Advertising Analytics: Transition to 2.0
By Achraf Bennadi
In my first post I had explored how “Big Data” and “Thick Data” complement each other. Now, as we are discussing different marketing tactics and advertisement channel, it is important to understand how marketing firms, agencies, and companies are analyzing this data and coming up with insights. Traditionally, companies have been measuring their advertising impact one medium at a time. Whether it be TV, radio, print, or online ads, companies used to measure each of these mediums as if they functioned independently to drive sales. It is no longer the case today, as these same ads are increasingly interacting with each other and drive customers to make a purchase together. For example, a billboard add can get someone to look it up online, and maybe end up watching a promoted Youtube video review and finally ending up in a sale. Being able to understand and dissect the data to get insights on how customers are being affected by omni-channel marketing tactics simultaneously allows for a more efficient and effective reallocation of funds. Without having to increase ad/marketing spend it is possible to see a significant uplift in sales. This is all great stuff to know, however how are these companies now able to process all this big data and get a clear idea of their “marketing performance, run scenarios, and change ad strategies on the fly”? (Wes, 2013).
Analytics 2.0 is the way to go! What I’m about to say might sound very obvious, like “duh” how come they haven’t been doing this for the past century, however it is important to note that it only is in the past decade that technology has evolved to a point where we can gather and dissect the huge amount of data. Once the data is collected there are three set of activities that the “engine” or “software” that are undertaken. These three activities are attribution, optimization, and allocation. Let’s go through each one:
Attribution
To put it simple without throwing a lot of tech and scientific words at you, this is the first step where data is gathered and basically refined. Typically, these analytical tools collect data for the following 5 categories: market conditions, competitive activities, marketing actions, consumer response, and business outcomes. Different metrics and KPI’s are also attributed within these different categories. Via very sophisticated statistics and analytical tools this data is then analyzed to “quantify the contribution of each element of advertising” and how they work together to drive sales (Jon, 2013).
Optimization
The next step, just as its name suggest, is where predictive analytics is used to run a lot of different scenarios for business planning (Knorex, 2020). For example, you want to know what will happen to revenue if you reduce your TV ads to save money. Or imagine you want to increase your YouTube ads and reduce TV ads. Optimization basically runs multiple different scenarios based on the data you have gathered and analyzed during the Attribution phase to give you what are the most optimized way you should allocate your funds or launch a strategic marketing campaign.
Allocation
Finally, the last step of the process is being able to allocate/redistribute your resources across all the marketing activities you came up within real time. Today, companies can shift their resources between the different tactics in matter of weeks and days. In some instances, when it comes to online, they can also reallocate their resources in matter of seconds. This allows companies to always spend at the most optimized way every time they receive a new info/data and run through the three steps we went though.
This is Huge! Powerful! Revolutionary you might say! However, don’t forget that at the end of the day the only way they companies are able to do all of this is thanks to the information/ data we provide to them, whether it be intentional or not. However, I will leave this discussion for another time.
Source: https://hbr.org/2013/03/advertising-analytics-20#
References :
- Wes, Nichols. (March2013). Advertising Analytics 2.0. Retrieved from: https://hbr.org/2013/03/advertising-analytics-20#
- Jon, Rognerud. ( 2013). Digital Advertising Analytics — Primer. Retrieved from: https://chaosmap.com/blog/digital-advertising-analytics-primer/
- Knorex. (August 2020). 3 Key Things Advertising Analytics Can Do for Your Business. Retrieved from: https://www.knorex.com/blog/articles/advertising-analytics
-
