Key Drivers for Great CX Implementations

Customer Experience
Covers a lot of ground in understanding customer behavior that leads to increased business success And then you apply these learnings at scale And now at Internet speed where seconds matter

Our white paper outlines what we think drives successful CX implementations in today’s fast-changing business landscape

8 Key Drivers for Great CX Implementations

  • Data scientists
  • Streaming data
  • Raw data
  • Engineered features
  • Reinforcement learning
  • Operations research
  • Easy, not esoteric
  • Quality management

Data Scientists
Great CX results, now and in the future, start with AI. Your DS team & AI vendors MUST be skilled in the art & science of machine learning and AI techniques such as natural language processing, and reinforcement learning, to name a few.

Streaming Data
We all know personalizing the enterprise is where CX is headed, & fast. Digital & e-commerce place enormous demands on CX systems – personalized actions, offers, web site & mobile app ads – all must be determined for each customer based on their unique customer journey, all in near real-time.

Raw Data
Customer events & demographic can account for hundreds of variables, with events in a reasonably large customer dataset, often accounting for billions of events in total. Customer transactions drive 70-90% of our understanding of customer behavior versus 10-30% for traditional customer demographics.

Engineered Features
Engineered features are created by data scientists to “amplify” the KPI “signal” in question. These features can easily out-number raw data variables. Mastering engineered features is critical to success in using AI and in ultimately getting the very best results for every KPI measured & scored.

Reinforcement Learning
Not all NBAs are created equally. The best NBAs generating the very best results require advanced RL models, especially when operating at digital speed. AI models excel in this type of problem set, and they have rendered rules-based engines obsolete.

Operations Research
AI develops great insights, but these insights must be optimized across various constraints such as profitability, risk, availability and other business constraints. Operations research is a powerful modeling technique and a total game-changer in automating & optimizing difficult constraining factors.

Easy, not Esoteric
AI is not simple to introduce into the art & science of solving business problems. At the same time, AI has to be made easy to use to get broad acceptance in any enterprise. As you bring AI from out of the shadows, you need powerful end-user tools and the finest UX designs possible.

Quality Management
Today, CX means working at the speed of digital, making customer decisions in seconds. To automate this process, you need to be sure your data & your models are always working. Only by monitoring data & models in real-time will you see consistent results across all CX KPIs you need to run your enterprise.

CX projects are proliferating & implementations are key. State-of-the-art CX now requires AI at its core, & that means we need to hone our data & machine learning (ML) skills