D. Lukose, A.R. Ahmad, and A. Suliman (Eds.): KTW 2011, CCIS 295, pp. 46–51, 2012. © Springer-Verlag Berlin Heidelberg 2012

The CRM of Tomorrow with Semantic Technology

Sheng-Chuan Wu

Franz Inc., 2201 Broadway, Suite 715, Oakland, California 94612 USA

scw@franz.com

Abstract. Customer Support or Relation Management (CRM) used to be a post-sale after-thought, an obligation stemming from product sales and a cost of doing business. No longer! In today’s abundance of product/service choices, CRM must be an integral part of business to maintain brand loyalty, which is becoming critical for any business to survive. What if you can anticipate what you can do for your customers before they know it, predict what your customers may like or dislike and take steps to address potential problems before your customers switch vendors, or target individual marketing campaign to very specific and appreciative group of customers instead of spamming. How would this improve a business bottom-line and change its operation? This paper discusses an Intelligent Decision Automation platform for such a CRM system of tomorrow. It was built by Amdocs and Franz using semantic technology, machine learning and scalable java middleware. The Semantic platform consists of a number of elements: an Event Collector, a Decision Engine, the AllegroGraph triple store, a Bayesian Belief Network and a Rule Workbench. Combined, this pipeline of technology implements an event-condition-action framework to drive business process in real time.

Keywords: Customer Relation Management, Semantic Technology, Machine Learning, Rule, Bayesian Belief Network, AllegroGraph RDF Database.

1 Introduction

Today’s market can be characterized by over-abundance of product offerings. Because of automation and the vast manufacturing capacity in Asia, one can find almost endless choices from many suppliers for any product with good quality and low price. Good products alone no longer suffice. This is particularly true for electronic devices and computing hardware. It becomes much harder to stand out in the market today comparing with just 20 years ago. Furthermore, in some sectors such as telecom, it has become a zero-sum game. For a vendor to gain a new customer, a competitor must lose one. Therefore, to stand out and survive, a business today must:

1) Resolve individual customer problems effectively and efficiently; 2) Anticipate potential issues with individual customers and address them before the

customers are even aware of or before they become a serious problem;

The CRM of Tomorrow with Semantic Technology 47

3) Offer added value to existing products or services, customized for individual customers rather than just spamming;

4) Cultivate product and brand loyalty among the customer base; and 5) Gain additional business from current customers for the long term.

2 Current Situation Analysis

Unfortunately, today most businesses fail to do all these five things well. How many times have you been frustrated by your long and ineffective interaction with the call center staff (Figure 1)? How often have you been bombarded by spam from your vendors trying to get more money out of you by pushing additional, unwanted products or services? According to a study, a call-center agent on average must go through 68 screens before getting the right data to address a customer’s problem, and the customer often needs to call more than once to resolve the issue. Another example is the continuous spam using scare tactics from anti-virus software vendors (built into most PC’s) pushing users to pay to upgrade.

Fig. 1. Typical Agent Interaction with Customers

What can businesses do to remedy the situation? Traditional Business Intelligence (BI) tools can help a business understand the pattern and trend of *all* its customers, but tell nothing about *each* individual customer (Figure 2). That’s why BI cannot provide personalized services to individual customers. Most current CRM (Customer Relation Management) systems, while intended for managing a company’s interactions with customers, clients and sales prospects, are mainly used to organize, automate, and synchronize business processes, principally for sales, marketing and technical support activities. There is little analysis and reasoning capability to proactively serve the customer base individually for their long-term loyalty.

Bill

Plan

Device

Past Interactions

(Memos)Statements

Calculator (avg peak

usage)

Past Payments The unknown – why

calling? How to help?

No context –real-time insight and guidance

Typical Interaction Begins in the Dark

48 S.-C. Wu

Fig. 2. The Shortcoming of BI Tools

3 The Solution – A New CRM System

Specifically, the future CRM system must provide personalized and proactive services for the customers. The only way to achieve such is to develop a critical and comprehensive insight into individual customers. A business must understand, to the extent possible without violating privacy concern:

• User characteristics individually such as gender, age, preference, educational level, profession, family, etc.

• Record of interaction with the product, the service, the company and its sales and support staff, etc.

• Spending history, credit worthiness and payment pattern.

From this understanding, a detailed model of individual users can be established to enable analysis, reasoning and inference to proactively provide better user services, to create customer loyalty and to discover additional future business opportunity. In essence, the future CRM system must be an integral part of any business.

4 Technical Challenges

Technically, such a CRM system must support multiple data sources such as sales, operation, support, accounting, etc, entailing many legacy data and yet-to-come new data. There are many challenges to integrate such diverse data together to form a complete view of individual customers, including:

• Heterogeneity of data distributed over multiple departments, functions and locations;

• Data stored with different database schema; and • Information encoded with inconsistent data semantics.

