RELATED: The Six Principles Of Sales Forecasting

In this article:

1. How Sales Forecasting Affects A Business’ Decision-Making Process
2. The Importance of Sales Forecasting
   1. Ensure Sales Reps Maintain Accurate CRM Data
   2. Make Your Sales Force Accountable for Forecast Accuracy
   3. Make the Forecasting Process Work for Sales and Finance
   4. Provide the Right Tools
   5. Augment the Art of Forecasting With Science
   6. Understand Your Marketing Strategy and Funnel

Making Accurate Sales Forecasting for Better Revenue

Click here to jump to the infographic.

How Sales Forecasting Affects A Business’ Decision-Making Process

An example of a sales forecasting chart.

What is sales forecasting? This process estimates future sales using actual sales data from your sales organization.

With so many business and finance leaders facing unprecedented market disruption and the need for wholesale transformation in their businesses, there has never been a more crucial time to get a grip on revenue forecasts. This is the perfect time for companies to take a serious look at making accurate sales forecasts.

Strong revenue predictability based on historical data provides a sound platform for the critical decisions that need to be made in these tumultuous times.

• “Intel to slash 12,000 jobs as forecast misses”
• “Expedia shares drop after missing revenue forecast”

These headlines spell out the obvious: predictability is something many businesses still can’t deliver. Although there is no silver bullet in revenue forecasting, my view is that the first place to look at is within the sales function.

Why? Well, I think it’s mostly dysfunctional.

The Importance of Sales Forecasting

In my experience, the sales profession considers forecasting as nothing more than a necessary evil that has been mandated by finance. I have lost count of the times I have heard, “It’s just a distraction from our real job of selling.”

What’s more, the sales data doesn’t lie, and there are endless metrics that showcase how dysfunctional sales forecasting is today:

According to SiriusDecisions, 79% of sales organizations miss their forecasts by more than 10%. CSO Insights reported in their most recent annual survey that about 54% of the deals forecast by reps never close.

So as the CFO, statistically, you might as well toss a coin if you want a more accurate forecast than the sales team is giving you.

According to SiriusDecisions, reps spend an average of 2.5 hours per week and managers 1.5 hours on forecasting. Yet, they often don’t anticipate missing targets or realize too late in the quarter to take action.

This isn’t sustainable for any business, so what can you do to fix this broken sales forecasting process?

Here are six strategies to use to build a more accurate sales forecast:

1. Ensure Sales Reps Maintain Accurate CRM Data

Make it mandatory and part of the culture to ensure that sales reps provide accurate data on deals and opportunities.

There are a number of ways to ensure this happens, including:

• Create dashboards to highlight good and poor data stewardship by team or by rep
• Setting up simple flags and alerts to highlight lazy and poor behaviors (e.g., an emailed alert to highlight deals with close dates in the past or deals that have close dates pushed by more than X times in a quarter)

Given the value of accurate data, some organizations have gone as far as linking KPIs and compensation to data hygiene. Ultimately, you need to hold sales leaders accountable for the data quality of their sales team.

You need the right data of your past performances to come up with an accurate prediction of future sales. Getting accurate sales forecasts relies on your sales managers’ ability to record the correct numbers of your current sales.

2. Make Your Sales Force Accountable for Forecast Accuracy

Link KPIs and compensation for forecasting accuracy.

Maybe this won’t be well received in the sales community, but nothing focuses the mind more than putting pay on the line. I have seen this work well when it was introduced alongside other change processes in sales forecasting (i.e., the implemention of a new tool or process).

In my experience, I have seen KPIs tied to a range of forecast tolerances, with the most common being in the range of +/- 5% of the opening forecast.

Set a doable sales process with a baseline for your sales reps to measure their performance with. This baseline should also be the basis for sales organizations to measure their overall performance.

Seeing a concrete and doable number as their monthly quota helps them focus on achieving their targets.

RELATED: 4 Bad Rep Behaviors That Lead To Inaccurate Sales Forecasting

3. Make the Forecasting Process Work for Sales and Finance

Working together for an accurate sales forecast

Keep it simple, and don’t overdo the frequency.

Nothing turns salespeople off more than making it time-consuming and tiring to forecast. Having to forecast too frequently or making the process overly taxing means it doesn’t get the sales team’s focus or attention it deserves.

You also end up taking away critical selling time, which hugely diminishes the sales team’s ability to deliver a sales forecast.

It’s impossible to use one single test to make forecasting sales easier, but you can develop a flexible process you can reevaluate and adjust whenever the need arises.

Sales managers need to consider other factors aside from the historical retail sales when making a forecasting report. They also need to look at other factors such as cash flow, product delivery, customer profile, and each sales rep’s sales history.

