Why Traditional Forecasting In Ad Sales Is Never Accurate (and what you can do about it)

by | Feb 16, 2021 | Ad Sales, CRM, Digital Advertising

It’s not news that sales organizations across most industries struggle to achieve the holy grail of an accurate forecast. There are many root causes for these challenges, ranging from overconfident sellers who inflate their pipelines to sellers who withhold bad deal data for fear of repercussions with management. Despite systems being in place, sellers oftentimes maintain two sources of truth between CRM and their own private spreadsheets. Exacerbating these issues, many organizations struggle to define and properly communicate any semblance of a true forecasting methodology, leaving sales managers and sellers reporting their numbers as they see fit. Typical forecast reports are consistently missing key data needed to roll-up a proper forecast across sellers and any data that makes its way into formal reporting is often riddled with inaccuracies, leaving sales managers second-guessing and spending hours adjusting numbers. To make matters worse, obtaining an accurate forecast at the executive level typically requires aggregation of forecasts across several departments, demanding heavy manual effort only to be left with data that is highly spotty and inflated at best.

In our 15+ years working in media, we believe there are even greater inhibitors to proper forecasting when it comes to the ad sales industry, even amongst some of the most technology-forward companies. Despite world-class CRM systems, advanced analytics, AI, and instances of high adoption, ad sales executives continue to scratch their heads as to why accurate forecasting is unachievable and frequently throw money at this sisyphean challenge only to come up short.

How can ad sales executives leap forward and overcome these challenges? It starts with understanding some of the root causes for inaccurate forecasting in ad sales. To that end, we have outlined three reasons why forecasting in ad sales is so challenging.

Three reasons why forecasting in ad sales is so challenging:

  1. Publishing groups are highly siloed and competitive. Many ad sales organizations represent publishing conglomerates–organizations boasting a multitude of individual titles and digital properties. More often than not, different publishing groups leverage different systems when it comes to tracking data like accounts, RFPs, IOs, and sales activities. But simply putting a centralized system in place is never the answer, as sales processes also need to be centralized across groups to ensure consistency around data entry. And even for ad sales organizations that find success in aligning systems and processes across different publishing groups, they face challenges in maintaining the more sophisticated security models that ad sales teams demand to ensure privatization of data between publications competing for the same advertising dollars. If the right security model is not in place, ad sellers may be reluctant to share accurate forecasts for fear of in-house competition. Without proper change management and the guarantee of security, forecasting across multiple publishing groups, even with centralized systems, may be impossible unless a more nuanced approach is taken.
  2. Capturing a meaningful forecast is complex given multiple media types. Most ad sales organizations sell across a variety of media types, spanning print, digital, events, subscriptions, agency services, and more. In order for forecasts to be relevant and helpful for managerial and executive-level decision making, forecasting at a more granular level is critical for ad sales organizations in order to understand projected spend by media type. However, due to limitations commonly found in CRM and order management systems, it’s typically difficult to understand projections at the media type level without deep investment in system architecture, data, integration, and analytics.
  3. Ad sales is oftentimes transactional in nature, resulting in data too late in the sales funnel. The oftentimes transactional nature of print ad sales, the complexities of digital, and the unavoidable black box when it comes to programmatic makes it even more challenging to build a proper forecast in ad sales. This type of sales environment demands more upfront input by sellers on their estimates for their accounts for a given period of time, versus waiting until an RFP or IO comes in, or when a digital campaign is already in the planning phases in order management. CRM systems are often designed around this transactional nature of ad sales, and essentially become de facto order entry systems resulting in ad sellers inputting key data needed for forecasting too late in the process.

So where can ad sales executives go from here? We have uncovered that one of the most effective ways to combat these forecasting challenges is to equip ad sales teams with solutions to perform early-stage account-based forecasting in addition to IO-based forecasting. This pushes sales to forecast at the advertiser account level versus purely relying on accumulated IO and order data to formulate their forecasts. Through customized account planning tools enriched with integrated last year and current YTD actuals, ad sales organizations have found success in capturing and rolling-up a more accurate forecast not only across advertisers, but also spanning titles, digital properties, and media types.

If you are interested in learning more about account-based forecasting and our customizable account planning and forecast management tools, connect with one of our team members today.

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