or most effective form of sales for the far majority of industries. There’s a big push for digital sales as more companies look to processes like online advertising, social media marketing, customer relationship management, and other digital sales strategies to help grow their enterprises. And as we move into a digital era, some parts of sales become easier. For instance, the way we gather and analyze data has now become easier for the most part.

Tips when analyzing sales data

The digital era is so closely tied with the big data boom. Cloud-based services and eCommerce systems make it easier to gather, analyze, and crunch data. It’s no wonder that business intelligence and analytics reached a whopping $15.75 billion in value in 2021.

Taking a more statistical analysis of sales data will help you determine points of improvement in your sales funnels and tactics that could lead to better returns. So if you’re a business that isn’t analyzing sales and marketing data yet, you definitely need to start as soon as possible. If you’re planning to do that, then here are some tips to get started with analyzing sales data.

1. Determine your objective

No matter how much marketing data you have, it’ll all be useless if we aren’t clear about what our objectives and targets are. The first step to effective sales strategizing and consequently sales data analysis is by determining your benchmarks first. Answer crucial sales questions, such as:

  • What sales channels do you want to grow most?
  • What is working well and what isn’t working well?
  • How much increase do you want to see in your sales?
  • How much are you willing to spend on sales and marketing?
  • What’s the return of investment you want to see on your sales initiatives?

2. Decide what data you want to analyze

Next, you want to make sure that you’re gathering and analyzing the right kind of data. There are so many data points to measure and observe, but not all of them really help you achieve your marketing goals. 

One sales data analysis example companies should evaluate is how they measure the impact of their social media following growth on their bottom line. But more followers on Facebook, Instagram, Twitter, TikTok, or any social media marketing channel doesn’t always mean more revenue. Many times it does, but not all the time. So you want to be sure that you’re measuring data that correlates directly with your sales. Some examples of that data would be direct sales attributed to each social media channel, customer lifetime value, and so on (we’ll discuss the data you should measure later in this article).

3. Automate your sales data gathering and analysis

Data gathering can be an extremely time-consuming activity. So you want to be sure that you can automate as many portions of your sales data gathering process. If you don’t know what tools can do that, then this list of 10 best sales tools should be a good start. Apart from helping you implement better sales strategies, many of them present the data that will support such efforts. 

Sales data your business should measure

So now that you know sales data analysis processes, the next thing to do is determine the sales analysis techniques you should use. Here are the most common sales data analysis techniques to apply in your business.

1. Sales trend analysis

Using sales trend analysis involves reviewing revenue data and detecting patterns in your revenue across a given period of time. This technique is particularly useful when budgeting or determining where you can grow your revenue more. Some of the common methods of sales trend analysis are bumping of sales per day, customer segment, region, or sales channel, among many others. The goal of sales trend analysis is to break down sales sources and maximize those sources.

2. Predictive sales analysis

If sales trend analysis looks at previous sales data to determine potential growth areas, predictive sales analysis tries to determine how much growth a company might see. This technique can usually be computed by CRM platforms and ERP solutions. While not guaranteed, predicted sales through these forms of analysis can be a relatively reliable way to make future plans for the company based on predicted sales for a given duration.

3. Sales pipeline analysis

A sales pipeline analysis involves looking at a company’s sales funnel and seeing where bottlenecks are. The goal of this technique is to determine what part of the company’s funnel needs to be improved. For instance, if companies aren’t seeing much leads move from inquiry to discovery, they could improve conversion rates by incentivizing a discovery call or adding better sales pages to convert leads to potential customers. 

4. Sales performance analysis

A sales performance analysis looks at your performance as a company sales wise and bumps it off your current goals. This technique banks on a few key preparations, such as determining goals of your business in terms of revenue and growth.

5. Product sales analysis

Performing a product sales analysis involves looking at individual sales performances of your line of products and services. By looking at each product and determining things like opportunity for growth, conversion rates per marketing dollar spent, and so on, you can make calculated decisions on which products you should focus on selling. A business can sell a variety of volumes of products. Some might sell a handful while others hundreds or even thousands of products. Product sales analysis should be performed to as many as possible to determine which products have the most potential to bring more sales in.

6. Sales effectiveness analysis

By doing a sales effectiveness analysis, what you want to understand is how well a certain sales or marketing effort performs based on the amount of effort put in. Let’s say for instance that your company makes $1 million from a sales activity but spent $500,000 to get that result. It wouldn’t be deemed as effective as running a lead generation funnel that only makes $500,000 but cost $10,000 to set up and run. 

7. Diagnostic analysis

Diagnostic analysis involves using sales data to identify the trends in your business’ sales and the correlations it has with different variables. This kind of analysis usually goes hand in hand with more descriptive processes. The goal of diagnostic analysis is to determine what external and internal factors cause certain trends in your sales and singling those variables out so you can deal with each one accordingly. For instance, if you see that the rise of a competition has a direct effect on the sale of a certain product, you’ll want to perform a diagnostic analysis to ensure that you’re looking at the right issue before making any course of action.

Big data is here to stay

By 2025, big data analysis revenue will have reached $68 billion worldwide. So there’s not a shadow of a doubt that sales data will continue playing a crucial role in business. Anyone who isn’t looking at their sales data is missing out. And surely, you wouldn’t want your business or sales department to be one of them.