The digital advertising space is a competitive and technically advanced industry. Trends change at the speed of technological progression, yet one objective remains fixed—accountability for ad spend and marketing. For many companies, one of the greatest aspects of digital marketing is the ability to track ad spend and subsequent revenue down to the penny.
There is a major caveat for certain companies or industries using digital media, however. If a corporation does not provide ecommerce through their web experience, revenue matching is elusive. Consider these two scenarios:
For an ecommerce company like Zappos, a user clicks a paid search ad in Google, they browse the Zappos website, enter the checkout, and purchase an item. This trail of data is very linear. The cost of the ad and the revenue at checkout enables a straightforward view into return on ad spend.
Now consider a company that does not participate in ecommerce, such as Goodyear. A user can click a paid text ad in Google, browser the Goodyear website, perform a series of actions, but none of these actions are linked to a purchase on or off-site. How can Goodyear understand how their advertising dollars impact revenue generation? Are the dollars spent providing a positive return to their business? These critical questions are not easily answered for many industries and business models. But do not despair, there is hope!
Ad Spend and Big Data
Insert that omnipotent phrase: Big Data. There are two necessary elements when deriving return on ad spend for non ecommerce businesses; Online Data and Offline Point of Sale Data.
- Online Data: with proper tagging strategies, companies can obtain massive volumes of data related to the user’s online experience. For instance, let’s stick with Goodyear as an example. As a user searches for tires, schedules in-store appointments and generally engages with the Goodyear site, the company can collect names, email addresses, home addresses, vehicle information, etc. There is opportunity for a plethora of information that aids in identifying who the individual is.
- Point of Sale Data: The secondary form of data is a no brainer. This data should absolutely exist already inside any sales-based organization. General elements within point of sales databases include: personal information (name, email, address), vehicle data, revenue, and timestamps. Envision the data being punched into a computer after you have your car serviced.
The disparate data sources can be collected into a data warehouse, scrubbed for validity and matched based upon a predefined matrix of equivalent data points. The end result is a revenue figure associated with specific media cost.
An example can help clarify the process: John Doe searches for tires on Google and clicks on a Goodyear text ad. He enters the website, selects his vehicle information, browses applicable tires and signs up for the email newsletter. He leaves a trail of data crumbs; vehicle information, email address, full name, etc. Then five days later a John Doe enters a Goodyear retail store with the same email, same first and last name, and same vehicle information. It is highly probable that this individual purchasing in store is the same individual who engaged on the website earlier in the week. Therefore a return on ad spend can now be calculated because revenue has entered the equation and closed the loop.
Solutions that utilize technology and big data are making far-fetched dreams a reality. An online to offline matching process is a remarkable example of the progressive nature of digital marketing. The process is an innovative step in the direction of precision optimization and budget efficiency.
[This post originally appeared on Asking Smarter Questions and is republished with permission.]
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