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.
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
1
2
3
4 6
5
Agile Analytics: A Value-Driven
Approach to Business Intelligence
and Data Warehousing: Delivering
the Promise of Business
Intelligence
Ken Collier, 2011