What is sales analytics?

Sales analytics is the process of gathering and analyzing sales data points to see how an organization is progressing toward its goals. Sales leaders can use these analytics to generate performance insights, identify what’s working and what might need an adjustment and create actionable steps to improve sales.

Through sales analytics, an organization can better understand past performance, identify trends, profitability and predict future sales outcomes. Sales analytics processes help turn siloed data into actionable insights—such as forecasting future sales and revenue more accurately—and use sales metrics to set attainable goals.¹ The modern sales analytics solutions are artificial intelligence(AI)-infused to automate workflows and deploy predictive forecasting.

While the primary goal for sales analytics is to reveal actionable insights that improve the sales process, sales intelligence is a separate process that involves gathering raw data.² Both processes ideally work alongside each other to achieve the mutual objective of business efficiency. A successful sales analytics strategy includes sales reps, sales analysts, business stakeholders and other tools and systems.³

The sales analytics process broadly speaking involves collecting, organizing and analyzing sales data. After that data has been collected and analyzed into understandable insights, it’s turned into actionable decision-making. By following these steps, an organization is much more likely to make smarter decisions and improve overall performance.⁴

Sales data is first gathered from varying internal sources. These sources can include customer relationship management (CRM) systems, sales activity logs, e-commerce data and product data. The data being collected can include a wide range of information like sales volume, revenue, customer acquisition cost, customer lifetime value (CLV) and sales cycle length. An organization can choose from a range of sales analytics software from vendors such as IBM, Salesforce and HubSpot.

There are many different analytics used for sales, but generally fall into four categories: descriptive analysis, diagnostic analysis, predictive analysis and prescriptive analysis. At this stage, an organization is going to determine which analytics it wants to focus on and then use specific sales metrics to gain insights.

A sales team is going to take those insights and interpret the findings into sales reports. The team is going to look at things like sales performance, conversion rates and other data from key performance indicators (KPIs) to figure out customer behavior and which sales functions work well.

The insights from data analytics are then put into action. An organization should implement changes based on the analytical findings, such as providing targeted coaching for sales reps to boost team performance and tailoring sales strategies based on customer buying patterns.

Once those changes have been made, the sales team must continue tracking data analytics to ensure that those changes had an impact. Sales analytics tools, such as visual dashboards, can leverage AI to provide real-time insights into an organization’s sales operation and automate routine tasks.

These analytics are the ones that track historical sales data and are used to provide a basic overview of sales performance. The popular sales metrics being analyzed are revenue, number of users, total sales, growth rates and conversion rates. The type of question being answered are “How many”, “when”, “where” and “what”.

The diagnostic sales analytics is examining why something specific occurred. It can be a success or a failure of a past performance, but the main goal is finding the root cause. Common sales analytics tools for this are data mining, correlation analysis, regression analysis and machine learning (ML). The type of question being answered is “Why did it happen”.

Predictive analytics uses historical data and sales trends to forecast future sales and customer behavior. The purpose is to have data that can help anticipate future sales goals and revenue growth. These data analytics typically involves using historical data combined with statistical modeling, data mining techniques and ML to predict future outcomes. The type of question being answered is “What is going to happen next”.

The primary goal of prescriptive analytics is to recommend specific actions to optimize future sales outcomes. The purpose of this type of sales analytics is to provide a sales team with actionable guidance to improve sales team performance.

It goes beyond the predictive analytics capabilities and adds a layer of decision intelligence by using ML, AI and generative AI to do enhanced sales data analysis. The type of question being answered is “What should we do about it”.

A significant benefit of sales analytics is the holistic approach to data. Through sales analytics tools and processes, an organization can visualize data through charts and graphs rather than a singular data point. Sales leaders need the entire picture to make an educated decision about sales forecasting and changes to future workflows.

Modern customers expect a personalized experience tailored to their preferences. But that type of customer journey is hard to build without data-driven insights and key metrics for customer behavior. In order to attract more customers and qualified leads, organizations must track factors like time on a certain website or social media or response rate from a particular marketing campaign.

Sales reps are constantly on the quest to achieve results and generate sales revenue. Sales analytics play a vital role in helping sales managers achieve things like increasing win rates and decreasing sales cycle length.

By identifying these areas of improvement, sales teams can optimize how they strategize for future business needs. Sales teams through lead generation and other analytics tools are getting an end-to-end visual of the sales pipeline, giving them many more data points than in the past.

Sales data analytics provides organizations with factual information about customer interactions and data visualization techniques so that sales leaders can make more informed decisions on product performance, marketing efforts and customer segments. By using sales analytics software, an organization can take extensive research into historical data and current customer data to make smarter pricing decisions and take advantage of more tailored sales opportunities.

Sales reps expect incentives as part of their role, but those incentives aren’t possible without accurate data to back it up. Organizations that keep records of sales activities and track sales outcomes incentivizes the sales team to continue to do good work. Separately, sales analytics can help simplify payroll management and help sales managers make sales commission structures that best suit their teams.

Sales analytics is vast and there are many different metrics to track, but some of the more relevant metrics are listed after this.

This sales metric measures total revenue generated by each sales representative over a specific period of time. It is crucial for assessing individual performance and for setting fair compensation plans. A higher sales per rep indicate greater efficiency and effectiveness in sales activities such as closing deals or upsells.

This metric tracks sales figures by geographical area. It helps identify strong and weak markets and market trends, making for a more effective sales strategy. It enables strategic decisions about resource allocation, market expansion and regional sales strategies.

A sales team should monitor the increase or decrease in sales over time, commonly compared to the same period last year. Positive growth signifies a thriving business, while negative growth indicates a need for corrective actions.

The sales target is a predetermined monetary value that a salesperson or an organization aims to achieve within a specified time frame. This metric serves as a motivational benchmark and a tool for measuring sales team performance. If a sales person excels this target year-over-year, then a sales leader might want to consider adjusting the target value.

This metric reflects the percentage of customers who stop doing business with a company during a specific period of time. High churn rates can indicate dissatisfaction, poor services or pricing issues, needing corrective action. A deeper dive into this metric can begin to pinpoint where in the sales funnel a customer might have lost the connection or become disinterested in the product or service.

This metric calculates the average total value of closed deals. It’s a crucial indicator of sales efficiency and how successful a sales team was in a specific period of time. The larger the deal size generally means a higher profit per transaction.

This key metric calculates the percentage of inventory being sold during a specific period. Through this measure of data, an organization can see sales trends per product or service and decide about inventory and their supply chain.

This metric refers to how quickly sales opportunities move through the pipeline from initial contact to a closed deal. A faster velocity often means quicker turnaround times and increased revenue, while a slower velocity might signal inefficiencies in the sales process.