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Predictive Sales Analytics: Everything You Need to Know

How to use sales analytics to improve your decision-making and expand your capacity to close more deals

A rapidly growing number of organizations are utilizing advances in data analytics to spot patterns and trends, maximize efficiency, and make more intelligent business decisions.

These organizations that rely on data and analytics outperform the competition in all areas. Research from McKinsey shows that companies that base their marketing and sales decisions on data improve their return on investment by 15%-20%. In addition, they are 5%-6% more profitable than their competitors.

Simply put, the days of relying on intuition and guesswork to make important business decisions are over.

In order to remain competitive, you must collect data on the right sales metrics, and be able to analyze that data in order to spot risks and make accurate predictions about future growth opportunities.

That is what predictive sales analytics is all about.

Sadly, even though business leaders now realize the importance of being data-driven, research by McKinsey also found that most sales organizations today (57%) still do not view themselves as effective consumers of advanced analytics.

The good news is, a predictive sales analytics program is relatively easy to set up, and it can deliver drastic profit increases by improving decision-making, increasing efficiency, and expanding your capacity to close more deals.

What is a predictive sales analytics program?

To define what a predictive sales analytics program is, let’s start by breaking it down piece-by-piece:


Metrics are the various aspects of your business that you choose to measure in order to determine how well you’re performing.

Metrics in business are similar to health markers in medicine. A doctor can choose to measure all kinds of “metrics” about your body, They can even use precise medical instruments to collect highly-accurate data on all of those metrics.

However, only a portion of those metrics (such as blood pressure, BMI, cholesterol levels, etc.) are truly useful for determining whether your body is healthy and functioning well. By measuring these key metrics, you can use the data to guide you toward reaching your health goals.

In the same way, the key to achieving your business goals is to always be measuring the right metrics, and make sure you are collecting accurate data on each of them.

Care must be taken not to base important business decisions off of misleading “vanity” metrics that might sound good on paper, but don’t actually give you an accurate idea of your organization’s performance.

Each individual business must decide which metrics are meaningful and indicate that their organization is on track to reaching their goals.


While you can choose which metrics to measure, and what your goals are for each, you can’t choose your data. The data just is what it is.

For example, let’s say one of the metrics your sales team has chosen to measure is how many SQLs are converted to Closed Deals each month. They’ve chosen a 10% close rate as their goal.

Last month, they only had an 8% close rate, missing their goal by 2%.

While the metrics and goals you choose to measure may change, that data (8% close rate) remains unchanged.

If one of your sales reps converted several SQLs into closed deals, but never recorded that data in your CRM, your metrics will now be incomplete and no longer accurately represent reality. This would completely throw off your projected revenue, and any decisions made based on that projected revenue will be flawed.

That’s why it’s vitally important to make sure that the data you are collecting on each metric are as accurate and complete as possible.


If metrics are “what” you choose to measure, then analytics is the “so what?”

Analytics consists of thoroughly examining all of your metrics in order to identify patterns and glean valuable insights.

Predictive analytics involves making predictions about the future based on those insights that you’ve gleaned from your data.

Fortunately, sales intelligence tools that are powered by AI can now quickly process large amounts of data, and then provide insights, suggestions, and accurate predictions about the future automatically.

5 ways predictive sales analytics can drastically improve the performance of your sales organization

We’ve identified 5 ways top-performing sales organizations are utilizing their sales data to make smarter predictions, generate more revenue, and outmaneuver their competitors:

1. Accurate sales forecasting

One of the most obvious applications of predictive sales analytics is predicting future sales based on historical data. Unlike ambitious goal-setting, historical data gives you an accurate and realistic picture of how much revenue your team should be generating within a certain time period.

By relating your current transactional and customer interaction data (emails, meetings, phone calls, etc.) to actual sales outcomes, you can predict future revenue to an extremely high degree of accuracy.

When you can accurately forecast what revenue will be and spot risks early, you can use that knowledge to allocate resources and manage your workforce more efficiently. In addition, cutting waste enables your organization to be more agile so you can quickly respond to changing market conditions.

Historical sales data also allows you to compare your organization’s performance to industry averages to see if you’re on track. If you discover that your sales metrics do not align with industry averages, you can now begin the process of identifying the root cause and making the necessary corrections.

2. Disruption and innovation

In order to remain competitive, you must be able to quickly adapt to changing marketing conditions, trends, and customer demands. In such a dynamic, fast-moving business environment, a well-designed predictive sales analytics program could easily become your competitive advantage.

Reliable data allows you to accurately predict changes in the marketplace. With this foresight, you can disrupt your own organization, leverage the latest technology to create innovative, new business models, and better meet the needs of your customers before a more nimble, data-driven competitor does.

