Role of user data in a DSP Platform - Part II

I had mentioned in my previous post about the importance of user data. Now I will share how one can leverage data more effectively in delivering a campaign via a DSP.

During my playful days that I spent with rich user data, I was able to achieve some really outstanding results during campaign optimizations. We undertook data integration into our DSP platform very seriously and made some good investments around data collection, organization and activation. As a result we saw really good traction and adoption of our platform and it proudly sat over a huge segment data of unique cookies in India region alone. That was some really rich user data that we used for audience targeting and retargeting purposes and delivered significant value for our customers.

During the exercise I saw some really interesting insights which I wanted to share here:-

1.   It takes some time, in some cases 2-3days to a week to 10 days, for user data to start showing desired results. One has to leave a campaign running for some days and do just enough investment for the DSP optimization algorithm to learn and then kick in. A DSP platform or any ad server for that matter run on algorithms and in order for the machine learning predictive algorithms to learn how to optimize themselves, they must achieve a point of statistical relevancy. The campaign has to serve some thousands of impressions over various sources of inventory, and register a couple of hundred clicks or conversions, for the DSP platform to confidently start predicting correct impression value.

2.   Campaigns running on audiences with small number of unique users generally struggle on exchanges for delivery. Small audiences are difficult to scale in terms of volume and the predictive algorithms couldn’t get enough data points to predict accurately on those audiences.

3.     Retargeting works really well for advertisers but only when it is used effectively with frequency capping. Absence of frequency capping leads to over exposure of retargeted campaigns to users thus irritating them and also making them virtually blind to those ads after a point.

4.     Dynamic creative optimization equipped with smart user data collection works really well for Ecommerce players. Customized offers and messaging to users based on their previous browsing patterns really helps in pushing them down the purchase funnel.

5.  Also audience targeting works really well on RTB or an exchange which has much greater reach in terms of unique users than what a managed network can provide. Hence audience targeting can do great wonders for a DSP platform.

I’ll just want to end this conversation by saying that what the online Ad industry is seeing or doing with user data currently is just like scratching the surface. There is hell lot more that can be done with user data. The next big challenge for most companies out there dealing with data is to use predictive analytics and statistical modeling techniques on data, to get more meaningful insights like determining look-alikes, duplicates, calculate user affinity, build algorithmic audiences,  decrease noise in audiences. revenue attribution to right audiences etc.