#3 Bid optimisation

Zhang solves the problem on the assumption that all placements have a homogeneous bidlandscape. This is not the case with us; we solve the optimisation problem mathematically with Lagrange. Interestingly, on the way to the solution the marginal campaign yield value also appears, i.e. the gain in the KPI that an additional euro in the campaign brings – a value of great interest to our partners.
Solving this optimization problem provides us with a bid function for each campaign and each placement, which assigns a bid to a user based on their KPI.
Conversely, the bids are determined to exactly spend the budget over the day and maximize the KPI.

A simulation shows that this bid strategy far outperforms ‘one bid fits all’ (OBFA) as well as linear bidding strategies (without taking placement differences into account). In this simulation, 2400€ are to be invested and clicks maximised.

Compass might generate fewer impressions here than OBFA. But that is not the optimization goal. Instead, it is clicks, where Compass clearly beats the other strategies.

As a result of the side condition of bid optimization, the daily budget of each campaign is spent exactly over the day (this is precisely how the bid strategies - in blue - were adjusted).

If this bidding strategy is logged in and in live operation, however, quite a lot can happen. Unforeseen shifts in traffic and changed bidlandscapes (e.g. large campaigns by competitors) can lead to campaigns deviating quickly from plan. This means that they either cannot fully place their daily budget or use up their budget early. So, there can certainly be deviations from the plan within a day.

This behaviour is not welcome and it can be demonstrated that bidding strategies are not completely efficient in this way. We therefore continuously feed the current deviation of the campaign between planned and actually committed costs into a ‘controller’ module; this models the correlation between bid height and plan deviation, in order to continuously adjust the bid strategies.

Compass delivers unprecedented performance and insights into our display advertising market.
By determining the bidding strategy, we are able to estimate for each campaign how much could be spent per website and placement, as well as how many auctions could be won with that sum. It can also determine why publishers can be left empty-handed at the end of an auction (often due to an excessively high hard floor price). Here, we can use Compass in a simulation to find optimal hard floor prices together with publishers.

The marginal campaign yields that Compass calculates along the way help partners estimate how far they can scale up in our network.
Our partners benefit from our modelling just as much as we do, as their campaigns are on par with ours. As a result, we expect high demand from our partners. Given that the market limits in the OTTO Display Network are higher than with our old setup, we hope to be able to meet a high demand for a long time.

Here's a look at the combined effect of ODiN, Orbidder, and Compass compared to our previous setup using a campaign as an example: