DISCOVER & MONETIZE CUSTOMER INTELLIGENCE GOLD MINES … · Group Significant Concepts • Most...

DISCOVER & MONETIZE CUSTOMER INTELLIGENCE GOLD MINES TO IMPROVE GOLD MINES TO IMPROVE SALES PIPELINE & REVENUE

Susan Chiu

Director, Customer Intelligence

B d C i tiBrocade Communications

March 7 2012©2011 Brocade Communications Systems, Inc.

AgendaAgenda

• Brocade Overview

• Project Background and Objective

• Structured vs. Unstructured Data

• The Sales Funnel

• Analytic Framework

• Case Study

• Summary

Q & A• Q & A

©2011 Brocade Communications Systems, Inc. 2

Brocade Communications

• Pioneer and leader in data center networking

• Founded in 1995

• 4,500+ employees worldwide

• Headquartered in San Jose CA• Headquartered in San Jose, CA

• Operating in more than 160 countries

$2+ billi i l

3© 2012 Brocade Communications Systems, Inc.

• $2+ billion in annual revenue

Project Background, Objective, and Approach

• Brocade Customer Intelligence partners with Marketing and Sales to implement analytics-driven lead generation programsHi t i ll t t d d t h b tili d f l ti d li g N d t id tif dditi l d t t

Background

• Historically structured data has been utilized for analytic modeling. Need to identify additional data sources toincrease modeling power

• Lead note data collected in the CRM system has been identified for exploratory analysis because of the richness of itscontent

• Identify and extract key words, or so-called key concepts in unstructured lead notes that are highly correlated with lead conversion (i e improving

Objective Approach

• Acquire a sample of structured attributes and unstructured lead note attribute from the CRM (Customer Relationship System) system salesforce com for a correlated with lead conversion (i.e., improving

lead conversion rate). Concept variables are structureddata.

• Work with the Sales and Marketing teams to create standardized new attributes based on the above key concepts

Relationship System) system, salesforce.com for a particular region and time period

• Utilize SPSS Text Analytics to extract key concepts • Use Chi-Squared Automatic Interaction Detection, or CHAID, in SPSS Modeler to identify key structured attributes and concepts that improve lead conversion

• Salesforce.com• IBM SPSS Text Analytics 14.2• IBM SPSS Modeler 14 2

CRM System and Analytics Tools

© 2010 Brocade Communications Systems, Inc.

IBM SPSS Modeler 14.2

Structured and Unstructured Attributes Definition • Definition

• Structured Data: Attributes that are categorical or numeric

• Examples

• Industry (Categorical): Healthcare, Government, Retail, Education, etc.

• Number of employees (Numeric): 1005, 500, 25

• Unstructured Data: Open ended text attributes such as lead notes and blog comments

• The lead note section in SFDC is an open text area where a sales rep can enter information regarding his interactions with or his assessment of a particular lead. Examples*:

• 11.25 The company has a particularly strong relationship with C XYZ ki g th d l g t XYZ h l tCompany XYZ, ranking as the second largest XYZ channel partner

• He wants to virtualize the ports 2 ports to 4. CDE, EFG, JKL

• 2/20/11- Talked with Robert. XYZ shop not looking to switch vendors. / / p gJust purchased new core switches. Sent a follow up email with Gartner article.

©2011 Brocade Communications Systems, Inc. CONFIDENTIAL: 5

* Confidential Brocade information is masked with random terms like XYZ, CDE, etc.

Structured and Unstructured Attributes (Continued) (Continued)

• 7.15- David said he is in a mtg, asked me to call back next week ========== First Time WLAN: Number of Current APs:19**Number of Planned: 30=============

8/10-Build budgetary quote and present to customer. Set up 3rd conf / g y q p pcall for end of August. Spoke to David.They are not happy with their Company ABC switches and routers Core: ABC 1234Edge: ABC 5678Edge: ABC 5678

Budget process: Sept - Dec '10, then network refresh, early CY 2011 Set up conf call for July 30th at 9am to intro the brocade team


The Sales Funnel

Prospect Project focusis to identify MQL sub-

Marketing Qualified Lead

is to identify MQL subsegments with high likelihood of convertingto SQL or opportunities

(MQL)

Sales Q alified LeadSales Qualified Lead(SQL)

Opportunity

© 2011 Brocade Communications Systems, Inc. CONFIDENTIAL 7

Customer

Analytic Framework

Align with

Business Objective

Identify

Data SourcesIdentify Analytic

Approach

Prepare Unstructured

Data

Extract Key Terms or Concepts from Unstructured Data

•Structured and unstructured data in the CRM system, salesforce.com (SFDC)

• Text mining• CHAID for model building• CHAID or Chi‐squared Automatic Interaction Detection, is a classification tree method using chi‐square

• Extract lead note data and import it into the SPSS workbench in formats like EXCEL, text, etc.

