What makes sales forecasting so difficult? Is it the people? What to measure? The dimensions, or periods, judgements or sources? The cadence, categories or the adoption of a standard tool?

The Sales Forecasting Dilemma

When forecasting an opportunity every company has a unique method to its madness. Most start by creating an opportunity and attaching a (more or less arbitrary) forecasted close amount to it. Some companies believe in sales stages, others use stage probability, and still others use forecast categories.

It’s madness to watch companies go through their Friday forecast call. It’s a combination of business intelligence tools, CRM, and always our best friend Microsoft Excel. With all of these tools and all of these methods, companies still struggle to accurately predict the future.

To get to the bottom of the forecasting debate I decided to turn our research arm, InsideSales Labs, at the problem. Although I don’t mind surveys, I wanted the truth about the accuracy of forecasting from the cold hard data. Fortunately, XANT has the largest sales database in the world, which makes it super easy to bring the art and the science of sales together. For this study we looked at 270,912 closed-won opportunities and their forecasted close amounts across more than a dozen companies. In order to make this more interesting, we examined these opportunities 90 days out from closing. We predicted that 90 days out, even big deals would be mostly accurately forecasted, but we were wrong.

By comparing the 90-day forecasted amount and the closed-won amount for each deal, we discovered that only 28.1% were accurate (were within 5% of the forecasted amount). Dang! That was worse than any of us had imagined. Diving deeper into the data, we found that about half of the opportunities we analyzed missed by a margin of more than half. What was interesting was which direction sales reps guessed wrong – i.e., did they tend overestimate or underestimate. . .

Sales Reps Tend to Overestimate

What do you think? If a sales rep misses their forecasted number, do they underestimate or overestimate? The truth: they overestimate by nearly 2x! When sales reps’ forecasts were off, they tended to overestimate by an average $91,000 while underestimating by only $47,000 on average.

So how do you fix this? Well first you need to understand which deals are good and which are bad using a tool like Predictive Pipeline from XANT. Tools like these analyze 200+ attributes of historical opportunities to determine what influences the success of a deal in your organization. These characteristics are then applied to new opportunities, adjusting the forecast up or down based on the likelihood that those new deals close. A human can’t do that. We don’t have the cognitive capacity to analyze that much information nor the emotional objectivity necessary to apply that analysis rationally.

Accurate Sales Forecasting

Look, your entire business is run off of projections – from how you staff to where you invest and new opportunities you pursue. If you consistently have trouble predicting your number, you can’t have a sustainable business…period. Sadly, all the methods we’re trying to use right now are not working so it’s time to try something new, don’t you think?

Check out the research findings by InsideSales Labs here: RESEARCH

This post was originally published on LinkedIn. Follow my musings there for more sales statistics.

The company had a global sales team offering data protection and information management systems. Their forecasting process was difficult, and the pipeline always seemed artificially inflated. Deals were added to the pipeline, but were consistently being pushed to the next quarter ‘while ironing out details.’

Sales leaders knew something had to change, but were not sure quite where to start.

They worked with XANT on an initial sales effectiveness assessment (SEQ), to see how sales reps were performing, and where they could improve.

Identifying The Good and Bad Sales Reps

The Sales Effectiveness Quadrant from XANT produced a segmentation of their reps based on performance. This helped them identify gaps in deal closing and pipeline building skills. Sales reps were placed in four categories, based on their skills:

• Deal makers – Sales reps who are natural closers. They love interacting with the customer in the final stages of a deal, but are lacking some pipeline drive
• Pipeline builders – The agents who are very good at finding leads and qualifying them for purchases
• Top performers – Sales reps who have both good pipeline building skills and a natural talent in closing
• Under performers – Sales representatives lagging behind in both pipeline creation and closing.

The assessment not only showed sales managers who needed help and where. It also highlighted what are the behaviors that pave the way to success. Having this information, managers were ready to setup a coaching plan. They were able to help their low-performing sales reps reach the same results as the sales superstars.

Weeding Out Bad Deals From the Pipeline

XANT allowed the company to gain visibility into the pipeline. They now could understand what were the behaviors of sales reps leading to an inflated pipeline. In this case, selling data protection services led to complex deals and long sales cycles. There were many technical specifications that needed working out. Deals were lingering in certain sales stages, and not closing.

The next step was to interpret this data in business context. With the insights provided by XANT’s Predictive Pipeline software, management was able to answer questions like:

• How are we tracking against quota?

