Sales Analytics: Everything You Need to Know

No matter how good or bad your total revenue is, the time will come to dive into sales analytics. Why? Well, there are two reasons:

If the sales metrics are great, you need to know that your customers like your products or services, and you should only strengthen their satisfaction.
If the metrics are poor, it’s a signal for you to change your sales approaches.

As you can see, in order to move forward with your business, you need material to refer to. That’s where sales data and its analysis are essential. There are various metrics to monitor, from conversion rates to customer lifetime value. The information can also range from historical sales data to up-to-date insights gathered in real time.

How can you make sense of all the available information? You need a structured sales analytics plan. In this article, we’ll define sales analytics, provide its advantages, and overview sales analytics software. We’ll also help you determine key sales metrics for your business and how to start your sales analytics journey.

What Is Analytics in Sales?

So, let’s start with the definition of sales analytics. At its core, it involves collecting, deciphering, and transforming sales data into actionable insights. You examine every facet of your sales process, from the initial customer contact right through to the final sale. The goal is to understand the why, what, and how of your sales results.

Suppose you’ve gathered the needed sales data. For example, you have a lot of website visitors who install your software, but you still have low revenue. The sales data analytics shows that those people don’t renew their subscriptions after the first month of using the tool. Knowing this information, you can invest more in user onboarding, email marketing, and retention strategies to keep the clients engaged.

Or let’s assume you’re a real estate agent or broker. As you’ve been diligently tracking your business activities, you’ve noticed a pattern: You’re getting a decent number of inquiries, and quite a few potential buyers are taking tours of the properties you’ve listed. However, despite this seemingly promising trend, the actual number of closed deals and generated revenue is disappointingly small.

Sales data analysis uncovers a critical insight: Many potential buyers show initial interest but don’t proceed past the first showing. They visit, they look, but they don’t come back to make an offer.

That’s where you can develop a solution. For instance, you might decide to enhance the initial property showing experience. Additionally, you might implement a follow-up strategy, communicating with prospects from the comfort of their own homes.

As such, sales analytics data doesn’t just highlight a problem—it also directs you toward strategic decision-making.

Types of Sales Analytics

Descriptive analytics: What happened?

Descriptive analytics pulls together data on past sales performance to identify trends and patterns. For instance, if your total sales revenue spiked in March, descriptive analytics will spotlight this event.

Diagnostic analytics: Why did it happen?

Some marketing campaigns thrive, others flop. Ever wondered why? To answer this question, you need to consult diagnostic analytics tools. They not only collect data but also find reasons behind successes or failures. Some helpful data sources for this analytics include customer feedback, sales reps’ performance, and external factors like market trends.

Predictive analytics: What will happen?

“Predictive” in this collocation means sales forecasting. That’s where historical data and current trends work together to inform your sales strategy. For example, predictive models might suggest that if you increase your customer engagement efforts, your sales could grow by 20% in the next quarter.

Prescriptive analytics: What should we do about it?

This is where analytics recommends actions. If predictive sales analytics tells you that increasing customer interactions boosts sales, prescriptive analytics will suggest practical ways to enhance those interactions:

increasing social media engagement;
personalizing email communications;
launching referral and loyalty programs.

The Benefits of Sales Analytics

Sales analytics is your team’s secret weapon for higher sales productivity. It’s essential at any stage of the sales funnel, whether it’s finding new prospects or prioritizing existing leads. By utilizing sales analytics, you can identify areas for improvement and assess the sales team’s performance more accurately. Here are some other reasons to employ data analytics for sales.

Enhanced Decision-Making Capabilities

One of the standout benefits of sales analytics is its role in making data-driven decisions. Apart from making informed reactive choices, you can also anticipate needs and act proactively. Sales analytics provides a detailed view of the following:

customer behavior;
sales performance;
sales trends, among other things.

It allows sales managers to make data-backed decisions. For instance, if analytics reveal that certain products perform better in specific regions during particular seasons, businesses can strategically plan their inventory and marketing efforts accordingly.

Improved Sales Forecasting and Operational Efficiency

Predictive analytics in sales performs two functions:

creating accurate sales forecasts;
analyzing patterns in historical data and external factors.

Take the average deal size and sales cycle length as an example. By understanding this data, sales teams can forecast future sales more precisely, such as how much time is needed to seal the deal, how many resources the negotiation will require, and the average value of a potential transaction. This capability boosts the operational efficiency of the sales process, reduces wastage, and optimizes expenditure.

