This document outlines an agenda for a workshop on organizational data transformation. The agenda includes exploring data transformation lessons learned through case studies, reviewing lessons learned, and discussing how to put the lessons into practice. The case studies will focus on challenges of introducing data-driven practices into a sales environment and critical success factors for technical, behavioral, and organizational change. The document provides context on one case study involving challenges of helping sales reps manage their territories using limited data and time. It summarizes findings and outcomes from three cycles of the case study work.
Live it - or leave it! Returning your investment into AgileChristian Hassa
Keynote at Agile Testing Days Berlin 2013
If you’re involved with software development, there is probably no way you can ignore it anymore: the agile approach. With everyone talking about it, there is a certain pressure to adopt agile methods. This brings with it the danger of introducing a bunch of practices without placing enough emphasis on the two main success factors: continuously improving software and continuously improving teams.
The latter is usually driven more or less automatically by the self-interest of the directly affected individuals – after all, nobody deliberately wants to be inefficient. "Continuously improving software" on the other hand will almost certainly go wrong at first, because trust and feedback are much harder to establish between stakeholders (customers, team) than within a team. This often leads to efficient teams building the wrong product, or, even worse, just investing into iterative delivery without enjoying any of its benefits.
Efficiency is therefore just one component for ensuring a good return on investment when adopting Agile. In this talk, I want to focus on the other part – effectiveness – and how it impacts on the way teams collaborate with their customers. I'll introduce a few techniques (Story Mapping, Specification-By-Example) that support this change and present examples from past projects in the financial and public sector where they proved successful.
Alliance 2017 - How to Plan a Pain-Free Upgrade or Transition to the CloudSparkrock
Presented by Diana Budreau on January 27th, 2017.
Are you considering moving your NAV or CRM solution to the cloud? This session will walk you through the steps you'll need to take to make sure it's the right decision, get your organization ready, and build a project plan to ensure that the move is a success.
Detecting Malicious Websites using Machine LearningAndrew Beard
We present a set of newly tuned algorithms that can distinguish between malicious and non-malicious websites with a high degree of accuracy using Machine Learning (ML). We use the Bro IDS/IPS tool for extracting the SSL certificates from network traffic and training the ML algorithms.
The extracted SSL attributes are then loaded into multiple ML frameworks such as Splunk, AWS ML and we run a series of classification algorithms to identify those attributes that correlate with malicious sites.
Our analysis shows that there are a number of emerging patterns that even allow for identification of high-jacked devices and self-signed certificates. We present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms.
3decision®: Bringing structural data analytics to the massesLaura Berry
Presented at the Global Pharma R&D Informatics Congress. To find out more, visit:
www.global-engage.com
Rational structure based drug design techniques are still used in >50% of all drug discovery projects. They strongly rely on structural data for proteins and protein:ligand complexes. However, widespread adoption of these techniques is hindered by the inconsistent data persistence and the complexity of analysing structural data. In this presentation, Peter Schmidtke from Discgine discusses 3decision®: a structural analytics platform aiming to facilitate and speed up the use of structural data.
There’s a massive difference between teams that rock and those that just don't. Not only do the teams that rock deliver some phenomenal, off-the-page results, they are a joy to work with and be part of. These teams act like magnets for more amazing people, deliver remarkable value for customers and inspire action in others.
This session explored the ideas beneath the Open Leader Method(TM), a unique leadership programme for leaders in IT.
Examples of procurement and payroll tests using SAP data in IDEA from a previous User Group. Recommendations are also made taken from the council's own experiences.
Mark Graban SHS 2014: Two Data Points Are Not a Trend: Using SPC to Manage Be...Mark Graban
Healthcare leaders often make bad decisions due to a lack of statistical understanding. This session will remind attendees that simple comparisons of two data points or comparisons to goals and targets can be misleading. Control charts allow us to better validate project success and make better ongoing management decisions.
It’s far too easy for improvement facilitators to draw incorrect conclusions about the success of their Lean event or Six Sigma project if they are simply comparing before and after performance. Likewise, healthcare leaders make bad decisions when they are likewise comparing two data points (today versus a previous month or year or today versus a target).
Basic Statistical Process Control (SPC) methods, like control charts, are a simple and proven alternative.
