Q&A with Alexander Group: How to Build Flexibility into Sales Plans

In our Experts in ICM Q&A Series, we’re sitting down with leaders from across the incentive compensation and sales performance management space to explore learnings, trends and opportunities that exist for today’s ICM and SPM professionals.

Today, we’ll chat with Alexander Group’s Rachel Parrinello, principal and sales compensation thought leader, and Isaac Hausman, principal and data science thought leader, on the “planning whiplash” that leaders face when dealing with uncertainty and change in the market. 

Our experts share their advice on how to build more flexibility and agility into the sales planning process to unlock sales success and reach business objectives in the face of change. 

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Whether it’s sales planning or incentive design, leaders are grappling with the same challenge: uncertainty. What does Alexander Group’s data reveal about how uncertainty is impacting go-to-market (GTM) teams?

Rachel Parrinello: We’ve been doing an annual trends survey for nearly 25 years, and the number one challenge has always been setting quotas. But this year, uncertainty trumped quotas for the first time ever.

That certainly says a lot about what professionals are dealing with today. Planning teams are no longer able to “copy and paste” their sales strategy year-over-year. In this world of constant change, what advice do you have for organizations that want to be more adaptable to change? 

Rachel Parrinello: First and foremost, your governance committee shouldn’t just meet for three to four months during the planning cycle to create the plan designs and get approvals. The committee needs to meet throughout the year to make sure that you have that ongoing governance to course correct as needed.

Companies must have a dashboard of all the core sales compensation metrics. Critical metrics include how well the business is doing, sellers quota attainment and sellers’ payouts. Depending on your business, you will need to look at the data from the company level, product level, regional level or even a segmentation level. It is important to drill down and make sure that the business performance, quota performance and payouts all match.

[BLOCKQUOTE
| Quote: Have some guidelines in terms of triggers. What are some data results that are going to trigger the need to make some updates, or maybe even trigger a SPIFF?
| Author: Rachel Parrinello
]

I always think about it from a seller standpoint. If anything is happening outside the sellers control that impacts the seller's payout, between 3 to 5%, then that's when you probably need to either get more cash in their hand through a sales performance incentive fund (SPIFF) or revisit quota setting, attainment, etc.  Anything that's in their control can see a swing of more than 5%. But when it's outside the seller's control, companies need to adjust and adapt quickly. 

Isaac Hausman: Agree, it's really thinking about it more as a system that can be automated, so that you're spending your time actually looking at results and making tweaks that are easy to execute versus the painful part of pulling reports together and adapting ad hoc. We need to do higher order thinking versus just getting the quotas out.

On one hand, you want to avoid changing incentive compensation plans all the time, because it gets confusing for reps. On the other hand, companies need to adapt more frequently to today’s market. What levers are companies using to adjust their plans throughout the year as reality hits?

Rachel Parrinello: There are a few clients of mine in the manufacturing space that have struggled to forecast their business, given the tariff unknowns. They’ve seen some migration from annual plans to maybe semi-annual or quarterly.

A lot of my clients that do quarterly planning just have that motion or that muscle in place to execute more frequently. Now, some of my clients who have annual plans think change is scary. But it is doable once you get that motion and automation in place.

For example, you can add flexibility with different SPIFFs that you can give your sellers to drive focus on areas that may not be executing according to plan. Usually, that falls in the realm of a new product launch or some sort of strategic product that may not be performing as planned. 

Or here’s another example. Europeans take a very length vacation during the summertime. It's hard to get business done during that time. So, companies may want to run a region SPIFF focused on driving business in a certain period of time when you see a bit of a dip. 

With regards to tariffs, a lot of companies have been asking if it makes sense to make plan or quota adjustments. A lot of times you need to wait and see. During this whole tariff situation, what we've seen is that demand and pricing have fluctuated. In some cases, performance is going up not because you're selling more, but because your prices are going up. But in other cases, performance is going down.

That said, you need to be upfront and clear with your communication. Always. 

[BLOCKQUOTE
| Quote: A good tip is to commit to the money, not the mechanics.
| Author: Rachel Parrinello
]

What we mean by that is to commit to your sales team that you're going to pay market competitively, both at target and on upside. 

But things are changing in this environment all the time, so planning teams need to communicate that they may have to tweak some things and make adjustments over time. 

If your sellers trust you as a leadership team, you need to commit to doing what's right for them. Then, they will be a good corporate citizen and weather the storm of changes with you. 

