Steve Silver is the Senior Research Director of the Sales Operations Practice at SiriusDecisions. As part of our webinar with Steve, we spoke to him about the current state of sales forecasting today and trends in sales forecasting technology.
Is forecast accuracy more important now than it has been in the past—and if so, what caused that change? Why?
Steve Silver: It's not necessarily more important, but it's certainly more urgent and the expectations are higher. Forecast accuracy is considered a critical indicator of the ability of the company and executive to manage the business. Investors can see it as a sign of confidence (or lack thereof) in the leadership team. This perception applies to both public and privately held companies—especially those that are trying to raise money or on a path to an IPO.
So where does that urgency and expectation come from, and why is it higher now?
Some of the pressure is internal and some of it is external. When you consider the ever-increasing amount of data about buyer behavior and business performance available today, and combine that with advanced analytics tools and data science, we see that expectations from investors, shareholders, board members, and senior executives for accuracy are increasing, and the willingness to tolerate surprises is decreasing.
Got it. When it comes to the actual sales forecast process within a company, sales ops may be primarily responsible, but what about the rest of the organization? What are their roles when it comes to building an accurate forecast?
We talked earlier about sales leaders and their role in enforcing the discipline of the forecast process. They have to use the sales force automation platform not just for forecast reviews, but for opportunity reviews, account reviews, call planning, and other sales-related activities. The key is to leverage any data you ask the rep to enter into the sales force automation platform.
Finance obviously has a role to play here and is usually closely aligned with sales operations in designing and managing the forecast process. The depth of finance involvement may vary depending on the organizational structure and the capabilities of the sales operations team. The rest of the executive leadership team—by which I mean product management, marketing, engineering, and manufacturing, for example—are all concerned (or should be) with resource allocation, budgets, revenue, and capacity planning, all of which are closely tied to sales performance.
Finally, you have the service delivery functions, such as customer service and professional services, that are also trying to manage demands on resources and conduct capacity planning.
Interesting. Could you tell me more about the role of customer service in the forecast process?
Well, perhaps it's more accurate to talk about both customer service and customer success. Those two functions are often involved in order fulfillment, provisioning, customer onboarding, training, and initial rollout—especially when software is involved. An accurate sales forecast is critical to their ability to manage resources and cost effectively deliver services.
Now that we know who's involved, let's talk a bit more about the sales forecast process itself. Of the forecast process components you listed in today's presentation, which would you rank as most important? Why?
If I had to pick on element, it would be a sales process that is aligned with the buyer's journey. This all starts with an understanding of your buyer, the knowledge inflection points, observable outcomes, and actions that the buyers take in their decision-making process. As the saying goes, the sales opportunity can only move as fast as the buyer is willing to go.
I agree. Because the buyer is in control of their process, the design of the selling process needs to reflect that buyer's process. I know this is a challenge for many sales teams. How would you suggest that teams shift their sales processes from the seller's perspective to that of a buyer?
We have a four-step process we call value-stream mapping. It starts with gathering internal knowledge from both marketing and sales about historical opportunities and buyer personas. Then, that knowledge is augmented with interviews with sales reps to develop a preliminary map of the buyer's journey. Step 2 is to validate that map with current customers and prospects. Step 3 is to conduct an internal workshop to add in sales assets, sales activities, and sales stages. Step 4 is to implement the model in the sales force automation platform and train sales reps.
Building process is one thing—adoption is a completely separate challenge. What advice do you have for sales ops leaders as they try to roll out these processes, especially across large sales organizations?
First, you have to make the process, both the sales process and the forecast process, easy to use for sales reps. Eliminate any duplicate steps or data entry. Examine every step of the process to drive out rework, confusion, or inefficiency and then add automation where you can.
Second, you must explicitly and repeatedly address the WIIFM (What's In It For Me?) issue for sales reps. In addition to making the process less burdensome, we want forecast and opportunity management to become a virtuous circle. The more data we can get about opportunities and buyer behaviors, the more insights and intelligence we can provide to sales reps. That enables reps to do a better job of qualifying new leads and opportunities and delivering the right message at the right time to the right buyer. This is not always readily apparent, so sales ops and marketing have to make a special effort to develop these insights and make them available to sales reps.
We hear a lot about predictive analytics today. Do you see more companies considering or deploying predictive capability, and if so, what are their main considerations when thinking about buying that tool?
Predictive is definitely a hot topic right now and will continue to be one for the foreseeable future. I think we're only beginning to see the power and impact from some of the analytics tools available right now. In general, we're recommending that most of our clients consider some sort of predictive tool if they haven't already deployed one. We feel that vendors will continue to evolve their capabilities and that the more data you start collecting now, the more valuable the tool will be. Of course, you have to consider the organizational capability to deploy and leverage the tool when making that decision.
What's the level of accuracy we can expect from a tool?
At a minimum, we would expect to see accuracy at or better than +/- 10%—that's the accuracy of the day 1 (or week 1) commit to final results. Most of the tools and vendors we follow can do much better than that when enough historical data is available.
How big should the organization need to be to benefit from a predictive forecasting tool?
We typically see organizations consider a predictive tool when they start to add multiple layers of sales management. As they grow to include 3 or more sales managers, each with 8 to 12 reps on their team, managing a forecast manually starts to be a big burden. It's also easier to put predictive tools in place when there are fewer people to train AND you start collecting data sooner rather than later.
To close, what's your "silver bullet" best practice to deliver better forecasts?
Align your sales process with your buyers journey, train the sales team, stick to a disciplined forecast process, and make sure you're delivering benefits to the sales reps in terms of reduced time spent on the forecast process and increased insight into buyer behavior.