Key Learning Objectives
1) Understand some of the common pitfalls in the creation and use of performance measures in various healthcare settings
2) See statistical chart analysis methods that allow for the best management decision making, such as knowing if we are improving and if a "bad day" requires investigation or if it is merely "noise" in the system's performance
3) Connect key principles of Lean management and the Deming philosophy into modern KPI and metrics management
By the end of this session attendees will
1) Understand the importance of "control charts" for management decision making
2) Be able to create and interpret a basic management control chart
3) Know of other resources for more learning
Mark Graban is author of the Shingo-Award winning book "Lean Hospitals: Improving Quality, Patient Safety, and Employee Engagement." Mark is also co-author, with Joe Swartz, of "Healthcare Kaizen: Engaging Front-Line Staff in Sustainable Continuous Improvements" (also a Shingo recipient) and "The Executive Guide to Healthcare Kaizen."
He serves as a consultant to healthcare organizations through his company, Constancy, Inc and is also the Chief Improvement Officer of the technology company KaiNexus.
Mark has a B.S. in Industrial Engineering from Northwestern University and an M.S. in Mechanical Engineering and an M.B.A. from the Massachusetts Institute of Technology’s Leaders for Global Operations Program. Mark and his wife live in San Antonio, Texas.
3PLs are a virtually perfect competitive business model. With highly variable costs to revenue, it is challenging to make a 3PL company thrive. Here is some research we have done with Lean Transit to achieve remarkable progress towards making 3PLs more profitable.
Why do Data and Analytics struggle in large organizations? This presentation explores the structural and causal issues at play through the lens of 'systems thinking' and 'business dynamics'.
Live it - or leave it! Returning your investment into AgileChristian Hassa
Keynote at Agile Testing Days Berlin 2013
If you’re involved with software development, there is probably no way you can ignore it anymore: the agile approach. With everyone talking about it, there is a certain pressure to adopt agile methods. This brings with it the danger of introducing a bunch of practices without placing enough emphasis on the two main success factors: continuously improving software and continuously improving teams.
The latter is usually driven more or less automatically by the self-interest of the directly affected individuals – after all, nobody deliberately wants to be inefficient. "Continuously improving software" on the other hand will almost certainly go wrong at first, because trust and feedback are much harder to establish between stakeholders (customers, team) than within a team. This often leads to efficient teams building the wrong product, or, even worse, just investing into iterative delivery without enjoying any of its benefits.
Efficiency is therefore just one component for ensuring a good return on investment when adopting Agile. In this talk, I want to focus on the other part – effectiveness – and how it impacts on the way teams collaborate with their customers. I'll introduce a few techniques (Story Mapping, Specification-By-Example) that support this change and present examples from past projects in the financial and public sector where they proved successful.
Alliance 2017 - How to Plan a Pain-Free Upgrade or Transition to the CloudSparkrock
Presented by Diana Budreau on January 27th, 2017.
Are you considering moving your NAV or CRM solution to the cloud? This session will walk you through the steps you'll need to take to make sure it's the right decision, get your organization ready, and build a project plan to ensure that the move is a success.
Detecting Malicious Websites using Machine LearningAndrew Beard
We present a set of newly tuned algorithms that can distinguish between malicious and non-malicious websites with a high degree of accuracy using Machine Learning (ML). We use the Bro IDS/IPS tool for extracting the SSL certificates from network traffic and training the ML algorithms.
The extracted SSL attributes are then loaded into multiple ML frameworks such as Splunk, AWS ML and we run a series of classification algorithms to identify those attributes that correlate with malicious sites.
Our analysis shows that there are a number of emerging patterns that even allow for identification of high-jacked devices and self-signed certificates. We present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms.
3decision®: Bringing structural data analytics to the massesLaura Berry
Presented at the Global Pharma R&D Informatics Congress. To find out more, visit:
www.global-engage.com
Rational structure based drug design techniques are still used in >50% of all drug discovery projects. They strongly rely on structural data for proteins and protein:ligand complexes. However, widespread adoption of these techniques is hindered by the inconsistent data persistence and the complexity of analysing structural data. In this presentation, Peter Schmidtke from Discgine discusses 3decision®: a structural analytics platform aiming to facilitate and speed up the use of structural data.
There’s a massive difference between teams that rock and those that just don't. Not only do the teams that rock deliver some phenomenal, off-the-page results, they are a joy to work with and be part of. These teams act like magnets for more amazing people, deliver remarkable value for customers and inspire action in others.
