For players, casino gambling is about random games of chance. But for casino marketers seeking differentiation in increasingly competitive markets, the use of multi-genre and predictive analytics, data integration and strategic interactions makes their chances of success far more predictable. The combination of these capabilities has become known as the “Customer Journey,” which helps shape and drive value from new business opportunities that operators need today. 

Long gone are the days when ads proclaiming the “loosest slots in town” were sufficient to gain and retain customers. Even many of the traditional database marketing tactics have become too broad and generic to effectively drive specific behavior change in many cases.

Customer-centric initiatives backed by analytics that are targeted and strategic, however, are far more personalized and becoming a competitive necessity. Technologies provide 360-degree insights of patrons across casinos and non-gaming venues like bars, restaurants, hotels, retail, spas and entertainment—in real time, an essential element to facilitate the interactive experience expected by newer demographics that represent growing market shares.

The need for a Customer Journey approach is being driven by more competitive markets and an increase in Millennial visitors. The gaming industry’s post-recession recovery can largely be credited to their ability to appeal to new market demographics with diversification of products and business offerings.  The results have created more complex behavior profiles of customers, for which the traditional segmentation models based on a monthly “snapshot” of primarily average gaming worth, is becoming less representative of the typical customer. Instead, Customer Journey encompasses a collection of behaviors and transactions that span lines of business, devices, social media and online platforms or numerous other internal and external touch points, and are more reflective of today’s actual customer experiences.

The pillars of the Customer Journey approach include the integration of the data sources, execution of analytics and then ultimately fulfilling targeted interactions throughout that path. Predictive analytics, which identifies patterns and applies statistical models and algorithms to capture relationships between data sets, is common in traditional and e-commerce channels to understand behaviors and outcomes across multiple and individual channels.

Unlike traditional descriptive analysis, which explains past behaviors, predictive analytics are based on future probabilities. Issues addressed by predictive analytics include: why something is happening, (which addresses profiling and segmentation); what if trends continue? (forecasting); what will happen next? (predictive modeling); and what is the best possible outcome? (optimization).

In recent years, the advent of Big Data has made data collection more comprehensive, timely and affordable. Information can come from online web logs, sensor networks, text and other digital venues. Statistics cover demographics, behavior patterns and other factors. On the software end, connected data capabilities integrate “outside” data with existing data. This lets operators generate intricate consumer profiles, create targeted campaigns and evaluate responses.

But customers, and their behaviors, change. Millennials, for example, are the most diverse age group ever. They have distinctive views regarding money, entertainment and other issues, and are very tech savvy. Connected analytics capabilities, such as optimal path analysis, can automate detection of these type changes and deliver actionable insights into how to improve customer experience and outcomes. With Millennials, it has been found that personalized games involving mobile or social media could have strong appeal.

Connected interactions capabilities provide campaign management and decision tools that help deliver personalized experiences across gaming and other channels via ads, loyalty cards, mobile/online offers and other mediums. If there are more customers at one restaurant than another, for example, operators can send offers to attract guests to the latter.

With predictive analytics and marketing, the sky is almost the limit for casino operators. Following Amazon’s lead, analytics can be used to project and suggest games or properties guests will like based on those frequented (e.g., “People who purchased this also bought…”). This could introduce consumers to new games and increase the number of games played during a casino visit.

By studying data across the entire Customer Journey and learning more about customer demographics and behaviors, marketers and analysts can be more effective with campaigns. Coupled with other customer-centric and cross-channel initiatives, this would bring even more value and ROI to casino operators and their portfolios, as well as improved customer experiences.