By Roberta Houle, Team Lead, Trading, The Exchange Lab
Today, one of the greatest challenges for an Ad Operations professional is finding the right application of data for optimal campaign performance. Since performance is by far the most important client ask, successful Traders are ones that have developed superior analytical skills.
This is a far cry from the desired skills for an ad operations role just a few years ago. Ad ops were simply known as “traffickers” – back then you understood a bit of JavaScript and could upload creative tags onto a platform. Today, with the growth of programmatic the skill sets in these teams have gone far beyond trafficking. Ad tech companies now place more value on someone with expertise in mathematics and laughs at the notion of a pivot table being “complicated.” Technical expertise is still highly valued, but the desired Trader is more heavily analytical.
What does it mean to follow the data? Well let’s take a deeper look into some of the techniques that could be used by a Trader.
The first thing actioned when launching a campaign is focused prospecting; a semi-narrowed down distribution of impressions tailored by our past experiences. This broad set-up creates a versatile learning experience that will be the base for the optimization strategy. With data flowing in we can start to segment data into the following buckets; high performing, mid-tier, non-performing, and the unknown, and then adjust our bids accordingly. Some examples of data criteria to look at include: sites, channels, users, time of day, frequency, geo location, context, etc. It’s critical to have strong analytical skills in order to understand the myriad of reports. Analyzing the reports ensures a refinement of the buying strategy and enables optimization across the campaign.
Many DSP’s have created algorithms that will manually adjust impression bids based on all of those factors, useful considering a person can’t analyze 24/7, BUT it is still important to have human eyes on the data. Humans consider elements machines may not; including seasonality, fraud, real life events, and the simple fact that being a human allows us to understand what the product is and how it is consumed – sorry machine; for now we’ve still got you beat here.
For example; a hotel client sees the most bookings on a Thursday afternoon right after a user checks up on their daily news. It stands to reason you should only bid on news inventory at that time; however, brand awareness is created on the days leading up to the optimal booking day, playing a key role in the decision process when booking. Excluding all inventory but news would be very harmful to the brand and the long term performance of a campaign. Adjusting the strategy requires a human to ensure scale is not compromised by the narrowing down the algorithm often does.
Programmatic has the potential to rid the ecosystem of inefficiencies with the right operations team. Take advantage of your own learnings. Always test, learn, apply, and most importantly, follow the data.