• What has changed in the pipeline?

• Which deals are at risk and which will move forward?

• Which sales reps need coaching, and how?

The AI analyzed data from the CRM, as well as billions of other transactions from XANT’s database. It then found patterns in how their sales deals were closing. This allowed them to understand characteristics of good sales deals and weed out ones that would never close.

Focusing on the Right People and the Right Deals

With these insights, reps now knew what deals they need to focus on to win. Forecasting accuracy increased, as reps were allowing less zombie deals to move to the next quarter as pending.

The company experienced a 10% increase in closed amount, as well as a reduction of the time deals were spending in the sales cycle.

Read the full story to find out more!

Photo credit: AlphaStockImages.

Sales leaders need to get tactical about how they run their pipeline calls and how they forecast revenue. There’s a key trend in sales to expect predictability of revenue results.

Forecasting With Data, Not Your Gut

Ideally, accurate forecasts are the result of a balance between human judgement and data insights. However, in most sales teams, forecasts are largely guided by intuition. Only 46.9% of deals close as forecasted (CSO Insights).

Data and science is the answer to sales leaders’ questions about revenue forecasting.

XANT has created a guide for sales forecasting that will help increase the accuracy of revenue projections and guide them towards a more accurate result. You can download “The 5 Secrets of Sales Forecasting Every Sales Leader Must Know”, to understand what factors influence forecasting and how to kee them in check.

In this white paper we discuss the following:

How to Identify and Promote the Right Rep Behaviors

Some reps are sandbaggers. Others wear rose-colored glasses. And a lot of reps stuff anything into their pipeline just to get their managers and sales operations off their backs.

Bad behaviors like these keep bad deals in pipeline 3X longer than good deals. Sales reps and managers must take a more data-driven approach to evaluating their people and processes.

How to Measure Pipeline the Right Way

Organizations often mistake a full pipeline for a healthy one. This can be a costly approach, because sales pipelines are often filled with deals that are not real or winnable. Reps end up losing over $200,000 a year in lost revenue chasing bad opportunities.

How to Capture and Understand Complex Pipeline Changes

Pipeline change can determine whether or not a deal will close, and what to forecast. Most sales managers hold weekly pipeline review calls with reps, but they can waste up to four hours just assembling data for analysis in Excel.

The more time managers spend evaluating the what and why of pipeline changes, the better equipped they’ll be at understanding deal progression and gaining confidence into committed deals.

Evaluating Data in Historical Context

Forecast accuracy is highly dependent not only on understanding what winnable deals look like but also on the path those deals follow to close. Historical data captures these insights, but most organizations don’t know where to look or they don’t account for the kinds of process and model changes a typical business experiences.

As a result, 79% of sales organizations miss their forecasts by more than 10%. Knowing what to look for and accounting for business changes will sharpen your forecasts.

Don’t Forget About Transactional and Newly Won Deals

Transactional and newly won deals (deals not currently in your pipeline, but which are likely to open and close within the period) can represent more than 30% of your forecasted number at the beginning of a period. For businesses with highly transactional sales cycles, it can actually reach to over 80%.

In order to eliminate the need for large judgmental plugs, adopt a model to more accurately account for gaps within your forecast at the beginning of the period.

Conclusion

The days of pure gut feel selling are over. Successful sellers today augment their intuition and experience with data supplied by predictive sales systems. We are working in the golden age of selling, and we have access to more data, insights and technology than ever before.

By leveraging these advances in practical ways, and by keeping up with industry trends and best practices, sales leaders and reps are able generate exponential revenue growth.

XANT can power your sales organization with the right tools to do this effectively. Just one example is the Predictive Pipeline software for pipeline management and sales forecasting. By using Predictive Pipeline and its machine learning capabilities, you can increase your sales forecasting accuracy by up to 30 percent.

Download the 5 Secrets to Sales Forecasting white paper to learn how to solve these pipeline management challenges and get to your goal faster.

“Our vision is clear—accelerate each step of the sales process to give our customers actionable predictive insights through the power of real AI and machine learning,” said Dave Elkington, CEO and founder, XANT.

“We use billions of pieces of cross-company data. We can predict the best telephone number to dial, the best email to use, and the best day/time to contact in conjunction with hundreds of other predictive insights. That is the power of real AI.”

XANT CEO Dave Elkington

Features Enhance Predictive Capabilities.

