The fundamentals of building a data-driven sales territory plan for 2025

When it comes to sales territory planning, the difference between high-performing teams and those that struggle often boils down to strategy.  

Mapping out territories isn’t just about historical sales figures or random geographic splits. It’s a finely-tuned balancing act where you’re aiming to unlock hidden potential within your customer base.

And here, your success starts with a paradigm shift. That is: forget looking solely at historical sales. Focusing on untapped growth potential is the real secret weapon.

As David Gerardi, our VP of Revenue & Operations at Forma.ai shared during our recent sales planning masterclass series:

Incorporating metrics like market share, industry trends, and the size of your customers’ businesses gives you the power to map territories that are not only fair but packed with opportunity.  

Here, we're covering some key recommendations from Masterclass session on data-driven sales territory planning so you have the fundamentals top of mind as you plan for 2025.

Why data-driven territory planning is critical

At its core, planning territories is an optimization problem. Your objective is to align seller capacity with the workload required to cover a sales territory effectively.  

When you get this right:  

• there's adequate sales coverage matched to territory potential,  

• you create better customer relationships,  

• you capitalize on untapped sales opportunities for creating revenue, and

• you maintain a motivated sales force who feels they can reasonably hit their goals.

On the other hand, with poor territory design you risk assigning far too much workload to one rep and too little to another, leading to lost revenue opportunities, employee burnout, inadequate sales coverage, and wasted sales potential.

The analogy Kyle Webster (Forma's Chief of Staff) and David Gerardi shared in our session was to think about a bar or cafe. Just imagine being the sales rep faced with this:

Versus the sales rep with a territory and workload that looks like this:

Naturally this impacts how much compensation is on the table, and you need to adjust for retention of your reps, among the other aforementioned factors.

The key is balancing the workload of each territory against the revenue opportunity. A good plan ensures that each rep has the right amount of accounts and opportunity to hit their targets.

It's extremely important that you think about how much potential or growth of revenue you want to distribute among your sales team.

Alright, so let's get into what to do once you have your sales force sizing covered.  

Note: For the scope of our fundamentals session, we focused on solving for how to optimally deploy reps via balanced territories *wherein the baseline work is already complete re: knowing how many salespeople are required).

How to create a data-driven sales territory plan [3 key pillars]

1. Run a potentialization exercise

If you're only looking at current sales and historical sales of your customer base, it's very simple to figure out where all of your sales reps should spend their time. With just one metric it's a relatively straightforward formula to figure out how to deploy everyone.  

But the point is to go one step further. You want to identify what is optimal for revenue growth.  

For this, you need to figure out:  

• What's the ultimate potential spend of all my customers?

• And, is the historical sales (plus potential spend) equally distributed amongst your sales reps such that every rep has a customer base with a similar workload and a similar kind of potential revenue target for the org.

This is potentialization.  

With potentialization—usually in the absence of market share—you're really trying to figure out growth opportunity based on a whole slew of different variables at your disposal.  

You'll estimate a total spend figure for each customer and use that in aggregate across all of your different territories. This'll help you identify whether territories are equally balanced and if there's an equal workload among them all.

Because potentialization can range in complexity quite a bit, in our session, we didn't cover every statistical model you can build to accurately predict potential; but, we walked through a toy-car example.  

The steps outlined below get you 95% of the way to the right direction.

Step 1. Create a definition of the full-target customer universe

As David shared in the session, there are two different approaches you can take for this:

1. A light touch approach (very high-level segmentation to drive reasonably good decision making)

2. A very deep analytical approach (which can involve a far deeper analysis more data re: correlation and relationships between variables).  

Overall, however, even with a light touch approach looking at a couple different variables at a high level and using the knowledge of your business and past customers, you can figure out core definitions to get to 80% of your answer.

In this step, you're basically figuring out: of all the people who could spend with you, what does that realistically look like?

Step 2. Segment your customers & estimate the total spend potential across the customer base

This gives you a directional indication of total outstanding potential, which can then be used in your territory setting.

This involves a very simple bucketing exercise segmenting all of your different customers into three different segments: small, medium, large.

This'll depend entirely on the company/employee size. In the end, it'll look something like this re: Segments and Potential:

In our example we estimated potential spend like this:  

• A smaller company (under 100) employees = max est. spend $100  

• A medium company (100-300) employees = max est. spend $100-300  

• A Large company (300-1000) employees = max est. spend $300-1000

You first want to know what someone's max potential spend is, so you can ultimately figure out the incremental potential (sometimes called uncapped potential). You'll layer this into your output, like this:

2. Build your data-driven territory index score

Once you've identified potential, you may discover that a given territory has far less incremental spend potential than another. But instead of just arbitrarily moving territories around, you can opt to do a more analytical approach with indexing methodology.  