You also need to follow a consistent sales model and reporting process to follow for all departments involved. Using a standardized format helps in comparing year on year data without hassle.

Know When to Use the Right Sales Forecasting Methodology

The best way to make forecasting work for both the sales and finance team is by letting them get intimate with the different sales forecasting methods there is. By getting to know these different forecasting methods, they have an easier time consulting the right forecasting results for each of their needs.

If the finance team wants to forecast financial models, then they need the right method to give them the results that they want. They can forecast possible revenues and other relevant information and see reasonable sales projections.

For the sales reps, if they want to see how likely it is that they can close a deal, then there are specific methods for that as well.

Here are some standard sales forecasting techniques below for your reference:

• Average Sales Cycle: The sales cycle for the product or service you’re presenting your prospect differs, and knowing the average sales cycle beforehand can help reps prepare.
• Historical Sales Forecasting: Using historical data, your sales forecasting tool tells you the likeliest sales results you would get.
• Regression Analysis: This is a more complicated sales forecasting method that analyzes the factors that affect your sales results. Given these results, one can tweak their sales process as they see fit.

4. Provide the Right Tools for Sales Forecasting Methods

Use a common set of tools for pipeline management and forecasting.

Ensure that sales and finance teams use the same platform for sales pipeline management and forecasting processes. Any disconnect here opens up data challenges and typically wastes endless time discussing the validity of the numbers.

When forced to use the finance forecasting tool, it gives sales a reason not to update their data in the CRM system.

Use your CRM as the system of record for pipeline data and avoid spreadsheets at all costs. When you’re juggling multiple spreadsheets and the complexities of a global or matrixed organization, the errors and inaccuracies pile up.

What’s more, building and maintaining these complex spreadsheets is time-consuming and often incredibly expensive.

Deploy a pipeline analytics tool that can easily show you what has changed and provide both finance and sales early insight into deal and pipeline risk.

Ensure that the forecasting platform you are using gives both sales and finance what they need. Excel gives finance the flexibility to roll up the numbers in multiple dimensions (e.g., product, region, sales org), but it isn’t designed to handle the complexity of matrix, overlay, and channel sales organizations.

Deciding to purchase a sales forecasting software may cost the company an initial investment, but in time, it helps ease the workload from sales teams. This allows sales reps to focus more on making revenue rather than catching up on their reports.

Doing so also changes the way sales reps see sales forecasting as a time-consuming report to put off as long as possible.

Take the Time to Train Sales Reps on the Sales Forecasting Tools

A common mistake organizations make when they assign a new CRM for their sales team or finance teams is they don’t give them the time to acquaint themselves with the new system.

Learning about the sales forecasting tool they’re using should be gradual. It’s not a one-time thing.

Conducting training sessions occasionally helps sales reps who get more acquainted with the system level up their understanding of the system. That way, they get to use more features of their sales forecasting tools.

Instead of organizing a one, long training session for the sales forecasting tools, you should start by conducting basic training first. Eventually, as the reps get used to what they know, then more training sessions are added for the more intricate parts of the system.

It’s also essential that the sales manager also attends these training sessions as well. Since sales reps likely consult the sales manager for issues in their sales forecasting tools, they must be equipped with the knowledge to answer their questions.

5. Augment the Art of Forecasting With Science

Use data science to score deals by comparing them to deals you have won in the past.

So many sales forecasts are based on the “gut instinct” of the sales team. There is always some subjectivity in forecasting, but objective data should be the basis of judgments.

Historical trend data and top-down run rate predictions have some value in forecasting, but in today’s rapidly changing markets, a bottom-up, deal-by-deal forecast is essential.

The challenge here is that anecdotal details and personal judgments can dilute the accuracy of the numbers as they are rolled up through the sales organization. Sales managers usually know the right questions to ask, but they often lack time to inspect every opportunity, so this isn’t the fail-safe that most companies believe it is.

To get around this, I’m increasingly seeing companies utilizing the data science to score deals by comparing them to deals they have won in the past.

This provides an objective data-based view of a deal’s likelihood to close and gives a great benchmark with which finance functions can compare the sales leadership numbers.

Although the accuracy of these data science-based algorithms is shown to outperform human judgment, my advice is to use them to augment human forecasting processes, not replace them.

So, my takeaways are that forecasting has become a painful, time-consuming process. Wasted time cripples everyone, and company leaders don’t have the reliable projections they need to build a predictable business.

Integrate Sales Forecasting Results into Qualitative Data

As mentioned before, forecasting techniques in sales are more reliable than human judgment. However, this should not be the sole focus of your sales forecasting efforts.

