UCC Diploma in Digital Business Lecture - Sep 2017

1. Organisational Data Transformation
Barry Magee – Client Analytics and Data Transformation Leader – IBM Digital Sales Europe
vs.

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

3. Set the Scene – problem context..... 15 mins
Case Study Round 1
(Aggregation & Interpretation)
- Set the scene
- Propose Next Steps
- What did happen
- Discuss
5 mins
10 mins
5 mins
10 mins
30 mins
Case Study Round 2
(Time, Urgency & Data)
- Set the scene
- Propose Next Steps
- What did happen
- Discuss
5 mins
10 mins
5 mins
10 mins
30 mins
Case Study Round 3
(Stakeholders, Models & Sprints)
- Set the scene
- Propose Next Steps
- What did happen
- Discuss
5 mins
10 mins
5 mins
10 mins
30 mins
Agenda
What’s the plan?

4. exploring data transformation

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

13. Page  13
Context – Round 3
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)
Do you have an X list
about Y?
How do I add a Prospect
retrospectively?
My sponsor wants me
to focus on Z!
26% of active clients
30% of target clients called – 1.5 calls/rep/week
8-10% lead conversion rate – calls to opportunities
$42m pipeline created
1 single outbound ‘funnel’ – no alternate ‘lists’
Customers and Time
Next Best Customer
(nice to have)
Direction
(urgency wanes)
What’s the best way
to use LinkedIn?
Engagement Cycle
(need to have)
Ren WinBk
Values
Customer Name Sales Rep NetNew Oppty?
IMT
Rank
Vol
Weight
Average
of
%
Direct
Average
of
%
Indirect
Renewals
Power
Storage
Mob
ICS
WinBack
NoCover
psWAXIT
9
to
5
SWMA
Drop-Offs
HWMA
No
SWMA
ETS
HMC
DataPower
XIV
Storwize
Dell
HP
EMC
Cisco
Juniper
Motorola
Linux
Oracle
Sun
SPECIALIST DISTRIBUTIO Shane Ronan-Duggan No 1 9,302 100 0 0 0 0 0 0 0 52 9,250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
SIG PLC Brian Royle Yes 2 3,960 0 100 0 22 0 0 21 21 1,151 0 0 1,144 1,102 0 42 0 0 439 0 0 0 0 0 0 0 17 0
ARROW ECS UK LTD Shane Ronan-Duggan No 3 3,575 100 0 0 0 0 0 0 0 0 3,575 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
VR012/PGDS LTD Emma Coyle No 5 3,092 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 3,044 0 0 0 0 0 0 0 0 0
NORTHAMBER Shane Ronan-Duggan No 6 2,695 100 0 0 0 0 0 0 0 0 2,695 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PRUDENTIAL Enda Scanlon No 7 2,356 0 100 0 0 0 0 0 0 157 415 0 754 50 0 0 0 0 980 0 0 0 0 0 0 0 0 0
TRAVELERS MANAGMENT LT Enda Scanlon No 8 2,314 100 0 0 0 0 0 0 0 0 0 22 0 0 0 127 75 0 2,089 0 0 0 0 0 0 0 0 0
IMPERIAL COLLEGE Anthony Murphy Yes 9 2,215 0 100 0 0 44 0 0 0 209 104 0 884 200 0 0 0 0 774 0 0 0 0 0 0 0 0 0
VR050/INTELLECTUAL Del Tillyer Yes 10 2,201 0 100 0 0 87 0 0 21 0 0 0 1,040 0 0 0 0 0 1,032 0 21 0 0 0 0 0 0 0
VR695/KINGSTON UNI Anthony Murphy No 11 2,141 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2,141 0 0 0 0 0 0 0 0 0
WILKINSONS Brian Royle No 12 2,117 0 100 0 0 0 0 0 0 209 311 0 806 0 0 42 0 0 748 0 0 0 0 0 0 0 0 0
ADMIRAL Enda Scanlon Yes 13 1,967 0 100 0 22 22 0 0 21 0 52 0 936 0 62 0 0 0 851 0 0 0 0 0 0 0 0 0
LOGICALIS UK Suneel Talikoti No 14 1,850 100 0 0 0 0 0 0 0 0 492 0 572 0 166 0 0 0 619 0 0 0 0 0 0 0 0 0
MCKESSON HBOC Louise Noone No 15 1,780 98 2 0 0 0 0 0 21 235 0 22 208 100 42 403 0 0 748 0 0 0 0 0 0 0 0 0
