Leading online floral retailer FTD has multiple customer touchpoints across radio, linear, connected TV (CTV), social, search engine marketing (SEM) and programmatic display channels. Ultimately, FTD wants to reach its target audiences and deepen relationships with them in their preferred channels in the most cost-effective and efficient way possible. To accomplish this, they need access to the insights through an intuitive, prescriptive interface.

As one of the most trusted online floral retailers in the world, FTD took an industry-leading approach to prepare for a cookieless future. The company used advanced data engineering techniques to consolidate their data into Google Cloud and BigQuery. From there, FTD prioritized building a new privacy-centric attribution model that could allow its marketing team to determine the most effective media mix. For this, they turned to Adswerve to help build a modeling solution that will meet today’s (and tomorrow’s) privacy standards, understand its customers' preferences, and see how to optimally allocate its marketing budget across its many online and offline touchpoints.

Using Google Cloud technology, Adswerve data scientists built a multifaceted, durable modeling solution to determine which media touchpoints will impact FTD’s revenue the most. 

The FTD data estate is in an advanced state which made it easy for Adswerve to review and validate FTD's Google Analytics dataset and then create, train and optimize a model using Python to help FTD understand how its media spend affects sales and to learn more about its spend allocation across media. 

Adswerve’s data scientists also used machine learning (ML) to predict which channels will reach select audiences and allow the FTD team to target its messaging to the right customers at the right times without oversaturation. 

Once the model was up and running, the Adswerve team developed channel attribution, adstock effects, and diminishing returns visualizations for the FTD team to see the insights the model gleaned from the data. 

Finally, Adswerve developed a proprietary budget tool that offers FTD weekly model-derived insights with recommended budget allocation. The easy-to-use app is available to FTD’s entire marketing organization and secured using Google Cloud. 

With off-the-shelf MMM products, you can’t see how the models work or customize them, and they often take an enormous amount of time and effort to implement. But using Google Cloud gave Adswerve the power, speed and flexibility to tailor models to FTD’s needs seamlessly.

About Mixed Media Models
With a mixed-media model, you can:

• See how each media channel impacts your target KPIs

• Understand how media channels perform with a change in spend

• Estimate the optimal budget allocation across your media channels 

Plus, see how much additional spend will improve channel performance and how long-lasting the effects will be.

Adswerve’s Google Cloud modeling solution delivered value right from the start. When it first tested the modeling solution, FTD determined its television campaign spend wasn’t optimal. Since then, the marketing team has reduced its TV campaign budget by 30% while still reaching the same audience. They were able to reallocate the savings into other channels that had more opportunity to grow. 

Moving forward, this new durable technology will drive FTD’s data-informed decisions about media purchasing. Every member of its marketing organization can easily access and seamlessly use Google Cloud’s machine learning models and insights using Adswerve’s app to make quick, revenue-boosting media decisions.