Instead, what you should do is to supplement your qualitative data with the quantitative data available to you. This helps you create smarter business moves that are a combination of good business sense and data science.

Think of it this way: your qualitative data is the one pitching ideas to you, but your quantitative data is the one to approve or disapprove of these ideas.

These two types of data work hand-in-hand with one another instead of against each other as a lot of people might think. Therefore, you should make a continuous effort to let them balance each other out.

6. Understand Your Marketing Strategy and Funnel

Figure out what converts your prospective customer into closed clients.

Before making any decision on your sales forecasts, you need to understand your organization’s marketing funnel. This helps you figure out what drives your prospective client from one step to the next in your funnel.

What is a marketing funnel? This represents the buyer’s awareness and consideration of a product or service from initial contact until their decision to purchase.

To figure out how your customer’s journey affects sales forecasting, you need to consider the following metrics:

• Opportunities
• Qualified leads for marketing
• Qualified leads for sales
• Website and/or social media page visitors

By figuring out these metrics, your team can predict the number of possible leads your marketing efforts can generate. It can also predict the conversion rate and funnel velocity of your leads.

Once you use the information from both the conversation rate and funnel velocity, you can then predict how many initial leads are possible opportunities and how long this process takes.
Don’t forget to download, save, or share this handy infographic for reference:

Bringing all of these together, you can be assured of a more accurate salesforce forecasting.

It’s time for the sales function to stand up and take accountability for its forecasting and become a real business partner to the finance team. For this to happen, the business also needs to deliver the right tools, processes, and cadence for accurate sales forecasting.

Use data and science alongside human insights in every forecasting judgment. Then, based on the sales forecasting results, you can set a realistic sales goal for your team and business.

What’s the biggest roadblock to accurate forecasting for your company? Share your experience with sales forecasting in the comments section below.

Up Next: 

• XANT Solves Sales Forecasting Challenges With Predictive Pipeline
• You Suck at Sales Forecasting, and I Can Prove It
• What Is Artificial Intelligence and How Can You Start Using It to Be Successful

Discover how predictive forecasting can help you improve your forecast accuracy by grabbing the free ebook below.

Editor’s Note: This post was originally published on October 21, 2016, and has been updated for quality and relevancy.

RELATED: The Six Principles Of Sales Forecasting

In this article:

• • Why Is Market Forecasting Important?

1. 1. Pinpoint the Problem
   2. Determine the Variables Sales

• • • Forecasting Schedule

1. 3. Choose Your Forecasting Model

• • • Qualitative Forecasting
    • Quantitative Forecasting

1. 4. Test the Data
   5. Cut Out Wasteful Data
   6. Data Analysis

• • • Use Visuals

1. 7. Verification
   8. Track Progress of Your Forecasting Strategy

• • Final Note

How to Market Forecast Simply

Why Is Market Forecasting Important? market forecast

Market forecasting is sometimes an overlooked part of business management, but it’s an essential tool for predicting future events. Knowing, or at least having an idea about future trends, can significantly help leaders make informed business decisions.

1. Pinpoint the Problem

Choose a data point or identify a problem. The question should be something like, “what will our sales look like in the next year” or “how will the market react to our new product.”

If you have a decent forecasting tool, this may seem a simple step. Still, in reality, some issues need evaluating by the people who will be maintaining and gathering forecast data. It will be difficult for any tool to help at this point because information on the customer base, competitors, and how the market works needs to be addressed.

2. Determine the Variables

The forecaster should now identify the appropriate and important data set that is needed to make the forecast, ones that are relevant to the business, and then decide how to collect the information.

Sales Forecasting Schedule

It’s normal to forecast each month for 12 months ahead and then annually up to five years. Forecasting too far ahead is likely to be inaccurate as the economy and your business will change within that time. That said, all forecasts are wrong to some degree because they are just the best guess. One advantage of this is that they are fluid and can be adapted.

3. Choose Your Forecasting Model

Select a model that fits your business the best. Ideally, you’re looking for one that gives you the best prediction, but at the start of a business, this will be an unknown entity. The data that you chose will affect which model is chosen, so pick the model that fits the dataset, assumptions, and selected variables.

Although there are numerous different methods by which a business forecast is made, they all fall into one of two categories: qualitative and quantitative.

Gather knowledge from experts as well as factual data. Historical data may not be available yet for new products, but judgments and opinions made by a variety of specialists will be; this is known as qualitative forecastings.

In contrast, quantitative forecasting uses available data to analyze and predict future values. An example of this might be a store stock check of a particular product used to predict sales and then used to refill stocks.

Qualitative Forecasting market forecast

This forecasting model is particularly successful with short-term predictions when there is limited historical data. It uses people’s views and can, therefore, be unreliable compared to measurable data.