BI can tell you ALL about the average customer

but NOTHING about the individual ones

The CRM of Tomorrow with Semantic Technology 49

Figure 3 below is a snap shot of a typical telecom IT system, encompassing many different database systems with applications written in nearly every programming language.

Fig. 3. Complexity of a Typical Telecomm IT System

Furthermore, the user model must be conducive to supporting semantic reasoning, rule-based inference, machine learning (supervised and unsupervised) and complex intelligent scenarios. And the CRM system architecture must be flexible enough to meet increasing and changing demands. Traditional data warehouse approach to data integration is totally inadequate and too rigid (both architecturally and schema-wise) to be useful for the future CRM system.

5 A Semantic Technology Based CRM System

A semantic model, employing URI (Universal Resource Identifier) and RDF (Resource Description Framework), uniquely represents data or more accurately concepts within the organization and globally in a schema-less RDF format (i.e., subject-predicate-object). It totally avoids the ambiguity and semantic inconsistence in most relational models that has plagued many data integration effort, while preserving maximum flexibility to meet future demand. Such a model can also connect easily to external metadata (also encoded in RDF and URI) from fast-growing information sources such as LOD (Linked Open Data cloud).

The RDF data model is also very natural for semantic reasoning (e.g., RDFS++ reasoning), machine learning and rule-base inference. Such analysis can be applied

Thousands of legacy systems

Huge integration projects

Languages: Nearly every languageDatabase: Relational- Oracle, RDB, NonStop SQL, DB2, Berkley, in memory

Business Systems, Operational Support Systems, Network Systems

Disparate data models

50 S.-C. Wu

directly on individual customers to achieve personalized and proactive services. That’s why Amdocs (the world’s largest billing service provider for Telco’s) chooses Semantic Technology for its next generation CRM system (AIDA) for its Telecom clients such as Bell Canada, Verizon and AT&T [1].

Fig. 4. AIDA System Architecture

The AIDA system consists of three main functional blocks (Figure 4):

1) Event Collector Here the system automatically extracts critical user events from very diverse sources with disparate data models, cleanses the data, and transforms (maps) them into semantic model (i.e., RDF and URI). For a telecom company with 80 million subscribers, it could generate 1 trillion RDF event triples every two months, an extreme scalability requirement for the AIDA architecture.

2) Decision Engine After the event triples are ingested into the triple database, a rule-based inference engine automatically classifies data of interest. For example, a customer may be classified as high credit risk or high retention risk. This classification process is run in the background controlled by a scheduler. Results of classification are stored back into the triple database as new properties of the entity being classified. Additionally, a Bayesian Belief Network (BBN) model that has been trained with historical data provides predictive analysis, forecasting potential consequence based on current observations (events). Both the rule-based inference and predictive analysis operate directly on the user model in the RDF triple database. This is the core of this new CRM system.

Events Decision Engine

ContainerContainer

ActionsSBA Application Server

“Sesame”

AllegroGraphTriple Store DB

EventIngestion

ScheduledEvents

Inference Engine

(Business Rules)

BayesianBelief

Network

Events

CRM

Operational Systems

Event Data Sources

Amdocs Event Collector

WorkBenchCRMRM

Amdocs Integration Framework

OMS

NW Web 2.0

AIDA

The CRM of Tomorrow with Semantic Technology 51

3) Action Support This block consists of two main functions, rule definition by business analysts and interface with existing CRM system in operation. A workbench is provided for business analysts to define business rules (e.g., what constitutes a customer with high-retention risk) by drag and drop. The workbench translates user defined rules into standard rule languages such as Prolog and Jess, which will be compiled and run by the system later. Additionally, all the inference and reasoning results are fed back into existing CRM system to help support staff to serve the customers correctly.

AIDA is currently being deployed in production at the call center of a major telecom company. According to an Amdocs study in a pilot deployment, AIDA provides significant operating cost savings by:

• Increases First Call Resolution by 15% • Reduce average handling time by 30% • Decrease training cost by 25%

This translates to a whopping 40% direct cost saving, besides the intangible but potentially even more significant benefits – good will and brand loyalty from the customers.

6 Summary

In summary, for a business to excel or even just to survive today, it must develop a comprehensive insight into its customer base and proactively and effectively serve them. Traditional BI tools and CRM systems cannot meet such requirements. Only a new CRM system based on a comprehensive user model with reasoning and inference capability can achieve such goals. With Semantic Technology, your business can realize such a future CRM system TODAY.

Reference

1. Guinn, B., Aasman, J.: Semantic Real Time Intelligent Decision Automation. In: Proceedings of Semantic Technologies for Intelligence, Defence, and Security Conference (STIDS 2010), pp. 125–128 (2010)