The demand intelligence industry, which is also often called demand forecasting, is a highly detailed and unique industry that is used on a B2B platform. It is typically engaged with by other businesses using to propel themselves forward in the market by better understanding consumer demand. Information detailing what demand intelligence is, how it works, the data that it uses to predict demand, and the resulting information for business have been detailed below. This research was compiled through company websites, articles written by industry experts, and even a few references to industry reports to better understand how demand intelligence operates and the impact that it has on those that utilize it.
In addition, research was compiled that identified six key industries that commonly make use of demand intelligence. These industries were identified by examining the websites of multiple demand intelligence businesses to see who they claim to assist, in addition to advice from industry experts related to who the data can help. For each industry identified, a brief description that details why demand intelligence is of importance to that market has been provided. From there, a few notable organizations that have used demand intelligence successfully in the past were also provided.

Demand Intelligence — Industry Overview

What is demand intelligence?

  • Demand intelligence, also referred to as demand forecasting, is the process by which future consumer demands, preferences, and needs are predicted using historical data for the purpose of making business decisions.
  • Businesses that utilize demand intelligence and forecasting can more effectively analyze and manage their inventories, budgets and pricing models, business operations, marketing methodologies, and the like to reach consumers and make a profit.
  • Silvon estimates that businesses making use of demand intelligence are approximately 30% more accurate in their supply management and customer satisfaction.

What type of data does demand intelligence require?

  • When demand intelligence is working to forecast demand for a product/service that is either new to the market or doesn’t have much data available on it, qualitative forecasting will be used to predict future demand. This data typically includes expert opinions and advice, market research, and comparisons to similar or related products/markets for insights.
  • Time series analysis is the process by which demand forecasting utilizes historical data that is relatively complete, such as product sales and revenue, trends, and patterns. This data typically involves historical graphs, sales charts, and the like to predict future demands of a known product.
  • For more complex predicting, some forecasting will make use of data that depicts the relationships between variables that affect demand. This includes competitors, the economy, socioeconomic factors, promotions, weather, and even unemployment rates. This is the most holistic approach to demand forecasting and intelligence, and also requires historical information to predict future demand.
  • To collect such data, demand forecasting and intelligence will make use of machine learning, statistical models, geocoding, natural language processing, historical data, and other similar systems for the most accurate planning and predicting.

What are the different types of demand intelligence?

  • Demand forecasting and intelligence can be used for both the short- and long-term. The data used to compile such intelligence can also be taken from a short or long time frame. As such, the short-term typically operates across the span of 1 year or less, while the long-term can be anywhere from 2-5 years.
  • Demand forecasting can be based upon the broad economic environment in which a given business is operating in. This type of intelligence will usually make use of the Index of Industrial Production (IIP) to measure the economy and see how a business stacks up against it.
  • Another type of demand forecasting will use the industry in which a business operates as a metric for comparison. This methodology will utilize historical product/service demands to predict future levels of the same. This can also be broken down to see how a company’s own product has fared in the past on a seasonal level to predict future demands.

What data does demand intelligence produce for business use?

  • Most commonly, the data produced by demand forecasting and intelligence include:
  • Other data points that are produced through demand forecasting and intelligence also include:

Demand Intelligence — Target Audience

Industries of Interest

  • Industry-wise, demand intelligence and forecasting is very useful for companies in the consumer packaged goods (CPG) space, as well as retail companies. Businesses in these markets tend to have very complete historical data sources to pull predictions from, and the planning processes post-data collection are typically quite accurate.
  • Retail organizations are a major target for demand intelligence companies, as the data on these markets are rather complete and can easily be used for predicting prime inventory levels, potential catalysts, workforce optimization, and even communication processes.
  • Electronic and technology brands are also prevalent contenders for using demand forecasting and intelligence, as the products themselves are capable of measuring demand and use and sending metrics back to the business that sold it.
  • The travel and accommodation industries are large targets for demand intelligence as data related to booking times, dates, locations, preferences, and like are easily accessible. Demand intelligence for these industries can assist with workforce optimization, demand drivers, capacities, pricing optimization, and marketing methods.
  • The transportation industry, specifically for land travel, is another main target for demand forecasting. Businesses in this market need to be able to know when consumers are going to need to travel, to/where from, and why so that they can manage wait times, trip times, pricing, and workforce planning.
  • Businesses in the automotive industry are also primary targets for demand intelligence, as the data on consumer demand in quality, operations, location, and timing have been found to increase marketing ROIs by up to 20% and sales forecasting by 30%.