Qualitative models include:

• • The Delphi method is a qualitative forecasting model which requires information and opinion from several field experts or Market Mavens. It is assumed that the forecast is more accurate because of the aggregate data.
  • The Market Research model requires a poll from a large number of people about their willingness to buy a particular product or service and to use this data to predict how many products will be purchased once launched.

Qualitative Forecasting

These models rely on having sufficient raw data to predict future values and avoids the unpredictability of the human element, which is removed from the equation.

Mathematical values exist to make these predictions that factor in variables like the unemployment rate, and Gross Domestic Product (GDP) to build long term forecasts.

Qualitative models include:

• • The Indicator Approach – looks at the relationship between sets of data
  • Econometric Modelling – assesses the stability of datasets and is used for a more targeted approach.
  • Time Series – uses past data to predict future events.

RELATED: 6 Forecasting Strategies For More Accurate Sales

4. Test the Data market forecast

Perform initial analysis of the data to see it is usable. Check trends and patterns shown in the data to see if they are helpful. Cut out any unwanted data.

5. Cut Out Wasteful Data

Making a 100% accurate forecast is impossible, so the process should be simplified as much as possible while still retaining a degree of accuracy. You will need to make some assumptions at this point to cut down the amount of unwanted data to make the forecast straightforward.

RELATED: How To Manage A Sales Team While You Work From Home

6. Data Analysis market forecast

Using your chosen model, run the data, analyze it, and make the forecast.

Use Visuals

Make your figures graphic and convert them into a chart or diagram.
This will make it so much easier and more enjoyable, to visualize any forecasting issues that can be addressed straight away.

7. Verification market forecast

When you can, check your forecast against the actual data. Check for accuracy and identify any problems. Ensure you make any changes to the variables or change any necessary steps.

8. Track Progress of Your Forecasting Strategy

Forecasting isn’t a measure just to get the business up and running. Successful companies use their market forecast to calculate their progress and use it as a management tool to run the business better.

Tools can help you to compare your forecast to accounting systems. Don’t do unnecessary work when a system can do it for you.

Have regular financial review meetings. Check your organization’s finances compared to forecast, see if you’re on track. Put measures in place to alter work if you’re surpassing goals or falling short, unless there are reasons not to. This way, your company numbers drive your plan.

Final Note

Ultimately, all market forecasts are educated guesses irrespective of whether they mirror the specifics of a business.

There are risks involved with forecasting because of the unlimited number of variables and relying on historical data or data based on opinion.

What simple market forecasting strategies do you use? Share them with us in the comments section below.

Up Next:

• • • 7 Ways To Increase Sales Productivity
    • You Suck At Sales Forecasting And I Can Prove It
    • XANT Introduces Industry’s First Mobile Sales Engagement Solution
      

In this article:

1. Reps Make Mistakes – A Lot of Them
2. The Four Bad Behaviors That Mess Up Sales Pipeline and Forecasting
   1. Pipeline Stuffing
   2. Sandbagging Deals
   3. Overestimating Deal Size
   4. Underestimating Sales Opportunities
3. Inaccurate Sales Forecasting Costs Companies Millions
4. How Artificial Intelligence Can Help the Sales Pipeline Management and Forecasting

Sales Pipeline | Get Accurate Revenue Forecasts by Avoiding These Bad Behaviors

Reps Make Mistakes – A Lot of Them

Sales teams can make actions that can impact the sales pipeline in a negative way. The sales forecasting process is riddled with errors in most organizations, whether human errors or the lack of predictability in the marketplace.

Around 79% of sales organizations miss their forecasting mark by 10% or more, according to SiriusDecisions data. In turn, they don’t reach their targeted sales goals. What we like to call human “errors” are often poor habits of sales reps or an entire sales team. Some of these occur repeatedly throughout the sales cycle.

One habit, in particular, is the end-of-the-month rush in sales, which can cost companies millions. It’s so widespread a customer now waits until the end of the month to buy a product. They know they have a chance to get a discount from a sales rep desperate to close a deal.

Data shows stakeholders are increasingly looking to invest in companies that are good at producing predictable and sustainable growth.

Sales leaders need to get used to handing over more accurate revenue projections.

The Four Bad Behaviors That Mess Up Sales Pipeline and Forecasting

There will be factors that influence your revenue projections outside of your control. These include the growth of the economy, the state of the industry, and the production costs. Some sales rep behaviors also play a part in inaccurate revenue forecasts.

