Optimizing Purchasing for Facebook Exchange and Other Emerging Media with Tag Management
In this ever-changing landscape of paid media channels available for purchase, it is critical for marketers to test new vendors rapidly and with limited impact in day-to-day operations. Having the right paid media mix means having the right vendor mix, and a TMS can offer that flexibility. Take for example the explosion of social network paid media, the evolving display remarketing space and mobile ad networks. It’s no secret that marketers who are first able to test these new media channels usually have a leg up on the competition. Those first movers gain valuable insight into the workings of the market, enabling them to optimize and build account history before the bulk of the competition heats up. For anyone in today’s age of revenue-driven marketing, this translates directly into increased ROI.
Tag management is uniquely positioned to enable marketers to test and adopt new digital media strategies much more efficiently. Let’s explain with a scenario in which an organization decides to test a new media channel: In this case, Facebook Exchange (FBX). To test this new media channel, the organization will want to run a proof of concept (POC) with two short-listed vendors for a period of time, compare the results and then finally deploy the winning vendor with a full-budget campaign.
Traditional Deployment Cycle
Today, marketers need to go through a pretty labor-intensive process to create a test for the scenario above. This would include a phased approach that involves launching one vendor for a period of time, removing that vendor, and then repeating the process with another vendor to complete the test. Usually, the addition of each vendor has to be scheduled into an existing deployment cycle and is reliant on expensive/limited IT and development resources. Moreover, the vendor installs are usually ‘light’ and not properly integrated in order to save time to get it up and running. Finally, the results may be tainted by external factors and jeopardize the validity of the final decision.
After the analysis is complete, the final step is to deploy the winning vendor back onto the web properties: This time with the complete integration. Outside the obvious challenges of coordinating all these moving parts is the cost of the dev resources and the often-overlooked challenges around proper analysis of data stemming from two very different time periods. Another big challenge is trying to account for seasonality in the data, not to mention a missed opportunity in lack of sales from this new media during the testing and analysis periods.
**Model Assumptions: In our test scenario we will assume a 200% ROAS for Vendor A,180% for Vendor B and 10% of proposed budget test.**
By the Numbers – Phased POC Approach yields approximately $104K/yr before factoring development costs
Low Cost A/B Test with TMS
Any basic TMS will enable much smoother testing of vendors by helping to reduce the cost of development when swapping out various tags; however, an enterprise solution such as Tealium is able to not only eliminate those costs, but provide a faster more integrated POC. Here’s how: With Tealium, we can use our Split Segment Extension, which creates first-party cookies representing 50% of the visitors as User Group A and the other 50% as User Group B. We then create a conditional rule that loads Vendor 1 on users in Group A and Vendor 2 for users in Group B. No development is required to set up the split-segment cookie or vendors as the Tealium iQ solution does it with a point-and-click interface. Upon activation, both vendors can run in parallel on their respective test segment thus eliminating temporal oddities during analysis. We can even map the visitor-assigned segment into a web analytics custom parameter for additional analysis later on. Further, both vendor configurations are fully integrated creating a complete feature test within the respective vendors, which eliminates the need to reintegrate post decision. This streamlined process not only reduces POC costs, but more importantly, it enables the marketing team to get up and running on the new media faster, which ultimately translates to immediate revenue improvements. In our scenario, this means activation of the new media (FBX) 4 months earlier, translating to a 56% increase in Revenue.
By the Numbers – TMS A/B approach Yields $184K
Agility Increases Revenue and Fuels the Bottom Line
Agility shouldn’t be a buzzword. An enterprise TMS is a very useful tool that can enable marketers to properly vet emerging media and vendors rapidly and efficiently. By reducing time to market and translating that time to real increases in sales, a TMS can in many cases pay for itself, reduce the need for internal resources, and help provide significant market insight.