Tailored Insights into Customer Behaviors and General Trends

Sales analytics breaks down sales data into smaller segments, showing nuances in customer behavior and shifts in market trends. These insights enable businesses to adapt their sales and marketing efforts to better meet their customers’ needs.

Suppose data shows a significant portion of customers prefer online interactions over in-store experiences. A business might shift its focus towards digital channels:

optimize the website;
create more online customer service channels;
capitalize on social media presence.

Moreover, these analytics can reveal opportunities for upselling and cross-selling. Here is how it works. You access purchase histories. They demonstrate customer preferences. Sales teams can better tailor their recommendations, significantly increasing the chances of making a sale and the average order value. Sales analytics also helps identify which clients are at risk of churn and why, enabling timely interventions to retain them.

Spotlight on New Opportunities

New sales opportunities arise as a business expands and its products change over time. By tracking sales data, sales agents can better assess product fit across industries and geographies. Plus, they can spot new tendencies, like a sudden spike in demand for a product category or an untapped customer segment, before their competitors can get ahead.

Enhancing Sales Team Performance

Before you take steps to improve your sales team performance, you need to understand their strengths and weaknesses. That’s where sales analytics can also help. It pinpoints areas where sales team members excel and where they need improvement:

whether they’re good at closing deals;
whether they struggle with initial customer contacts;
how they ensure customer retention.

If there are some issues, you can implement targeted training to enhance those specific skills, improving individual performance and overall team effectiveness.

Key Sales Analytics Metrics to Monitor

What key performance indicators (KPIs) should serve as a basis for your sales analysis? Key metrics include:

Sales Growth and Sales Targets

Sales growth is the percentage showing whether your revenue generated in the current period is larger or smaller than the revenue for a similar period in the past. To calculate it, subtract the last period’s total sales from this period’s, divide the result by your past revenue, and multiply it by 100%. For instance:

$100,000 of sales: last quarter;
$120,000 of sales: this quarter;
(120,000 – 100,000) / 100,000 * 100% = 20% (revenue growth).

Sales targets are the objectives you want to accomplish. Setting and tracking them is equally crucial to comprehending your current standing and how close your sales team is to hitting them. Aims can be monetary or revolve around a desirable number of new accounts to win over. For example, if your annual sales target is $1 million and you’ve hit $720,000 by the third quarter, you know a 28% gap remains.

Average Deal Size, Conversion Rates, and Customer Acquisition Costs

Conversion rates reveal different things:

whether people subscribe to an email newsletter;
whether they schedule a meeting;
whether they create an account, and so on.

But in sales, the CR denotes how effectively your team turns sales-qualified leads into paying customers. A low conversion rate might signal a misalignment between your sales strategies and customer needs or perhaps a gap in your team’s skills—both of which are fixable with the proper focus.

Average deal size is another telling statistic. It represents the average sale value and is calculated as the total sales revenue divided by the total number of closed deals over a given time frame. When looking at different constituents of this metric, you can measure whether your sales team is capitalizing on big-ticket sales or smaller, quicker wins.

Track this indicator over time to see how your sales performance changes. If it’s declining, it can be a sign that your salespeople are struggling to convert high-paying clients.

Customer acquisition cost (CAC) tells you how much you spend acquiring each new customer. The calculation formula is as follows:

The total cost of sales and marketing over a specific period is divided by the number of new customers acquired during that period.

You need to keep this metric to a minimum, as spending too much to acquire customers can quickly erode your margins.

Sales by Region, Product, and Channel

Sales by region reflect how popular your products are across different geographical areas:

territories or states (for domestic companies);
countries or continents (for global businesses).

This breakdown helps determine where to focus your efforts. Maybe you’re a hit in urban areas but not making much headway in rural markets. Or perhaps certain products sell well on the East Coast but aren’t as popular in the West.

Sales per product or channel also provide a layer of insight into what’s working and what’s not. For instance, you might discover that while overall sales are good, certain products aren’t pulling their weight. This can lead to strategic decisions on whether to keep developing certain products or discontinue them.

Retention Rates and Customer Lifetime Value (CLV)

Customer retention and CLV zero in on the potential for long-term success. Retention rates measure how well you keep your customers returning, encouraging them to renew their subscriptions or make repeat purchases. A falling retention rate is a red flag that something’s amiss in customer satisfaction or value. Here is how to estimate it:

How many subscribers stay at the end of a specific time frame divided by the total number of clients you had when the same period started and multiplying the result by 100%.

