The document outlines 5 key steps to effectively manage data for an event marketing and sales campaign. It discusses: 1) Setting goals and data requirements based on event targets. 2) Conducting data discovery through audience mapping and segmenting existing internal data. 3) Performing a gap analysis to determine what additional data is needed. 4) Implementing data processes and acquisition methods to fill gaps, including primary and secondary research. 5) Measuring data performance against goals. The overall goal is to maximize chances of event success through strategic data management.
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...Big Cloud Analytics, Inc.
The document discusses how organizations can fund their big data and analytics initiatives through incremental revenues, cost savings, and more effective marketing spending. It outlines typical stages in a company's analytics journey and provides case studies showing how analytics has been used to increase revenues by 2.5x, decrease costs by $2,190, and improve customer satisfaction by 63%. The key message is that most companies are not fully leveraging the data they already have and that properly implemented analytics can significantly improve customer engagement and drive strong returns.
1. The document outlines strategies for making data actionable, including developing a big data strategy, setting milestones, identifying relevant data sources, and using data to create organizational efficiencies.
2. It emphasizes focusing marketing and data use on engagement, lead generation, and utilization metrics. Data should be used to move from broad approaches like blast marketing to more targeted segmented marketing.
3. Key steps include gathering, organizing, and identifying relevant data, then using data to track activities and outcomes, store results for future use, and continuously tweak marketing based on lessons learned.
Marketing Data Management: Squeeze More Revenue From Your Tech Stack #LLCSeriesG3 Communications
This document summarizes a webinar about squeezing more revenue from marketing technology stacks through effective data management. It discusses common data management challenges companies face and provides four exercises to improve data strategies: 1) enriching leads from events with a data partner, 2) customizing content using data fields, 3) verifying lead profiles ready for sales, and 4) analyzing customer data to define target audiences. The webinar emphasizes having a process to leverage technology data for business goals and ensuring data receives budget to properly define buyers.
Content marketing analytics: what you should really be doingDaniel Smulevich
My presentation from Digital Marketing Show 2014. #DMSLDN
A journey through web analytics processes, from setting up KPIs to integrating data sources and automating reports.
1) The document is a presentation about using data to drive insights and experiences. It discusses defining objectives, assessing relevant data sources, and generating insights and data outputs.
2) A key point is that data alone is not valuable - insights create meaning from data. Insights should challenge conventions and uncover underlying motivations.
3) Measurement is important to optimize performance and should be tied to objectives through KPIs and diagnostics. Tagging data properly allows tracing activities to understand what works.
Digital-Warriors-Marketing Roadmap with Big Data AnalyticsJaysonBowden
This document outlines a presentation on driving marketing programs with big data analytics. It discusses the evolution of analytics, provides a 4-step roadmap for implementing big data marketing, and reviews some key risks. The 4 steps are: 1) proof of concept, 2) datatification, 3) governance, and 4) consumerization. Each step provides critical people, process, and technology considerations. The presentation also reviews big data enablers and provides references for further information.
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...Big Cloud Analytics, Inc.
The document discusses how organizations can fund their big data and analytics initiatives through incremental revenues, cost savings, and more effective marketing spending. It outlines typical stages in a company's analytics journey and provides case studies showing how analytics has been used to increase revenues by 2.5x, decrease costs by $2,190, and improve customer satisfaction by 63%. The key message is that most companies are not fully leveraging the data they already have and that properly implemented analytics can significantly improve customer engagement and drive strong returns.
1. The document outlines strategies for making data actionable, including developing a big data strategy, setting milestones, identifying relevant data sources, and using data to create organizational efficiencies.
2. It emphasizes focusing marketing and data use on engagement, lead generation, and utilization metrics. Data should be used to move from broad approaches like blast marketing to more targeted segmented marketing.
3. Key steps include gathering, organizing, and identifying relevant data, then using data to track activities and outcomes, store results for future use, and continuously tweak marketing based on lessons learned.
