June 24, 2015
Getting Forecasting Right: 4 things to consider when thinking about your forecasting tool
By Ben Johnson, Sr. Product Manager at OpenX
Accurate forecasting is a critical tool for anyone in a business selling goods or services. For publishers selling digital advertising, getting it right – or wrong – can have a significant impact on revenues. It’s surprising, then, to see how many publishers settle for tools that are unable to help them maximize their inventory management. While it’s true that forecasting tools have traditionally been cumbersome to use and hit-or-miss on accuracy, there are solutions that break the mold.
There are a variety of factors that complicate forecasting – constant fluctuations in Internet traffic, increasingly complex targeting capabilities, and the dynamic nature of today’s digital sales team. In order to maintain a holistic view of how all of these affect selling your inventory, there are four key attributes your forecasting tool needs to have. They are: data transparency, a consumer-grade user interface, consideration of pre-campaign revenue impact, and accommodation for seasonality.
Data Transparency: Trust but verify
How often do you go back to check the accuracy of your forecast reports? That’s sort of a trick question but an important one nonetheless. Most forecasting engines don’t provide users with that information.
A post-mortem analysis of your forecast report will tell you how accurately you’re selling your available inventory and provide valuable insight that can help pinpoint discrepancies that can be adjusted ahead of the next campaign. More importantly, by regularly comparing what was forecasted to actual results, publishers learn to trust their forecasting systems, and their teams will have more confidence when responding to clients about inventory availability.
Make sure your current or prospective forecasting tool provides you access to the data you need to do a post-campaign analysis in order to assess the accuracy of your forecasts.
Consumer-grade User Interface: Let’s not make it more complicated than it needs to be
Most forecasting systems are difficult to use to start with. That difficulty is compounded when you consider the different users of these tools and why they use them.
Forecasting needs vary from one department to another. For example, direct sales teams generally use these systems when a client asks about inventory availability. Sales team members are naturally more people-focused than software-focused. So, they need a tool that makes it easy for them to go into and out quickly to find the limited information they’re seeking.
Meanwhile, ad ops teams are power users who spend the majority of their day using ad serving software. They need a comprehensive forecasting tool that gives them control over every lever that can be adjusted to optimize campaigns, while also keeping the needs of the sales function in mind. The right balance can be found in tools that have focused heavily on both creating an intuitive design and building powerful capabilities on the backend.
Another important aspect of intuitive design to consider is the ability to save your forecasts. Your future self will thank you! It sounds so basic, but the ability to save a forecast is not common for most forecasting systems. They usually limit users to keeping a forecast only as long as their browser remains open. This leaves Sales reps with only limited – and frustrating – options. They must either export the forecast or take a screenshot of it to send it to a client. Even worse, sales reps might stumble upon the perfect set of targeting criteria, but without the ability to save the forecast, there’s a good chance that they may never rediscover the exact permutations that led to those results. This can easily lead to loss of the sale.
The solution is simple: your forecasting system should be intuitive enough to work well for users throughout your organization, and include basic functionality like enabling forecasts to be saved so they can be referenced and reused anytime.
Revenue Impact: Go beyond the buying model
If you’re reading this blog post, then you likely operate in a complex advertising environment that runs thousands of campaigns simultaneously. Whenever you consider an offer from a client, there’s always the chance that the hypothetical campaign will divert impressions away from existing ones. Wouldn’t you like to understand the overall revenue impact of the campaign when making your decisions?
The problem is most forecasts typically rely on the buying model as the main tiebreaker when deciding which campaigns should serve over others. But what happens in cases where two campaigns with similar buying models compete for the same impressions? In such cases, price should be the deciding factor. Unfortunately, most forecasting tools can’t take price into consideration.
Forecasting tools should allow your sales teams to enter both the buying model and the price the advertiser is willing to pay, allowing the engine to predict the number of available impressions as well as the net impact on revenue.
Forecasting systems – or people – can’t predict spikes in traffic that occur when unanticipated world events create an impact, but they should take into account anticipated seasonal events. For example, we know sporting events such as the Super Bowl and the World Series occur annually and affect traffic. We also know the World Cup and the Olympics consistently take place once every four years. Today’s forecasting systems can only track events that happen every year. However, the better forecasting systems allow users to manually override specific anomalies or events whose effects we know will be felt.
Make sure your forecasting tool covers the basics of accommodating for seasonality and also allows you to input information that make it work even smarter.
These features seem like they would naturally be included in any forecasting system worth its salt – and they should be! However, this goes beyond what’s available with the average forecasting tools available today. Data transparency, an intuitive interface, and consideration of revenue and seasonality impacts are all issues that directly affect yield for nearly every publisher I’ve ever worked with.
Forecasting really is the foundation of any ad serving operation, on top of which everything else is set up and executed. So, you can see why it’s important to make sure you have the right tools in place to do it well and to make it accurate. When you do, you’ll see the difference in your top and bottom lines.