Some brands will harness AI to form deeper customer relationships, while others will not survive the rise of AI.
Is a bottle of water just a bottle of water? When you’re drinking it, hopefully? But if you’re selling them, it’s really 30-50 attributes, if we’re doing our jobs right.
We’re way, way past broadcasting messages to the average.
Rather, we’re continuously sensing what people want, and then delivering continuously adaptive value.
Rapid innovation, faster and faster, for increasingly micro-segmented markets.
It’s about devotion to customers. Enterprise Artificial Intelligence (AI) makes it happen. The machine can crunch combinations in an ontology that our brains cannot absorb simultaneously, simple as that.
Patterns, clusters and ratings drive automated actions in goal-driven yield management, guided by human judgement and creativity.
Whereas ERP systems enabled innovation that leveraged core assets, and traditional analytics made those transactions systems smarter, new Enterprise AI capabilities are enabling breakthroughs in cross-enterprise precision, speed and alignment.
This alignment provides a compelling ROI alternative to the traditional approach of deploying analytics, so that your existing investments in core ERP, CRM, BI, EPM and other business analytics technologies are more fully realized.
Enterprise AI fuses data into an AI platform, from which machine learning algorithms recommend goal driven actions, which then are federated back into to existing processes and systems.
Micro-concept innovation of AI translates diverse sets of data into consumer attributes, preferences and behaviors.
The quantifiable success of data science relates directly to aligning it with Enterprise AI – an intersection between people and machines, the ontology and the yield. Internal and external data is mapped to rich attributes. Enterprise AI provides big data automation, machine learning clustering, collaborative filtering, and cluster engineering. Clustering may then discover patterns, rate offerings/content and predict actions.
Clusters may be grouped along the most relevant consumer drivers to create micro-concept archetypes.
Machine learning actions then guide people with micro-forecasted yield optimizing recommendations, such as for personalized loyalty, omnichannel orchestration and demand forecasting.