Marketing Data Management: Squeeze More Revenue From Your Tech Stack #LLCSeriesG3 Communications
This document summarizes a webinar about squeezing more revenue from marketing technology stacks through effective data management. It discusses common data management challenges companies face and provides four exercises to improve data strategies: 1) enriching leads from events with a data partner, 2) customizing content using data fields, 3) verifying lead profiles ready for sales, and 4) analyzing customer data to define target audiences. The webinar emphasizes having a process to leverage technology data for business goals and ensuring data receives budget to properly define buyers.
Content marketing analytics: what you should really be doingDaniel Smulevich
My presentation from Digital Marketing Show 2014. #DMSLDN
A journey through web analytics processes, from setting up KPIs to integrating data sources and automating reports.
1) The document is a presentation about using data to drive insights and experiences. It discusses defining objectives, assessing relevant data sources, and generating insights and data outputs.
2) A key point is that data alone is not valuable - insights create meaning from data. Insights should challenge conventions and uncover underlying motivations.
3) Measurement is important to optimize performance and should be tied to objectives through KPIs and diagnostics. Tagging data properly allows tracing activities to understand what works.
Digital-Warriors-Marketing Roadmap with Big Data AnalyticsJaysonBowden
This document outlines a presentation on driving marketing programs with big data analytics. It discusses the evolution of analytics, provides a 4-step roadmap for implementing big data marketing, and reviews some key risks. The 4 steps are: 1) proof of concept, 2) datatification, 3) governance, and 4) consumerization. Each step provides critical people, process, and technology considerations. The presentation also reviews big data enablers and provides references for further information.
Using big data to increase the bottom line for vacation rental management com...Amy Hinote
The document discusses how vacation rental property managers can use big data to increase revenue. It defines big data and explains how data from various sources within a property management system can provide insights. Examples are given for how data can be used to optimize pricing, marketing, customer retention, and owner acquisition/retention. The key is collecting clean, accurate data from sources like the PMS, call center, website analytics, and using that data to determine objectives, identify metrics, find patterns, and take actions that increase bottom line revenue.
This document summarizes a presentation on leveraging data for demand generation. It discusses using pre-campaign analytics to refine target lists and messaging by validating data, aligning expectations with available prospects, and enhancing prospect profiles. Real-time analytics are used to optimize campaigns by measuring key metrics and adjusting targeting and messaging. Post-campaign analytics provide insights for future initiatives by analyzing results based on variables like event timing and data sources. Modeling techniques like an "ideal prospect" algorithm are also discussed for developing highly targeted lists. Case studies demonstrate how these data-driven strategies improved results.
[AIIM16] How Regulatory Data Can Set the Narrative for an Analytics OpportunityAIIM International
The document discusses how regulatory data can be used to create analytics opportunities. It defines regulatory data as structured data that firms are required to retain for compliance purposes. There are two main types of regulatory data: customer communications data like statements and reports, and transaction reporting data like logs and ledgers. The document argues that while this data is currently used only for compliance, it can be analyzed to create value for businesses and customers by transforming static documents into interactive experiences through personalized and predictive analytics. This allows firms to enhance customer relationships and better meet evolving customer expectations.
The document outlines seven critical considerations for developing a successful single customer view (SCV): 1) Getting data sources may be challenging due to different data ownership; 2) Getting cross-department commitment requires understanding different perspectives; 3) Choosing a fixed price or agile development model depends on project needs; 4) Allocating sufficient resources for data understanding is essential; 5) Determining the value of various data sources; 6) Three essential data sources are customer, transaction, and communication data; 7) Defining "customer" is complex for business-to-business contexts. Maintaining quality and proving value are also discussed.
A Path to Predictability at Scale - Sales Workshop for Foundation CapitalJoanne Chen
- how to assess product/market fit using the sales process
- how to design the first sales process and teach someone else to sell your product predictably
- how to move from qualitative sales to data-driven sales
- how to scale a sales org and use data to measure success
- a lot more!