Here are a few of the bad sales rep tendencies you need to cull to make forecasting more predictable and increase revenue:

1. Pipeline Stuffing

People in charge of lead generation and management stuff the sales pipeline with opportunities at the end of the month. It is to make sure they become high performers. Studies show that on the last day of the month, sales reps triple the number of opportunities they have in the pipeline. They also make many more calls to prospects. It’s a form of procrastinating and then panicking before your deadline.

2. Sandbagging Deals

There are different ways to describe the sales process. Two of the most popular are sales funnel and sales pipeline. Both can illustrate the buyer’s journey, but they do have notable differences.

A pipeline creates a visual on where in the sales pipeline stages the sellers and prospects are. Depending on the stage, they may be qualified leads or sales-ready leads.

Other reps might keep good opportunities from moving into the next stage in the pipeline in the current quarter. They will eventually move them into the next to make sure they make quota.

This might help them in the short term, but long-term, it paints a distorted picture of how the company is actually doing. This can have repercussions on how it can obtain financing or hand out compensation.

3. Overestimating Deal Size

Sales reps are notoriously optimistic. Hey, you have to be to work in this profession. Most of the time, they will overestimate how good sales opportunities really are. This leads them to input inaccurate numbers on the deals they have. They end up being disappointed at the end of the quarter due to not meeting their sales metrics.

4. Underestimating Sales Opportunities

Other times, they might simply pass up good opportunities. They follow their gut and intuition rather than the predictive scores. It often happens when they don’t have all the information about the factors that influence their deals or their sales performance.

Predictive sales tools that tap into crowdsourced data can change this. They can analyze billions of data points in an instant. They can send reps alerts in real time on internal factors that influence their deals. These can be past transactions and their outcomes, as well as changes in account information. They can also add information on external factors. These are weather, traffic, stock information, company acquisitions, and mergers.

Inaccurate Sales Forecasting Costs Companies Millions

Out of all these pitfalls of forecasting, opportunity stuffing at the end of the month is the most common. It is also the most toxic for accurate sales forecasting.

Sales reps are under the pressure of performance review calls, and this means they will either:

• Scramble to work harder than they did the whole month (and as a result, their closed won rates fall by nearly half)
• Try to push any deal over the finish line at all costs to increase their numbers, giving away discounts in the process (this leads to a median drop in the deal size of 34.5%)

The results are inflated sales pipelines and inaccurate forecasts. Overall, this lowers the confidence in the ability of the team to produce reliable financial projections.

How Artificial Intelligence Can Help the Sales Pipeline Management and Forecasting

New predictive sales tools powered by artificial intelligence (AI) is just one type of technology sales managers can use to avoid bad sales rep behaviors. AI can dramatically enhance human judgment when it comes to sales pipeline management and forecasting.

AI sales systems can analyze data from the company’s CRM software, as well as the external data and crowdsourced information, to reach more accurate conclusions about the following:

• What has changed in the pipeline and how can this impact deals?
• Which deals will close and which are at risk, and how do you to focus only on what matters?
• Who are the sales top performers, and how can you coach others to success?

To err is human, but some mistakes may be costlier than the others. They can hurt the pipeline velocity and conversion rate, among others. Sales managers can explore using an AI-powered sales pipeline platform. This tool can help ensure the reps avoid the above-mentioned behaviors.

Do you know of other sales behaviors that can hurt the sales pipeline? Let us know in the comments section below.

Up Next: 12 Tips For Evaluating Sales Reps Performance

Editor’s Note: This post was published on July 16, 2018, and has been updated for quality and relevancy.

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.

RELATED: 4 Bad Rep Behaviors That Lead To Inaccurate Sales Forecasting

In this article:

1. Building a Sales Forecasting Strategy
   1. Define Sales Stages
   2. Determine Stage Probability
   3. Build Forecast Categories
   4. Establish a Forecast Cadence
   5. Measuring Pipeline Metrics

How to Create an Effective Sales Forecasting Strategy in Six Steps

Building a Sales Forecasting Strategy

A strategic forecasting method can help a business increase revenue and improve efficiency. The importance of forecasts can’t be understated since they play a big role in determining a company’s investments and expenses.

Have time to spare? Check our in-depth guide to sales forecasting and our recommended steps in the full version of Building a Forecasting Strategy That Works.  Get it here!

1. Define Sales Stages

The first step to building an effective sales forecasting strategy is to identify and define your sales stages. It’s important to know the sales process inside-out before defining your sales stages.

If you’re still starting to build your forecast, these points can guide you:

• What type of sales model are you running — transactional or relational?
• Create descriptions, milestones, and outcomes for the sales stages.
• What key steps does the customer go through in the customer journey?
• Define distinct stages.

2. Determine Stage Probability

Once you’ve determined your sales stages, it’s time to check the probability of your salespeople securing deals while following the sales process. This step analyzes the likelihood of a sales rep actually closing a sale.

