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How AI improves the accuracy of sales forecasting
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“We’re killing it and our reps are on fire. We’ve got a promising new product in the pipeline. Just look at our past performance. The influencers on social media are raving. I have a feeling we’re gonna knock it out of the park next quarter!”

When it comes to estimating future sales, does this type of forecasting — er, wild guessing — seem like it will work?

Believe it or not, at many companies, this loose form of doing revenue projections for a particular time period — a methodology obviously devoid of fancy Excel spreadsheets, key metrics, and science-based input — passes as legitimate sales projection. An often-haphazard  traditional forecasting process has taken root in the absence of a standard successful way of pinpointing future average sales. And many companies’ sales forecasting models can still be summed up as essentially taking a shot in the dark.

Historical forecasting: a bit backwards

And for good reason: predicting the future sales pipeline in any kind of accurate way has historically been a crapshoot because corporate sales projections haven’t had much substantive data to go on. Plus, in sales, historical data is typically complex, and past sales data often varies from quarter to quarter. And sales forecasting methods have been largely based on current selling activity and win percentages, which can change pretty drastically along with a slew of factors from quarter to quarter.

Sales data and wins, customer behavior, the competition, and whatever fluctuations are going on in the market at the time, not to mention other external factors (Global Warming, the local weather, pandemics, employees jumping ship for higher pay), are all wild cards that can wreak havoc on the ability of an organization to confidently pinpoint its future number of sales. 

It’s safe to say that the process of consulting the corporate-sales crystal ball and factoring in historical sales data has been a bit like counting chickens before they’re hatched: an often hopeful exercise but not exactly a reliable one.

The good news, if it can be called that, is that if accurate sales forecasts have been eluding your company, you’re not alone. In fact you’re in extremely good company. SiriusDecisions (2016) found that 79 percent of sales organizations miss their forecast by more than 10 percent. Forrester went so far as to conclude that “most sales organizations are terrible at forecasting.”  

The ripple effects of misguided forecasting 

Yet many companies are still basing their pivotal selling-related decisions on inaccurate sales forecasts; they have no other choice. That means negatively impacting their budgets and planning, their inventory management, ultimately their future sales performance. How their stock performs or fails to perform is largely based on what’s gone into the sales projection equation.

So yes, inaccurate sales forecasting techniques are problematic across the board, with lasting ramifications for companies of all types, whether they’re brick-and-mortar stores or online, whether they’re startups or small businesses or massive enterprises that hold a huge percentage of market share.

Of course, there’s a better way to do sales forecasting, which you already suspected from reading the title of this blog post and deciding to check out our thoughts on this topic.

AI sales revenue forecasting to the rescue

Artificial intelligence has come to the rescue in many ways these days, and sales projection is just one domain that’s benefitting.

The contrast with the old way of doing things is stark. Time-honored statistical modeling techniques are simply no match for sales forecasting software that can crunch the many available forecasting metrics and KPIs out there. Deep-learning models can expertly assess relationships between all kinds of data that sales managers would be hard pressed to stumble upon.

The upshot: sales forecasting using linear regression pales by comparison with machine-learning-aided AI, which lets you accurately identify emerging data trends to get a super-sharp estimate of your total sales and expected profitability.

Enter AI and machine learning algorithms

Artificial intelligence can accommodate unbelievable amounts of data (we’re talking potentially thousands of metrics, many of which don’t seem related in any way, shape, or form). They typically include measures such as customer-experience ratings and churn rates, along with expected indicators such as previous sales and other previous year data, to make forecasts decisively more on point. AI and machine learning can make sense of all of this data and decipher correlations in the form of comprehensive time series forecasts, as well as stay abreast of changes in the larger world of Big Data.

Being able to thoroughly interpret the data can also have positive ramifications beyond accurate forecasting. It can serve to guide sales organizations and sales managers in the right direction, giving sales teams the actionable tools they need to go out with a workable business plan and achieve their lofty sales goals. 

