15. Old Business Process + Expensive New Technology
= Expensive Old Business Process
16. Shift in way of doing things
• Conventional way of doing business
• Long planning processes
• Canceling program if fails
• Not feeding the learning output
17. From Lean Startup Methodology
• Act like a startup
• Create fast learning cycles
• Learn and iterate
• Accelerate feedback loop
18. Bi-Modal IT
Reliability Goal Agility
Price for performance Value Revenue, brand,
customerexperience
Waterfall, V-Model, high-ceremony IID Approach Agile, Kanban,low-ceremony IID
Plan-driven, approval-based Governance Empirical, continuous, process-
based
Enterprise suppliers,long-term deals Sourcing Small, new vendors, short-term
deals
Good at conventional process, projects Talent Good at new and uncertain
projects
IT-centric, removed fromcustomer Culture Business-centric,close to
customer
Long (months) CycleTimes Short(days, weeks)
19. Creating a Learning Organization in Digital
• Act like a startup
• Discover Insights, act fast
easily
• Time-to-market in digital
channels
• Accelerate feedback loop
• Self-learning offers by
customer history and
behaviour
• Learn and iterate
22. What to do?
• Board Room Agenda
• Way of doing business - processes needs to change
• Sometime you need to change people
• You definitely need new technology
• Capability to provide a good Digital Experience has become a
Unique Value Proposition
26. Information is internal only. Do not distribute.
An offer ending is the most common event before the end of the save window.
Network issues and plan changes were also common end events.
6 events prior 5 events 4 events 3 events Churn
Save
Window
27. Information is internal only. Do not distribute.
All Normal Client Transfers to Agent – Change
IVR
28. Items Bought Together : by Category (1 Year Period)
Category A
Category D
Category B
Product Z
Product X
Product Y
Category C
The team used nPath and Graphgen (sigma) visualization functions to analyze consumer buying behaviors, by campaign, region, store, month and demographic, over multiple years. The transaction fact table analyzed was over 22 billion rows. Each row is identified by a customer transaction ID (basket) , customer ID and the item. The sigma graph is purely data driven based customer buying behavior.
Products naturally cluster into categories. For the first time the client was able to validate what products and categories “naturally” attract to each other based on customer behavior. The client can explore a customers’ trip type ( e.g. special occasion trip , a stock-up trip , seasonal trip) overtime and proactively make customized recommendations prior to the customers next shopping trip. Operationalizing theses insights are described in the next few slides.