PEGA Decisioning - Next Best Action Decisioning Artifacts

PEGA Decisioning - Next Best Action Decisioning Artifacts 

Proposition Management - is how propositions are defined, stored and managed in PDM. Propositions are anything that can be offered to a customer incl. ads, products, offer bundles even customer service actions.

Decision Strategies - match propositions with customers, combine data about the customer with rules and predictive analytics to determine the Next-Best-Action during each interaction.

Interaction History - gives Pega Decision Management its long-term memory. Captures every customer response to every NBA and provides it as input for determining what the NBA should be.

Visual Business Director - is a 3-D graph environment for simulating and analysing the results of Decision Strategies and customer interactions, helps to ensure that strategies are on target to meet goals.

Adaptive Decision Manager - enables automatic development and deployment of adaptive predictive models which learn and gather data on-the-fly to predict future customer behaviour.

    Each of these key features leverages the different types of decisioning artifacts: Rule Types, Landing Pages, Decision Components, and Flow Shapes that are the building blocks of Pega Decision Management.

 

PEGA Decisioning Landing Pages

Landing pages- here certain key capabilities are configured, monitored and managed, they provide a shortcut to viewing the rule types as well as the data behind these capabilities.

The Decisions landing page enables you to define and manage all aspects of the decisioning hierarchy incl. the propositions, strategies and data flows that determine NBA.

The Predictive Analytics landing page enables you to manage all aspects of the development and implementation of predictive and adaptive models.

The Monitoring landing page enables you to monitor decisions through three types of reports: the adaptive model reports, Interaction History reports and Visual Business Director.

The Infrastructure landing page enables you to configure, view and manage resource usage and various operational settings.

 

PEGA Decisioning Flow Shapes

Each element in a process flow is called a flow shape, they allow seamless integration of PDM with Pega Business Process Management.

There are 2 types of flow shapes:

The Decision flow shape extends the capability of the decision flow shape in the Pega Platform by enabling it to invoke predictive models and decision rules, such as decision tables and decision trees.

The Run Interaction flow shape invokes a decision strategy, which determines how and when to make a proposition to the customer, and then captures the customer’s response to the proposition.

 

PEGA Decisioning Rule Types

The Strategy rule type, the main rule type, utilizes all other rule types to determine what the NBA should be.

Decision Tables and Decision Trees classify customers by characteristics for the purpose of creating segments.

Scorecards are used to implement industry standard scorecards such as the FICO scorecard.

The Predictive Model rule type is used to execute predictive models created with Pega Predictive Analytics Director.

The Adaptive Model rule type - configures the adaptive analytics decision models that are managed by the Adaptive Decision Manager service.

The Interaction rule type - executes a decision strategy from a workflow and capture the details of the customer interaction.

PEGA Decision Management - What is mean by Next Best Action?

The primary role of Pega Decision Management is helping to determine the Next-Best-Action in customer-facing business operations.

NBA is a new approach to any type of customer interaction:

 

• Marketing
• Sales
• Service
• Retention
• Collections
• Fraud
• Data collection

It determines the optimal action, which the one that satisfies customer expectations while also meeting business objectives. 

It begins with the customer taking some sort of action such as calling into a call centre or responding to an email => system registers customer’s intent for this action. Then the data is being taken into consideration by Pega Decision Management, and decision strategies are being applied to create a mini business case, that will calculate the NBA for the interaction. Once its done, then the decision engine returns the Best-Action to take with the customer: either no action at all, service action, or make an offer or other. This is a never ending loop throughout the customer lifecycle.

Pega Next-Best-Action Marketing = Next-Best-Action Advisor, which advises agents on how to interact with customers (inbound) and can be used for outbound as well.

Interaction: Listen, Learn and Act accordingly => Interaction History captures customer responses to every NBA. Pega Decision Management combines traditional business rules with predictive analytics and interaction history to come up with the NBA.

 

The ultimate goal of Next-Best-Action is to optimize customer value:

• maximize the profitability of each customer relationship
• help retain profitable customers
• To do that NBA bases each decision on 2 things: what does that customer need right now, and then, what are the business objectives for this interaction?

