Sales Analysis 101: Types, Benefits, and How to Perform One

Julia Lopes
sales/analysis

Sales revenue is the lifeblood of every business, and, without it, a company cannot operate. That’s why a sales analysis plays such a critical role in business. It allows teams to uncover new opportunities and identify and solve problems in their sales process. 

A sales analysis considers all aspects of the sales process including the underlying factors driving sales, the efficacy of key performance indicators (KPIs), and whether or not resources are being used effectively.

For most companies, it gets more complicated (and urgent) as the business expands and sales ramp up. Sales managers and teams aren’t always sure what a mature analysis process looks like or where to begin. 

In this guide, we’re going to explore what a sales analysis is, explain the benefits of sales analyses and their importance, and consider the most common types. By the end, you’ll have a better understanding of how it can benefit your company and how to go about implementing your own process.

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What is a sales analysis?

A sales analysis is a structured review of your company’s sales data. A thorough analysis can reveal what’s working, what isn’t, and bring opportunities for improvement into clearer focus. For example, it can tell you which sales channels are delivering the most ROI, what factors are impacting conversion rates, and can help you determine your customer acquisition costs. 

Why it is important

It provides a wide range of metrics and data that can help your sales team improve their sales pipeline model and make timely and informed decisions that drive revenue and reduce waste. The more teams know about their sales process and their customer’s experience, the more effective they will become at landing new deals and expanding sales with existing customers.

Benefits of a sales analysis

A sales analysis delivers benefits to every company, no matter the size, sector, or complexity of its sales process. Some of the most meaningful benefits companies see from sales analysis include understanding patterns in customer behavior, causes of revenue growth, and using data to make informed sales and business decisions. 

Better understanding of customer behavior

You can’t manage what you don’t measure. This is a popular phrase that’s absolutely true when it comes to understanding and influencing customer behavior.

By tracking all aspects of the buyer journey — from points of contact and messaging to engagement with content and more — you can give your sales team a deeper understanding of how to target potential customers and retain existing ones. 

An in-depth sales analysis can reveal patterns in behavior that can help your sales team create more effective buyer journeys and increase pipeline conversion rates. This is important because it will help your team replicate what’s working, adjust their sales process accordingly, and prioritize leads who fit the profile for those likely to convert. 

Increase revenue

The primary goal of most analyses is to increase the pace and size of new sales. That means identifying elements of the sales process that aren’t working well and optimizing tasks and activities in order to expand the sales pipeline capacity. 

It’s common wisdom that scaling an inefficient process only scales the inefficiency, so it’s important to optimize the sales process before scaling it. A sales analysis can help teams resolve inefficiencies and problems with handoffs before ramping up sales efforts. 

Make data-driven decisions

All sales processes need the flexibility to change over time. Whether that’s in response to customer feedback, falling short of sales targets, or competitor activity, sales teams have to be ready to modify their sales process. But in order to make smart and confident decisions, they need access to data.

A sales analysis provides the information sales teams need to remain ahead of the competition by creating better buyer journeys and ensuring that sales efforts are delivering the best results. 

9 common types of sales analysis

Sales is a complicated process, and a single analysis may not provide all the insights a team needs. Fortunately, there are a few different types of sales analysis, each of which highlights a particular aspect of the sales process. The table below summarizes the most common types, followed by more detailed explanations.

Analysis typePurposeQuestion answered
Sales trend analysisLooks at historical data in order to detect patterns
Useful for planning, budgeting, and forecasting
How do sales appear in a historical context? 
Sales performance analysisIllustrates patterns in overall sales
Compares actual sales with sales targets
How do actual sales compare to sales targets? 
Predictive sales analysisConsiders historical data, current performance, and future variables
Useful for strategic and financial planning
What should the business expect in terms of future sales? 
Sales pipeline analysisUseful for understanding buyer behavior and how to optimize the buyer journey
Helps pinpoint opportunities for improvement and identify any inefficiencies
What does the buyer journey look like? 
Is it working? 
Product sales analysisProvides insight into which products are the most successful and why 
Indicates how well a product is matched to prospective customer needs
How well aligned is the product to prospect? 
How is the product fit impacting the sales cycle? 
Sales effectiveness analysisMeasures effectiveness of sales strategy and its influence on sales performanceHow effective is the sales process at converting leads into sales? 
Diagnostic analysisHighlights trends or anomalies in sales process
Answers why they happened and what caused them 
Why did something happen? 
Prescriptive analysisInforms problem solving related to the sales process or forecasts What should be done about it? 
Marketing researchHelps understand interests and pain points of existing and potential customersWho are the target customers? What are their pain points? 
What do they value? 
Who are the competitors? 

Sales trend analysis

A sales trend analysis takes a high-level view of the sales process. This type of analysis helps your team determine if sales are increasing, decreasing, or remaining constant. A sales trend analysis looks at historical data in order to detect patterns. This information is useful for planning, budgeting, and forecasting. 

Sales performance analysis

While a sales trend analysis illustrates patterns in overall sales, a sales performance analysis compares actual sales with sales targets. This is important for determining whether or not the current sales process is optimized to meet sales goals. 

If the performance analysis reveals that sales are behind projections, then leaders can adapt their strategy or optimize the process to correct the trend. A sales performance analysis considers metrics such as the conversion rate, average deal size, and sales cycle length. 