This session explored the ideas beneath the Open Leader Method(TM), a unique leadership programme for leaders in IT.
Examples of procurement and payroll tests using SAP data in IDEA from a previous User Group. Recommendations are also made taken from the council's own experiences.
Mark Graban SHS 2014: Two Data Points Are Not a Trend: Using SPC to Manage Be...Mark Graban
Healthcare leaders often make bad decisions due to a lack of statistical understanding. This session will remind attendees that simple comparisons of two data points or comparisons to goals and targets can be misleading. Control charts allow us to better validate project success and make better ongoing management decisions.
It’s far too easy for improvement facilitators to draw incorrect conclusions about the success of their Lean event or Six Sigma project if they are simply comparing before and after performance. Likewise, healthcare leaders make bad decisions when they are likewise comparing two data points (today versus a previous month or year or today versus a target).
Basic Statistical Process Control (SPC) methods, like control charts, are a simple and proven alternative.
Key Learning Objectives
1) Understand some of the common pitfalls in the creation and use of performance measures in various healthcare settings
2) See statistical chart analysis methods that allow for the best management decision making, such as knowing if we are improving and if a "bad day" requires investigation or if it is merely "noise" in the system's performance
3) Connect key principles of Lean management and the Deming philosophy into modern KPI and metrics management
By the end of this session attendees will
1) Understand the importance of "control charts" for management decision making
2) Be able to create and interpret a basic management control chart
3) Know of other resources for more learning
Mark Graban is author of the Shingo-Award winning book "Lean Hospitals: Improving Quality, Patient Safety, and Employee Engagement." Mark is also co-author, with Joe Swartz, of "Healthcare Kaizen: Engaging Front-Line Staff in Sustainable Continuous Improvements" (also a Shingo recipient) and "The Executive Guide to Healthcare Kaizen."
He serves as a consultant to healthcare organizations through his company, Constancy, Inc and is also the Chief Improvement Officer of the technology company KaiNexus.
Mark has a B.S. in Industrial Engineering from Northwestern University and an M.S. in Mechanical Engineering and an M.B.A. from the Massachusetts Institute of Technology’s Leaders for Global Operations Program. Mark and his wife live in San Antonio, Texas.
3PLs are a virtually perfect competitive business model. With highly variable costs to revenue, it is challenging to make a 3PL company thrive. Here is some research we have done with Lean Transit to achieve remarkable progress towards making 3PLs more profitable.
Why do Data and Analytics struggle in large organizations? This presentation explores the structural and causal issues at play through the lens of 'systems thinking' and 'business dynamics'.
Simple Principles for Complex Data-Led Organisational TransformationBarry Magee
Digital Transformation Lab - Best of Practitioner Research - Jun 2021 - Barry Magee
I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results.
https://www.linkedin.com/in/barrymagee/
Culture Hacking with Data - front line experiences in Data Driven TransformationBarry Magee
UCC PGDip in Innovation Studies - Feb 2021 - Barry Magee
I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results.
https://www.linkedin.com/in/barrymagee/
Data Strategy for Digital Sales : Case Study & Best PracticeBarry Magee
Citrix Peer Exchange : Indeed.com - Oct 2020 - Barry Magee
I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results.
https://www.linkedin.com/in/barrymagee/
Data Driven Customer Engagement: Workflow and Feedback SystemsBarry Magee
Citrix Peer Exchange : Dun & Bradstreet - Jul 2020 - Barry Magee
I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results.
https://www.linkedin.com/in/barrymagee/
Intelligent Tooling for (Digital) SalesBarry Magee
Sales Institute - Nov 2017
I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results.
https://www.linkedin.com/in/barrymagee/
How to structure, implement and evaluate an innovation management programmeBarry Magee
How to structure, implement and evaluate an Innovation Management Programme. Lessons Leant from IBM and its Collaboration and Technology Innovation Programmes
Data Driven Transformation for Sales - SMART Territory ManagementBarry Magee
Design, lead and implement framework to harness enterprise, organisational and external data to create a data driven territory management programme for IBM Digital Sales Europe.
The aim is to embed an end-to-end data driven approach to cleint engagement across all sales lines based on a prototype methodology that has undergone 4 iterations with significant user-led enhancements over 2 years in a pilot business unit.