The Data-Driven Event: Battle-Tested Data Insights for Event Professionals Hubb
The document discusses how event professionals can leverage data insights to improve events. It notes that while many collect data, it often goes unused. It recommends defining objectives and metrics before events to guide data collection and analysis. Examples show how to build dashboards to track objectives, audiences, meetings and more. Analyzing perceptions before and after can reveal shifts. The document advocates embracing data for more insightful, targeted and impactful events.
The document discusses data mining and data warehousing. It describes data mining as a technique that enables companies to discover patterns and relationships in data with a high degree of accuracy. Typical tasks for data mining include predicting customer responses, identifying opportunities for cross-selling products, and detecting fraud. The document also discusses why companies build marketing data warehouses - to more efficiently and profitably serve customers by integrating customer data from various sources and analyzing purchase histories. Key considerations for ensuring success include having the right support team, quantifying benefits, and prioritizing deliverables in a phased approach.
Making your analytics talk business | Big Data DemystifiedOmid Vahdaty
MAKING YOUR ANALYTICS TALK BUSINESS
Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders' expectations, how to shift from metrics to "real" KPIs, and how to approach an analysis in order to create real impact.
This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you're most welcome as well: join us to share your experiences so that we can all learn from each other and improve!
Bios:
Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha.
Rplus offers analytics solution for retail industry through cloud based DemandSense application and big data analytics platform. Retail companies can leverage data to improving the profitability and efficiency of operations at a low cost in a faster timeframe.
This document discusses measuring social media return on investment (ROI). It begins by outlining the benefits of social media for businesses in communicating with customers and saving costs over traditional channels. It then notes that most organizations do not measure social media ROI due to difficulties in tracking metrics and setting clear objectives. The document provides examples of key social media metrics like fan growth, website visits, and transactions. It emphasizes focusing on metrics most important to the business and calculating the value of each. Finally, it discusses building business cases by illustrating short and long-term impacts to justify increased resources for social media efforts.
How Customer Intelligence Will Future Proof Your Event PortfolioBear Analytics
Virtual events provide event organizers with valuable customer data that can be used to future-proof their event portfolios. Collecting the right data at registration and through various engagement metrics allows organizers to understand attendee demographics, interests, and behaviors. This customer intelligence enables organizers to personalize messaging, showcase program impact to sponsors, and identify new markets. While virtual events are disruptive, those who leverage customer data to their advantage can differentiate their offerings and position themselves for success.
Web analytics involves measuring, collecting, analyzing, and reporting online data to understand how a website is used and how to optimize usage. The goals are to better understand users, make data-driven decisions, improve the website, and boost conversions and sales. However, with data coming from many sources, the key challenge is taking action. A structured approach is needed to identify the metrics that matter and ignore unimportant ones, using actionable metrics to drive changes.
This document provides an overview of the Accelerator App product. It summarizes the key features as:
1) Tools to help with coaching, mentoring, meeting scheduling, and program management for accelerators and startups.
2) Tracking of startup progress through coaching notes, setting of metrics, and productivity tools.
3) Scheduling of meetings and events integrated with Google and Microsoft calendars.
4) Creation and management of internal events, opportunities, and customized events for accelerators.
The marketing plan summarizes strategies to target the West Coast of the USA for an offshore IT services provider. It includes a situation analysis and identifies target services. The budget focuses on understanding current spending. The target market is California and Washington for their advantages of being open to offshoring. The marketing strategy includes pull strategies like advertising, press releases and customer events as well as push strategies like events, branding, and mail campaigns. Execution details activities for each strategy with goals of generating qualified leads and converting accounts. Implementation covers resource needs and targets. Financial projections include assumptions, pro forma statements, and break even analysis. Scenarios predict the future and plans are outlined quarterly.
INO 585 Group 3 Case Study - Target.pptxNihalNaveen
Target Corporation is an upscale discount retailer providing high-quality merchandise at attractive prices. It has 1,774 stores in the US and was founded in 1902. Target uses data analytics and predictive modeling extensively. It analyzes customer purchase data to personalize offers, which led to controversy over predicting a customer's pregnancy. Target continues investing in analytics, hiring experienced leaders from competitors to develop analytical capabilities. Recommendations include benchmarking analytical maturity, integrating data across functions, and emphasizing tools and dashboards.