Here are some ways to determine stage probability:

• Just shoot from the hip and have a sales rep or a sales operations specialist define the stage probabilities.
• Work with default probability by stage (a few CRMs or Customer Relationship Management have this built in). Typically, Stage 1 is 10%, Stage 2 is 25%.
• Run reports of deals closed won that show exactly what the probability is by stage.

3. Build Forecast Categories

Creating an effective sales forecast strategy

The next step is to make forecast categories. These categories are dependent on the company’s internal business processes. In some cases, the categories may be assigned to sales pipeline stages.

A standard way of referring to these categories is essential so everyone is on the same page. Check out our table of forecasting categories in the full version of the ebook here.

4. Establish a Forecast Cadence

You now have your sales stages, probabilities, categories, and predictions. In the next step, you need to establish a forecast cadence to manage your data.

Regular sales forecasting meetings remind your team of their pipeline progress. It also keeps you updated on how much work still needs to be put in to achieve your sales goals.

5. Measuring Pipeline Metrics

Not all sales forecast strategies are set in stone and you’ll definitely encounter changes throughout your timeline. Thus, you’ll need to know which metrics you need to measure to determine progress and success.

Here are some metrics you can consider:

• Velocity of deals coming in
• Number of deals a rep manages
• Quality of the deals a rep makes

Having a good forecasting strategy can help your company improve efficiency to hit sales targets and boost revenue. It also helps sales leaders keep investments and expenses aligned with revenue forecasts.

Another thing to remember—ensure your strategy uses the right tools and methods. These go a long way in helping produce more accurate forecasts.

Download the entire Building A Forecasting Strategy That Works eBook here!

What are some challenges you encounter in making sales forecasts? Share them with us in the comments section below.

UP NEXT:

• Building a Sales Forecasting Strategy that Works
• 5 Strategies For More Accurate Sales Forecasting
• 3 Keys to Unlock the Potential of Sales Structure for Enterprises

The article featured a ground-breaking research study by InsideSales Labs, the research and best practice arm of XANT. The study highlights the constant struggle between when is optimal for the seller to have the prospect purchase (the optimal selling window) and when is optimal for the buyer to purchase (the optimal buying window). Typically, sales reps push prospects to buy in the current period due to compensation and company cycles grounded in time-based compelling events. Through multiple sales engagements, buyers have become educated on the tactics and strategies of the optimal selling window. These experiences, in combination with massive amounts of information, empowers the buyer to push for the optimal buying window that aligns with their time-based compelling events. This consumerization of the enterprise sale reaches a climax as buyers define their place in the sales process, forcing sellers to follow the optimal buying window.

Many executive management teams myopically focus on the increased deal flow of these trends and believe that these end-of-period efforts lead to valuable increases in the number of deals and associated sales revenue. Because of this, sales representatives are encouraged to procrastinate sales opportunities or pull deals forward, not understanding the full impact these behaviors have on company revenues. However, in expanding the data to look at end-of-period losses as well as deals, a different picture emerges—one that suggests a high cost for companies embedded in an end-of-period mentality.

Wanting to better understand the behaviors associated with the optimal selling and buying windows related to time-based patterns, InsideSales Labs analyzed data from deals both won and lost across nine quarters. InsideSales Labs focused on deal sizes (total dollar per contract) and win rates (deals won divided by deals won and lost) of 9.8 million sales opportunities from 151 companies using data from the XANT HD Forecast product. The analysis analysis revealed significant patterns related to weeks, months, and quarters:

Weekly Closing Strategies

• Sales reps tend to lose more opportunities on Fridays than any other day. Tuesday is the day reps have the best rate of closing deals. Tuesday’s win rate is 14.72% higher than Friday’s.

Monthly Closing Strategies

• At the end of the month, reps appear to be pushing deals that are not ready. There is a 2.90x increase in number of deals closed at the end of the month but an 11.43x increase in number of deals lost. o Reps are less effective at closing deals at the end of the month. Win rate decreases by 51.11% at the end of the month and deal size decreases by 34.50%.
• Inappropriate end of month sales behaviors cost companies millions. The decrease in deal size and win rate results in an estimated $98.02 million per year in lost revenue for the average company in our data set. This number represents a potential increase of 27.21% in revenue per company if properly addressed.

Quarterly Closing Strategies

• The “end-of-month effect” is exaggerated at the end of the quarter. There is a 1.08x increase in number deals closed at the end of the quarter but a 1.77x increase in number of deals lost when compared with other ends of month that don’t fall at the end of a quarter.
• Reps close at a more effective rate until the last week of the quarter. Win rate decreases by 12.26% at the end of the quarter compared with other ends of month. Deal sizes grow steadily toward the end of the quarter and are at their highest in week 12 then drop 11.51% in the last week of the quarter

To learn more about the study visit XANT Labs

Review the Harvard Business Review Article here:

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.”

