How to Make Your Sales Forecasts More Accurate

Read the Published Article on Harvard Business Review

Consistently accurate sales forecasts are gold. They deliver the revenue predictability that is essential for companies to accelerate their growth and success. Unfortunately, consistently accurate sales forecasts are rare. That’s because many companies fail to align their sales and marketing departments, and that alignment is a prerequisite for forecast accuracy.

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Throughout my three decades in high tech — as a sales rep, chief revenue officer, CEO, board member, and now as a venture capital adviser and investor — forecasting sales has often seemed closer to gazing into a crystal ball than to developing the math equation that is required for repeatable success. But this misalignment between sales and marketing is not just a tech phenomenon. I regularly see the same challenge in many other market spaces.

It’s easy for marketing and sales teams to wind up at odds. There’s often competitive friction between the heads the two departments — both of whom usually report to the CEO. Sales will grumble that marketing isn’t generating enough leads to make the number. And marketing will scoff that sales isn’t following up on the leads that marketing delivers. This bickering intensifies whenever it looks like a quarterly sales target is about to be missed.

When I joined Black Duck Software as CEO in 2013, I very quickly experienced the pain of sales and marketing misalignment as we missed our sales target for three straight quarters. Although there was a service level agreement between sales and marketing regarding the volume of leads that marketing would generate for the quarter, it was effectively useless because each department defined “lead” differently. Marketing had its definition of marketing qualified leads, and passed the required number of them to sales each quarter. Sales had its definition of sales qualified leads, but too often their quality was insufficient to close enough business to hit the forecast. When we missed the target each quarter, the finger-pointing and blaming ensued.

What we needed was a better equation for how to create the right forecast. This equation starts with a well-crafted definition of a lead or prospect that shows interest in a product or service. That is followed by an understanding of the rough number of stages the prospect goes through (including interest, evaluation, and budgeting) on the way to a purchase.

A big hurdle we faced in creating the success equation was establishing both the definition of a sales qualified lead and how that related to our marketing team’s compensation. For most companies, including Black Duck at that time, marketing’s compensation and incentives are based primarily on the sheer volume of leads passed to sales, irrespective of their quality or of whether the company makes or misses the bookings number.

The problem is that when marketing’s compensation is based on raw leads that have not been fully vetted (perhaps the lead is interested but not in urgent need of the product), sales winds up chasing unqualified leads. In my experience, if the sales and marketing goals are identical and closely tied together, this is unlikely to happen.

Companies can achieve sales-marketing alignment and avoid end-of-quarter angst by agreeing on a single version of the truth. A prerequisite is a very clear definition — by both sales and marketing — of what constitutes a lead that a salesperson will be able to close in the month, quarter, or year. This definition of lead viability needs to be ratified by the entire senior management team — the CEO, CFO, CSO, CRO, CMO, and COO. And, just as important, the group needs to agree not to change the definition on the fly.

Why the rigidity? It is very often the case that when a sales target is missed, there is a temptation to redefine the metrics and tinker with the definitions: “OK, we missed, but we’ll be able to improve our execution if we define our leads more clearly.” The problem with this approach is that the conversion rates that tell you what your sales math equation is need to be grounded in many quarters of data. Every time you redefine definitions or metrics, you have to wait a few quarters for there to be enough data that is statistically significant.

Defining a sales qualified lead (SQL) was one of the most valuable things we did at Black Duck. But getting there can be a contentious, time-consuming process. The sales, marketing, and sales operations teams had to take the time to analyze the characteristics of the leads that resulted in closing a sale, and come up with a set of clearly defined and agreed-upon criteria that had to be met for a lead to attain the designation of SQL. The head of sales operations was the independent — and final — arbiter of any dispute between sales and marketing regarding whether the criteria were being applied correctly. In other words, there were no back channels to the CEO.

And it worked. Armed with the SQL definition, marketing was able to look at the programs, campaigns, events, webinars, and web/collateral content that were the most productive in generating the SQLs that sales closed. This made time allocation more efficient and resource utilization much more targeted.

The sales organization knew from the get-go that the bookings number was chiseled in granite. And sales knew how much business it needed to close in order to make the number, based on expected revenue from renewals coming up during the quarter, bottom-of-the-funnel deals expected to close, and the average deal size of and average time to close new and existing SQLs in the funnel.

The single-version-of-the-truth process made it clear that marketing’s compensation had to be based primarily on the department’s ability to generate SQLs. In our case, the marketing team needed to live, breathe, and be measured by SQL production. Relentlessly focusing on SQLs definitely streamlined and sharpened marketing decision making.

After we implemented the SQL metric, we stuck to it. It gave us the single version of the truth that we desperately needed, and after a few quarters of using this method, our forecasting improved markedly. We subsequently hit 10 straight quarters and improved our accuracy to plus or minus 3% in any given quarter.

This made my role as CEO far easier. The predictive benefits of understanding our sales math equation helped us understand early in the quarter how we were progressing on our quarterly goals. If SQL creation was trending below our forecast, we knew we needed to adjust our activities during the time remaining. If we were trending ahead of forecast, we knew we would be over plan, which meant we could ramp up spending to accelerate the momentum.

This system also improved my relationships with the management team and the board. I no longer had meetings where the heads of sales and marketing complained to me about each other. They were too busy collaboratively working toward a defined set of clear, mutually agreed-upon company goals. Best of all, as we gained credibility with forecast accuracy, we were able to spend more time on other board-level topics — strategy, competition, culture — that we never got to in quarters where we missed our sales forecast.

Putting this into practice isn’t easy. But finding this single version of the truth will assures sales and marketing alignment and, in my experience, increase sales forecast accuracy. And that is absolutely essential to building a successful high-velocity sales model.