The document analyzes the market opportunity for vertical portals and Aeneid's product offerings. It finds that the number of vertical portals is increasing, especially in high tech, finance, and healthcare. Portals need new revenue streams and tools to manage increasing content. The best opportunities for Aeneid are in developing features to differentiate sites, increase user engagement, and make money for portals via a revenue sharing model without sharing customer data. It recommends Aeneid partner with portals and content toolmakers to create research services.
Banalytics - Monetizing corporate big data | InstareaMatej Misik
How to use corporate big data for external applications, remain legally and ethically compliant and create a solution with clear public good? At the marketing edition of Banalytics in Bratislava, Matej Misik shared our approach to big data monetization for telcos, banks and other data rich industries.
Instarea is a "laboratory" for innovative big data monetization ideas within the international Adastra group. A young committed team, fresh thinking and a lust for adventure define us as a company. We yearn to change the world for the better through data.
More Related Content
Similar to PPA Data for Event Professionals_Preso_JW
Using big data to increase the bottom line for vacation rental management com...Amy Hinote
The document discusses how vacation rental property managers can use big data to increase revenue. It defines big data and explains how data from various sources within a property management system can provide insights. Examples are given for how data can be used to optimize pricing, marketing, customer retention, and owner acquisition/retention. The key is collecting clean, accurate data from sources like the PMS, call center, website analytics, and using that data to determine objectives, identify metrics, find patterns, and take actions that increase bottom line revenue.
This document summarizes a presentation on leveraging data for demand generation. It discusses using pre-campaign analytics to refine target lists and messaging by validating data, aligning expectations with available prospects, and enhancing prospect profiles. Real-time analytics are used to optimize campaigns by measuring key metrics and adjusting targeting and messaging. Post-campaign analytics provide insights for future initiatives by analyzing results based on variables like event timing and data sources. Modeling techniques like an "ideal prospect" algorithm are also discussed for developing highly targeted lists. Case studies demonstrate how these data-driven strategies improved results.
[AIIM16] How Regulatory Data Can Set the Narrative for an Analytics OpportunityAIIM International
The document discusses how regulatory data can be used to create analytics opportunities. It defines regulatory data as structured data that firms are required to retain for compliance purposes. There are two main types of regulatory data: customer communications data like statements and reports, and transaction reporting data like logs and ledgers. The document argues that while this data is currently used only for compliance, it can be analyzed to create value for businesses and customers by transforming static documents into interactive experiences through personalized and predictive analytics. This allows firms to enhance customer relationships and better meet evolving customer expectations.
The document outlines seven critical considerations for developing a successful single customer view (SCV): 1) Getting data sources may be challenging due to different data ownership; 2) Getting cross-department commitment requires understanding different perspectives; 3) Choosing a fixed price or agile development model depends on project needs; 4) Allocating sufficient resources for data understanding is essential; 5) Determining the value of various data sources; 6) Three essential data sources are customer, transaction, and communication data; 7) Defining "customer" is complex for business-to-business contexts. Maintaining quality and proving value are also discussed.
A Path to Predictability at Scale - Sales Workshop for Foundation CapitalJoanne Chen
- how to assess product/market fit using the sales process
- how to design the first sales process and teach someone else to sell your product predictably
- how to move from qualitative sales to data-driven sales
- how to scale a sales org and use data to measure success
- a lot more!
The Data-Driven Event: Battle-Tested Data Insights for Event Professionals Hubb
The document discusses how event professionals can leverage data insights to improve events. It notes that while many collect data, it often goes unused. It recommends defining objectives and metrics before events to guide data collection and analysis. Examples show how to build dashboards to track objectives, audiences, meetings and more. Analyzing perceptions before and after can reveal shifts. The document advocates embracing data for more insightful, targeted and impactful events.