The data doesn’t lie

With guidance from AI, customer experiences can also be tracked in detail, giving business leaders and sales reps real-time awareness of their unique challenges, their shoppers’ motivation, what’s needed to attain better customer satisfaction, how to enhance retention on their website, and other critical data. 

One helpful indicator for improving sales forecasting accuracy is buyer intent, which can help you discern your customers’ plans to buy — e.g., assess demand forecasting — which can of course then influence your sales planning, supply-chain decisions, and various steps you might take. With accurate buyer intent data in hand, you can confidently add a level of specificity to your sales forecasting.

B2B intent data is especially pertinent. Gartner has referred to aggregate business buyer intent data as “the future of B2B lead generation,” which means it’s one key indicator of how to hit the sales-forecasting nail on the head.

The secret of sales forecasts: predictive analytics

Predictive analytics looks in depth at various patterns (think prior sales cycles, current environment, macro-economic impacts) and then predicts outcomes based on what it sees. When done right, predictive models project revenue from the current sales pipeline by figuring out which deals are going to close and the anticipated timing. Predictive analytics go beyond that, though also factoring in estimated income from deals that aren’t yet even on companies’ sales radar.

Companies using predictive analytics have been able to make future revenue forecasts that are up to 82% accurate, according to Entrepreneur. Maybe someday we’ll get to 100%, but that’s considerably better than “terrible.”

The underlying benefits of AI-assisted sales forecasting

More-useful lead scoring

Assigning values to business leads is a common practice geared toward prioritizing focus, but again, not something that’s necessarily been done well. As with sales forecasting overall, scoring may be assigned according to incorrect indicators such as deceptive buying signals. It’s no wonder then that a significant percentage of B2B salespeople don’t think lead scoring is worth their team members bothering with.

With AI algorithms, a company can comb its volumes of relevant data and come up with an intelligent assessment of which sales leads are truly worth pursuing. AI can factor in every indicator that matters and then recommend the best leads. 

Better ability to close deals

A great close rate can be so elusive — competitors are trying to steal your business, and a sale can easily get stalled. A solid win rate is never guaranteed. Closing can’t be taken for granted, and in fact, not even half of projected deal closings are completed, according to CSO Insights. 

What AI provides: that all-important ability to look into the most likely movers and shakers who can make the transaction happen and bring in the new business, and what their strengths would be in the closing process. Knowing key details about people and relationships, including influential players whose roles may not be highly visible to leadership, means being able to more accurately predict the best way to complete the sales process. 

Better customer retention rates

With AI helping you more confidently understand your customers’ needs and project what they’ll want next, you also strengthen your ability to keep them interested. How’s that for a way to meet your (more accurate) sales forecasts?

Quicker business decision making and forecasting 

Unless you’re using a crystal-ball sales forecasting process such as making an optimistic guess, it’s going to take your team a while to alight on the right sales forecasting details.

With AI, your relevant numbers can be summarily crunched and the data-driven results digested in a short period of time. Not only will you improve the accuracy of your projections, you’ll be able to immediately start moving forward with optimization and sales strategy and implementing the right sales quotas and next steps to make strong prospective sales happen in the right time frame, whether that’s next month or in the next decade.

Upgrade your sales forecasting tools

Want better forecast accuracy? You can implement better sales forecasting that supplies invaluable insight for your sales leaders who want to go above and beyond, whether they do it next quarter or next year. You can improve your odds of forecasting sales in an accurate, reliable way, and Algolia can help you do it.

Our API-first search and discovery platform facilitates accurate prediction of ecommerce customer behavior and other details that are directly (and indirectly) connected to your sales outlook.

Get in touch with us and let’s talk about how you can enhance your search and thereby your sales forecasting accuracy, hit your sales targets, increase your conversion rates, and, ultimately, boost your ROI for unparalleled success.

About the author
Catherine Dee

Search and Discovery writer

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