Broad Audience

  • For the most part, any company that wants to use demand forecasting and intelligence can successfully make use of the data. However, it is up to a business to decide that they want to pay for the software and predictive information.
  • According to PredictHQ’s website, the average cost for demand intelligence varies significantly from business to business because of size, goals, and data availability.

Example of Demand Intelligence Customers

  • Airport Rentals and Travel Appeal are two travel/accommodation businesses that have made use of PredictHQ in the past to increase customer engagement, satisfaction, demand, and bookings.
  • Lineup has utilized PredictHQ in the past to forecast demand at separate restaurant locations to increase profits and optimize staffing.
  • Other notable businesses that have used PredictHQ include, Domino’s, Accenture, and Uber, among others.

Demand Intelligence Industry – Demographics

After an exhaustive search through the industry publications, media articles, websites & publications of leading companies such as PredictHQ, and surveys, we were unable to find any details in the public domain regarding the demographic profile of an average customer of the demand intelligence industry or leading companies in the industry. However, we were able to collate a few useful findings regarding the type of customers and industries using the PredictHQ’s solutions, and their likely demographic profile based on the profile of the top executives/C-suite in the US. Below is a summary of the same.

Useful Findings

  • PredictHQ is the demand intelligence company that enables businesses to predict and adapt to the impact that real-world events have on customer demand.
  • The company’s customers primarily consist of businesses and organizations from varied industries such as accommodation, retail, aviation, and transportation, among others. The customers in the accommodation industry use company’s solutions to enhance their yield management strategy, the aviation industry companies use them to identify demand drivers for price and packages, retail companies can predict a shift in foot traffic and make decisions around marketing promotions and staffing, and the transportation industry can estimate demand surge using the company’s solutions.
  • Its solutions and services are used by some top companies globally, including Uber,, Alexa, Accenture, Alaska, First Data, and Domino’s, among others, to build more accurate forecasts, intelligent products, and operational strategies. The company’s clients also include some Fortune 500 companies.
  • A detailed list and profiles of PredictHQ customers and clients were available behind a paywall. B2B Sprouts has aggregated a list of customers using PredictHQ by location, by job titles, by industry, by company size & revenue, and by target key decision-makers. However, the same is available only through a trial.
  • While there is no information available in the public domain regarding the demographic profile of the company’s customers, some customers stories featured on its website indicate that the company’s products and solutions are primarily used by the top executives and heads of various departments such as CEOs, CIOs, CTOs, founders, product leaders, accounting department heads, and Head of Data Science, among others.
  • Given the fact that top US company executives and department heads leverage PredictHQ’s products and services, a demographic profile of the top executives/C-Suite members in the US can provide a close proxy for the demographic profile of the PredictHQ and demand intelligence industry customers. As per data from Fortune, 79.5% of the Fortune 500 senior managers and 93.6% of the CEOs are males, while 20.5% and 6.4% are females in these two roles, respectively.
  • Also, 73% of the senior executives in the US, men and women, are white, 21% Asian, 3% Latino, 2% black, 0.6% belong to two or more races, 0.2% are Native American, and 0.1% are Native Hawaiian or Pacific Islander. Additionally, nearly all the top executives and CEOs are bachelor’s and graduate degree holders, and roughly half of them have obtained a Master of Business Administration (MBA).
  • As per Payscale, the average Chief Executive Officer (CEO) salary in the US is $155,906. Also, as per an article by Korn Ferry, the average age for a C-suite member in the US is 54 years, varying from 51 years (CIO) to 58 years (CEO). Hence, based on the above, it is likely that most of the clients using the company’s solutions are white males in the age group of 51-58 years with a bachelor’s or a graduate degree and earning greater than 150k annually.

Demand Intelligence Industry – Psychographics

The specific market that PredictHQ competes in has customers that need scalable, high-quality event data to improve the accuracy of their predictive or demand forecast models. These customers seek demand intelligence products such as those offered by PredictHQ because they find event data collection and cleanup time-consuming and laborious. Their considerations for choosing a vendor include data quality and ease of use, and their preferred content formats are likely case studies, webinars, and thought leadership content that is both timely and brief.