CLV gives you a snapshot of the total worth of a customer to your business over the whole period of your relationship (how much a company can expect to earn by working with this client). A customer’s value increases with their CLV. If you know the CLV, you can better determine how much you can spend on acquiring similar users or buyers.

Step-by-Step Guide to Sales Data Analysis

So, let’s get a bit practical and discuss how to start your sales analysis journey, what sales analytics tools to choose, and what to look for.

Collecting and Analyzing Sales Data

An effective sales analytics process starts with the basics:

selecting who or what you want to analyze;
choosing the frequency at which you want to review your sales effectiveness.
identifying trends and patterns;
setting sales goals and targets;
specifying the types of data most relevant to your sales goals;
monitoring key metrics using various sources within your sales organization (CRM systems, sales analytics platforms, customer feedback).

There are two sales analysis methods for interpreting this data: quantitative and qualitative. Quantitative analysis includes calculating growth percentages, average deal sizes, or conversion rates—in short, numbers. Qualitative sales performance analysis taps into patterns or trends like sentiment in customer reviews, sales managers’ feedback, and customer behavior. These sales reports indicate underlying strengths or weaknesses in your sales processes.

Leveraging Technology

Choosing the right sales analytics tool is critical to effectively monitoring and making necessary adjustments to your sales strategy. Here are main features to look for in sales analytics software:

Integration capabilities: The sales analytics tool should be compatible with your existing systems, such as CRM, ERP, or email marketing software, and unify the data from these tools. You don’t want a solution that requires more time to configure and upload all the information manually than analyzing the data itself.

Some CRM software already comes with native features for performing data analytics in sales. A prime example is AI-powered Salesforce Einstein Analytics integrated into the SF platform.

Ease of use and user-friendliness: The sales analytics tool shouldn’t take long to get to grips with, either. A user-friendly interface is the ideal choice for all team members, regardless of their skills. A sales manager should focus on selling and building relationships rather than navigating complex software.
Comprehensive data visualization features: That’s what user-friendly interfaces typically provide. These features are graphs, heat maps, and dashboards. They make it easy to interpret complex data sets and share insights across the organization.
Cost-effectiveness and scalability: The cost is among the decisive factors when exploring sales analytics tools. The price can get too hefty depending on the number of users each plan supports or other factors. You need to find what fits your current budget and what can scale with your growing business without significant additional costs.

Top Sales Analytics Software for Small Businesses

Here’s a quick overview of some leading tools tailored for small businesses:

Tool	Benefits
HubSpot Sales Hub	Integrates with existing CRM, offers comprehensive analytics and user-friendly dashboards.
Zoho Analytics	Known for its deep analytical capabilities and ease of use, it supports multiple integrations.
Mixpanel	Excellent for behavior analytics, helping businesses understand user interactions with their products.

Analyzing Data and Adjusting Your Sales Strategies

Once the sales analytics software is in place, you can proceed to gather information and generate sales reports. Look for the specific sales, compare those figures to their counterparts, and draw conclusions about which products are most attractive to your target audience.

Did you spot issues with your overall sales performance? Examine data more closely to find the cause of the problem and ways to resolve it. Let’s assume sales cycle data indicates that lead transitioning from the opportunity to the closed-won stage takes a long time. Then, you may want to examine your sales manager to make sure the team successfully responds to client objections and concerns.

Overcoming Challenges in Sales Analytics

Companies usually face these three common challenges when implementing sales analytics (and how to overcome them):

Substandard data quality. To tackle this problem, add context to your data, such as its purpose and audience. Explain anomalies and identify patterns to provide clarity.
Inadequate equipment. You may either buy a tool that doesn’t cover the needed use cases or, in contrast, is too complicated and expensive for your day-to-day operations. Outline your specific needs and invest in tools that meet the requirements.
Poor data culture. To foster a strong culture, regularly train your team to understand data and utilize tools. Learning should be ongoing with two-way communication.

Sales Analysis: Where Do You Go From Here?

Sales analytics refers to the process of interpreting data from sales activities. Why is sales analytics important, you may ask? How do analytics and sales co-exist?

Sales analytics is a powerful toolset that transforms how sales teams operate. It shows sales performance and areas for improvement. During this process, you need to monitor specific metrics like sales pipeline velocity, CLV, and overall sales growth.

However, you can’t do it manually. Explore existing solutions, choose ones that match your goals, and set up your first analysis. Integrate analytics into your sales strategy, optimize your sales pipeline, and accelerate your sales cycle. We hope your sales performance will reach new heights.