• Predictive Playbooks is the sales cadence tool which increases conversations with the right prospects and customers and builds pipeline for sales teams. It now includes a Best Email feature. This feature leverages the same cross-company data to predict the best email address for best contact results.

“While we have many premium AI capabilities, ‘Predictive for All’ is our goal,” Elkington said. “No other enterprise solution provider has the breadth of predictive features powered by data and AI, and makes those features available to every customer.”

XANT – A Leader in the Sales Acceleration Space

XANT offers the industry’s leading AI-fueled sales acceleration platform powered by Neuralytics. Neuralytics is a predictive and prescriptive self-learning engine that drives revenue growth. The platform helps companies acquire new customers faster, improve cross-sell/upsell conversions, and rep performance.

XANT has received numerous industry awards including the 2017 AIconics Award for Best AI Application for Sales and Marketing and Forbes Cloud 100 list. We’ve won CNBC Disruptor 50, AlwaysOn Global 250, OnMedia 100 Top Private companies, and more.

We’re proud to now offer the benefits of Artificial Intelligence in sales to all of our customers.

We expect this update will bring improved sales results for all our customers – some XANT clients have seen improved results of up to 30 percent after adopting our platform.

Sales departments are constantly lagging when it comes to forecasting, shows Gabe. “Most of us are flipping a coin when it comes to our sales forecast strategies,” says Gabe. Industry research shows that 79% of sales organisations miss their forecasts by more than 10%.

Why Is Sales Forecasting So Difficult?

So why is forecasting sales such a difficult task? One reason is its complexity, shows Gabe Larsen. Sales forecasting includes all the factors that influence whether a deal will close or not. The other reason is limitations of the Customer Relationship Management (CRM) software.

“The problem is a lot of people try to do forecasting inherently in CRMs. This is a multi-tool framework that goes about this deep in 150 areas. They lack a lot of the things they want to do around forecasting. They lack the system and the capability so that they end up maybe doing a lot on the side and it gets a little bit messy,” explains Gabe. “Specific forecasting tools are more like a chef’s knife. They’re designed for a specific tool and they’re going to do one thing 150 feet deep. This is one thing I find is often a problem,” he adds.

Sales Forecasting, Done the Right Way

Gabe Larsen advocates for a model based on discipline and flawless execution, when creating a sales forecasting strategy. Here are the action items you should have on your list for a winning sales forecasting strategy:

1. Define sales stages
2. Determine stage probability
3. Build forecast categories
4. Use predictive forecasting
5. Establish a forecast cadence
6. Know what you’re measuring

He uses a few stages for creating this strategy, and optimizes based on company goals and specific factors that influence sales. These may be target audience, deal sizes or customer journey:

1. Understanding the model: transactional vs relationship selling
2. Create descriptions, milestones and outcomes
3. Ensure stages cover the entire customer journey
4. Define distinct sales stages

Transactional vs Relationship Selling

Whether the company uses transactional vs relationship selling is important for the sales forecast, as it influences building sales stages, shows Gabe Larsen.  The transactional selling model focuses on achieving quick sales. The rep will not have a deliberate attempt to form a long-term relationship with the customer. Relationship selling is about sales striving to develop a relationship with them first, and then try to close the sale.

“A lot of companies obviously want to move up stream. They want bigger deals. We all want bigger deals. The problem with moving up stream is that it’s going to be a relational selling model. It will be bigger deal sizes, longer sales cycles. Whether you like it or not, it is probably going to be a different business model than your high velocity [sales],” shows Gabe Larsen.

He recognizes that many companies try to use both models in their strategy, however they are not always successful. “Better companies are recognizing that they are very different and they have a completely different go to market strategy for their transactional business, different marketing, [they are] different sales plays,” adds the InsideSales VP.

Create Descriptions, Milestones and Outcomes

Creating milestones and outcomes you are expecting during the sales process is a crucial step in the sales forecasting strategy, shows Gabe. He recommends taking the time to build this document to understand how to optimize the process. Some of the sales stages might be (each can have its own different milestones, outcomes and description):

• Planning
• Opportunity Qualification
• Opportunity Strategy
• Executive Sponsorship
• Solution development
• Solution confirmation
• Closing

The milestones will help you progress each deal from one stage to the next. It will also give an accurate view of how much time sales reps spend on each of these steps.

