How to Overcome the 5 Challenges of Accurate Sales Forecasting
If there’s one thing that’s always a constant in an organization’s sales pipeline, it’s change. While that’s a positive factor when deals are progressing forward and are moving from discovery to proposal to closed won, change is also often negative, caused by deals going dark or falling out of the pipeline altogether.
Sales leaders have the incredible challenge of keeping up with all of this change and are simultaneously held to forecasts usually set at the beginning of the year, the quarter, or the month. The problem is, with so much movement, accurate forecasting can be an impossible challenge – or at least one that requires constant observation and management. The challenge compounds when executives and Board of Directors become involved. Sales leaders who aren’t keenly aware of every single deal at any given time can fall into the trap of guessing and relying on weighted pipeline that is fraught with errors and challenges.
In this post, we’ll take a look at 5 of the most common sales forecasting challenges, and we’ll also share some key takeaways for how sales managers can better predict deal outcomes and therefore, sales pipeline.
1. Dependent Upon Correct CRM Data
While CRMs present plenty of their own challenges, they are certainly a necessary tool to any successful sales organization. However, without proper reinforcement and specific guidelines for data entry, it’s common practice for sales professionals to include placeholder text or numbers, skip fields, or include text in a “notes” field that cannot be easily categorized or filtered.
In addition, sales professionals who want to minimize attention to their deals or downplay potential value may sandbag deals by including a lower dollar amount than is probable. In contrast, others may have overconfidence that their deal will close in a shorter time period or may want additional internal support from solutions consultants or sales engineers and may therefore indicate that the deal is more valuable than it likely is.
For sales leaders, any of the above scenarios can cause faulty data, which ultimately results in error-prone forecasting. The problem with CRM data is that the data is only as good as it is accurate.
2. Limited Visibility into Specific Deals
Coaching and weekly 1:1 engagement with sales professionals is paramount to proper visibility into the pipeline, although many sales leaders are challenged for time as it is. We’ve previously explored the challenges of deal mechanics versus business case and the inaccuracies that are present when sales professionals are laser focused on the tactics of a particular prospect account versus the value-based reasoning that will ultimately lead them to buy.
In our definition, deal mechanics refer to the “black and white” aspect of the sales process, wherein the data points are typically recorded in Salesforce via a drop down menu or a single type-in field (expected ACV, anticipated close date, etc). On the other hand, we define business case as the components that build upon deal mechanics. A business case gets to the heart of what’s really taking place during the discovery process, the value conversations, and the progression through the sales funnel. A business case provides a deep level of insight into:
- What problem is the sales professional solving for the prospect?
- What is the actual root cause of that problem?
- Why is that problem (or solution) important to them?
- How does the sales professional know that a budget has been dedicated to solving the problem?
- And so on…
When sales leaders rely on deal mechanics versus the business case, they are likely only getting part of the story – the part that omits the human component of the sales process, which is what ultimately can sway a decision one way or the other.
3. Assumed Consistent Quarters and Sales Cycles
In enterprise sales, seasonality and evolving sales cycles are often culprits to forecasts being off target. Not only do most sales leaders have to account for sales cycles to various industries, such as retail or finance, they also need to factor in seasonality. For some industries, Q1 may be the hottest time to buy, whereas Q4 may be best for others looking to spend budget before the end of the fiscal year.
While most sales organizations have tracked buying cycles long enough to have a fairly good idea of which quarters are best and how sales cycles align, fast growing sales teams or those that have pivoted into a new market or have adjusted product market fit may not have the line of sight needed to make the right predictions. When sales leaders aren’t confident as to the length, the seasonality, or probability of sales cycles, forecasting can be a major challenge.
4. Lacking or Missing Deal Knowledge
One of the biggest problems facing growing sales organizations today is a lack of process and methodology. While many sales leaders have in place rigorous sales cycles, along with documented discovery questions, objection handling best practices, and even a proposal and finance checklist, it’s difficult for sales professionals to ensure each item is checked off if the process isn’t clear cut. It must be easily accessible to them while they’re having a conversation with a prospect, in meetings, or scheduling follow-ups.
When sales professionals go “off script” and ask their own discovery questions or rely on a Word Doc or spreadsheet to keep track of what they need to ask and when, the result is often missed questions and a lack of knowledge of the intricate (but ever so critical) aspects of a deal. Sales leaders know all too well that if their sales professionals don’t gather certain data points early on or don’t confidently handle potential objections, that the deal could easily fall through during later stages, such as when the proposal is sent. Therefore, even though the CRM may have deemed a particular prospect 80% likely to close, a deal that is lacking specific components or one that didn’t follow the team’s process is much less likely to close, therefore throwing a curveball at the forecast.
5. Insufficient Sales Stage Definitions
While most enterprise sales organizations have well-defined sales stages, definitions, and activities documented and watched closely for accuracy by a Sales Ops team, many growing teams struggle with staying consistent. This is specially true if an organization’s market or target buyer is evolving or moving upstream to a larger segment.
For sales leaders to properly forecast, it’s imperative that each of their sales managers and sales professionals refers to the same sales stages, uses the same terminology, and ultimately adjusts deals in the CRM the same exact way. For example, if one sales professional marks all of his deals that have had a single conversation as an active opportunity with a 20% likelihood to close rate while another sales professional marks his similar conversations as a lead with a 10% likelihood to close, then the inconsistencies – even though they may be only a few percentage points off – can make the entire forecast faulty. Now, consider the consequences when these “small” inconsistencies take place across a large enterprise sales team with hundreds of field and corporate professionals.
Overcome the Sales Forecasting Nightmare
By adopting an agile sales methodology, sales leaders can access the most up-to-date sales information so they always have proper insight into the deals being worked. At the same time, leaders can view key insights for each deal in real-time so they can not only forecast effectively, but can iterate quickly and collaborate with sales professionals to get deals across the finish line. In addition, with the help of a platform to support this methodology, sales leaders can see visuals and stats to identify what’s working, what’s not working, and what needs to be adjusted and iterated upon for future quarters.
The best part? Sales leaders can have eyes on every deal today, and can accurately plan forecasts for next month, next quarter, and even next year.
“Data is the new oil,” has become somewhat of a trope in the tech community: a quippy statement to illustrate the vast amount of data in the universe…