This helps you figure out how unbalanced a territory truly is and redistribute to ensure it's more balanced.  

So what is a territory index score?

The territory index score is a powerful analytical technique for balancing territories. It helps you to objectively compare different territories based on quantitative measures, like historical sales and potential sales, and ensure that no one territory is either overburdened or underutilized.

The output of this is a score at the territory level based on key metrics like historical sales and potential.

Your goal in this step is to arrive at a number that represents the territory workload across all territories and distribute it as evenly as possible.

A well-balanced territory will have an index score that falls within a specific range. If it’s too high, a rep is likely overloaded. If it’s too low, the territory may not have enough opportunity to meet revenue targets.

Moving accounts or redistributing geography helps even out the workload, ensuring that no one rep is overwhelmed while others are underutilized.

Here's an example territory index score:

In the above example:  

• From 800 points to 1000 represents a well-balanced territory (that doesn't necessarily require change or isn't where we would necessarily focus our effort for re-carving). You can think of this as achieving a well-staffed and well-attended bar (from the example above).

• Anything less than 800 points represents a territory that doesn't have enough opportunity (it'll have an underutilized sales resource)

• Anything more than 1200 points is a territory with too much opportunity and work for one salesperson to handle.

3. Run your data-driven territory index analysis

When you run this analysis, you'll produce an output of territories ranked on your index, and it'll look something like this:  

• The vertical axis displays territory index score  

• The purple dash lines running across the graph at 800 and 1200 effectively show the range for a balanced territory based on our index score

• Each territory is represented by a purple dot so you can visualize the territories scattered with the lowest index score and the highest.

In the example above we can see a good chunk of our territories are within a reasonable index score range, but we have three outliers at the bottom that are potentially too small and need more accounts more geography added four territories in the top right that are potentially under resourced.

Overall, the index score gives us effectively an objective measure to evaluate territory balance and look at territories relative to one another easily during the sales planning process.

Here are the steps involved in how to calculate a territory index:

1. Identify metrics: The first step in building a territory index is selecting relevant metrics. You'll identify which ones correlate to assessing a territory's overall 'worth' or score. This varies depending on what type of role and territory you're creating the index for.  

For example, if looking at a role that's primarily focused on renewing software contracts, the value of the upcoming renewal contracts is a metric you might consider for your index. If looking at a salesperson selling residential building products, new housing starts as a metric in a geographic area (which could be a proxy for potential).

The most common metrics to use are historical sales and customer potential, but there are a number of different types you can use (such as activity and capacity type metrics, like call volume requirements for different segments of accounts).

2. Aggregate data, defining your index parameters: Here, you'll collect and aggregate all customer and territory level information. You'll define parameters such as the number of territories you'll be balancing and determine the total number of index points to allocate. You'll also define the weighting that you want to assign to each metric.  

For example, if you want to prioritize future potential, you might weigh potential sales at 60% and historical sales at 40%.

3. Run the analysis, allocating index score by territory: Allocate index points across accounts / territories and create summary views (like the one above) for assessment. Run the analysis to assign index points to each territory.
   

4. Adjust territories based on index scores: lastly, you'll reshuffle and reorganize territories and accounts until the territory scores are more appropriately balanced.

Below you can watch Kyle Webster walk through the example:

Working through this analysis with sales managers and leaders, the index score takes multiple important metrics into account, rolling them into a single benchmark that can be used to support the recarving process.  

You can work with Sales Management live to move around the accounts or the geographies and see in real time how certain moves impact the index score as you to balance out territories.  

It's a good way to synthesize the different data points.

And remember, other sales force effectiveness levers (including compensation plan design) are far more effective when applied to a salesforce with equitable territories to begin with, so it's worth the effort! The index isn't nearly as involved as some of the potentialization models.

How a full-stack SPM platform enhances your sales territory planning

In our session we covered relatively simple analysis examples (the toy car model), but there are far more advanced models that you can use.

Advanced territory planning really comes down to whether you have the interconnected data that allows you to profile your customers and segment them such that you can see:  

• What a high spending customer walks and talks like, and

• If you have the data and modeling techniques to identify correlated customer variables that remain untapped.