The framework aggregates and collates data across a wide range of sources, integrates and delivers sales focussed insight back to end users. It uses both standard analytics models as well as innovateive sales expertise codification to rank clients for 'next best customer' selection.
Results consistently show that the data-driven approach delivers 4x the average lead conversion rate for traditional approaches within a sales environment and that 80% of new business opportunities come from clients and offerinsg that lie outside traditional 'hunting' approaches.
In doing so, the sales team reduce the risk within business forecasting by identifying and winning leads outside the traditional 'top' customers where sales tend to continually farm for revenue.
Design and deliver a startup series across employees in all IBM Ireland business units within an accelerated 6-week timeframe to create client-centred value initiatives through design thinking approaches.
Framework brought client-valued focussed initiatives through a series of stages from ideation, business and resource planning through to final pitch to a panel of executive 'dragons'.
Widespread engagement throiughout IBM business units in Ireland brought the engineering and sales community together in a first-of-a-kind programme which saw 140 ideas propgress through the process.
With 32% of 'ideas' making it through to formal submisison we improved attrition on previous start-up initiatoives and the process delivered over 12 fully fleshed project submissions that were executive reviewed for implementation across healthcare, human resources, big data and analytics and sales industry segments.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Plan for the morning....
exploring data transformation
lessons learnt
What really happens when you attempt to introduce data-
driven practices into a sales environment?
What are the critical success factors for technical, behavioural
and organisational change consideration?
• 3 cycles – we’ll walk through and work in groups
• What would YOU do next?
• 5 lessons –walk through and work individually
• 1 application in you own environment
• 1 critique or weakness
putting it into practice How could you put this into practice in your own organisation?
• 3 key tools
• The importance of value chains
• Key Performance Questions and KPIs
• Using “Job To Be Done” to bring it all together
3. A) Case Study Workshop
Set the Scene – problem context.....
15 mins
Case Study Round 1
(Aggregation & Interpretation)
30 mins
- Set the scene
- Propose Next Steps
- What did happen
- Discuss
5 mins
10 mins
5 mins
10 mins
Case Study Round 2
(Time, Urgency & Data)
30 mins
- Set the scene
- Propose Next Steps
- What did happen
- Discuss
5 mins
10 mins
5 mins
10 mins
Case Study Round 3
(Stakeholders, Models & Sprints)
30 mins
B) Lessons Learnt
C) Putting It Into Practice
- Set the scene
- Propose Next Steps
- What did happen
- Discuss
5 mins
10 mins
5 mins
10 mins
Agenda
What’s the plan?
5. Page 5
• 1,000 sellers and support
• 80% of volume of IBM Europe
• Full Portfolio – all product lines
• ‘Long-Tail’ part of business
• Mix of sales tasks any given day
• Each seller has 500-1,000 clients
Context
What’s the setting and what is the problem to be solved?
6. Computing is entering a new
cognitive era.
Implementing data at the
centre of sales
What we’d LIKE to have….
7. Organisational environments, however, are
designed and run with a lot of inefficiencies.
Transforming sales and creating value is
harder than it sounds
What we ACTUALLY have….
8. Page 8
Context
What’s the setting and what is the problem to be solved?
So, what’s the problem?
Who?
• Sales Reps attempting to manage their sales
territory
When?
• Deciding who to call next with limited time and
multiple choices – 30 mins/day 1000s of clients
Why?
• Traditional engagement cycle focus on renewal
events alone – 12% of customers only
5% clients
engaged quarterly
9. Page 9
Context – Round 1
What evidence is there of the problem to be solved....the clues
What’s activity is happening on floor?
What are end users (sellers) saying - feedback?
What data evidence is normally available?
a
f
d
Renewal Dates Shift Proposal Build Work
8 Teams – 70 Sellers
2 Systems of Record (Contracts)
2 Systems of Record (Inventory)
1 Opportunity Mgt System
3% of Renewals/Qtr People and Tools
80% ‘direct debit’
20% fixed term
Matrix Stakeholders
15k active clients - approx. 500k ‘inactive’
20% of time ‘selling with customers
24% of time ‘pre-sales admin’
11% of time ‘post-sales admin’
Customers and Time
9.50
10. Page 10
1. Data aggregation creates process value
2. Tolerance for data accuracy is very low
3. Visualization drives ‘discovery’
4. The right data delivery process is critical
5. Time sensitivity of information is important
6. There is over-confidence in ‘effectiveness’
Findings
Round 1 Outcomes
Apr 2012 – Feb 2015
What did we do?