VR012/ EUI LIMITED Enda Scanlon No 16 1,732 0 100 0 0 0 0 0 0 0 0 22 0 0 0 85 0 0 1,625 0 0 0 0 0 0 0 0 0
VR012/HARGREAVES L Suneel Talikoti No 17 1,677 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,677 0 0 0 0 0 0 0 0 0
VR695/INTELLECTUAL Del Tillyer No 18 1,647 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,625 0 0 0 0 0 0 0 0 0
VR522/NISA RETAIL Brian Royle No 19 1,627 0 100 0 0 0 0 0 0 0 492 0 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0
TECH DATA LIMITED Shane Ronan-Duggan No 20 1,555 100 0 0 0 0 0 0 0 0 1,555 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
APACHE NORTH SEA LTD Sarah Knox No 21 1,551 0 100 0 0 0 0 0 0 0 104 0 442 526 416 21 0 0 0 0 42 0 0 0 0 0 0 0
VR522/SAGA SERVICE Louise Noone No 23 1,494 0 100 0 0 0 0 0 0 0 0 22 0 0 0 234 0 0 1,238 0 0 0 0 0 0 0 0 0
VR695/SURREY COUNT Anthony Murphy No 25 1,260 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,212 0 0 0 0 0 0 0 0 0
RAILWAY PROCUREMENT James Gray Yes 26 1,256 100 0 0 22 22 0 0 21 78 0 0 858 0 0 127 0 0 0 0 63 0 46 0 0 0 17 0
KIER GROUP PLC Marese Clarke No 28 1,236 92 8 0 0 0 0 0 0 131 104 22 442 125 0 0 0 0 413 0 0 0 0 0 0 0 0 0
NHS LANARKSHIRE Sarah Knox No 30 1,206 0 100 0 0 0 0 0 0 0 0 0 520 200 125 0 0 0 361 0 0 0 0 0 0 0 0 0
C & J CLARK Marese Clarke No 31 1,174 0 100 0 0 0 0 0 0 0 0 0 624 50 458 42 0 0 0 0 0 0 0 0 0 0 0 0
VR012/2 SISTERS GR Brian Royle No 32 1,157 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0
VR012/ECCLESIATICAL IN Louise Noone No 33 1,156 0 100 26 0 0 0 0 0 0 0 0 0 0 0 21 0 0 1,109 0 0 0 0 0 0 0 0 0
HRG C/O ARGOS Brian Royle Yes 34 1,138 0 100 0 0 0 0 0 21 0 52 0 442 125 166 0 0 0 0 0 105 20 0 0 0 0 206 0
VR695/DUMFRIES & G Sarah Knox No 35 1,111 24 76 26 0 0 0 0 0 0 492 0 0 0 0 0 0 0 593 0 0 0 0 0 0 0 0 0
SAGA GROUP LTD Louise Noone No 36 1,104 0 100 0 0 0 0 0 0 0 78 0 494 0 146 0 0 0 387 0 0 0 0 0 0 0 0 0
HMV RETAIL LIMITED Sarah Knox No 37 1,076 9 91 0 0 0 0 0 0 0 0 0 598 0 478 0 0 0 0 0 0 0 0 0 0 0 0 0
VR012/HAIRMYRES HO Sarah Knox No 38 1,058 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,032 0 0 0 0 0 0 0 0 0
VR012/ATCORE TECHNOLOG Suneel Talikoti No 39 1,056 0 100 0 0 0 0 0 0 0 0 0 806 0 250 0 0 0 0 0 0 0 0 0 0 0 0 0
SCC Suneel Talikoti No 40 1,035 0 100 0 0 0 0 0 0 0 0 0 520 25 0 0 0 0 490 0 0 0 0 0 0 0 0 0
ECCLESIASTICAL Louise Noone Yes 41 1,022 0 100 0 22 65 0 0 21 0 0 0 468 0 62 21 0 0 361 0 0 0 0 0 0 0 0 0
WILKINSON Brian Royle Yes 42 1,005 0 100 0 0 22 0 0 0 26 492 0 0 0 0 0 0 0 464 0 0 0 0 0 0 0 0 0
HALFORDS LTD Brian Royle No 43 1,004 100 0 0 0 0 0 0 0 340 0 0 338 326 0 0 0 0 0 0 0 0 0 0 0 0 0 0
VR012/PLYMOUTH UNI Anthony Murphy No 44 980 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0
VR695/KIER GROUP LTD Marese Clarke No 46 954 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0
OCADO Marese Clarke Yes 48 934 0 100 0 22 44 0 0 21 0 0 0 416 250 125 21 0 0 0 0 0 0 0 0 0 0 34 0
VR012/ISLE OF WIGH Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0
VR012/HAMPSHIRE COUNTY Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0
VR012/IMPERIAL COLLEGE Anthony Murphy No 51 925 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 903 0 0 0 0 0 0 0 0 0
Attach Hardware Software Multi-Vendor
Channel
A RAIN of lists
Average of 100 unique
“lists” per Europe mission
10.50

