![]() ![]() Now the orderId parameter is a base64 string with the following value:Īs you can see the actual integer order ID is still there but there is a GUID after it. The fixĭominoes have since fixed this with the release of their new tracker which resembles a creepy HAL-like thing. ![]() From the output of the script you can work out the order rate over time and produce the graph above. ![]() The actual code is a lot messier and a bit more complex, I found that some dominoes orders are never fulfilled and so you also need to check current + 1 and current + 2 to be sure. Import requests, time start = 1234 # The orderID of a yummy pizza you have literally just brought current = start def order_exists ( id ): resp = requests. You can then log how long that took, and just increment orderId by X again and repeat. So an idea was born, what if I made a script that uses this to roughly track how many Dominoes were ordered online over time? Once you have a ‘start’ orderId you would just need to increment it by X, and keep requesting that ID until a valid response came back and the orderId was ‘filled’. I also discovered that it wasn’t tied to a users session so you could send any arbitrary order ID in the parameter and get the status of that order. While I was writing my dissertation I ordered a few too many pizzas over a couple of weeks and I noticed that the orderId parameter was just an incrementing number. This URL returns a JSON response with some information that was used to update the tracker, such as the status (cooking, prepared, out for delivery etc). After opening up Chrome’s dev tools I noticed that it was making a request to the following URL: Next, Domino’s and DBi turned their efforts toward connecting valuable datasets.After you order a delicious (if a bit expensive) Dominoes pizza you have the option to track your order as it is being cooked and delivered. Despite there being a large number of unique containers, data layer consistency makes it easy to duplicate tags and rules - a significant time-saver and error preventor for Domino’s.Ĭonnecting datasets provides holistic customer insights Because the data layer stays independent of the HTML page structure, it remains consistent when the page content is updated and provides reliable, unchanging data sources for Google Tag Manager containers to pull from.ĭBi deployed Google Tag Manager across many of Domino’s apps and websites, setting customized tags for all of the company’s ecommerce tracking and reporting needs. Having taken strategic steps in its partnership with DBi, a Google Analytics Premium Authorized Reseller, Domino’s has turned its team goal of unified marketing measurement, holistic insights, and efficient actionability into a day-to-day reality.įor all of this to be possible, DBi leveraged the power of the data layer, a repository of information written into the page code used to store and send information to Google Tag Manager.
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