The document discusses data mining and data warehousing. It describes data mining as a technique that enables companies to discover patterns and relationships in data with a high degree of accuracy. Typical tasks for data mining include predicting customer responses, identifying opportunities for cross-selling products, and detecting fraud. The document also discusses why companies build marketing data warehouses - to more efficiently and profitably serve customers by integrating customer data from various sources and analyzing purchase histories. Key considerations for ensuring success include having the right support team, quantifying benefits, and prioritizing deliverables in a phased approach.
Making your analytics talk business | Big Data DemystifiedOmid Vahdaty
MAKING YOUR ANALYTICS TALK BUSINESS
Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders' expectations, how to shift from metrics to "real" KPIs, and how to approach an analysis in order to create real impact.
This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you're most welcome as well: join us to share your experiences so that we can all learn from each other and improve!
Bios:
Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha.
Rplus offers analytics solution for retail industry through cloud based DemandSense application and big data analytics platform. Retail companies can leverage data to improving the profitability and efficiency of operations at a low cost in a faster timeframe.
This document discusses measuring social media return on investment (ROI). It begins by outlining the benefits of social media for businesses in communicating with customers and saving costs over traditional channels. It then notes that most organizations do not measure social media ROI due to difficulties in tracking metrics and setting clear objectives. The document provides examples of key social media metrics like fan growth, website visits, and transactions. It emphasizes focusing on metrics most important to the business and calculating the value of each. Finally, it discusses building business cases by illustrating short and long-term impacts to justify increased resources for social media efforts.
How Customer Intelligence Will Future Proof Your Event PortfolioBear Analytics
Virtual events provide event organizers with valuable customer data that can be used to future-proof their event portfolios. Collecting the right data at registration and through various engagement metrics allows organizers to understand attendee demographics, interests, and behaviors. This customer intelligence enables organizers to personalize messaging, showcase program impact to sponsors, and identify new markets. While virtual events are disruptive, those who leverage customer data to their advantage can differentiate their offerings and position themselves for success.
Web analytics involves measuring, collecting, analyzing, and reporting online data to understand how a website is used and how to optimize usage. The goals are to better understand users, make data-driven decisions, improve the website, and boost conversions and sales. However, with data coming from many sources, the key challenge is taking action. A structured approach is needed to identify the metrics that matter and ignore unimportant ones, using actionable metrics to drive changes.
This document provides an overview of the Accelerator App product. It summarizes the key features as:
1) Tools to help with coaching, mentoring, meeting scheduling, and program management for accelerators and startups.
2) Tracking of startup progress through coaching notes, setting of metrics, and productivity tools.
3) Scheduling of meetings and events integrated with Google and Microsoft calendars.
4) Creation and management of internal events, opportunities, and customized events for accelerators.
The marketing plan summarizes strategies to target the West Coast of the USA for an offshore IT services provider. It includes a situation analysis and identifies target services. The budget focuses on understanding current spending. The target market is California and Washington for their advantages of being open to offshoring. The marketing strategy includes pull strategies like advertising, press releases and customer events as well as push strategies like events, branding, and mail campaigns. Execution details activities for each strategy with goals of generating qualified leads and converting accounts. Implementation covers resource needs and targets. Financial projections include assumptions, pro forma statements, and break even analysis. Scenarios predict the future and plans are outlined quarterly.
INO 585 Group 3 Case Study - Target.pptxNihalNaveen
Target Corporation is an upscale discount retailer providing high-quality merchandise at attractive prices. It has 1,774 stores in the US and was founded in 1902. Target uses data analytics and predictive modeling extensively. It analyzes customer purchase data to personalize offers, which led to controversy over predicting a customer's pregnancy. Target continues investing in analytics, hiring experienced leaders from competitors to develop analytical capabilities. Recommendations include benchmarking analytical maturity, integrating data across functions, and emphasizing tools and dashboards.