“A lot of people actually take this to the next level and build some of these concepts into CRM to help people really be able to manage this more effectively. I’m a big proponent of systematizing it but I think the first step is really getting it down on paper in a way that allows people to know different sales stages are,” said Gabe Larsen, during the webinar.

The Probability That Your Deals Will Close

Successful sales forecasting has a lot to do with probability. Probability is the likelihood that a deal will close or move something into a closed state. To determine probability, you might use the following processes:

1. Have rep subjectively assign probability
2. Default a probability by stage
3. Run reports to determine probability by stage

Often, companies will use a default probability for their industry – or one built into their CRM, shows Gabe Larsen. He adds that he doesn’t recommend the first strategy: subjectively assigning probability. However, intelligent companies will use reports to determine probability by stage.

“You really want to get to that point where you’re starting to run some reports to be able to determine what the probability is by stage. […] This method is easily understood and depending on how you use your probabilities, it’s objective. There is no management call, […] a lot of the emotion is taken out of it,” said Gave Larsen.

One point of caution would be: probability must consider sales cycle length, he adds.

Building Forecasting Categories

Each organization will build different forecasting categories based on its goals, says Gabe Larsen. However, it is a crucial step of sales forecasting.

“You can just be completely subjective here and allow sales managers, sales reps, etc. to define forecast categories. A lot of organizations will actually bucket them into these different sales stages,” says Gabe. He adds that each company will have different verbiage (they may call their categories ‘pipeline’, ‘commit’, ‘upside’ or other).

Pipeline = a newly created opportunity that is not likely to close this quarter;

Best Case = identifies opportunities that are not guaranteed to close this quarter. They may close if everything goes as planned;

Commit = these are opportunities that are going to close this quarter;

Closed = you have received an order and closed an opportunity.

Using Predictive Forecasting

Predictive forecasting will take some of the subjectivity out of the sales forecasting process.  Predictive forecasting tools will look at the variables which influence results (predictors), to forecast outcomes for your sales results. The process uses data mining and probability to create a statistical model.

[Predictive forecast] looks at historical conversion rates. What’s winning percentage of similar opportunities that have been like this in the past? It’s going to look at current sales pipeline and number of opportunities in the time frame. It will also consider other variables: average deal size, time, engagement, the number of times something has been shifted. All of this data driven sales forecast rolls up into predictive forecast. This is going to make your sales forecast substantially a lot tighter than the intuition of reps,” explains Gabe Larsen.

Predictive forecast tools will account for these variables and help you get a more accurate number on sales forecasting. They will consider, for example, the commit risk. When a deal has shifted its close date three or four times, this reduces the probability to close and constitutes a commit risk.

“The predictive forecast gets you a truer number and it then becomes very effective at helping reps get closer to their number. […] Once we have sales stages identified, it helps show how effectively are people moving through them,” shows Gabe.  Moreover, predictive forecasting tools will flag any slowdown of the sales cycle.

“Predictive forecasting is best utilized when you have larger, complex deal sizes than transactional one call closes,” added the InsideSales VP.

Sales Forecasting With XANT

If you’d like to learn more about creating a winning sales forecasting strategy, watch the webinar with Gabe Larsen: “Building a Forecasting Strategy that Works.”

Up-to-date data is an essential component of Playbooks, which brings the software directly to reps in their browser. Owler’s data has been proven by over one million business professionals to be the most reliable company information on the market, which is why we chose Owler to help sales reps get the scoop on their prospects.

Playbooks uses four kinds of external data to augment our Neuralytics database comprised of 110-billion strong (and counting) sales interactions.

Those are:

• Demographic
• Histographic
• Geographic
• Firmographic

See Owler data incorporated into the Playbooks sales acceleration platform.

Owler now supplies Playbooks with firmographic data, including revenue estimates, company size, industry, URL, logo, social profiles, and more. InsideSales also leverages Owler’s Competitive Relationship Graph to predict future customers and help reps take advantage of competitive intel and company-news to close more deals. The data now available in Playbooks is the same information within our web platform that helps our 1 million member community make their most important business decisions.

Up-To-Date Info is Essential to Sales

Savvy salespeople are leveraging Playbooks to prospect, prioritize and connect, as well as to automatically sync all activities to the CRM without manual data entry.Today, the typical sales rep struggles to manage up to 200 or 300 different accounts at a time, each at different stages of the sales cycle. Playbooks solves this difficulty by prioritizing and targeting specific accounts with personalized sales engagement plans, driving higher conversion and close rates. Additionally, Playbooks is also the first solution of its kind to follow across the web, even across multiple browser tabs, giving immediate access to critical sales resources, no matter what website the rep is on.