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 – present
We need more
lists!

15. Inside Out : Where we think clients and markets are….
Outside In : Where clients actually are…
What do we do differently?
identify &
prioritise
We codify expertise into models
that match clients with needs and
prioritizes engagement driving
sellers to the right clients
B
All things considered, who is
best customer to engage next?
Ren WinBk
Values
Customer Name Sales Rep NetNew Oppty?
IMT
Rank
Vol
Weight
Average
of
%
Direct
Average
of
%
Indirect
Renewals
Power
Storage
Mob
ICS
WinBack
NoCover
psWAXIT
9
to
5
SWMA
Drop-Offs
HWMA
No
SWMA
ETS
HMC
DataPower
XIV
Storwize
Dell
HP
EMC
Cisco
Juniper
Motorola
Linux
Oracle
Sun
SPECIALIST DISTRIBUTIO Shane Ronan-Duggan No 1 9,302 100 0 0 0 0 0 0 0 52 9,250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
SIG PLC Brian Royle Yes 2 3,960 0 100 0 22 0 0 21 21 1,151 0 0 1,144 1,102 0 42 0 0 439 0 0 0 0 0 0 0 17 0
ARROW ECS UK LTD Shane Ronan-Duggan No 3 3,575 100 0 0 0 0 0 0 0 0 3,575 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
VR012/PGDS LTD Emma Coyle No 5 3,092 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 3,044 0 0 0 0 0 0 0 0 0
NORTHAMBER Shane Ronan-Duggan No 6 2,695 100 0 0 0 0 0 0 0 0 2,695 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
PRUDENTIAL Enda Scanlon No 7 2,356 0 100 0 0 0 0 0 0 157 415 0 754 50 0 0 0 0 980 0 0 0 0 0 0 0 0 0
TRAVELERS MANAGMENT LT Enda Scanlon No 8 2,314 100 0 0 0 0 0 0 0 0 0 22 0 0 0 127 75 0 2,089 0 0 0 0 0 0 0 0 0
IMPERIAL COLLEGE Anthony Murphy Yes 9 2,215 0 100 0 0 44 0 0 0 209 104 0 884 200 0 0 0 0 774 0 0 0 0 0 0 0 0 0
VR050/INTELLECTUAL Del Tillyer Yes 10 2,201 0 100 0 0 87 0 0 21 0 0 0 1,040 0 0 0 0 0 1,032 0 21 0 0 0 0 0 0 0
VR695/KINGSTON UNI Anthony Murphy No 11 2,141 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2,141 0 0 0 0 0 0 0 0 0
WILKINSONS Brian Royle No 12 2,117 0 100 0 0 0 0 0 0 209 311 0 806 0 0 42 0 0 748 0 0 0 0 0 0 0 0 0
ADMIRAL Enda Scanlon Yes 13 1,967 0 100 0 22 22 0 0 21 0 52 0 936 0 62 0 0 0 851 0 0 0 0 0 0 0 0 0
LOGICALIS UK Suneel Talikoti No 14 1,850 100 0 0 0 0 0 0 0 0 492 0 572 0 166 0 0 0 619 0 0 0 0 0 0 0 0 0
MCKESSON HBOC Louise Noone No 15 1,780 98 2 0 0 0 0 0 21 235 0 22 208 100 42 403 0 0 748 0 0 0 0 0 0 0 0 0
VR012/ EUI LIMITED Enda Scanlon No 16 1,732 0 100 0 0 0 0 0 0 0 0 22 0 0 0 85 0 0 1,625 0 0 0 0 0 0 0 0 0
VR012/HARGREAVES L Suneel Talikoti No 17 1,677 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,677 0 0 0 0 0 0 0 0 0
VR695/INTELLECTUAL Del Tillyer No 18 1,647 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,625 0 0 0 0 0 0 0 0 0
VR522/NISA RETAIL Brian Royle No 19 1,627 0 100 0 0 0 0 0 0 0 492 0 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0
TECH DATA LIMITED Shane Ronan-Duggan No 20 1,555 100 0 0 0 0 0 0 0 0 1,555 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
APACHE NORTH SEA LTD Sarah Knox No 21 1,551 0 100 0 0 0 0 0 0 0 104 0 442 526 416 21 0 0 0 0 42 0 0 0 0 0 0 0
VR522/SAGA SERVICE Louise Noone No 23 1,494 0 100 0 0 0 0 0 0 0 0 22 0 0 0 234 0 0 1,238 0 0 0 0 0 0 0 0 0