In every industry, the winners of the future will be the organizations that can leverage data to identify market changes quickly and be the first to respond with solutions that best meet customer needs.

3. Sales process optimization

By becoming data-driven, you can ensure that you are always saying the right thing, to the right customer, at the right time.

Here are the different ways you can leverage predictive sales analytics to optimize your sales process, cut down the sales cycle, and drastically increase revenue and profits:

Segmentation and targeting

Based on data points you have on your most profitable customers, you can now target and acquire more customers only if they exhibit similar behaviors and characteristics. This effectively allows you to “clone” your most profitable customers.

By utilizing data to improve the targeting of your sales outreach and advertising efforts, you can avoid wasting time and money targeting customers that aren’t likely to be a good fit for your company.

Lead scoring and prioritization

By analyzing demographic, transactional, and customer interaction data, you can now segment leads in your pipeline based on how profitable they are likely to be and how engaged they are (an indicator of how quickly they are likely to close). 

Instead of wasting time reaching out to leads that aren’t likely to be interested in your products, sales intelligence tools can now use your sales data to generate a list of the most viable and profitable opportunities to contact first.

This data can also allow you to identify and fix weak points and bottlenecks where leads are getting stuck in the sales process, or falling out of your pipeline completely.

Positioning and messaging

Most organizations find it extremely challenging to develop value propositions that are effective at convincing each segment of customers they target to buy from them. While most companies opt for a one-size-fits-all approach, data-driven companies are able to test many different value propositions on different segments of customers to identify which are the most effective.

By collecting and cross-referencing many data points, it’s possible to build highly-personalized value propositions tailored to the specific needs of each customer segment.

Personalized outreach

Personalization is often the difference between a hugely successful email  outreach campaign, and one that’s just a waste of time, effort, and resources. In fact, marketers have noted a 760% increase in revenue from segmented campaigns, according to Campaign Monitor.

According to MarketingSherpa, targeted, personalized email marketing has 2x higher return than cold calling.

While basic personalization (such as allowing customers to select the type of emails they want to receive, or addressing each customer by their name) has been common in email marketing for a while, sales intelligence tools now allow you to get far more sophisticated. For example:

  • Optimized subject line and body text: Sales intelligence tools can now automatically generate and test hundreds of different subject lines, and optimize them over time (based on open and click-through rates) to be as persuasive as possible.

These tools can also optimize the body text, CTAs, and even images in order to maximize conversions.

  • Optimized email timing and frequency: Sales intelligence tools not only take the guesswork out of what to write, it also determines the exact right frequency of emails to send, and what day and time to send them, based on each of your customers’ individual time zones, activity patterns, and habits.

For example, one customer might be the most likely to buy something on a Thursday during her lunch break, while another customer might be more likely to respond on a Saturday afternoon while relaxing at home.

Fortunately, sales intelligence tools make it possible to send the right email, at the right time, and at the right frequence to optimize sales for each individual customer.

Demos and presentations

Over time, you can also gather data on which types of marketing collateral, demos, and sales presentations are the most effective at converting different types of prospects.

You may learn that some prospects prefer short, concise sales presentations, while other prospects prefer more in-depth, detailed demonstrations of the product.


Another challenge is setting the price of new products and services to ensure maximum sales and revenue. Using market data and dynamic-pricing engines, companies can test many different price points to determine what the optimal price is for each solution, and even for each segment of customers.

Some companies have discovered that, in order to maximize revenue, they actually needed to raise prices. While price increases may cut the number of potential sales, by growing the average size of each sale, you may be able to achieve an increase in overall revenue.

Discounts and promotions

You can also increase sales significantly by personalizing discounts and promotions to each prospect.

For example, if the data shows that a certain type of prospect typically ignores a 10% discount, but often makes impulse purchases once a product is 40% off or more, you can send them a customized promotion to increase the likelihood of closing the sale. 

This same level of personalization can be applied to all kinds of other incentives, such as free shipping, freebies, etc.

4. Customer retention

Research done by Frederick Reichheld of Bain & Company shows that increasing customer retention rates by 5% increases profits by a whopping 25% to 95%.

Your sales team should know who their big buyers are and be focused on taking care of them to ensure the highest customer satisfaction and retention rates possible.

You probably know who your largest accounts are, but transactional data can help you identify other accounts that are growing quickly.

You simply can’t afford to lose those large accounts, so you need to be able to identify them, and then make them a top priority for your team.

According to research by Esteban Kolsky, 67% of customers report bad experiences as a reason for churn, but only 1 out of 26 unhappy customers complain.

Predictive sales analytics gives you the ability to identify the top factors that cause customers to churn, so you can spot at-risk accounts, and proactively reach out to them to address their concerns and make sure they are thoroughly satisfied.