• Leverage Concept Model Nuggets* in the text mining module of SPSS Modeler workbench•Select appropriate modeling criteria like max or min frequency

• Improve lead conversion rate

g qstatistics to identify optimal splits

C t Determine Whether to Group Key Concepts to

Categories

• Concept variables vs.

Create Standardized

Variables Based on Key Concepts or

Categories

Combine Standardized Concept or Category Variables with Other Standardized Variables

• Each record in the data

Build Analytical Models

• The dependent variable

Test and Rollout

• Create marketing C bi

pcategory variables: It depends on whether you have a comprehensive library and whether concept level data is predictive and more appropriate for “story

Each record in the data set represents an SQL (sales qualified lead). One customer may have multiple SQL’s. Structured variables and structured concept variables are columns in

• The dependent variable is categorical (Opportunity, Other, SQL) and the independent variables are numeric or categorical (some binary)

campaigns based on the key concepts that are predictive. For example, a campaign with an offer on a particular technology solution or a campaign targeting a

• Create binary structured concept variables (e.g., a variable called ‘concept_wireless’)

© 2011 Brocade Communications Systems, Inc. 8

8

pp p ytelling”

variables are columns in the dataset. particular competitor’s

customers or a vertical.

* Terms that are grouped under a concept: synonyms, extracted plural/singular terms, permuted terms, terms from fuzzy grouping, etc.

Concept ExamplesConcept ExamplesConcept Original Text in Lead Note

switches Switch Switchs Switcheswitches Switch, Switchs, Switche

opportunity size size of the opportunity

brocade solution brocade solutions, brocade's solution, brocade'ssolutions

data center data centers, data-center, datacenter, datacenters, data center co

windows server window servers, windows servers

timeframe time fram time frame time frames time frame

9

timeframe time fram, time frame, time frames, time-frame, timeframes


Case Study

• A sample data set for a particular geographic region and time period15 146 MQL (745 t iti 1 265 SQL 13 136* ith SQL t iti )

Data

• 15,146 MQLs (745 opportunities, 1,265 SQLs, 13,136* neither SQLs nor opportunities)• 7 structured categorical demographic variables and 1 unstructured variable (lead note)

•10,597 MQLs (511 opportunities, 872 SQLs, 9,214neither SQLs nor opportunities)

Model Building (~70% of the Data) Validation (~30% of the Data)

•4,549 MQLs (234 opportunities, 393 SQLs, 3,922neither SQLs nor opportunities)

Variables Analyzed

• 1 Unstructured Variable (Lead Note)

• 7 Structured Demographic Variables

• 495 Structured Binary Concept Variables

10

• 502 Structured Variables


* Including marketing qualified leads to be nurtured.

Group Significant Concepts

• Most significant concepts can be

Call to Action

concepts can be grouped into categories or dimensions

Competitive Landscape

Purchase Driver

Improve Lead

Conversion

dimensions

• Nine key categories (or dimensions) are

Pricing/Discount

Project Timeline

dimensions) are uncovered

• Picture an ideal

SolutionSelling

ExistingR l ti hi

Route toMarket

dialogue with a potential customer….

Follow UpRelationship


Partial Results of CHAID Model #1Partial Results of CHAID Model #1Concept Variables Contributing to High Lifts in Conversion

• The most powerful predictive

Competitive Landscape Concept B

The most powerful predictive variables are concepts derived from lead notes, not structured demographic variables

• Leads associated with notes that contain competitive landscape concept B or

Technology A

p pTechnology A have much higher conversion rates than the overall average


Gains Chart of CHAID Model #1Gains Chart of CHAID Model #1Double Digit Conversion Rates in Top Three Deciles

• Over 75% of the opportunities are captured in the top three deciles


SummarySummary

11 Information derived from unstructured data can be more powerful

than structured data in predicting conversion rate

22 Grouping concepts into categories can facilitate story telling of

your analytical results or design of future campaigns

33An optimal dialogue with a potential customer may be multi-

faceted involving topics like competitors, pricing, technology

solutions, and project timeline

Lead note analysis can be applied in two areas:44 Lead note analysis can be applied in two areas:

- Prioritize your sales team’s lead follow-up effort

- Design future campaigns or offers based on predictive concepts


Contact InformationContact Information

S ChiSusan Chiu

Director, Customer Intelligence

Tel: (408) 333-2071, (415) 987-6069

email: [email protected]

www.brocade.com


Appendix


Brocade EcosystemThe power of open solutions

HYPERVISOR

SERVER

HYPERVISOR

NETWORK

SERVER

NETWORK

SECURITY

STORAGE

17© 2012 Brocade Communications Systems, Inc.

Thank YouThank You


DISCOVER & MONETIZE CUSTOMER INTELLIGENCE GOLD MINES … · Group Significant Concepts • Most...

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