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Digital Retail Intelligence
Transcript of Digital Retail Intelligence
Digital Retail Intelligence
Actions are the speciﬁc call to action that has been issued and the direct result of that call to action i.e. accept offer, promote offer to friends via Facebook, purchase now, click on link, log-in to loyalty app etc.
The result action then can trigger an event with additional attributes based on the pre-deﬁned rules.
All actions are logged and action attributes feed the predicative analytics model
Results maybe derived directly from the actions or are fed via third-party sources such as POS or CRM systems
The results module is used to determine exactly what the result of the action is (no action, purchase with value level, products or offer options transacted)
It is primarily designed to correlate the economic outcome to the prior processes
Provide Retailers with Actionable Analitics
Deliver automated expert retailing and customer engagement systems that align with business objectives
Create disruptive solutions that become the must have digital retailing toolset
Import data in real time where possible via API's from all relevant sources
Collect relevant matching data
Utilise Master Data Management (MDM) capabilities to match dataset and relationships between objects and entities
Enable visualisation of relationships (devices, people, customers, actions, locations, retailers, etc)
All new data matched to existing known entities and objects in realtime
Enable intelligent queries of related objects and attributes
Feeds analytics and predictive analytics engine
Deﬁne rulesets under “if this then that” structure
Rules look for patterns of matches and relationships that then trigger speciﬁc actions/events or grouping of objects (target customers)
Rules trigger events or look for speciﬁc event attributes and events trigger rules to be actioned
Rules can be simple of highly compound clusters of rules. (If a prospect is in a location and a time of day, has provided consent and has speciﬁc attributes and has accepted similar offer previously, then offer a proposition with the following attributes….)
Rules are reﬁned over time based on event outcomes to create more precise and relevant offers with higher transactional yield and proﬁtability via Predictive Modelling
Based on historical data, creates a statistically accurate predictive model of behaviour
Model is exported into a PPLM XML structure and becomes an executable ruleset with prescribed event outcomes
Ongoing result input continually creates a more statistically accurate models. This becomes the core IP of the system.
Offers are either personal or public offers based on an event. The event maybe time of day, surplus of product or a person matches speciﬁc attributes
The offer maybe presented to the person(s) in a number of methods. Email, in-app message, digital signage, SMS etc when actioned.
The offer content is managed within a CMS with the variables managed by the Offer module (speciﬁc discount, price point, timeframe etc)
Each offer is has an audit trail to determine what was offered, based on what rules and speciﬁc attributes. This enables it to be analysed and used for predictive modelling.
Offers can be varied to determine what speciﬁc offers provide the best yield under different conditions
Rules trigger events or look for specifﬁc event attributes and events trigger rules to be actioned
Rules can be simple or highly compound clusters of rules. (If a prospect is in a location and a time of day, has provided consent and has specifﬁc attributes and has accepted similar offer previously, then offer a proposition with the following attributes….)
Events trigger offers with specifﬁc attributes to one or multiple parties.
Events are actions or a collection of actions that are triggered by rules or have occurred and have been reported by an external systems (i.e. a purchase or app download)
When an event occurs it can trigger a rule or be sent to a message broker to update or inform a third party system.
This function orchestrates all system messages between components and external systems and sources
Each component is either a publisher or subscriber (or both) to speciﬁc sources or events
Event Broker interrogates the data and decides which competent requires what, in which format or structure
Collection of Independent Data
Spontaneous actions to live stimuli
Context Intelligence (CRM, Loyalty)
Big Data Analytics (MapReduce)
Data Driven Decision Management