Social media and machine learning are revolutionizing the way we think about our customers and how we relate with them from only a few years ago.
CRM solutions are complex and impact many departments throughout the organization and beyond. The tools and technics used to manage these complex implementations and integrations are also evolving.
In the early years CRM was considered a novelty or luxury. Today CRM is as crucial to running a business as the company’s accounting system. In fact, they are tightly integrated to manage the processes for selling, pricing, inventory, and a customer’s credit balance. Customer support is no longer an afterthought. It too is built directly into the customer life cycle and CRM.
Looking into the future, social media will continue to make a big impact on CRM from the way companies use it to advertise, sell, and support customers. A.I. as augmented information or artificial intelligence will continue to evolve and impact CRM implementations and the way we manage them.
Learning Objectives:
- Understand the origins of Customer Relationship Management CRM
- How CRM is interconnected and integrated within the organization and beyond
- What’s the impact of artificial intelligence and machine learning for the organization and customers?
The Robotics Automation is very attractive to many organizations. Bots can directly affect the customer experience. It is a straightforward way of improving customer service and reducing manual, unattractive labor without a big investment in a new systems. But how to start? This presentation will walk you step by step through a Bot implementation. My company’s very successful journey into implementing robotics will be used as a case study. You will learn how to set up the criteria for vetting processes, how to calculate the ROI for each bot and how and when to prepare requirements for a bot. You will have the opportunity to try all of this for yourself.
Learning Objectives:
- How to select bot processes
- Calculating bot ROI
- Requirements taylored for the bot
It’s no secret that organizations have been increasingly turning to advanced analytics and artificial intelligence (AI) to improve decision-making and achieve immediate and long-term improvements in areas from churn reduction and pricing optimization to fraud prevention, talent retention, predictive maintenance, and more. The organizations that succeed these days are those that most quickly make sense of their data in order to adapt to what’s coming. And the professionals that excel these days are those who can shift their perspective on how work gets done to unearth meaningful insights from data and turn those into competitive advantages. This session will describe concrete strategies business analysts can start applying today to add advanced analytics and machine learning to their “bag of tricks” and help their organizations use data to its full potential.
Learning Objectives:
- Understand why machine learning is becoming the foundation for the next wave of advanced analytics.
- Explore why the human mind still is—and will continue to be in the foreseeable future—a key element for businesses to realize the full potential of AI and machine learning for innovation.
- Learn concrete strategies you can use to become a valuable contributor to the process of turning vast amounts of business data into fast discovery, deeper insights, and competitive advantage.
How can I improve straight-through processing with RPA? How should I enhance my business decisions for optimized automation? These and many similar questions have been answered anecdotally by business automation practitioners for years. Yet, automation capabilities such as workflow, decision, or RPA are usually approached as silos and the use of data science models have been confined within these silos.
However, the democratization of AI is expanding the automation horizons: Machine learning techniques can now be applied reflectively, observing the activities and outcomes of every aspect of a business process, and use them as training data in order to automatically and continuously find ways to improve this process. In this presentation, we will review the key digital business automation capabilities and the type of business events they generate and show how these individual pieces of business insights, collected in a data lake, can be exploited through machine learning to prescribe tangible business process improvements.
Learning Objectives:
- What kind of insights can be gathered from observing business process, decision, bots execution
- How machine learning can be applied to the collected insights
- What kind of impact can be expected on business process improvement