Tech Connect Conference - May 2017

1. Using Analytics to improve Prospecting and reduce time to Pipeline targets
Barry Magee – Client Analytics and Demand Generation Leader – IBM Digital Sales Europe

2. Page  2
Context
What’s the setting and what is the problem to be solved?

3. Lessons from the Transformation Journey
Action Design Research – structured iteration through change cycles, pivoting and learning throughout
1 2 3 4 5 6
Design Thinking
Prototype and Iterate
Are we
there yet?
2012 2017
messy learning
always enhancing

4. Lessons from the Sales Execution ‘Layer’
Moving towards data-driven Sales Sprints – 6 stages

5. Rain of Lists
TIME is the killer in sales – multiple
stakeholders, multiple directions
1
rain of lists
single process

6. Quality => TIME
Each doubling of lead conversion rate halves
time taken to get pipeline
2
prospect quality is “thin edge of wedge”
1%
2%
5.5%
1
Year
6
Mths
2
Mths
lead conversion rate
time to achieve target
A 2% Lead Conversion rate on a list of a 1000 prospects means 980 ‘wasted’ calls
at approx. half an hour per call between prep and engagement.
• $1m Target
• $100k avg Value
• 20% Win Rates
• 15 Min Prep
• 15 Min Call
• 2 Hours A Day
• Quality of List = Lead Conversion Rate

7. Savings Account
Investment Funds
Car Loan
Mortgage
Travel Insurance
Home Insurance
Given constraints on time and
resource, for a given combination of
products and services who is the next
best customer that will give me the
best return on time or money
invested?
1
2
3
6
5
4
1
model for x model for n=all to ∞
Prioritization
Only 5% of lists have prioritization and lists at
odds with each other
3

8. Sparsity of Info
How do you model when you have little or no
‘historic’ data?
4
Sales Pipeline
Market Share
Market
limited hard data codify and model from organisational wisdom
risk
?
?
?
“inside-out” model

9. Feedback Loop
You need to know why you’re NOT appealing
to customers. Capture all feedback.
5
limited market data codify and model from organisational wisdom
??
What would I
like to know? ?
?
?
1%
If not
interested
why not?

10. Sprint to Learn
You need to incorporate feedback loops so
your models can learn from execution
6
observe &
adjust
observe &
adjust
observe &
adjust
observe &
adjust
“inside-out” model
“outside-in” model
capture all outcomes and integrate into sprint cycles
2%
10%
Time
Lead
Conversion

11. Inside Out : Where we think clients 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
2
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
3 observe &
adjust
End-to-end management system
on all outcomes allowing continual
course correction to maximize
performance
4
Where are the clusters of
best opportunity found
1
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?
8% steady state lead conversion rates compared
with 1-2% average for outbound
5
marketplace
targeting
Provide analysis of high-
performing client/product
clusters to marketing to target
inbound marketing spend
What are the best lead
indicators of client need?
What is the data-driven
reason of call?