In order to make savvy account decisions, salespeople need to have accurate account data. That’s where Owler comes in. Every 1.3 seconds, members of Owler’s professional business community provide unique data and insights on private company revenue & employee estimates, competitive relationships, CEO approval ratings, likely business outcome, and more. Owler’s 12M+ company profiles from the most accurate and up-to-date account directory in existence. These real-time updates are connected to XANT via API, ensuring that their customers have access to the most up-to-date account information in the world.

Here are a few ways InsideSales customers benefit from Owler’s top-tier account data:

Lead mapping: Generating a huge number of leads is great. But you know what isn’t great? When these accounts float around, unmapped to accounts, where they can’t be scored and distributed to the correct reps. Now, thanks to Owler and InsideSale.com’s’ Neuralytics, when marketers map leads to accounts, they’ll be able to accurately map and score the maximum number of leads.

Real-time account information: Sales territories matter, as does delivering the right leads to the right reps at the right time. Let’s say your company only does business with companies that generate over 20M in revenue, and your sales team has an account they’ve been itching to call, but it only generates 17M in revenue. When that company’s estimated revenue increases from 17M to 21M in revenue, that information is reflected in InsideSales via Owler real-time data integration. Now, your sales rep has the green light to work that account.

New companies added daily: Does your company sell to startups? Do you want to be the first to know of a new competitor of a company you do business with? Look no further. New companies are added to Owler daily, providing InsideSales customers with a ripe set of accounts to target with their solutions.

Clean data structure: New accounts are great, but duplicate accounts create problems. Owler automatically maps each account to the company’s URL—a globally-known, free and open standard that functions as a unique identifier and build’s Owler backbone of data. This way, you can be sure that each company in your system is uniquely and accurately identified. Any type of action, be it a cold call, cold email, marketing campaign, or anything else is connected to the correct account, and only that account.

Find new opportunities: Want to know where you’re likely to see success in sales? Leverage Owler’s competitive graph to help identify potential opportunities that are similar to current opportunities. Since sales reps employ similar strategies to go after similar, competing companies, using the competitive graph to pull in past successes can make your job simpler and make you more efficient.

The latest news: Easily discover trigger events with a concise summary of recent press on the companies you care about. When a salesperson lives in a platform like outreach, and they call an opportunity, they then have the most recent news on that opportunity. Owler provides sales reps with a distinct relevancy when making phone calls.

These are just a few of the many ways harnessing the most accurate account data benefits InsideSales users. This is also why more and more companies are leveraging Owler’s API to power their marketing automation, sales enablement, SaaS, and financial services offerings.

Big data refers to the mounting repository of information, whereas predictive analytics is the process that turns that data into something useful.

In other words, predictive unleashes data’s potential through algorithms that can actually learn from themselves. This can have far-reaching benefits in anything from sales acceleration to crime fighting and health care.

However, it’s not a simple process. In speaking with many different organizations and customers, I’ve found two common misunderstandings around data. Before any organization can make progress on the predictive front, it must understand:

• Proper data hygiene
• Implementation timing

1. Proper data hygiene

Complications in creating predictive models often come from dirty data. Far too many organizations think they can simply upload whatever data they have into the system and receive the perfect outcome.

That’s not how it works.

Organizations need to make sure their data is clean. That means it must be relatively error-free. If data is duplicated, inaccurate or outdated, it’s not going to give you accurate results.

In other words, garbage in means garbage out.

To help improve your data and ensure it’s ready to fuel a predictive engine, you must scrub it. That means amending or removing data in a database that is incorrect, incomplete, improperly formatted or duplicated.

Typically, this process involves updating records to create a single view of the data, even if it is stored in disparate systems.

2. Time to implement

In their eagerness to adopt predictive solutions, organizations often misjudge how quickly things will be up and running.

There are no microwaveable dinners when it comes to building predictive models. You can’t push a few buttons and expect a ready-to-eat meal in less than a few minutes.

I often have to help temper customer expectations when it comes to predictive solutions, helping them understand that this process takes time.

Organizations should plan for an implementation that can range from several weeks to several months. They must also constantly build and retrain their models with additional information to refine them and ensure they are working properly.