This also includes identifying customers who have signed up for a trial of your product, but haven’t begun using it. When you are able to identify these accounts in the trial stage, you can reach out to them to offer assistance or tutorials to help on-ramp them and help them see the full value of using your product.

5. Upsells and cross-sells

Using data to identify underserved customers and making personalized up-sell or cross-sell recommendations to them is a quick and sustainable way to boost revenue.

According to the book Marketing Metrics, businesses have a 60% to 70% chance of selling to an existing customer, while the probability of selling to a new prospect is only 5% to 20%.

By looking at purchasing patterns among each of your customer segments, you can generate personalized recommendations for what customers can buy next, based on what similar customers have bought in the past. 

According to McKinsey, a full 35% of purchases on Amazon come from these kinds of highly-personalized product recommendations.

How to implement a predictive sales analytics program

According to McKinsey, successful companies take 5 specific actions in order to implement an effective sales analytics program:

sales-data-analysis--sales analytics program
Source: McKinsey & Company

  1. Internal and external data sources: The first step is to begin allocating time and resources toward data collection, both internally, and from external sources.
  2. Analytics talent: The second step involves acquiring talent with advanced analytics skills who can translate insights into actionable guidance for reps in the field.
  3. Analytics tools and technology: The third step is to implement low-cost analytics tools, many of which can be deployed quickly and easily from the cloud.
  4. Sales workflows: Fourth, begin embedding analytics into pre-existing sales tools (such as the CRM system) and related processes.
  5. Change management: Finally, in order to maximize adoption, insights must be accompanied by clear communication, incentives, training, and performance management.

Unfortunately, even with the best of intentions and the right steps in place, many organizations encounter problems in places they least expected.

Most predictive sales analytics programs don’t fail because of a lack of advanced technical skill or the growing complexity of their tools and algorithms.

They fail because of basic, fundamental, human reasons.

Why do predictive sales analytics programs fail?

You can only derive valuable insights from your sales data if you’re collecting it in the first place. This is where the biggest challenge lies for most sales organizations. 

The process of manually entering sales activity and customer data into a CRM such as Salesforce™ is often extremely tedious and frustrating, decreasing the chances that your sales reps will actually take the time to do it.

In fact, sales professionals cited manual data entry as their #1 biggest challenge to using their CRM, according to HubSpot’s State of Inbound: Sales Edition Report.

Source: HubSpot’s State of Inbound: Sales Edition Report

The report goes on to state:

“Data entry time negatively correlates to user satisfaction. Practitioners and executives alike prefer time spent selling to time spent on manual tasks that software should help avoid….”

There’s no wonder why only 40% of sales updates are ever entered into the CRM, on average, according to ActiveCampaign. Even when data is entered into the CRM, there’s no telling how accurate it is. Even small typos by hurried salespeople can ultimately add up to major errors down the road.

Because critical sales data isn’t being tracked in any kind of systematic or consistent way, business leaders are too often forced to make strategic decisions based on poor data, guesswork, or intuition.

While getting sales reps to enter accurate and thorough data is an uphill battle, you simply can’t afford to not have this data.

The only viable solution is to find a way to eliminate manual data entry entirely and make the whole process automatic. However, until recently, there has been no reliable technology capable of doing this.

How to automate sales data entry

Fortunately, Sales Force Automation (SFA) tools have become extremely helpful at automatically handling all kinds of mundane, tedious administrative tasks.

According to SiriusDecisions:

 “These improved systems are capturing information automatically so that reps spend less time doing data entry, and they’re analyzing the data they capture to help reps target the best prospects and accounts, offload tasks such as calendaring, and guide reps using insights from the SFA data. SFA systems are finally becoming a valuable tool for reps and sales operations.”

Cirrus Insight, for example, is an SFA tool that works transparently in the background to automatically record and track 100% accurate sales activity data so your team is free to focus on more important activities, like attracting, delighting and retaining your customers.

With Cirrus Insight, your sales reps can carry out their work like they normally do, sending emails, booking appointments, making phone calls, etc. As they work, Cirrus Insight automatically tracks all of their sales activities, without them having to think about it.

By eliminating manual data entry, Cirrus Insight gives your sales team hours of valuable time back that they can use for high-impact sales activities and coaching.

It’s like having a virtual sales assistant working behind the scenes to record events in Salesforce, book meetings, and find the data-driven insights your team needs to deliver sales quotas.

With Cirrus Insight:

  • Your sales reps always know how to prioritize their pipeline
  • Managers know where to focus to improve team performance
  • Sales leaders can easily see the data they need to optimize strategies and goals

Try Cirrus Insight Free for 14 Days

If you’re ready to see how Cirrus Insight can help your sales organization become data-driven, sign up for your free, 14-day trial here.

Amy Green
Amy Green

Marketing Director at Cirrus Insight

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