When I started my career in operations in the early 2000s, it was in sales operations, and I spent an extraordinary amount of time designing territories. Most of that was squinting at maps and rerunning account lists against zip code assignments. The company was in B2B technology, but we sold digital marketing services to automotive dealerships (I cannot be blamed for subsequent events at CDK Global 😂).
Because car dealerships dealt with face-to-face transactions all day and the owners tended to be technology-averse, selling online wasn’t an option yet. We had under 50 field representatives and 20 inside salespeople to call into sparsely populated areas.
Field sales reps lived and died by territory design. Each territory had to take several factors into account:
If we gave them the wrong territory, even the best salesperson failed.
This isn’t what territory planning looks like today for SaaS sellers. Driving distance is rarely a major consideration because field reps can usually supplement their territory using video call technology. Many sales reps are nearly exclusively remote.
However, the old way of territory design has something that many SaaS companies neglect:
It considered how many accounts a salesperson can efficiently cover and quirks in the market.
Today, we’re too focused on equally dividing total opportunity and not focused enough on improving efficiency.
According to the Harvard Business Review, optimized territory design can increase sales by 2-7% without any changes in enablement, process, product, or talent.
Best-in-class territory design considers market opportunity, the effort it takes to win an account, the capacity sellers have to sell, and market conditions unique to your product. The draft is then tweaked over time to account for territory quirks—like accounts with an exclusive agreement with a competitor, for example.
Market opportunity is the one aspect of territory design that SaaS companies understand well. Market opportunity is the number of sellable accounts in a territory, whether it’s defined by a combination of vertical and geographic data or some other profiling mechanism (firmographic, technographic, etc.).
Ideally, your team uses algorithms to determine which account profiles generate the most revenue for your business (and verify it multiple times against actual opportunity data – I see too many early startups running with an intuition-based ideal customer profile).
Early-stage companies typically have a ton of market opportunity per salesperson. If you can only hire three account executives (AEs) and you have a SaaS product that is $10,000 and has a wide range of use cases, your AEs will have more accounts than they could cover for years to come.
As companies add sales reps, operations and the VP of Sales will work together to balance opportunity across salespeople. But stopping here is not what the best-in-class organizations do.
Sales capacity is not the number of hours in a workweek multiplied by 52. Sales capacity should factor in PTO, average hours sales reps take leave (a yearly average based on historical trends divided across the number of reps to spread it out evenly because we can’t predict emergency leave or parental leave), and time necessary to perform administrative tasks. The complexity of your product and the amount of enablement required to be successful also need to be factored into your equation.
For more on calculating sales capacity, check out this workbook by our friends at Lative.
Level of effort used to be a simple calculation of the number of meetings booked against accounts in a day in a given geography. Sellers are no longer restricted to scheduling meetings to accommodate driving routes. Now level of effort should look more like the volume of activity required to generate a qualified opportunity.
The level of effort shouldn’t be calculated as a high-level average across all sellers and territories. Operations should take the time to look at how teams are targeting accounts and how those account profiles impact effort.
An example of where SaaS gets an estimate of the level of effort right is for high-price software sold to enterprise organizations. Sales managers know it’s unrealistic for an enterprise seller to give many accounts the level of care necessary to acquire the logo and keep that customer happy.
SaaS often gets this wrong, forgetting to factor in market conditions that increase the effort to sell into a particular subset or profile of accounts.
Market conditions and the amount of effort that goes into a sale have drastically changed in the past few years for the majority of sellers in SaaS. They’ve lost a lot of inbound lead volume due to budget cuts and stakeholder turnover in their target market, and budget cuts to their marketing department. Many have lost their inside sales reps or now work with outsourced inside sales reps working off scripts and an ICP list (which they may or may not adhere to, speaking from experience). Sales cycles have lengthened, and buyer committees have grown.
This has made selling more labor-intensive in SaaS nearly across the board.
The nuance many companies fail to calculate is the expected reduction in productivity when a salesperson is assigned a market that has received zero marketing coverage or if they have been assigned to a new vertical or profile of accounts that do not currently exist in the company’s customer base.
Machine learning applied to lead routing makes a lot of sense. Circulating accounts based on engagement or intent can help inside sales representatives target accounts more efficiently. However, round-robin account assignment for full-cycle sellers with a product that isn’t strictly transactional doesn’t make sense. We need to let full cycle sellers focus more on relationship building and should provide them with more intentional territory design.
Using AI or a machine learning model to identify patterns in accounts – or, better yet, identify gaps in the data, suggest ways to solve it, and then analyze for patterns in your customer base – has far more potential in the short term. These tools are maturing rapidly, but it is a mistake to implement these solutions without feedback from your sales team and manual oversight.
Before we design territories, we need to understand the ground rules. We must know our target revenue number for the year and how we’re splitting sellers into specialties or profiled accounts.
In this scenario, we’ll say we’re selling to other businesses that also sell technology. We’ve divided our account executives so one arm handles a specialized vertical, one handles enterprise accounts, and one handles mid-market. These three profiles don’t overlap – we’ve extracted the specialized vertical from enterprise and mid-market.
To understand the ideal territory, we need to analyze how many accounts must be contacted to create an opportunity in each profile. We also need to understand the average initial contract value for each sale.
Then, we need to factor in sales capacity by vertical. This should also vary by profile.
We may find that enterprise account executives tend to be the most experienced sales reps, and they don’t quit as often.
Depending on how products vary by profile, the enterprise and specialty sellers may have less capacity to spend on selling because of the amount of training and one-on-one time with their managers.
Mid-market account executives may have one eye on the door because they are either less experienced and more prone to missing quotas or frustrated that they haven’t been promoted to more senior positions.
When factoring in the capacity per rep per vertical, the level of effort necessary to sell into an account, and the number of viable accounts in the market, you can design what a minimum viable territory would look like per profile per year – ideally with some room to grow, but you’ll also assume that your sellers will become more efficient as they learn, enablement programs improve, etc.
Territory design, quota assignment, and compensation planning should happen in lock-step. The products your reps can sell and the types of accounts they can sell into should inform your compensation plan just as much as the selling behavior you want to see in your salespeople.
Ideally, your territory design helps inform how the teams are staffed and the quotas assigned to each seller profile.
Unfortunately, we’re often told the minimum number of salespeople we need to have because your Sales VP has a patented formula:
Using a more advanced method to calculate how many accounts each profiled seller can realistically cover can go a long way in convincing a Sales VP that their math is outdated. And while it’s true that territories will never be entirely fair, you’ll have a much better starting point, which gives your management team far less cause to believe a salesperson when they blame their territory for failure.
About the Author: Camela Thompson is the Head of Marketing at the RevOps Co-op. Before switching to marketing, she spent 15 years in operations, supporting sales, marketing, and customer success. She has been a system administrator for HubSpot, Salesforce, Zoho CRM, Marketo, Eloqua, and more, and she’s helped manage SQL data warehouses and led a team of analysts. Camela has advocated for end-to-end go-to-market operations before pundits declared it a “thing.”
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