1. Aggregated multiple datasets into a
central set of views for sellers.
2. Created visual ‘HeatMaps’ to allows
sellers see and determine ‘valuable’ clients
for engagement.
3. Created client engagement planning and
execution management process – who
did you call and when?
4. Creation of infographic style 360° view of
customer - Client-On-A-Page.
5. Delivery process and integrated with the
Opportunity Management system
I’m too busy!
11. Page 11
Context – Round 2
What evidence is there of the problem to be solved....the clues
What’s activity is happening on floor?
What are end users (sellers) saying - feedback?
What data evidence is normally available?
a
f
d
Renewal Cycle
(need to have)
We like the process
but we simply don’t
have time You said customer
had 12 assets - they
were all gone!
I don’t need this - I
know what my
customer needs’ are
26% of active clients
42% of target clients called – 1.5 calls/rep/week
20% lead conversion rate – calls to opportunities
56% win rate – opportunities to wins
52 mins saved per seller per day
Customers and Time
New Business Cycle
(nice to have)
Hot & Cold
(urgency wanes)
How do I say no
to alternate lists?
10.20
12. Page 12
What did we do?
1. Focus on agile approaches – value-
mapping, feature evaluation and iterative
artefacts.
2. Re-designed workload shift and created
extra 52 mins per rep per day time
3. Developed seller and SME based ‘lead
indicator’ ranking model
4. Invested $250k in technology platform to
scale up and onto real-time web solution
5. Started work to expand approach to
other sales teams
6. Completed ‘list audit’ to determine what
alternate business direction was being
given to sellers
7. Datafication finds your weaknesses first!
8. The role of analytics is secondary
9. Management layers lack line of sight
10. Sellers aren’t doing what we think they are
11. Stop old practices as well as starting new
12. multiplicity drives irrational behaviour
13. Your sponsors may become impatient
Findings
Round 2 Outcomes
Mar 2015 – Dec 2015
I’m too busy on
other stuff
14. Page 14
What did we do?
1. Paused work with Renewals team and
expanded to other sales teams
2. Integrated all the Lists sellers were being
given from matrix of stakeholders
3. Implement Next Best Customer and All
Thing Considered Next Best Customer.
4. Altered process to capture client
feedback on each engagement
5. Implemented Sales Sprints idea instead
of open ended execution runs
6. Started focusing on Market Feedback
views to assess ‘best’ strategy.
7. Integrated with more downstream tools
to simplify research process
8. Commenced Worldwide deployment
14. You must manage the rain of lists!
15. Identify a cohesive strategy isn’t easy!
16. Issues with lack of cohesive direction
17. Issues with ‘perceived wisdom’
18. Mission creep happens invisibly over time
19. Separation of direction from execution key
Findings
Round 3 Outcomes
Jan 2016 – May 2018
We need more
lists!
15. Page 15
What did we do?
1. Developed brand new cloud-based AI
system from ground up for sales
2. Contains both expert models and territory
ranking to provide proscriptive guidance
3. Combines internal data, competitive install,
live buying signals – 13bn calaculations
4. Formal pilot programme with CEO
sponsorship and steering committee with
PMO
5. Technical team complimented on sales
floor with a transformation leader –
digital
6. Technical team complimented on sales
floor with an adoption leader – field
14. You must have an adoption programme!
15. Beware of technical debt in CRM!
16. Issues with account ownsership
17. Issues with account strategy
18. Issues with pipeline build incentives
19. Focus on programmatic change agenda
20. Never waterfall – always agile and iterative
Findings
Epilogue
Mar 2019 – Aug 2019
We need more
guidance
15%
-20%
40%
3%
52
days
0
Con Rate
Sales Cycle
Total Pipe
77%
Seller Satis
17. Page 17
Interpretation
Build a visual sales narrative
17
??
There’s a LOT of STG activity on this
account at the moment and none of it has
any maintenance attached! Is there a tech
refresh happening? Seems to be Power +
Storage…must investigate.
1
There’s been no
TSS NetNew on this
customer in last 6
months…..
No renewal
currently this
Quarter and the
178 boxes under
cover with us are
worth $19k per
annum but new kit
Oppties suggest
scope to expand.