Through our interactions with these customers, XANT has noticed that as the forecasting abilities within organizations mature, they each follow a similar journey.

If you’re looking to adopt a predictive forecasting solution for your business, you’ll want to know these four critical steps to becoming a predictive sales organization.

1. Assess your pipeline’s health

Imagine you’re coaching a professional sports team that is only a few months away from their first game of the new season. In order to get ready, you need to know who on your team is healthy, who needs extra training, and which specific aspects of your play you need to focus on.

Similarly, before a company starts forecasting, they need to assess if their pipeline is healthy enough to produce the desired results. Often, opportunities will need to be cleaned up to start with an accurate picture of reality.

Some important questions to consider:

• Does my data reflect the true status of where I am in the sales process?
• How much of my pipeline is realistic?
• Do I have any blind spots?
• Do I understand how my pipeline changes over time?

All of these considerations will help you set the right foundations for proper forecasting.

2. Follow a single source of forecast truth

Once you’ve decided how healthy your team is and where you need to improve, your next step is to figure out which drills and training regimen to follow.

You can’t base your success on some program you found while browsing online. Professional teams have trainers whose job it is to create a specific program the entire team needs to follow to make sure everyone is aligned.

In order to prepare yourself properly, you need to find one source of truth and stick to that program.

The same goes for any organization looking to improve their forecasting. Companies need to make sure there’s a single source of truth for their forecast.

Imagine running a company with thousands of customers and sales reps worldwide. That leaves a lot of room for people to start making their own projections based on their own methods.

Without a common understanding of the sales process, it is next to impossible to interpret the forecast that is rolled up all the way to the top.

In order to maintain consistency, organizations need to standardize each step in the sales cycle and rely on a single source of forecast truth.

You can accomplish this by having a firm grasp on where all the opportunities are in your pipeline, so you roll up a consistent number and communicate a reliable forecast.

3. Learn from your historical yield

Let’s return to our sports analogy. Chances are your team’s past athletic performance is indicative of how your team might compete in future events. If your team played average last year, and you haven’t made any significant changes, you can expect the same kind of performance this season.

Successful teams look back on last year’s performance and pinpoint important factors, like who the high scorers were and which plays worked best, so they can make adjustments. 

Sales is no different. By evaluating performance trends and applying a statistical analysis against the current pipeline, you can make a projection about how you’ll finish out the quarter.

Our customers sometimes refer to this as historical yield or run-rate analysis. Keep in mind, this is a mathematical approach and does not leverage emerging predictive science technologies or machine learning.

4. Apply machine learning to your forecast

Sports are no longer just played on the field. Professional sports teams are now making use of player data and analytics to improve in-game performance.

The future of sports management will include looking at all the current and active data (heart monitors, fitness and competitive data) to take action on the fly.

Similarly, the last phase in this evolution involves using data science and predictive analytics to determine where you can expect to finish, without inputting the numbers yourself.

Not every organization has the luxury of hiring a team of brilliant data scientists to build a predictive algorithm to tell you where you will land at the end of the quarter.

But even if they did, they’d lack access to the vast amounts of data that is needed to prove the algorithms and fine-tune them to your specific business scenarios.

For example, do you have a lot of open and closes in a month, do you have seasonality, do you have a holdout process for a specific line of business?

At the end of the day, sales forecasting is a necessary evil. No ones like to be involved in the process because it takes time away from selling. But at the same time, no organization, big or small, private or public, can afford not to do it. 

No one likes surprises – not your executives, not your board members and not your investors.

Having a consistent and accurate view into your sales forecast is key to the success of your company’s growth.

Where is your company in the forecasting journey? Learn how to take it to the next level in the free ebook below.

Free eBook:Becoming a Predictive Sales Organization

Discover how data science can help you improve your sales forecasts and increase revenue.

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Tracking sales performance behavior is a complicated process. That’s because as leads and opportunities develop and solidify in the real world, the underlying data is constantly changing.

And while there are a wide variety of technologies built to monitor sales performance, most traditional platforms are often ill equipped to handle changes in data, leading to inconsistent reporting.

To meet the demands of constant change and provide sales teams with a greater degree of forecasting accuracy, organizations need to adopt tools that support version history.

Doing so will help them gain a significant competitive advantage.

What is version history?

Version history offers the ability to track changes over a period of time, as well as the ability to recreate those changes at a given point in time.

The process works something like the change-tracking feature in Microsoft Word. As you edit a Word document, you can see all the specific changes you and others have made.

