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
©2011 Brocade Communications Systems, Inc. 6
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
© 2011 Brocade Communications Systems, Inc.
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
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• 502 Structured Variables
© 2011 Brocade Communications Systems, Inc.
* 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
© 2010 Brocade Communications Systems, Inc.
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
© 2011 Brocade Communications Systems, Inc. 12
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
© 2011 Brocade Communications Systems, Inc. 13
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
© 2010 Brocade Communications Systems, Inc. 14
Contact InformationContact Information
S ChiSusan Chiu
Director, Customer Intelligence
Tel: (408) 333-2071, (415) 987-6069
email: [email protected]
www.brocade.com
© 2011 Brocade Communications Systems, Inc. 15
Appendix
©2011 Brocade Communications Systems, Inc. 16
Brocade EcosystemThe power of open solutions
HYPERVISOR
SERVER
HYPERVISOR
NETWORK
SERVER
NETWORK
SECURITY
STORAGE
17© 2012 Brocade Communications Systems, Inc.
Thank YouThank You
© 2011 Brocade Communications Systems, Inc. 18