Data Driven Marketing For 1,200+ Retail Locations

Challenge

Monro, Inc., a leading auto service and tire provider operating over 1,200 retail locations under ten different brands across the United States needed a way to unify and analyze cross channel marketing data. This included website, organic and paid search, social media advertising and listings analytics. They desired a shareable way to see performance in different hierarchies; individual stores, collections of stores organized into custom territories, individual brands, holistically as an entire organization.

Solution

Mason Digital gave them exactly what they needed. Our team utilized cutting edge software and cloud service solutions used by data scientists around the globe. We wrote Python programs to clean up and organize data and pipe it into cloud storage and compute systems. Our databases were joined and visualized in Tableau and Google Data Studio.

Together these technologies provide near real-time access to all of our partner’s data, organized and visualized the way they need to see it.

Users of these dashboards can explore locations on a map to see key metrics. They can highlight collections of stores within their custom territories. They get a window into what is happening in Designated Marketing Areas (DMA’s). They can see trends over time.

This data is also married with third-party sources to track competitive advertising spend in similar geographic areas. Best of all, we are able to bring in actual store visit data from some of our advertising platforms. This tells us whether advertising actually leads to foot traffic in the stores.

Results

The ability to peer deeper into the data allowed us to find insights that might have otherwise been missed. For example, after a major Google Ads account overhaul to accommodate organizational changes, analysts saw an average cost-per-call higher than our target. Alarm bells went off. However an average is just that, the middle of a data set. The average alone doesn’t represent all that is happening across the thousands of campaigns actively running.

In truth, 60% of all campaigns were beating the average. Trends showed many other campaigns were trending toward the target. Using histograms helped us visualize the real story and gave us a plan of attack to achieve better performance. Rather than just look at the average, we showed the bulk of our performance was trending left (lower cost than our target). Instead of a wholesale set of changes, we simply needed to focus on what was trending right (higher cost than our target).

The simple interpretation that cost per call was blankedly “too high” across the board lacked context. Using Tableau we could easily extract the campaigns and ad groups that needed attention and focus on discovering why some campaigns were lagging in performance.

This led us to specific insights that drove targeted changes. Within a few weeks of implementation, overall cost per call dropped 22% and call volume increased.

In today’s world, we see businesses racing to build systems like the one we made. However, building them is only half the challenge. Harnessing raw data to achieve the bigger purpose of meeting our clients goals is what we always set out to achieve.

Data is useless until it has been transformed into something that can lead humans towards insight and improvement. Monro, Inc. owns eight brands and over twelve hundred retail locations. They needed complete visibility into how marketing efforts tie back to each individual store as well as into their custom sales territories.

Giving our client this visibility required Mason Digital to build custom software to extract, transform and store the massive amounts of data produced by our efforts. This data had to be captured at the most granular of levels, the actual physical location of consumers when they interact with marketing and advertising messages.

Mason Digital constructed systems to extract data from all of the various advertising, analytics and other data systems utilized. Using Tableau, we mapped custom geographic territories to build large datasets that are then used to categorize raw geographic data into the world the way our partner needs to see it.

We wrote custom software solutions in Python to transform this raw data. We then utilized flexible cloud data warehouses to store it. This transformed data is available in interactive dashboards where our team and our partner’s internal analysts can slice and dice information in ways that were previously impossible. This marketing data is matched to the rest of the company’s operations information and together they provide a clear understanding of what is happening.

As an example of the many ways we use this information, after a major overhaul of the company’s Google Ads campaigns we noticed average cost per call was rising. This led some to believe that all efforts were not delivering.

Looking beyond the average and applying frequency distribution visualized in a histogram, we saw that 60% of campaigns were meeting or exceeding the target. The problem actually lied with a small number of campaigns that were negatively weighting the average. With our focus on figuring out what fundamentally made some campaigns perform vs others in this new campaign setup, we were able to implement targeted optimizations leading to a 22% overall decrease in cost per call with an increase in call volume.

While we take great pride in our ability to build complex data collection, transformation, storage and visualization tools, our ability to analyze, interpret and actualize this data to achieve our partners’ goals is the mission that drives us.

Visualizing Data Driven Marketing For 1,200+ Retail Locations

Challenge

Solution

Results

Mason Digital Expert Team

Matt Weaver

Micaela Contente

Jeff Commaroto

More Case Studies

Challenge

Solution

Results

Mason Digital Expert Team

Matt Weaver

Micaela Contente

Jeff Commaroto

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