If you’re an online publisher, chances are you’re inundated with data from a myriad of sources. You’ve got tons of data about your audience, data about your accumulated content, data about your ad inventory, data about mobile, tablet and desktop performance -- and, of course, data about your data.
Data by itself is just data. The magic happens through analysis of that data and the resulting insights. By leveraging and accurately analyzing all this seemingly incongruous data, publishers more often than not can drive additional ad sales and revenue. Indeed, connecting the data dots between audience, content and ad inventory will allow you to:
-- Win more advertiser RFPs, because you’ll know what choices to offer at different price points.
-- Do less ad rate discounting, because you’ll know which inventory has high sell-through rates.
-- Move lower-value remnant ad inventory into the premium price range, because you’ll sell more ads based on audience, behavior, geography and other targets.
-- Cut down on costly ad make-goods and bonus impressions, because you’ll minimize under-delivery.
-- Gain better insights about your advertisers, hit your ad sales projections, and maximize your overall ad revenues, because you’ll know
more about advertisers’ buying histories and patterns.
-- Better focus your ad sales team, because your rate card will be set more accurately.
Sounds great, but how do you get there? Let’s start with some general steps for strategically managing that rate card:
-- Identify and group your ad inventory by type of ad (i.e,, banners, video, interstitial, takeovers, mobile)
-- Take note of how much you’re currently charging for each type of ad, making sure you account for discounts and for such pricing models as CPM (cost per thousand impressions) and CPD (cost per download). Also note which types of ads are your top sellers
-- Monitor market pricing trends and discounting for your top ad types
-- Evaluate and adjust your ad rates based on supply (available impressions combined with the number of advertisers purchasing your inventory) and demand (how much inventory you’re actually selling combined with advertisers’ historical buying patterns).
But how can you make sure you set your ad rates to maximize your sales? After all, if you set them too low, you’ll lose dollars with every sale; if you set them too high, you’ll lose sales to begin with. Here are some tips to maximize your ad sales revenues:
Be more selective with your ROS (run of site) ad sales in favor of ad inventory pools containing highly targeted inventory of similar types. For example, you may have high demand from health enthusiasts and discover that 30% of your ROS inventory consists of New York-based people interested in getting more information about health. You can then sell that inventory at a higher rate.
Establish simple discounting policies, particularly for your most valuable premium inventory, based on real-time sell-through of those impressions. For example, if your inventory is 90% sold through, don’t discount 30% off rate card. That inventory is likely in high demand and you should hold out for the best price.
Of course, once you’ve set your rates and sold your ads, the job is hardly done.
Now it’s time to strategically manage and evaluate campaigns you’ve sold in order to further optimize your ad sales opportunities. In this ongoing process, you should:
1. Run advertiser-by-advertiser forecasting reports based on impressions and revenues.
2. Track the availability of your top-selling inventory, optimizing impressions by reviewing ads contending for overlapping inventory. A campaign that takes up a large portion of your inventory may not yield a lot of revenue. Use your analysis of campaigns to understand where to strategically allocate inventory based on profitability or strategic potential.
3. Catch potential problems before they happen by tracking line-item progress. Predictive analysis will allow you to understand the repercussions of making changes to order lines. A small change in one campaign can have a ripple effect across many campaigns. Understanding this can decrease your chances of profit or loss. Predictive analysis can also help you understand the future patterns of campaigns and to act on potential under-delivery problems before they occur.
Done correctly, this process helps publishers:
-- Increase direct sales CPM and sell-through rates of your most valuable inventory. A small yield gain on high value inventory will drive meaningful bottom line revenue growth.
-- Understand the interconnection of your overlapping ad inventory, connecting context and audience.
-- Set intelligent pricing, and more
clearly understand advertiser demand for products and packages, across display, mobile, video, as well as direct and indirect channels.
-- Overcome the operational burden of gathering key data and reports.
-- Improve overall visibility into revenue management with a singular “bird’s-eye view’ of the business.
In short, publishers can use the very data onslaught that so tantalizes advertisers to wrestle back control of their own ad sales process from the real-time bidding, programmatic juggernaut that threatens their very existence. They can then use this data to work with brands and ad agencies to better target and reach specific custom audiences. So it’s a win-win situation for everyone.
After all, if publishers don’t find ways to increase revenues, where would brands and agencies place their ads?
Thanks to Big Data, we won’t need to find out.