Data-Driven Attribution



Google Data-Driven Attribution







In April 3rd I attended a workshop at Google where they explained what how our current time, technology has improved many aspects of our lives and work; we have technology that can virtually display information or virtual reality device to enter a new world. We are in a world where people consume media on a daily basis. What does that mean for advertisers? TV? Display? Social? Paid Search? As tech improves our capacities to track and monitor data also increase, for instance, the use of the “persistent cookie” which tracks a user to all points of interaction for a brand whether they visit the official site to seeing a banner ad or an ad on google or another search engine. Being able to track that data and the consumer journey is vital for companies, and this is where marketers and Google come in.

Google has been a leader in a search engine as well as other verticals, but one of the latest tools they have created for marketers is DDA. What’s DDA & how can it help marketers? DDA stands for Data-Driven-Attribution and this consist of taking the data points and interactions that consumers have with a brand/product and evaluating the strategy by assign credit to how the click interacts with the usurer journey. Within Search Engine Marketing (SEM) there are a couple of attribution models which focus on giving the “last click” or “first click” the credit for a conversion (this can be from downloading free information to purchasing an item). This often leads to skewed strategies where a TV ad can get the credit, or the SEM ad receives the credit. This isn’t as impactful as it could be because the marketing mediums help each other to garner conversion. If you remove all print and tv ads, no one will know to go on the engines and search for items just like if only TV ads are available users can’t search online and do research on products before purchase. Therefore, it is essential to have a media mix that will satisfy user experience to obtain the goal desire.

DDA provides a solution to this, essentially the different media types don’t know who is doing what regarding impact, DDA tracks each active media type and constructs the user journey and marries the data. It can accurately follow the users and see patterns that a user has to assign values to each media type correctly. Instead of TV getting all the credit it assigns the value of .3 then a display ad also contributed and received .3 of credit and then search ad is clicked and the conversion happens, and this will receive a score of .4 totaling the journey the consumer had to reach that 1 conversion. Now we can see that tv and display were vital to achieving that conversion and can optimize towards having that tv ad and display ad drive users to help the conversion rather than focusing on tv and expecting the conversion to happen without the help of other media types.

What makes the connection between data and assigning credit to different steps in the consumer journey is thanks to Google machine learning. What machine learning does is similar to an AI, the google system begins to see how the campaigns are being run and gathering the data to be able to understand it. After this can look at the patterns and can marry data becoming more accurate on the assigning the percentage of the click to the corresponding media type. It will be able to understand that before the conversion takes place a user clicked on 3 ads and each leads to the next one and see how much higher the chance of conversion happening is depending on how many ads are clicked or on which ad the user started with and if that makes a difference. This leads to the advertiser being able to see where their investments are more efficient and optimize as the campaign is live; across different media placements. Elaborating a consumer journey that leads to their main KPI.

 Being able to leverage that kind of data and see the real impact the media types have a brand is crucial. Based on this learning session some of the key takeaways are: One, DDA can show us the patterns and help us evaluate our media strategies so we are focusing on the highest amount of conversion so we can better perform for clients while understanding the user journey. Followed by being able to see whether how a digital placement or a keyword contributed to the conversion. It is essential to know how each activation leads to the desired goal. It will be easier to understand how everything plays an important role or how it can be optimized for better results. Lastly, Having technology improve and taking advantage of data will continue to grow so we can better understand the user experience. In the end, everything comes together and it is important to understand how everything is measured for the benefit of the client.

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