The document analyzes the market opportunity for vertical portals and Aeneid's product offerings. It finds that the number of vertical portals is increasing, especially in high tech, finance, and healthcare. Portals need new revenue streams and tools to manage increasing content. The best opportunities for Aeneid are in developing features to differentiate sites, increase user engagement, and make money for portals via a revenue sharing model without sharing customer data. It recommends Aeneid partner with portals and content toolmakers to create research services.
Banalytics - Monetizing corporate big data | InstareaMatej Misik
How to use corporate big data for external applications, remain legally and ethically compliant and create a solution with clear public good? At the marketing edition of Banalytics in Bratislava, Matej Misik shared our approach to big data monetization for telcos, banks and other data rich industries.
Instarea is a "laboratory" for innovative big data monetization ideas within the international Adastra group. A young committed team, fresh thinking and a lust for adventure define us as a company. We yearn to change the world for the better through data.
Similar to PPA Data for Event Professionals_Preso_JW (20)
5. No
of
Delegates
required…
x50
for
your
data
qty
requirement
Example
200
dels
x50
=
10,000
Current
database
7,000
unique
records
Budget:
the
magic
number
=
50
To
build…
3,000
unique
records
Average
cost
per
contact
£1.25
Data
investment
=
£3,750
6. No
of
Visitors
required…
x50
for
your
data
qty
requirement
Example
700
visitors
x50
=
35,000
Current
database
30,000
unique
records
Budget:
the
magic
number
=
50
To
build…
5,000
unique
records
Average
cost
per
contact
£1
Data
investment
=
£5,000
7.
1
Financials:
Data
Goals
&
Requirements
• Type
of
Event:
Launch
• Data
Project:
Event
Specific
i.
Start
at
the
end:
define
event
goals
&
clarify
expecta,ons
ii.
Numbers
onsite
&
revenue:
delegate
&
sponsorship
For
example…
a
typical
launch
event:
• 100
dels
/
£80,000
(£800
del
yield)
/
£100k
Sponsorship
• 20%
benchmark
of
dels/rev
to
acquire
thru
databuild
(20
dels/£16,000)
• Qty
of
new
data
(expanding
on
exis4ng
+
new
outright)
required
to
hit
this
campaign
sub-‐goal?
=
3,000
qty
data
requirement.
8.
2
Data
Discovery:
Targe,ng,
Mapping
&
Segmenta,on
2a)
Event
specific
audience:
who
precisely
are
we
targe,ng?
Planning
&
Prepara,on
10.
2
Data
Discovery:
Targe,ng,
Mapping
&
Segmenta,on
2b)
Internals:
what
internal
data
do
we
have?
Match
current
internal
data
to
target
audience
Planning
&
Prepara,on
11.
Internals
audit…
12.
2
Data
Discovery:
Targe,ng,
Mapping
&
Segmenta,on
2c)
Segment
final
data
sources
and
communicate
to
the
new
data
accordingly,
using
marke4ng
&
sales
channels.
Planning
&
Prepara,on
13. Where
and
how
do
I
invest
in
data
for
a
specific
event?
3
Cri,cal
Gap
Analysis:
Plugging
the
Gap
3a)
Internal:
primary
data
research
• In-‐house
or
Outsourced
(usually
the
laber)
• Brief
data
company
• Allow
4-‐6
wks
for
turnaround.
If
comple4on
8wks
or
less,
no-‐go
• Agree
contact
channels:
phone/email/mail/fax
ie.
par4al
or
complete
• Agree
level
of
research
-‐
web
research
only
and/or
phone
verifica4on
• Agree
expected
conversion
rates,
base
on
75%±
comple4on.
If
1,250
names
required,
send
1,325
• QC:
Ask
for
a
sample
ager
1wk
to
check
on
track
• Agree
phase
comple4on
dates
if
urgent,
ie.
1-‐3
batches.
14. Where
and
how
do
I
invest
in
data
for
a
specific
event?