Predictive sales analysis

Another measure that businesses depend on is predictive sales analysis. These analyses are useful for strategic and financial planning. A predictive analysis considers historical data, current performance, and future variables, in order to determine what the expected sales totals will be in the future. 

Sales pipeline analysis

Understanding the sales pipeline is essential for managing the sales strategy. Without clear insights into how prospects become qualified leads (and eventually customers) and how sales reps engage with leads in order to convert them, it’s difficult (if not impossible) to influence buyer behavior or optimize the buyer journey. 

Analyzing the stages of the sales pipeline helps companies pinpoint opportunities for improvement and identify any inefficiencies or problems that may be negatively impacting the sales process.

Also read How to Turn Prospects into Customers

Product sales analysis

The product sales analysis provides insight into how well a product is matched to prospective customer needs. It can also indicate which products are the most successful, when and why certain products are most likely to be purchased, and which types of consumers are likely to buy a specific product or service. 

Sales effectiveness analysis

A sales effectiveness analysis answers questions like: 

  • How effective is your current sales strategy? 
  • How is your sales team performing? 
  • Are they being effective at converting prospects to sales?

It focuses on conversion rates, win ratios, and pipeline performance. If any of these types of metrics fall below targets, this type of analysis can help teams identify and address the problems that are impeding the sales process. 

Diagnostic analysis

A diagnostic analysis is a deeper dive into trends or anomalies that appear in the sales process. The goal of a diagnostic analysis is to answer why they happened and what caused them.

A diagnostic analysis may reveal changes in customer expectations, shifts in the market, competitor activity, or the impact of recent change to the sales process. In some cases, the diagnostic analysis may uncover the impact of changes to the existing tech stack on the sales process, such as the addition of a new app or issues with data. 

Prescriptive analysis

When issues or problems with the sales process or forecasts are discovered, a prescriptive analysis helps teams determine the right action to address the problems. 

Marketing research

While not a sales analysis per se, marketing research plays a pivotal role in determining the overall success of the sales process. Marketing research can help teams understand the interests and pain points of their prospects, identify primary competitors, and map out their buyer journey. All of the data collected through marketing research informs the sales process. 

How to perform a sales analysis

Up to this point we’ve considered what a sales analysis is, looked at its benefits, and summarized the most common types, as well as their purpose and the questions they answer. 

This section of the guide will walk you through the process of performing your own analysis process, one step at a time. 

1. Identify the question you want to answer or goal of the analysis

At the outset, the first thing that needs to be done is to make sure you know what question your analysis is trying to answer. Define the purpose and limits of the analysis, and what problem or opportunity it will address. Be clear, specific, and transparent. This will help establish alignment and avoid any unnecessary work from you or those helping you complete the analysis. 

2. Define relevant metrics 

All sales analyses require data, but only certain types of data will be useful. Since collecting and organizing data takes time and effort, it’s important to focus only on relevant data points. Otherwise, the analysis can become overwhelming and derail the analysis. 

Gather the questions you want to answer and then work with the members of your team (or, if available, a data team) to determine which metrics and data are needed and how to best track them. 

3. Crunch the numbers

Now that you have the data you need, it’s time to organize it, interpret the findings, and start drawing conclusions. As with the previous step, you may need to reach out to your data team for help managing the data, or request access to tools that can do this for you. 

4. Get other perspectives 

After drafting your preliminary report, try to get some additional feedback from trusted team members who understand the problem. This can help address any blindspots that your analysis may have, or identify opportunities to bring clarity to your results. This added insight can bring more credibility and confidence to your final report and recommendations. 

5. Summarize and present your findings and recommend action

Teams receive reports and analysis all the time. To prevent your analysis from getting lost in the noise, your report needs to be simple, straightforward, and easy to act on. 

To do this, make sure the most important findings are presented first and that you keep them clear and brief. Present the finding and follow by telling your audience what it means. Include specific action items (where appropriate) and be prepared to provide access to raw data and other information. Expect someone to ask for it. 

How to simplify your sales analysis

Conducting a sales analysis may seem intimidating, but here are two strategies any sales team can use to perform it more easily and to bring visibility into the sales process. 

Get on the same page

First, make sure you are working with a structured process. If each team or sales rep is managing their sales through their own unique or ad hoc process, it’s going to be difficult (if not impossible) to produce consistent results or control the buyer journey.

If the analysis reveals a sales process that’s fractured, or variations of the target sales process, then the first priority is to define how the sales process should be carried out. 

Use the right tool to automate and seamlessly integrate sales processes

The second strategy for simplifying sales analysis is to use tools that make it easier to see each aspect of the sales process, from marketing handoff to closing.

Most teams rely on a variety of apps and systems such as a CRM, email automation platform, and email and scheduling software. One of the most effective ways teams can manage complex stacks is to use a low-code automation tool.

Low-code automation makes it easy for sales managers (or team members with permission) to modify the sales process as strategies evolve and customer behavior changes.

Low-code automation uses a visual interface to simplify these changes and to make it easier to automate manual tasks such as routing incoming leads, sending emails, or scheduling meetings. Low-code automation integrates with and complements existing stacks, to help teams achieve stack extensibility. In other words, to get more from existing apps and systems.

Also read The Complete Sales Automation Guide

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Written by
Julia Lopes
Senior Marketing Analyst @ Pipefy. Passionate about sharing valuable and informative content. Currently writing about the unique ways teams and businesses can use low-code process automation to optimize processes and adapt to digital change.

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