VR695/SURREY COUNT Anthony Murphy No 25 1,260 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,212 0 0 0 0 0 0 0 0 0
RAILWAY PROCUREMENT James Gray Yes 26 1,256 100 0 0 22 22 0 0 21 78 0 0 858 0 0 127 0 0 0 0 63 0 46 0 0 0 17 0
KIER GROUP PLC Marese Clarke No 28 1,236 92 8 0 0 0 0 0 0 131 104 22 442 125 0 0 0 0 413 0 0 0 0 0 0 0 0 0
NHS LANARKSHIRE Sarah Knox No 30 1,206 0 100 0 0 0 0 0 0 0 0 0 520 200 125 0 0 0 361 0 0 0 0 0 0 0 0 0
C & J CLARK Marese Clarke No 31 1,174 0 100 0 0 0 0 0 0 0 0 0 624 50 458 42 0 0 0 0 0 0 0 0 0 0 0 0
VR012/2 SISTERS GR Brian Royle No 32 1,157 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0
VR012/ECCLESIATICAL IN Louise Noone No 33 1,156 0 100 26 0 0 0 0 0 0 0 0 0 0 0 21 0 0 1,109 0 0 0 0 0 0 0 0 0
HRG C/O ARGOS Brian Royle Yes 34 1,138 0 100 0 0 0 0 0 21 0 52 0 442 125 166 0 0 0 0 0 105 20 0 0 0 0 206 0
VR695/DUMFRIES & G Sarah Knox No 35 1,111 24 76 26 0 0 0 0 0 0 492 0 0 0 0 0 0 0 593 0 0 0 0 0 0 0 0 0
SAGA GROUP LTD Louise Noone No 36 1,104 0 100 0 0 0 0 0 0 0 78 0 494 0 146 0 0 0 387 0 0 0 0 0 0 0 0 0
HMV RETAIL LIMITED Sarah Knox No 37 1,076 9 91 0 0 0 0 0 0 0 0 0 598 0 478 0 0 0 0 0 0 0 0 0 0 0 0 0
VR012/HAIRMYRES HO Sarah Knox No 38 1,058 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,032 0 0 0 0 0 0 0 0 0
VR012/ATCORE TECHNOLOG Suneel Talikoti No 39 1,056 0 100 0 0 0 0 0 0 0 0 0 806 0 250 0 0 0 0 0 0 0 0 0 0 0 0 0
SCC Suneel Talikoti No 40 1,035 0 100 0 0 0 0 0 0 0 0 0 520 25 0 0 0 0 490 0 0 0 0 0 0 0 0 0
ECCLESIASTICAL Louise Noone Yes 41 1,022 0 100 0 22 65 0 0 21 0 0 0 468 0 62 21 0 0 361 0 0 0 0 0 0 0 0 0
WILKINSON Brian Royle Yes 42 1,005 0 100 0 0 22 0 0 0 26 492 0 0 0 0 0 0 0 464 0 0 0 0 0 0 0 0 0
HALFORDS LTD Brian Royle No 43 1,004 100 0 0 0 0 0 0 0 340 0 0 338 326 0 0 0 0 0 0 0 0 0 0 0 0 0 0
VR012/PLYMOUTH UNI Anthony Murphy No 44 980 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0
VR695/KIER GROUP LTD Marese Clarke No 46 954 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0
OCADO Marese Clarke Yes 48 934 0 100 0 22 44 0 0 21 0 0 0 416 250 125 21 0 0 0 0 0 0 0 0 0 0 34 0
VR012/ISLE OF WIGH Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0
VR012/HAMPSHIRE COUNTY Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0
VR012/IMPERIAL COLLEGE Anthony Murphy No 51 925 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 903 0 0 0 0 0 0 0 0 0
Attach Hardware Software Multi-Vendor
Channel
integrate &
simplify
We translate the data into simple,
understandable Reason of Call
visualizations and integrate into
Sales Connect
C
What is the data-driven
reason of call?
observe &
adjust
End-to-end management system
on all outcomes allowing continual
course correction to maximize
performance
D
Where are the clusters of
best opportunity found
A
aggregate &
analyse
We aggregate thousands of
data points most valuable as
lead indicators of need into a
central repository.
What are the best lead
indicators of client need?
10% steady state lead conversion rates compared
with 1-2% average for outbound