Isaac Hausman: Here’s another good example of staying nimble. A lot of companies are deploying product recommendation, cross-sell-type models to either general reps, farmer teams or CSMs. These models are in inherently nimble if they're built right and should be looking at—even on a weekly or monthly basis—the most recent product purchasing history and regenerating new recommendations. These models can also be used as a nimble account planning tool in the field. You could downshift certain product recommendations in the model if your margins get compressed because of tariffs, for example.                                                             

So, there are those sorts of enablement tools, separate from planning, that also allow for you to be nimble in the field. 

How should sales compensation and RevOps leaders think about balancing the art (their experiential know-how and instinct) with science (the data) when it comes to planning and incentives?

Rachel Parrinello: It starts with doing the analytics. I'm surprised with how many client engagements we do where there's no detailed set of analytics. But the analytics isn’t going to tell you what the problem is. The data can show you that there is a problem, but you have to marry that with knowledge. So, that means interviewing people, doing custom analysis, etc.

For every sales compensation project, we do a common analysis—which is a histogram of quota attainment, assuming you have a quota-based plan. Sometimes, we'll see something called bimodal distribution where there are clear winners and the losers. Okay, great. But why are there winners and losers? Why isn't this a nice, beautiful bell curve distribution? You can then slice the data in different ways and look at things like attainment versus tenure. Is it that our more tenured people have the good territories and the good books versus junior professionals? 

Armed with the data analytics, the next step is to understand why it is happening.  This generally means reaching out to sales leaders to get insights if you don't have it yourself. 

Isaac Hausman: As someone who's steeped in the data all the time, you actually see how quickly it can go awry if you are a little too focused on just the data. We certainly see that with data science teams, as well—not listening enough to the actual commercial teams and incorporating their feedback. Maybe even adding some statistically questionable trends into a model, because it's what leadership feels is right. 

Looking at the data is imperative, but you must have something to compare to and something to drive the thinking and question your thinking.

And believe me, I've seen a lot of companies get their ideal customer profile (ICP) wrong, and it was not what they told us it was based on the data. But, I've also seen weird things happen in a cross-sell recommendation model where it's basically showing you there's a better fit for a new product if they already have one of your competitors. There's a mathematical reason why that popped in the data, but that's obviously not the case. 

It's just being aware that those things can happen in the modeling process. Press some of your data providers a little bit to really go deep and explain their methodologies. That way, you can take the right approach with that data given maybe some flaws in their collection systems. Think of things like intent data sources or technographics, some of those outside enrichment pieces. 

[BLOCKQUOTE
| Quote: You can put it on a scale. The more confident you are in the data, the more you should listen to the conclusions. There shouldn’t be much debate about it.
| Author: Isaac Hausman
]

Know that a lot of data is imperfect. That's where the art becomes important. 

What impact do you think AI is having on how organizations approach planning, and what impact do you see AI having on the future of planning? 

Rachel Parrinello: I have one large, tech client that’s been using machine learning (ML) for their quotas for the last five or six years. I was talking to their head of sales compensation, and she said this year, for the first time, they will only allow leader adjustments by exception. Before, they would have their machines spit out the quota, and then they allowed leaders and managers to adjust it based on their knowledge. So, in five years they got pretty comfortable with their ML modeling. This also shows that you need to get people used to the tool. 

When I think of what AI tools that professionals will lean on in the short term, it’s going to be incentive compensation management (ICM) tools. ICM providers are coming out with AI capabilities to clean data, fix data and do modeling. 

Where I think AI is going is that agentic help bot for sellers and leaders alike. So, you have a tool that a seller can ask, “Who should I be targeting in this account? What should my message be? How much am I going to make?” These tools will tie all that different data together to help a seller maximize their goals.

Isaac Hausman: Keep in mind the concept of “garbage in, garbage out.” Those agentic co-pilots may not be able to surface great insights because the data's not there to surface insights with.

Rachel Parrinello: Good point. I’d be remiss if I didn’t mention, “The GenAI Divide: State of AI in Business 2025” report by MIT’s NANDA initiative found that 95% of enterprise AI or GenAI pilots produced no measurable profit or loss impact. Only about 5% delivered meaningful business returns. We've gone through a period of a lot of testing, and maybe wishful thinking, kind of projects. 

Isaac Hausman: I ultimately see planning converging into some kind of singular AI system where you can use natural language to interact with all the data that you have and run multiple concurrent simulations around: What if we do this segment, territory, quota and capacity?

And it'll basically model out scenarios, maybe different macroeconomic scenarios plus different internal planning scenarios, to help you decide what might be best in that area. 

It's all been moving faster than I thought, but we’re still at least five years away from that. In the interim, it's just going to feel more like automation on some of those individual components.

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