When we first introduced Customer Journey Analytics (CJA) as a new offering in Adobe Analytics, our hope was to help organizations see their blind spots. Our teams had observed far too many examples of important decisions that were being made with incomplete data, or topline metrics that skimmed the surface when it came to an understanding of consumer behaviors. In the retail industry for instance, brands were having a difficult time grasping how online activities were driving in-store purchases.
Part of the issue, however, was that digital had long been considered a second fiddle. In recent months, that has changed substantially. The ongoing COVID-19 pandemic has moved us from treating digital as an afterthought into an all-digital economy. And even when we do return to some normalcy, aspects of this will persist. The growth in digital adoption will make it even more important for brands to understand how consumers engage across different channels.
Anomaly Detection (available today)
As one of the most popular features in Adobe Analytics, Anomaly Detection has traditionally been used for website activity. Brands can use it to see areas of the web experience that drive or hurt sales. It allows teams to ramp up specific campaigns, or issue quick fixes in real-time.
We are now making Anomaly Detection available in Customer Journey Analytics. For the first time, users can get a better sense of the “hand-off” that happens between channels such as customer support and the mobile app. It is purpose-built AI that helps them identify pain points, or areas they can further refine the experience. For consumers, it means interactions that feel more connected and intuitive.
Anomaly detected in a call center
Contribution Analysis (sneak preview)
When teams see anomalies in their data set, some benefit from an additional analysis of the root causes. Consider a media company, who sees a significant dip in user engagement. With Contribution Analysis, they may see that a specific browser is the prime suspect, providing them a clue to investigate further.
We will be bringing this capability to Customer Journey Analytics as well. Consider a scenario where engagement with a loyalty program has been falling substantially. With Contribution Analysis on CJA, the team can better understand the disconnect between member benefits that their customers see online, versus what representatives are presenting in-person.
Intelligent Alerts (sneak preview)
In developing AI and machine learning features in Adobe Analytics, we realized early on that teams don’t know what they don’t know. There are signals hidden deep in data, that even skilled data scientists may not fully scope when they build their organization’s analytics practice. With intelligent alerts, part of the virtual analyst in Adobe Analytics, brands can rely on Adobe Sensei to do the heavy lifting—uncovering the “unknown unknowns.”