Your forecast is just a number. People say this all the time, but it completely misses the mark. Just a number implies that your forecast holds no real value — no purpose behind it. If that’s the case, why do it at all? Why spend all the time and effort to arrive at a useless number? Because bottom line, both the initial number and the ability to meet or exceed it, absolutely do matter. 

Forecasting is all about precision. It enables your organization to establish a budget and allocate it appropriately with the goal of optimizing revenue and growth. The closer your forecast aligns to actual earnings, the more efficient and effective your organization runs. In fact, a 3% increase in forecast accuracy increases profit margin by 2%, according to AMR Research.

Unfortunately, forecasting inaccuracy is a tale as old as time. According to SiriusDecisions, four out of five reps miss their target number by more than 10%. This is a systemic issue, but one that can be righted. All it needs is a little help from machine learning.

The Common Sales Forecasting Misconception

When you think about your sales forecast, do you see a target or a benchmark? One rewards accuracy while the other rewards you for simply surpassing it.

Salespeople are commonly trained to use their forecast as a benchmark. As long as they hit their numbers, they secure their commissions and bonuses — regardless of how far past their number they land. This tells your reps that precision has no value, only volume. Do you see the problem this poses?

Accuracy is the Real Incentive

Organizations often confuse forecasting for goal-setting when, in actuality, they each tell a different story. Reps need to see accuracy and volume as separate goals, rather than one in the same. Why does it matter, you ask?

One could argue that forecast accuracy diminishes the incentive to continue selling. If you are rewarded for accuracy, why not push a sale to next quarter to retain your bonus? This is a valid concern, but one could also argue that volume-based bonuses dissuade reps from submitting more realistic or competitive forecasts.

This is not to say that outperforming your forecast is a bad thing. Great performance should be rewarded. However, individual performance and company goals need to align. If reps continually lowball their forecasts, they limit the money the company can spend to further improve and grow their operations. By placing an emphasis on forecast accuracy, you push your reps to raise their own bar — and as a result, your company’s expectations.

Machine Learning Raises the Bar

Part of the reason reps continue to miss the mark when forecasting is because organizations struggle to hold them accountable. This is often related to dated forecasting capabilities.

Forecasting has been managed in spreadsheets for far too long. This antiquated method provides sales managers with no historical tracking, overrides or intelligence — leaving you to rely on rep judgement which is often filled with bias, happy-ears and neglected information. 

For you and your reps to forecast more accurately, you need more intelligence. You need more data and better ways to analyze it. This is where machine learning will change the game for you.

Machine Learning Brings Intelligence to Forecasting

Nothing can instantly transform your forecasting approach from good to bad, but machine learning can help you improve on an incremental basis. The more data (and more complete data) you collect, the smarter your algorithm becomes — and the smarter you become.

Successful forecasting ultimately comes down to intelligence. It’s about knowing which buttons to press to push a deal forward, but also knowing when to deprioritize a deal and pursue another. Machine learning helps you to identify those critical inflection points within a deal and take swifter and more precise action against them.

There are numerous inflection points to consider, and each of them carry particular weight within your model. This data is unique to each company, and often unique for different cycles in a company, and can be used to guide your coaching and overall deal strategy. The best machine learning models identify:

  • The key number of meetings and point of diminishing returns
  • The number of activities decision-makers should be involved in
  • The ideal number of contacts

By assessing past deals and understanding the trends behind your successes and failures, your model can establish best practices that enable your reps to be more prescriptive with every action they take against each account.

This is further enhanced by guided selling, which automatically sends this intelligence to reps via Actions. With real-time guidance based on established intelligence, reps have the knowledge to focus on the right deals and close them faster.

Machine Learning Provides Validation

If you receive an Excel forecast from your rep, you’re in a lose-lose scenario. Not only does it take significant time and effort to understand the reasoning behind their forecast, but you have no real-time data to validate their claims against. InsightSquared has many machine learning models. Two of the most commonly used models leverage machine learning to give sales leaders the clarity and confidence you need.

Confidence to Close

The cornerstone of every forecasting model is knowing whether a deal is likely to close or not. This helps identify which deals you should spend time with vs those that should be removed from the forecast. You can listen to your gut, but your gut can’t analyze the millions of data points available. Our Confidence to Close score gives you an accurate depiction of whether a deal will close by comparing it to established thresholds for winning based on previously won deals.

When push comes to shove, you and your reps know the deal best, but having a custom score to validate your feelings gives you that extra level of confidence. Not only that, but it adds another level of credibility that leadership cannot get enough of.

Ideal Opportunity Profile

You cannot view every deal equally. So when you review the forecast submitted by your rep, you need to make sure they are allocating the proper time and effort to each one. Our Ideal Opportunity Profile score helps you to identify the right deals to target based on commonalities and trends between previously won and lost deals.

There are only so many hours in the day, so it’s important that you have reps focusing on the right deals. Time and resource management is key to accurate forecasting. By identifying those key pull-forward deals ahead of time and taking a more strategic approach, you will find your forecasts far more reliable.

If forecasting is to improve in your organization, it needs to be approached the right way. That means seeing it for what it is — a target, not a benchmark. 

The next step is leveraging machine learning. Forecasting is a data-driven activity, so the more insight you have into the sales process, the better you can leverage that data. With machine learning, the bullseye looks a lot more attainable.

Request a machine learning-driven sales forecasting demo today.

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