A similar feature exists for sales forecasting technologies.

Version history allows you to trace every move a deal makes for unparalleled sales pipeline management.

Why do organizations need version history?

Measuring sales performance is easiest and most accurate when its progress is measured through a continuum of stages.

With version history, you have the ability to compute how long an opportunity was in a given stage.

Version history also allows you to monitor change analysis and view a detailed breakdown of pipeline changes between two points in time, allowing you to see the deltas in close dates, pricing and probability.

What are the consequences of not having version history?

Many sales organizations suffer from “sandbagging.” This is when a sales rep reports a much lower deal opportunity than what actually exists, manipulating the sales process to artificially improve his or her attainment for a future sales period.

It’s not hard to see how this manipulation makes accurate forecasting next to impossible.

Without version history capabilities, sales leaders are unable to view pipeline changes over time and see if a sales rep enters data on an opportunity at a later stage.

This restricted view leaves organizations in the dark. Without an accurate understanding of your pipeline’s overall health, you can’t determine which specific changes brought you to this point.

Without a platform that features version history, you will not be able to measure the current state of your pipeline and determine what it will take to save deals that are on the bubble.

For more on the value of version history, download the PDF below. 

Free White Paper: Data Versioning Capabilities in HD Forecast

This white paper presents an in-depth look into the benefits of using a platform that supports version history as a core feature.

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With that in mind, sales reps are often hesitant to commit to anything, preferring to under-commit and over-deliver.

While playing it safe is understandable, it makes sales forecasting unpredictable and difficult to manage.

All too often, managers and sales reps are left guessing which potential buyers will make it through to close, and when it’ll happen.

What impact does this have?

According to this article on Entrepreneur.com, 54 percent of deals forecasted by reps never close.

To help correct this problem, we’ve outlined four best practices that will strengthen and improve the accuracy of your sales forecasts.

1. Your pipeline begins in marketing and ends in finance

When organizations talk about their pipeline, they’re often just referring to their list of opportunities.

In reality, it’s much more than that.

Your pipeline actually starts in marketing with lead generation, funneling into how many leads have been nurtured, qualified and closed.

Even then, your forecasting pipeline doesn’t end when an opportunity closes. It continues through finance and ERP systems to ensure order capture, demand planning, billing and invoicing.

If teams base their forecasts on just a percentage of their pipeline, they’ll be wildly inaccurate.

Consider more than just your list of opportunities.

Take an in-depth look at the entire process from start to finish to capture a true revenue forecast.

2. Forecast based on your business model

No two companies forecast the same way.

Even separate departments within the same organization may forecast differently.

Some forecast based on revenue and profit, while others may focus on margin and product.

An organization’s forecasting must match its business model.

If you define your forecasting process to behave one way, there will be hurdles for those who aren’t used to operating that way.

Without alignment with the business, organizations are unable to change the way they forecast.

With that in mind, as a manager, you must come up with a forecasting solution that is flexible and adapts to the business model(s) of your organization.

3. Create views for different groups of users

Different groups of users have different data needs.

For example, territory-based forecasts might work for your organization’s sales teams, but your product teams, industry business units and other groups will need to cut deals along different dimensions.

An effective forecast allows you to “pivot” so deals can be aggregated by account, product, geography, vertical and more.

It’s important that each forecast you create is able to deliver role-specific views, adapt to organizational changes, and use past, present and predictive data.

4. Adhere to a repeatable cadence

Every organization needs a forecasting cadence.

Having a repeatable cadence not only gives managers a chance to frequently forecast, but also constantly monitor quota, judge accuracy, and report on the entire execution.

A typical schedule might look something like this:

Adhering to a regular cadence can also be useful in helping a business determine where it might be trailing.

This gives the team time to adjust and make up numbers elsewhere by finding deals on the cusp and bringing them in.

Finally, having a regular cadence is essential in helping businesses manage expectations for the quarter.

Stay tuned for predictive forecasting

These four steps are a solid starting point for organizations looking to clean up their sales forecasts.

But it doesn’t stop there.

According to Thomson Reuters, 40 percent of S&P 500 companies typically miss sales estimates in a given quarter.

Next time, we’ll discuss the benefits of data science and predictive analytics, and how they mesh with sales forecasting to further improve accuracy.

Until then, you can learn how to become a predictive sales organization by downloading the free ebook below.

Related content

4 Steps to Avoid Wasting Your Time on Bad Sales Opportunities

How Marketo Tamed the Sales Data Beast

3 Essential Questions to Ask in Every Sales Pipeline Review

3 Deadly Sales Forecasting Mistakes (And How to Avoid Them)

Image credit: Christophe Verdier