May 14, 2021

Written by

Collective[i]

  • Posted in
  • Sales Forecasting
  • Sales Forecasting Methods

What are the different types of forecasting methods?

To get ahead of the competition and meet consumer demand, successful sales teams will often look for any added advantage available to them. That includes insights that can be garnered from different types of sales forecasting methods.

This process involves equal parts taking stock and improvising: Collect as much market and consumer data as possible, formulate which sales forecasting methods will make the best use of that data, then make informed, tactical decisions based on their learnings.

Markets, consumers, and technology change rapidly — each influencing the other — and sales teams must learn to adapt.

Here are the methods sales teams have relied on in the past, and the new approaches available to keep sellers nimble and ready to seize every opportunity.

What are the three types of forecasting?

While many specific types of forecasting models exist — from qualitative forecasting to market analysis — they all fall into a few categories based on the type of data utilized and how that data is applied: opinion forecasting, historical, and — most recently — AI-enabled, prescriptive forecasting. Sellers make the best choice of which sales forecasting method to use based on the type of data and tools they have readily available.

Opinion forecasting is the original form of forecasting. Broadly speaking, it involves compiling relevant information available to sellers and forecasters based on characteristics and market features that can’t be described with numerical data. Percentages and variable data might be included, but it’s generally collected and utilized in the form of questions to sellers — and maybe surveys or questionnaires that value less empirical data.

Historical forecasting looks at past data that can be used to identify trends over a given period of time and extrapolate future trends accordingly. This kind of concrete information can also be found by looking at the cold, hard facts of the market to show what’s trending — and maybe why.

In practice, many sales teams bring opinion-based, qualitative tactics into their quantitative, numerical based data to produce their sales forecasts. They might use quantitative data to produce an Opportunity Stage forecast, for example, but also temper predictions based on what individual sellers know about the inner workings of a given deal. Technology — including AI algorithms — have even been brought in to speed up the process of compiling historical forecasts. But the premise of a forecast is inherently flawed if it relies solely on historic information to make future predictions.

Prescriptive forecasting relies on all these types of data to make the most comprehensive analytical predictions. Leveraging networked data, it takes into account both quantitative and qualitative data, leveling up by adding in real-time market data and trends. When utilized correctly, it has the potential to equip sales teams with updated, unique market information gathered from each type of business forecasting method.

Opinion forecasting methods

So how helpful is it, really, to look at opinion-based data to predict how sales will perform? Qualitative forecasting methods are still often used by sales teams — and this form of data is representative of the human element that quantitative forecasting can’t include. Some useful methods include:

  • Jury of executives: A peer review of expert opinion to modify market predictions.
  • Sales opinions: Sales data collected from personal sales team relationships that might reflect trends.
  • Expert opinions: Insights from industry leaders and academics who have a finger on the pulse of the market.

Sales teams continue to put a lot of trust into qualitative forecasting. Who better than those interacting with customers to provide perspective on how the market will perform?

But there is some tension between these projections and what quantitative forecasting suggests — and the last thing sales teams need is a shouting match between qualitative and quantitative forecasting methods.

Historical forecasting methods

More data-driven forecasting methods rely on more inputs to reveal trends. Their value is in providing objective, clear predictions based on previous results. Methods like quantitiative forecasting, opportunity stage forecasting, and regression analysis forecasting each look at data from a unique, unbiased perspective to create predictions within a specific model:

  • Opportunity stage forecasting: Grouping leads by their stage in the buying process and assigning each stage a close probability based on historical data.
  • Market analysis: Investigating macroeconomic forces and their impact on future sales.

Time series forecasting methods, for example, are a specific type of historical forecasting that look at patterns and trends in sales strictly based on a measured period of time, or in a sequence. This type of methodology doesn’t include other factors that might have contributed to a rise or fall in sales — like product marketing, changes in economic conditions, consumer behavior fluctuations — but instead takes a distanced, unbiased approach to just look for patterns in data over time.

What are the methods of sales forecasting that drive results?

Forecasting strictly based on quantitative and qualitative data fails to provide a comprehensive understanding of what the future holds for a given sales team — and, more and more, these methodologies are proving obsolete, leaving sales teams in a lurch to find technology relevant enough to keep up.

The missing components in traditional methods of sales forecasting are the right data and technology, which is being alleviated by AI-enabled forecasting tools coming to the market. Collective[i] is the only one that uses a neural network to combine historical data and outside sources to drive revenue growth for sales teams.

  • CRM automation: Machine learning reduces the time spent on data management by your team, automatically inputting and clarifying data input to provide ongoing, real-time data analysis in the form of actionable recommendations.
  • Forecasting improvements: AI-enabled technology synchronizes quantitative and qualitative data to provide quicker, more comprehensive analysis to your sales team on a daily basis.
  • Networked intelligence: As data is collected in a neural network from more and more unique sources — including other businesses and verticals — the accumulated information is leveraged to provide more informed recommendations. Collective[i] expands what is typically available to a given team of sales forecasters to help them make quick market-relevant decisions.

Ready to see what this predictive type of sales forecasting looks like in action? Explore Collective[i] today.

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