2
Odd. They have
most of their
assets covered
on a Direct
contract but
some isolated
boxes under a
contract with a
Business
Partner. Why?
Must
investigate…..
?
3
Hmm…a lot of
boxes on low levels
of only 9 to 5 cover.
Seems odd given
that they have
mission critical
applications for
Wimbledon. Must
ask.
?
4
We lost a TSS
Oppty to get
more business
out of this
customer a Year
Ago. I should
call to see how
they’re getting
on with the
service.
5
No multi-vendor that we can see but WinBack suggests there’s
more we could do that haven’t captured yet. Must investigate…..
6
11.30
18. Feedback Loop
2%
10%
Time
Lead
Conversion
observe &
adjust
observe &
adjust
observe &
adjust
observe &
adjust
“inside-out” model
“outside-in” model
The integrated feedback loop allows for Marketing and Sales sprints
1%
application to commercial market ‘search’
How is this relevant?
How do we know where the clients are if
we’ve never had them before?
We don’t have enough meaningful ‘historic’ data
to tell us where the market potential lies...
If anything, this historic data is dangerous – existing
commercial clients probably look a lot like our industrial and
enterprise clients and very different to where we should be!
11.40
20. WHERE – IMTs and Industries WHY - patterns
% Clients with Positive Outcomes per Lead Indicator
Outcome Status for Prospects
9%
Oppty
Conversion
17%
Nurture^
Conversion
47%
Misaligned
Value Prop*
What Market Feedback Did We Receive? 46%
Positive
Feedback
Did customer respond
positively to value
proposition? Are we on
target with message?
Did customer respond negatively to value
proposition? Are we off target with
message?
hit
What Market Feedback by IMT?
What Market Feedback by Industry?
What factors link strongest with Positive Feedback?
miss
hit
WHAT - outcomes
Iterate Your Models
observe &
adjust
observe &
adjust
observe &
adjust
observe &
adjust
“inside-out”
model
“outside-in”
model
Challenge Bias and Iterate Your Understanding of the Problem
12.00
21. Solve for issues that will have impact not just blip
No Management System
No course correction => suppressed results
Multiple Lists
Poor Contact Data
Little or No Market Feedback
Segments not Clusters
Weak or No Reason of Call
How do I use this info to open a client call?
Little Client Insight
data - not client insight provided
Who versus Why
Lists of CMRs with little else
Ease of Use
Designed visually to help discovery of insight
No Prioritization or Ranking
high opportunity cost of execution
Time to Research vs Engage
sellers spend 30 mins avg researching per prospect
Integration Results
Productivity
Confused Strategy
Opportunity Cost of Execution
Suppressed Lead Conversion
Revenue Left on Table
Understand Causes Vs. Symptoms
12.05
22. • What are the right questions?
• What is the best process to capture the answers?
• Then implement a data-driven transformation
Understand what you need to know & what your data is NOT telling you
process data insight
+ =
??
?
?
?
?
What would I
like to know?
“Stage” Your Problem
12.10
12.10
23. Possible Application Critique or Challenge
1 Interpretation Build a visual narrative to
bridge from data to insight
2 Feedback Loop Integrated feedback loop
allows for Marketing and
Sales sprints
3 Capture New Data Ask the right questions first
4 Iterate Your Models Challenge Bias and Iterate
Your Understanding of the
Problem
5 Causes Vs. Symptoms Solve for issues that will
have impact not just blip
5 “Stage” Your Problem Understand what you need to
know & what your data is
NOT telling you
Apply & Challenge
25. Recommended READING
Building Better Business
Cases for IT Investments
Ward, 2007, Cranfield
School of Management
What Are Key
Performance Questions
Bernard Marr, API
Institute - www.ap-
institute.com
Big Data: A Revolution That
Will Transform How We
Live, Work and Think
Viktor Mayer-Schonberger
and Kenneth Cukier 2013
The Signal and the Noise:
The Art and Science of
Prediction
Nate Silver, 2013
Marketing and Sales
Analytics: Proven
Techniques and Powerful
Applications from Industry
Leaders
Cesar Brea, 2014
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3
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5
Agile Analytics: A Value-Driven
Approach to Business Intelligence
and Data Warehousing: Delivering
the Promise of Business
Intelligence
Ken Collier, 2011