Centralized Decision Hub delivers NBA to any available channel: CC, mobile or retails store at any moment. It continuously build on each other across every interaction in any channel.

To satisfy customer needs NBA has to have 4 characteristics:

 Contextual- must take into account the very last input the customer gave to the company
 Timely- must be taken in an appropriate timeframe, via the right channel at the right moment. This means in real-time in the inbound channels
 Relevant- must be right for that particular customer based on everything known about them at the moment of communication.
 Consistent- consistent across every channel, the messages and propositions they receive must be the same

On business side- 4 key strategic business directions: Growth, Service, Retention and Risk Mitigation, but also other things like budget and resource constraints (availability of a product) must be considered.

We will see more about this topic in coming days. Stay tuned.

Webpage performance tuning tips

It has been very long time. I thought I can share my recent leanings Webpage performance tuning tips.

1) Yslow (http://yslow.org/)–

To evaluate your site’s speed and get tips on how to improve performance. This link has more insite about the best practices to be used to improve the web application speed.

2) Google’s PageSpeed Tools (https://developers.google.com/speed/pagespeed/) –

To learn more about performance best-practice and automate the process. If we give an existing application web URL, it will give the comprehensive details and the detailed fix to address the performance bottleneck.

Happy Learning!

Nanjundan Chinnasamy

More detailed developerworks article on web application speed can be found at http://www.ibm.com/developerworks/library/wa-speedweb/

Google’s Cloud Service - Google App Engine (PlatformAsAService)

It has been very long time posted my blog. It is much needed too. Yes, an most important much need life changing Event. Now I am back to normal with my regular BusinesAsUsuall work.  Yes, it is time to write my technical blog too. 

Long back, I have explored Google’s Cloud computing solution “Google App Engine”. I have created sample applications using Spring, JSF too. Recently, I have deployed website with static pages for one of my personal need. One of my friend hosted his static website using Google App Engine for his high availability and high scaleability requirement. I strongly believe Google App Engine will be the right choice for many individuals and startups to host their web application to meet their needs. Here is the sample Google App Engine application he hosted http://rainbowmobiles.co.in. 

You may have questions like What is Google App Engine? When to go with Google App Engine?, What is the problem it is trying to solve?. No worries, you will find an answer for such questions by end of my blog. 

Google App Engine is a Platform as a Service (PaaS) that lets you deploy and run your applications on the Google infrastructure without having to worry about setting up your own hardware, Operating System or server. Google App Engine lets you run web applications on Google's infrastructure. 
• Easy to build. 
• Easy to maintain.
• Easy to scale as the traffic and storage needs grow.

Cost:
All these are Free. Yes, free for up-to 1 GB of storage and enough CPU and bandwidth to support 5 million page views a month. 10 Applications per Google account.

Use App Engine when:
• You don’t want to get troubled for setting up a server.
• You want instant for-free nearly infinite scalability support.
• Your application’s traffic is spiky and rather unpredictable.
• You don't feel like taking care of your own server monitoring tools.
• You need pricing that fits your actual usage and isn't time-slot based (App engine provides pay-per-drink cost model).
• You are able to chunk long tasks into 60 second pieces.
• You are able to work without direct access to local file system.

Language Support:
• Java, J2EE (Selected framework with some restrictions)
• Python
• PHP

Data Store Support:
• NoSQL schema-less object based data storage, with a query engine and  atomic transactions.
• Data object is called a “Entity” that has a kind (~ table name) and a set of properties (~ column names).
• JAVA JDO/ JPA interfaces and Python datastore interfaces.

Google cloud SQL:
• Provides a relational SQL database service.
• Similar to MySQL RDBMS.
• Fast, scalable and highly available solution. 

Other services:
App Engine also provides a variety of services to perform common operations when managing your application.

• URL Fetch: Facilitates the application’s access to resources on the internet, such as web services or data.
• eMail: Facilitates the application to send e-mail messages using Google infrastructure. 
• Memcache: High performance in-memory key-value storage. Can be used to store temporary data which doesn’t need to be persisted.


I am hoping many new IoT solutions will evolve in near feature using Cloud solutions like Google App Engine. Let’s hope.

Happy Learning
Nanjundan Chinnasamy