Unifying Customer Conversion Data Across Devices

Increasingly, customer to brand engagement is cross-device. Complex user interaction paths underpin how prospective customers are attracted, engaged and converted to your brand. Large global e-commerce and travel brands have known this for some time, however the complexity of the conversion path is ostensibly similar for all of e-commerce, and iGaming, and in particular online casino, is no different. Historically the iGaming sector has had a very insular path to conversion or attribution view, in essence, dominated by single session last-click events almost always isolated by device type. Of course, many operators have matched player IDs across device to understand usage and, in some instances, particularly for large landbased brands, the opportunity to match online to offline has existed. and in some cases been exploited. However similar logic has not been applied to try and understand the cross-device and more complex offline-to-online interplay of prospects, i.e. pre-customers. Although the simplicity of single session last-event tracking is appealing, it is not only flawed, it’s financially irresponsible. More progressive brands have taken the view that they will try and understand user behavior within the purchase funnel. However, a purchase funnel now defined by device multiplicity is increasingly difficult to tackle.

The typical journey

Take a typical casino customer acquisition journey from point of first exposure and first-time cookie drop to first-time deposit and user ID (UID) assignment. A young male (USER A) is accessing content on a Thursday evening. An exit pop-under is served exposing USER A to BRAND B. USER A is using a tablet device for his browsing session. A few days later, USER A via a series of desktop-based search behaviors and content consumption is added to a behavioral segment called “Casino Intender”. BRAND B via a desktop display campaign intentionally targets said segment and via a premium ad unit on a sports new site serves an ad three times to said user. At this point, USER A’s desktop device is mapped via a placed cookie. Another 24 hours later, USER A (with friends) visits a land-based operation owned by BRAND B. USER A decides that a live casino experience doesn’t match his taste, however online poker is of interest. Having now had five cross-device and offline exposures to BRAND B, he conducts a brand search from which he accesses the site via a paid search affiliate direct linking with consent to BRAND B’s online poker offering, using his iPhone 6 mobile device. Based on four different nonunified customer touch points, BRAND B decides to cancel its pop-under and display campaign and instructs affiliates that mobile – specifically iOS – users convert well! Alternatively, and had one prior touchpoint been on desktop, they could have decided to upweight the buy due to its first-event impact on affiliates. Clearly both decisions are poor, and made in isolation of highly important cross-device considerations.

Cross-device attribution

Ultimately cross-device understanding requires complex ad technology support and equally as complex business intelligence and analytical resource. Unfortunately, adoption of enterpriseclass ad technology within iGaming has been limited, and when considered alongside comparable sectors, rather poor. One might argue that in less competitive times the need for media optimization coupled with heavy reliance on affiliates was unnecessary. This is no longer the case. Despite restrictive US gambling policy, indications from highly competitive casino markets make it clear that adoption will become a necessity, moreover with anticipated shifts in US policymakers’ attitudes toward online gaming, the opportunity to embed the correct data, tracking and technology infrastructure from day one is there.

Clearly, the viewpoints expressed above rest on the principle of, at the minimum, unified ad serving and thus reporting and attribution. However, it is increasingly the case that not all ad technology is made equal, and in today’s market, only a few obvious choices exist. Broadly speaking, cross-device attribution is achieved in two ways. Via mapped UID data, typically in the possession of large global networks commanding login access such as Facebook, allowing Facebook to assign multiple devices to singular users based on their login behavior, essentially allowing for conversion reporting that is interlinked to a UID only. Alternatively, other global ad technology and online utility providers, primarily Google and associated entities, can cross-device-map based on scaled deterministic datasets with high probability, using elements such as login, behaviour, and location etc. to form a rich data-led understanding of user behaviour. Far from coincidentally, the aforementioned Google and Facebook both own ad technology products, DoubleClick and Atlas, that power cross-device attribution based on the huge datasets respectively available to them. Google and Facebook are now the dominant ad technology vendors globally, Facebook being of particular interest based on how quickly they were able to translate their data into ad technology value via the acquisition of Microsoft’s Atlas.

Although both formidable data giants, Facebook likely lags in terms of available data, whereas Google, via its virtually universal online reach, is probably a more robust core solution. That said, it’s plausible that large operators would use both in parallel, however aiming to rely on DoubleClick to overlay Atlas’s data depth. Of course this may change over time, depending on the data richness of assets on Facebook’s acquisition horizon. Of course, apart from media optimization and efficiency, cross-device attribution also has an important part to play in product optimization. By way of example, if after a mobile web product relaunch there is a spike in cross-device conversions, this may indicate that users are aborting the mobile web experience due to product failures. Again, and in an increasingly competitive iGaming sector, insights such as these can be hugely beneficial to operators looking to secure a high as possible percentage share of wallet. Broadly speaking, the opportunity for iGaming operators to adopt best-in-class ad technology, and specifically device agnostic customer data, is significant. Consequently, operators preparing for the next phase in development with antiquated technology and practices are doomed to fail.

“Although the simplicity of single session last-event tracking is appealing, it is not only flawed, it’s financially irresponsible.”

“If after a mobile web product relaunch there is a spike in cross-device conversions, this may indicate that users are aborting the mobile web experience due to product failures. Insights such as these can be hugely beneficial to operators.”

“One might argue that in less competitive times the need for media optimization coupled with heavy reliance on affiliates was unnecessary. This is no longer the case.”