Leveraging Data for Demand Generation

Success

Background:

Since 1990, Winn Technology Group has supported over 800

technology firms with thousands of demand generation solutions.

Since 2001, WaveLength Market Analytics: Specializing in combining knowledge of technology markets,

products and services with data management & quantitative analysis

for strategies & programs that deliver superior results.

Speakers:

Kate Healy

Principal

Wavelength Analytics

19 years experience in technology

marketing

Geoffrey Swallow

President

Winn Technology Group

32 years experience in technology

marketing

Challenges:• Need to optimize marketing and sales budgets

• Data feast or data famine

• Prospect quantity vs. prospect quality

Agenda:

• Data Analytic Strategies• Pre-Campaign

• Real Time

• Post-Campaign • Data Strategies to Drive Quotas • Using Intelligence from Data

• More about the “Ideal Prospect” Algorithm

• Strategies for Sourcing and Managing Data

Pre-Campaign Analytics

• Validity

• Aligning expectation

• Refining

• Modeling

Target Universe Data Strategies

Pre-Campaign Case 1Fortune 500 Software Firm

• Program Objective: Telemarket to 200,171 contact records to promote User Conference.

• Pre-campaign data analysis identified 1,996 invalid records (1% of total database)

• Based on cost per dial, >$3,700 of campaign budget would have been spent calling invalid records.

• Total dials placed:! ! ! 249,138

• Cost per dial:! ! ! ! $1.86

• Post campaign analysis identified 56,171 invalid records representing 28% of total database.

• Cost of calling invalid records: >$104,478

User Conference Event Promotion Impact of Data Validity

•Develop on-going nurturing process, integrating lead cultivation with a regiment for data re-validation.

•Leverage lower cost maintenance procedures leveraging a funnel concept for data validation.

•Map internal data to third party for re-validation and or raising of confidence in existing prospects.

•When using third party data - pilot multiple sources (measure invalidity cost vs. data costs) (3.5% - 30%).

•Scrub data for duplication, competitors, customers, contact validity, bad fits (out of industry scope).

•Overall, minimize wasted dollars spent on activity due to irrelevant and inaccurate data.

Strategies

Pre-Campaign Case 2Leading Virtualization Firm

Aligning Expectation With Available Universe for > 20 City Road Show

•Develop realistic goals based upon expected % conversion and available universe.

•Compare existing data coverage on target universe with actual universe.

•Too small universe: Re-align expectations where universe is limited.

•Too large universe: Look for ways to stratify database based upon key attributes and BI to optimize.

Strategies

Pre-Campaign Case 3Refining Target Universe for Demand Generation and Lead Nurturing Initiative

Fortune 500 Collaboration Firm

• Program Objective: Develop demand generation/lead nurturing initiative to 525 target accounts.

• Developed scoring model to measure suspect value and lead status.

• Deployed several strategies to enhance data prior to campaign launch:

• Mapped target firms to Winn Enterprise Database for contact, demographic, and technology data.

• Conducted Internet Research to further enhance account knowledge,

• Identified > 3500 related entities linked to target accounts.

• > $800,000 booked opportunities converted to sales - additional 100 nurture leads in funnel.

•Developing intelligence prior to program launch can enhance messaging, targeting and prospect stratification.

•Intelligence gained prior to program launch also provided better territory alignment.

•Developing corporate relationship profiles assisted with cross and up sell opportunities.

What we learned

Real-Time Analytics

• Drive campaign success via the measure of key metrics

• Develop front-loaded metrics that can be measured during campaign.

• Develop key questions in call guide to help drive BI and key metrics.

• Leverage those metrics for innovative practices:

• Messaging - message testing

• Targeting

• Tracking

• Lead / Suspect scoring (Suspect Value / Sales Status).

• Overall and comparative demand center productivity by rep.

Real-Time Case 1Appointment Setting / Lead Generation / Lead Nurturing Program

VAR 500 Firm

• Program Objective: Develop sales-ready opportunities and nurturing leads for on-going initiative.

• Challenge: too much data (number of prospect contacts to company ratio).

• Winn analyzed the type of functions and titles that were driving higher lead rates.

• Winn pre-coded and isolated higher level target contacts and stratified the database.

• All other contacts were made viewable in account rep portal for use when referred.

•Reps were better focused on key accounts vs. canvassing too large of an audience.

•Campaign lead rates improved by 4X after re-aligning the data.

What we learned

Real-Time Case 2Cross-Industry Lead Generation Campaign

Emerging Technology Software Firm

• Program Objective: Develop sales ready opportunities and nurturing leads for on-going program.

• Client unaware of best market to focus on at program launch.

• Winn pre-loaded key data metrics including industry classification for all prospects selected for the campaign.

•Relative to resolved records, three industries represented 5X greater lead rate.

•Campaign adjusted to focus on remaining prospects in Manufacturing, Services, and Construction.

•The more coded data you can pre-load in a campaign, the more opportunities you have to analyze and shift program to drive results.

What we learned

Post-Campaign Analytics

• Leveraging knowledge gained from programs

• Key metrics established up-front

• Value of data collection and codification of tactical data

• Business / competitive intelligence

• Re-alignment of messaging and targeting

Post-Campaign Case 1Post-Campaign Analytics to Multiple Events

Fortune 500 Technology Distributor

• Objective: Analyze results of 21(on-site) events covering similar technologies over one year.

