May 17, 2021

Written by

Collective[i] Team

  • Posted in
  • Sales Forecasting
  • Prescriptive Forecasting
  • Artificial Intelligence

Why prescriptive forecasting is the way forward

In the past five years, artificial intelligence has resulted in major leaps forward for sales forecasting. Step by step, new technologies have worked to address many of the challenges sellers have faced when wrestling with the complicated work of predicting future revenue. First came tools to connect siloed customer data together, offering a more complete picture of not only historical revenue, but current efforts to close deals and nurture existing customers. CRM automation tech has helped to cut down on busywork in maintaining accurate customer data, reducing the room for human error and improving the data predictive models can use to make projections.

All together, these advances have greatly improved what the industry now refers to as predictive forecasting. No longer hampered by historical data and seller opinion alone, more advanced predictive models have been able to incorporate current information to make increasingly accurate projections.

But even the most accurate projection is just a guess. What really matters is what sellers are empowered to do with those predictions. The move from improved accuracy towards actionable insights represents the next quantum leap forward for sales forecasting, and it’s what Forrester Research calls prescriptive forecasting. It’s the difference between having a prediction and knowing exactly what needs to be done to help make that prediction come true — or to exceed it.

Prescriptive forecasting is the way to make good on the promise of better predictive forecasting. Here’s why:

Past results can’t guarantee future performance

Even in the era of AI-enabled sales forecasting, the best tools sellers have had to forecast revenue were ones that would help find patterns to support future predictions. Statistical models like those using the “Forecast Sheet” function in Excel would look for patterns over time. More modern tools took this a step further by incorporating current data and automating data capture to make the process faster.

When an AI can be trained across more data, more sellers, and more importantly different companies, the better the prediction. Even the most basic forecasting tools can spot factors like seasonality on a long enough time horizon and account for them in projections.

But what about those factors that no historical data can account for? Sales teams often build out their quantitative forecasts by marrying them with qualitative information — things like a seller’s opinion of how a sale will go, or what a group of internal experts have to say about the current state of the market.

Opinions are inherently subject to bias, though, and anyone who has ever bought into a mutual fund knows the old legal maxim governing investments: Past results are no guarantee of future performance.

Even the most detailed historical forecast — or smartest experts — couldn’t have predicted something like the COVID-19 pandemic. And if 2020 has taught sellers anything, it’s that even the most “accurate” historical data — whether from last year, last week or even yesterday — is useless when new, disruptive activities occur. If something new happens before a deal closes — like a merger, a round of layoffs, or yes, even a pandemic — shouldn’t your forecast account for that information as soon as possible?

But that agility has been one of the hardest elements to work into sales forecasting — and it’s one of the reasons almost 80% of sales organizations report that their forecasts are at least 10% off from reality.__

Yardsticks, not targets

Until recently, most technologically-driven advances in sales forecasting have been limited by their own data. AI-enabled forecasting tools have done a lot to make the historical data used in predictive forecasting models more reliable, doing things like automating CRM data capture and contact management. For many forecasting tools, that automation and simplification in and of itself is the end goal. Objectively, more accurate historical data will improve projections, but that is only one piece of the puzzle.

Many sales teams use projections as reliable targets to hit. And the more faith management has in the accuracy of a forecast, the more pressure there is for sellers to hit their numbers — at all costs.

This is where developments in neural networks and machine learning of the past five years provide a clear advantage, if not a total paradigm shift in how businesses are utilizing sales forecasts. With adaptive AI learning from a broader set of historical and current market data from a larger set of sources, forecasting not only becomes more accurate, but more focused on the activities that will increase revenue.

The promise of prescriptive forecasting, then, is that forecasts can evolve from arbitrary targets into intelligent yardsticks that measure a sales team’s performance on a daily basis and help them focus only on the actions that will drive revenue.

Prescriptive forecast tools transforming sales outcomes With prescriptive forecasting tools, sales managers don’t have to put the bulk of their energy into micromanaging sellers to make sure they’re hitting their activity targets. In turn, that helps sellers move away from a spread shot approach to selling — work every lead and chase what seems like the best chance to close a sale — and turn towards a more informed, nimbler approach to sales.

Bain & Company frames this kind of selling as a “do this, not that” scenario. Each day, based on what a neural network knows about buyer activity pulled not just from one organization’s sales efforts but a broad network of sellers, it can tell teams which leads are worthwhile, which ones aren’t, and which steps they can take right now to move their best opportunities forward. While historical sales forecasting methods and models might have shown that one lead is likely to buy big and an individual seller might have a gut feeling that confirms it, the neural network can show that this prospect isn’t buying right now. From anybody.

Sales managers, then, have the freedom to focus on the more human elements of a sales strategy. They can support their sellers to do the same, shifting priorities as the situation evolves. That means more of a seller’s time can actually be spent selling, rather than wrestling with customer data or chasing leads that are DOA.

In our own experience among sales teams that leverage the prescriptive forecasting tools of Collective[i], that translates into increased revenue that doesn’t just meet sales goals — it exceeds them.

Experience the market’s first truly prescriptive sales forecasting tools

At Collective[i], our goal is clear: improve revenues by taking the guesswork out of selling. We’ve seen a growing network of modern sales organizations make this paradigm shift and outperform old-school forecasts as a result. By leveraging the industry’s first true networked intelligence forecasting technology, we’ve helped sellers not only automate CRM, but make better decisions with recommendations pulled from daily insights.

Prescriptive sales forecasting isn’t just the future of sales — it’s the present. It goes everywhere your sellers can’t be to give the best perspective available, just like any good advisor should.

Connect with Collective[i] today to see how our sales secret weapon can transform your business.

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