Omnichannel execution has become imperative for retailers as consumers increasingly combine their shopping activity across online, brick & mortar and mobile channels.
With omnichannel, when a customer engages in any channel, the retailer is aware of their prior interactions in other channels and uses that knowledge to optimize the interaction in the current channel.
A well executed omnichannel strategy requires real time decision capabilities - the ability to process events as they occur, combine the event with other valuable data, gain intelligence from the data and decide on an action to improve the customer interaction. All of this done in real time.
Real Time Decision Process
A Real Time Decision Process can be viewed as five distinct steps.
Execution of omnichannel real time decisions is triggered by an event. An event can be any number of things but is usually initiated by a customer action; examples include,
- Customer visits website
- Customer uses mobile app
- Customer visits store
- Customer calls contact center
- Retailer geo-locates customer.
The event presents an opportunity to engage and must be captured in order to initiate the real time decision process.
Sometimes, knowledge of the event is sufficient information to take action. More often, additional data must be leveraged to improve intelligence. Many different types of information is potentially needed including
- Customer profile
- Transaction/sales history
- Channel interaction history
- Social activity history
- External data like weather, traffic, national/local events.
Data virtualization is the technical process that integrates these disparate data sources in real time into a usable format.
Intelligence must be derived based on the event and virtualized data to determine the optimal action. Predictive analytic capabilities are necessary and a wide range of decision frameworks must be available, including
- Rules engine
- Complex event processing
- Classification / clustering
- Machine learning
- Artificial intelligence.
The breadth of decision frameworks is necessary because different business objectives require different analytical approaches. For example, a rules engine works great when recognizing a customer for a milestone. Likewise, event processing is well suited for identifying potential customer dis-service scenarios. Finally, optimization techniques work well when making decisions about which promotions to place in front of the customer.
Once determined, the decision must be integrated with a customer facing channel or business process in order to impact the outcome in real time. The types of actions should be related to achieving core objectives such as
- Offer optimization
- Winning/completing the sale
- Customer lifecycle milestones
- Customer service (cross channel coordination, surprise & delight, recovery).
Feedback takes two forms. First, the outcome of the action/decision is fed back to the algorithms used in the Intelligence step. This can be done in real time for online learning algorithms or stored and leveraged in a batch mode for off line learning algorithms.
Second, data and insight from the Real Time Decision Process is fed into enterprise data management processes like customer relationship management (CRM) and customer data management (CDM).
It's important to note that real time decisions are related but separate to enterprise data management processes in which both rely on each other as inputs to one another. As a consequence, implementation of real time decisions does not require a multi-year, multi-million dollar enterprise data management project to be complete.
Execution of real time decisions is a complex set of capabilities that include predictive analytics, data virtualization and real time decision software. However, when done well, real time decisions delivers the full promise of omnichannel benefits.
What do you think? We'd love to hear your thoughts.