16. Page  16
lessons learnt

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. Agile Sprints
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

19. Capture New Data
??
What would I
like to know?
Ask the right questions first
11.50

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 Agile Sprints 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

24. Possible Application Critique or Challenge
1 Interpretation Build a visual narrative to
bridge from data to insight
2 Agile Sprints 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. Which customers
are in each
campaign?
WHY
engage?
WHO
to call?
WHAT
to offer?
WHEN
to engage?
WHICH
resource?
HOW
to engage?
WHERE
to adjust?
Marketing
brand and marketing drive
prospects into top of
funnel
plan
execute
Lead Dev
Either a dedicated bus dev
rep or sales rep on bus dev
activity
plan
execute
Sales
Sellers managing a
territory of clients or
pipeline of opportunities
plan
execute
Identified
Validated
Qualified
What are the best
market
segments?
What are market
pain points or
CRAs?
What products
match market
needs?
Which digital
toolset do I use to
engage?
What is market
feedback and
where to pivot?
Which campaigns
should we run?
Which digital
toolset do team
use?
How are teams
performing?
When do I plan
for campaign
execution?
Which Reps
should cover
which campaigns?
Which clients and
Oppties are
priorities?
Jobs To Be Done
Which Reps
should cover
which accounts?
How can I see
market pain
points?
How do I define
“best” segment?
How do I “fit”
product to
segment?
How do I “fit”
product to
segment?
How should
feedback adjust
campaigns?
How do I “fit”
product to
prospect?
Who do I call
next?
How do I
prioritise all my
calls?
How do I triage
my prospects?
How do I know
where to adjust
my actions?
Who do I call
next?
What are the
market trends?
What are the
market trends?
What’s best
product for my
customer?
When is best
time to engage
my customers?
How do I triage
my prospects?
How do I know
where to adjust
my actions?
How are teams
performing?

26. reading

27. 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