Purchase Motivations

  • Businesses and organizations purchase demand intelligence products such as those offered by PredictHQ mainly because they have a need for scalable, high-quality event data. The need for event visibility is the main reason a business or organization would partner with a vendor such as PredictHQ.
  • According to PredictHQ, “without having a reliable and comprehensive events data layer, forecasting models and teams lack valuable context to make their predictive models more intelligent.”
  • PredictHQ shares that one of its clients, restaurant prediction platform provider Lineup, made the decision to purchase a demand intelligence application programming interface (API) when it realized that it does not have the resources to collect and clean the event data it needs for its platform to provide accurate, actionable, and timely insights.
  • PredictHQ also shares that Legion, a client of theirs that provides an AI-powered solution to help employers remove labor inefficiencies and increase employee engagement, decided to look for a demand intelligence API when it realized that event data collection and cleanup are a ‘time suck‘ or a laborious and time-consuming activity for its data scientists.
  • The need to determine demand drivers seems to be another purchase motivator. According to PredictHQ, Lineup is working with them to determine if there are other demand drivers apart from event and weather data.
  • Uber availed of PredictHQ’s product to gain a better understanding of “how many drivers it needs on the road at any given place or time.”

Purchase Criteria

  • The quality of the demand intelligence data provided by the vendor seems to be the most important purchase criterion for buyers of demand intelligence products. This quality is defined by the level of correlation that exists between the buyer’s own collected data and the vendor’s demand intelligence data.
  • PredictHQ shares that it was chosen by Lineup because Lineup found through a few quick tests that a strong correlation exists between PredictHQ’s data and Lineup’s data. Lineup’s platform is dependent on “meaningful, reliable data,” so Lineup must have found that it can fully trust PredictHQ’s data.
  • PredictHQ also shares that Legion’s criteria for selecting a demand intelligence provider can be summed up by the following question: “Can this company give us useful event data better than we can and ultimate improve our workforce optimization efforts?”
  • Other purchase considerations include ease of use, enrichment of event data, and rankings of events. HQ revenue, a client of PredictHQ that provides real-time online booking rates and demand, chose PredictHQ not only for the quality of its event data but also for its ease of use, its enrichment of event data, and its intuitive rankings of events.

Research Habits

  • Buyers of demand intelligence products appear to explore multiple data sources before making a purchase decision.
  • Lineup, one of PredictHQ’s clients, found out about PredictHQ while reviewing possible data sources. Wheelhouse, a client of PredictHQ that provides a dynamic pricing platform for short-term rental owners, reviewed several potential partners before selecting PredictHQ.
  • The research process of buyers of demand intelligence products also seems to be a lengthy one. According to PredictHQ, HQ revenue spent months researching demand intelligence providers before eventually deciding to work with PredictHQ.
  • Business conferences are one way for interested buyers to discover and get acquainted with potential vendors. Some airline companies first learned about PredictHQ at a Cannes conference. To get the attention of airline companies at that event, PredictHQ representatives wore colorful patterned suits and spent hours networking with airline representatives.
  • Given the findings of Demand Gen, Edelman, and LinkedIn on the behavior of B2B buyers and purchase decision makers, it is likely that customers of companies such as PredictHQ conduct a web search and consult vendor websites and review sites first.

Media Consumption Habits

  • Information specific to the media consumption habits of customers of demand intelligence providers could not be located in the public domain.
  • However, the findings of Edelman and LinkedIn on the behavior of B2B purchase decision makers indicate that more than half or 53% of B2B purchase decision makers spend at least an hour per week consuming thought leadership content, 33% spend one to three hours, and 20% spend at least four hours.
  • B2B purchase decision makers prefer thought leadership content that is timely and brief. They prefer thought leadership content that offers new perspectives, solutions, or challenges they have not come across before.
  • The majority or 41% of B2B buyers engage with three to five content pieces before contacting a vendor to talk with a sales representative. Case studies and webinars are the influencer content formats they find most valuable.
Glenn is the Lead Operations Research Analyst at The Digital Momentum with experience in research, statistical data analysis and interview techniques. A holder of degree in Economics. A true specialist in quantitative and qualitative research.


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