• Analysis of event success by:

• Event timing (day/time/season)

• Venue type

• Lead time for event promotion

• Technology Theme

• Based upon analysis, leverage knowledge gained to drive future initiatives.

•Special events had the lowest cost per registration (4:1).

•Events promoted with a 15+ day lead time out performed those with < days over 50%.

•Technology theme dramatically effected cost per registration (as high as 10 X).

What we learned

Post-Campaign Case 2Post-Campaign Analytics for Appointment Setting / Lead Generation Program

Global 100 Technology Firm

• Objective: Analyze results of demand generation initiative.

• Analysis of program conducted by:

• Data source lead rates

• Data source validity rates

• Opportunities generated vs. areas of interest

• Based upon analysis, leveraged knowledge gained to drive future initiatives.

•Although the average cost of an actionable lead = $151, list sources ranged from < $100 to > $1000.

•Re-focus of campaign and future targeting of higher yielding list sources.

•Messaging altered to focus on areas resonating with target audience.

What we learned

Modeling

• Data Strategies to Drive Quotas • Using Intelligence from Data

• More about the “Ideal Prospect” Algorithm

• Strategies for Sourcing and Managing Data

Cost-effective & Successful Customer Acquisition Using Intelligence from

Data - Why It’s Important

Targeting the RIGHT prospects within the organization

Making sure the message is going to resonate with the

target persona

Knowing and understanding

trends and buying transitions

Understanding motivators and

concerns of your target buyer

More about the “Ideal Prospect” Algorithm

• The Ideal Prospect algorithm is based on the relationship between revenue growth and employee growth.

• Developed based on primary research that segmented the enterprise market using some key attitudes and behaviors about enterprises technology needs, how they use it, and what they expect it to do for them.

• Leading segment in new technology & adoption, termed “Strategic IT Spenders”:• On average, higher revenue growth & profitability, composing approximately 24% of the global

enterprise market & existing in all countries and industries.

• Buy IT for productivity gains more than cost savings.

• Perceive their organizations to be highly dependent on real-time transactions in daily operations, and involve many different channels for both IT information and sales.

• Always on the lookout for new technologies that make them more efficient.""

Strategies for Sourcing and Managing Data

Project Goals, Budgets, & Timelines

Internal DataExternal 3rd Party Data

Primary Research

Manually Collected

Modeled Data

•Variety of possible places

• POS data from the channel

• CRM data

• Financial data

• Harte-Hanks

• Jigsaw

• D & B, Hoovers

• US census

• Postal zip maps

Case Study 1

• Problem: Build sales funnel

• Solution: Create highly targeted list of enterprises with 800 company names based on Ideal Prospect Algorithm, developed using primary research segmentation model.

• Focus 4-quarter demand generation program using demand center tactics to nurture list of OWNED names.

Data Center Switch Vendor

Case Study 1

• Filling the sales funnel tightly couples data and outbound marketing programs.

• The process begins with defining and building a highly targeted Enterprise target list to nurture.

• In partnership with the Demand Center, populate the contact list, and deliver a set amount of A, B and C leads.

Program Specifics

Case Study 1Results

WaveLength Approach

Standard eDM Approach

List Size Requirements based on a 2% response rate

1800 names 50,000 names

Cost Per Name for Rental 0.90

Total List Rental Fees $45,000

1 Inside sales rep to qualify leads based on 80 calls per day

12.5 days @ $320/day or $4.57/call = a total of 4000.00

WL Program $32,000

Sales-accepted leads 99 22.7

Cost per qualified sales lead $323.23 based on 80% of the project being completed- cost will

go down as reach our final lead goals

$2,158.75

TOTAL COSTS $32,000 $49,000

Collaborative Case Study

• Goal: Gain an in-depth understanding of the fast evolving cloud marketplace. We looked at buying trends, buyers and influencers personas, and the role of the channel.

• Some results: Nearly 42% of sample of medium to large enterprises (n=151) are deploying or testing some type of cloud model.

Winn-WaveLength Cloud Computing Primary Research

Collaborative Case Study

• Cloud buying habits of the early market: Target the right persona

• Internal IT plays largest role – from creating the strategy to developing, migrating and managing application.

• Top business management is highly involved for current cloud users.

• Role of trusted, third party partners in enterprise buying currently very limited among early cloud adopting enterprises.

• Systems Integrator and Consulting Partners.

• Software vendor partners.

• And NOT telco service providers or hardware vendors.

Cloud Computing Primary Research

Collaborative Case Study

• Created demand generation message based on motivators in why the cloud transition is happening:• Operating cost reduction

• Rapid application deployment/more nimble IT

• Addressed concerns about deploying cloud services in demand generation campaign:• Security breaches for applications and for stored data (a.k.a at rest)

• Perceptions of high start-up costs

• Lack of “trusted” 3rd party relationship

Cloud Computing Primary Research

Summing it up...• It takes a whole lot of leads to get to a few sales, so make sure

you’re targeting your RIGHT market, in the RIGHT way and getting the most out of your efforts.

• Data is key to your success in your campaign - for targeting, messaging, and addressing concerns in an evolving market.

• Enterprise Migration to the Cloud study is a perfect example of how research helps:

• Target top IT executives

• Mitigate adoption concerns, e.g., security issues, and high start-up costs

• Demonstrate how cloud solutions results in cost savings and faster application deployment

Thank you very much for joining us today!

Q/A

winntech.net wlanalytics.com