3
Cri,cal
Gap
Analysis:
Plugging
the
Gap
3b)
External:
extend
data
reach
beyond
internal
data
only
• Content
marke4ng
/
lead
genera4on
(PR,
Partners,
PPC
etc)
• Referrals:
marke4ng
specific
campaign,
speakers
&
telesales
research
• List
swaps:
with
partners,
other
publishers
and
events
co’s
–
why
not!
• Industry
lists:
Top
company
lists/co
rankings/directories/awards
shortlists
&
winners
–
research
co’s,
target
4tles
• Sponsor/Exhibitor
‘wishlists’
• Social
media
(Linkedin
&
Twiber
research:
automated
&
human
controlled
tools)
• Associa4ons
(Published
member
lists...)
• List
rental/purchase
(Beber
for
top
line,
generic
4tles,
not
complexity).
15. 4
Establishing
a
process
of
data
collec,on,
upda,ng,
cleansing
&
building
4a)
Data
Collec,on:
• Always
capture
full
details
at
point
of
registra4on
• Important
capture
approving
managers
and
PA’s
-‐
important
influencers
who
approve
budgets
&
manage
dec-‐makers’
diaries
• Use
your
data
team’s
data
capture
template
for
import
-‐
ensure
all
codes
and
naming
conven4ons
match
the
database
• If
star4ng
from
scratch,
agree
&
create
relevant
demographics
for
your
market.
Don’t
underes4mate
this
project,
repercussions
later
-‐
what
info
is
non-‐nego4able?
Ie.
priori4se
‘Must-‐Have’s
vs.
‘Nice-‐to-‐Have’s.’
• Quality
checks.
16. 4
Establishing
a
process
of
data
collec,on,
upda,ng,
cleansing
&
building
4b)
Cleansing
&
Upda,ng:
• Ensure
all
data
that’s
been
updated
and
captured
throughout
the
campaign
from
various
sources
&
channels
is
imported
back
into
the
database
within
1wk
-‐
automated
or
manual
process
depending
on
your
CRM/database
• Essen4al
that
all
fields
match
the
database
format,
otherwise
this
won’t
get
done
17. 4
Establishing
a
process
of
data
collec,on,
upda,ng,
cleansing
&
building
4c)
Databuild:
• Approach:
numerous
op,ons!
i)
Work
with
‘refreshing’
and
‘expanding’
on
your
exis,ng
data,
eg.
Update
‘Goneaways’
&
‘Replacements’
(Refresh)
&
add
new,
relevant
‘4tles’
(Expansion)
ii)
For
new
data,
expand
on
companies
not
in
the
db,
thru
lead
genera4on
&
build
from
scratch
using
iden4fied
data
gaps,
plus
purchase
lists
(if
you
must!)
• Outsourced:
Communica4on,
brief
and
buy-‐in
is
key
-‐
get
the
right
‘fit’
with
a
genuine
ouqit
that
clearly
understands
your
target
market
&
wider
event
goals
• In-‐house:
Setup
rigid
process
and
KPIs,
incen4vise.
Must
be
efficient
for
good
ROI.
18. • Review
del
list
weekly,
ensure
new
data
is
matching
booking
profiles
• Track
and
measure
new
data
lists,
through
key
channels:
website
traffic
&
conversions,
telesales,
e-‐marke4ng,
direct
mail…
• Demonstrate
ROI:
Report
results
and
final
data
performance
within
1-‐week
of
the
event
–
good
prac4ce
for
historical
analysis
&
securing
future
investment
• Compile
an
annual
performance
dashboard
for
mul4ple
events,
to
support
future
benchmarking
&
decision
making.
5
On-‐going
analysis
of
data
performance
and
dynamic
data
management
during
an
event
marke,ng
campaign
19. Summary
–
Top
Takeaways
þ
Plan
from
6mths
out,
finish
4mths
out
þ
Capture
approving
managers
&
PA’s
þ
Data
volume:
more
for
events
vs.
subs
þ
Capture
all
contact
details
at
registra4on
þ
Consistently
monitor
the
delegate
list
þ
Be
crystal
clear
who
you
are
targe4ng
þ
Budget
Magic
Number
=
50!