Fortunately, a full-stack Sales Performance Management (SPM) platform can centralize all the critical data you need to drive optimal territory decisions. With an SPM platform, you can integrate all customer, sales, and performance data, making it easier to assess territory potential and allocate resources effectively.

Not only are the more advanced analysis models readily available in Forma.ai, but one of the greatest benefits is the ability to make real-time adjustments. I.e. As new data flows into your SPM platform, it can provide predictive insights on territory adjustments, allowing you to tweak plans and ensure continuous optimization. This means that when your sales force size fluctuates during the year, your territories can automatically get adjusted accordingly in the system. When you increase or reduce the number of sales reps, you can see the recalibration regarding the number of accounts assigned to each territory to maintain balance.  

The data can finally talk to one another vs. remain in silos.

Best practices for sales territory planning or rebalancing

Based on some of our session recommendations, here's what you'll want to do to run your own optimal territory design:

1. Isolate high-value customer profiles to determine which metrics to use for territory assessment

To build the most effective territory plan, you need to base your index on metrics that represent both customer potential and workload. This includes factors like industry, customer size, and historical performance.

High-value customers should get more attention, while lower-value customers need less coverage. On this:

• Focus on identifying/isolating the characteristics of a high-value customer. Analyze all the available data points, such as industry, company size, historical spend, and other attributes that contribute to a customer’s value. From there, you can build profiles that help identify similar customers with untapped potential.
  

• Group up or bucket these customers into cohorts or segments of high, medium, and low-value customers, and analyze everything you can about them to determine what drives their spend or value. Remember to look at both existing and potential customers as you do this.
  

• Use available data to correlate high-value indicators: If more detailed data (like market share) isn’t available, you can use proxy info such as company size or employee numbers to estimate potential spend. While this is a simpler approach, having some kind of structured segmentation (like company size as a proxy for revenue potential) can still be valuable.

As David explained:

2. Think ahead: Gather your metrics for territory assessment early in the sales planning period

As Kyle Webster shared, “When we think about a data-driven approach to territory planning, it happens mostly in the steps before you get to the territory carving."

As early as the beginning of August—when leading-edge organizations start the enterprise sales planning cycle—gathering the right stakeholders and reviewing mid-year performance data, it's a great time to look at historical metrics around your territories.  

From there, during September to mid-November, you can run your potentialization and indexing exercises.  

3. Design your territories based on workload & value (not rep tenure)  

When designing sales territories, it's essential you prioritize workload over other factors like sales rep tenure or customer relationships, which can create imbalances. The key is to ensure all territories are within +/- 10% of each other in terms of expected workload when you see them on your index. Ignoring this principle can result in unbalanced and unproductive territories.

For example, when a (theoretical) pharmaceutical company moved one of its top reps from a Manhattan territory to a suburban area, they decided to allow this rep to keep servicing 20 high-value Manhattan clients. This decision significantly increased the rep's workload in their new territory, while the Manhattan territory saw a major drop in both workload and value. The imbalance caused the once high-performing rep to struggle, while the new Manhattan rep had far fewer demands.

By sticking to the +/- 10% workload rule and minimizing exceptions, your organization can maintain fairness and create more sales productivity overall.

Make territory planning a strategic advantage

Ultimately, sales territory planning can be a strategic advantage for revenue growth. Organizations that use data-driven methods to plan their territories are better positioned to capitalize on new opportunities, grow customer relationships, and keep their salesforce motivated.

Frequently asked questions in sales territory planning

What metrics should be used in a territory index?

When deciding on the right metrics, consider customer attributes like industry, size, and historical performance. The goal is to choose variables that accurately represent both workload and potential revenue.

As David suggested, "Start by identifying your high-value customers and understanding what makes them valuable—whether it’s their industry, size, or historical spend."

You can create cohorts looking at things like:

• Industry  

• Products they've purchased in the past from you

• (if looking at renewals) the number of complaints they've issued in past year

• NPS scores

Anything to say, "is this a high-value customer spending with us". And once you define the variables, then working to identify everyone in your total addressable market that fits these.  

Determine which variables you have access to, and how you can correlate these to the customers you know are spending a certain threshold (whatever high/med/low looks like to you).  

Remember: you can also add multiple dimensions to this, accounting for multiple factors.  

How do you measure the impact of rebalancing?

Measuring the success of territory rebalancing can be tricky, but it often comes down to sales uplift and increased rep productivity. After rebalancing, evaluate if reps are better able to cover their territories and hit their targets.

Kyle shared, "Look for increases in retention rates and new sales penetration after rebalancing. These are strong indicators that your changes are working."