Introduction – The Key Event Types
First, check out my latest article on this topic recently published: Shush – Listen for customer signals with event-based marketing & service —- Out of that article surfaced these distinct event categories:| Event Category | Example |
| Account status | Average bank account balance trending down (by X standard deviations) |
| Behavior-Account | Roaming charges incurred or within x% of the limit |
| Behavior-Device | Failure of device/machine |
| Behavior-Person | Digital browsing – showing purchase interest/intent |
| Calendar | Major shopping holiday approaching |
| Contract-Account | Changes in the account/contract terms & conditions |
| Environmental | Severe weather alert – hurricane warning |
| Forecast | Model score updated – Churn/attrition score rises above a threshold |
| Inactivity | No activity (of a certain type) in the last 30 days – e.g., no deposits |
| Law/regulation | Change in the overall privacy policy |
| Milestone | Birthday – Age changes (milestones such as 18, 55, 65, etc.) |
| Product/Service | Replenish – consumable products, such as printer ink |
| Product-wide/Service-wide | The interest rate on all accounts of type X increases by x% |
| Profile-Person | Investable assets increase (or decrease) by x% (or crosses a threshold) |
| Transaction status | Order status change (disruption in availability, timing) |
Vertical Complex Event Processing Use Cases
Fiserv: Consumer Banking and Credit Cards
- Unusual account activity (e.g., large deposits/withdraws)
- Unusual account activity trend (e.g., average daily balance down by two standard deviations)
- Inactivity pattern (e.g., no transactions in last week)
- Missed transaction (e.g., missed direct deposit)
- Credit card spend activity use pattern (by spending category)
- Insufficient funds pattern
- Web or mobile click activity indicating an interest in a product
Insurance
- Fraudulent claims activity
Operations
- Predictive maintenance systems
Media and Communications
- Dropped call pattern or degradation of signal/service
- A customer has increased roaming (or other unusual account usages) behavior
- Customer in route to a foreign country pattern
- Popular programming based on a set-top box and social media insights
- Prepaid consumption detection and stimulation
- Churn detection
Healthcare
- Claims fraud
- Care interruption pattern
- Fitness monitoring
- Hygiene procedures pattern
- Healthcare patient monitoring
Horizontal Complex Event Processing Use Cases
Customer Service Center / Retention Department / Loyalty
- Customer struggling to get help pattern
- Payment due
- Strange returns activity
- Customer likely wants to cancel service
- Customer’s birthday
- Customer’s service anniversary (e.g., been a customer for X years)
Marketing / Cross-sell & Up-sell
- Customer online interest in a product or service
- Customer in store interest in a product or service
- Customer in the proximity of a store
- Customer usage stimulation – Drop off in use of a product/service
- Increase in use of a product/service
- Loyalty Program – Monitoring points activity
- Loyalty Program – Monitoring points expiration date
- Monitoring social sentiment
- Monitoring social influencer
Non-CX use cases
Here are some examples that are not for CX, but instead to improve business and operations efficiency:- Algorithmic stock trading such as if Stock A rises by X% and Stock B doesn’t automatically buy Stock B
- Transportation security and fraud detection such as an id card used twice in a short time frame (e.g., piggybacking) or high volume transactions on a new account – and then automatically alerting the right parties
- Detecting transportation congestion and incidents, and proactive notification of alternative routes
- Inferred detections suggesting that a vehicle has crashed (and severity of crash), such as when an airbag has deployed
- Communications security such as false alarms going off in a certain time window, not followed by other alarms that would be expected (false positive alarming)
- Communications security such as network monitoring for detecting denial of service attacks, and alerting the right parties of this situation