Start with strategy

The key is to have a well-thought-out data strategy.

Start by asking yourself what are the areas of your business where you can gain the most meaningful insights.

The XANT Cloud has positioned itself to help organizations develop and build their data strategy.

The XANT Cloud represents the evolution of big data applications for businesses, allowing companies to realize the full potential of predictive technologies in their own applications, business processes and sales practices.

To learn how predictive analytics can increase your sales success, download this free ebook.

• All three NBA teams disappointed their fans last season.

• They all overpaid for the talent on their rosters.

• And they all have failed to embrace analytics to make data-driven decisions.

ESPN conducted extensive research into every team’s analytics usage and concluded that the Knicks, Nets and Lakers sorely lagged behind the rest of the league, categorizing them as “nonbelievers.”

The NBA’s best teams ranked significantly higher for their investments in analytics.

The study’s findings confirm the Moneyball theory made famous by Billy Beane and the Oakland A’s, who strategically used data science to fill their roster with undervalued — and therefore less expensive — players who helped the team win a boatload of baseball games.

Let’s take a closer look at NBA team performance to see what sales leaders can learn from professional sports.

The power of analytics

ESPN researchers placed the San Antonio Spurs and Houston Rockets in the top tier of analytics users, categorizing them as being “all in.”

The Golden State Warriors, Cleveland Cavaliers and Atlanta Hawks earned spots in the second-highest tier, designated as “believers.”

The Warriors won the NBA title last year, defeating the Cavs in the finals, and the Spurs have long been regarded as one of the league’s best-run organizations.

So, it’s pretty clear that analytics give these teams a competitive edge.

But here’s the crazy part: The teams that invest in analytics often outperform bigger-market teams with larger payrolls.

What this means for sales teams

The implications are obvious for sales leaders who are responsible for achieving greater results with fewer resources than ever before.

To win in sports and in sales, you must think differently. You must leverage data and analytics to give your teams the best chance at success.

See how innovative companies like OutboundEngine are using predictive analytics to dramatically improve their sales results in this brief video.

To discover how your sales team can take advantage of predictive analytics, get the free ebook below. 

Free eBook: The Science of Lead Scoring, Prioritization & Sales Success

79% of marketing leads never convert to sales. That means inbound reps waste a lot of time chasing the wrong leads.

Get eBook Now

Melvin Conway

Microservices, a term coined by Martin Fowler, serve as a great tool and opportunity to help with the communication between teams and to bring focus to the overall system. This is for teams internal to XANT as well as external.

It is not only giving XANT the ability to improve internal consumption of our services, but more importantly allows for the easier consumption of our technology services by XANT clients and our partner community.

We are working on a new set of APIs. To start with, we are rolling out our products on Microsoft Dynamics, the XANT Predictive Cloud and the new XANT Mobile Now platform.

Everything has or is moving toward an API model so that it can be consumed by any device, browser, mobile or external headless services.

Sales teams are investing in data science to gain a competitive edge, the Salesforce research showed.

In fact, high-performing sales teams are 4 times more likely to use predictive analytics than underperforming teams, according to the “2015 State of Sales” report.

“Data science is transforming the sales industry,” said Dave Elkington, CEO and founder of XANT. “Self-learning systems, powered by predictive analytics, can now accurately predict who’s most likely to buy and prescribe the best time to call or email them and the most relevant message to share.”

Separating the best from the rest

Not surprisingly, the top teams also reported a higher level of confidence in their ability to use predictive analytics to improve sales performance.

High-performing sales leaders were 8 times more likely to rate their predictive analytics capabilities as “outstanding” or “very good” than their underperforming peers.

“The gap between the very best sales teams and all the rest is widening,” said Ken Krogue, president and founder of XANT. “It’s no longer about throwing big bucks at top sales talent. The difference now boils down to technology and how well you use it to improve the performance of your people.”

OutboundEngine’s sales leaders are early adopters of predictive analytics technology. They used it to build a model that separated their best leads from their worst leads.

By redirecting their sales reps’ efforts to the leads most likely to convert into sales, OutboundEngine achieved a 24% increase in revenue.

To see how predictive analytics can dramatically improve your sales performance, get the free ebook below. 

Free eBook: The Science of Lead Scoring, Prioritization & Sales Success

79% of marketing leads never convert to sales. That means inbound reps waste a lot of time chasing the wrong leads.

Get eBook Now