IFIP Decision Support Science - Jun 2016

1. Page  1
Introducing data driven practices into sales environments
DataBergs!
Barry Magee
Data Transformation Leader IBM Europe Digital Sales

2. Page  2
• 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?

3. Page  3
What’s the problem?
Who should I talk to next?
3% clients
engaged quarterly
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 – sales suppressed and unhappy clients

4. Page  4
Identify with sellers all Sights, Sounds and Smells that would indicate a potential customer
need and we’ll go get it…
Contract
Data
Pipeline
Intelligence
Attach
Intelligence
Install
Base Data
Technical
Upsell
Digital 360°
view of customer
Market
Intelligence
Competitor
Intelligence
Account
Intelligence
Everything We Know about Client…
2 key deliverables
Actionable ‘Next Best
Customer’ selection
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
2
1
What did we do?
Where to start on the question of client engagement?

5. Page  5
• low traction but high yield
• 42% completion
• 20% lead conversion rate vs 4% normally
• 56% win rate vs 50% normally
• $23k average deal size vs $21k normally
• $1.7m new business opportunity
• 80% with customers we wouldn’t otherwise engage
How did we do?
What happened next…..
Process
is critical

6. Page  6
Data-driven processes, tools and endeavours are organisational
transformation initiatives not just tool or technology deployments
irrationality and behavioural
economics!
Final thoughts…

7. Page  7
Framework
What are the Critical Success Factors?

8. Page  8

9. Page  9
Benefits Framework for SMART Analytics Programme – Cycle 3
Time
Process
Effectiveness
Client
Engagement
Sales
Effectiveness
Financial
80% of New Opps no Renewal in Qtr
> developing new engagement cycle
> spreading forecast risk
20% Lead conversion vs 4% Lead Development avg
56% WinRate vs 50% bau avg
$23k Avg winvs $21k bau avg
1.5/Rep/Wk - 13 SMART calls per Rep
Lower than target Call Rates
51% of Targeted Clients called
Terr Penetration increased from 15% to 29%
$1.7m Net New Pipe
Sellers spending > 3 Hrs per day on Pre-Sales
Admin
Sellers spending 1’20” per day on Customer
Research
1095 Customers On A Page created
Production Capacity 11 CoaPs/Rep/Wk
93% clients have some upsell hooks
2H process allowed Sales Mgrs prioritise
Seller Time Customer Contact
Scale - Platform Development
Data Quality
Gamification
Expanded client views
Improved Client Selection
Drive Sales URGENCY
• low traction but high yield
• 524 calls = 1.5 per/rep/wk drove
• 20% conversion rate
• $1.7m new business opportunity
• $1.1m in new business wins
Results
What happened in our case?

10. Page  10
Framework
Pillar Practice # WHY - Questions of interest?
1 What patterns do end-user interviews produce regarding problem statements?
2 Where does sales organisation reside on maturity model assessment?
3 Who does the business consider to be the universe of active and prospective clients?
4 What current clients do sellers engage?
6 Is sales engagement client or product centric?
7 What client organisational structure do sellers need when engaging?
9 Do sellers have a clear process to direct them to specific clients for engagement?
13 Do sellers need organisational or persona based selection models?
14 Do we have a set of target products/offerings we are trying to push out to market?
15 What current criteria do sellers use to decide on next best client?
17 Do sellers have a clear process to provide a comprehensive reason of call for each engagement?
19 What are seller-advocated LEAD INDICATORS for each given client set and/or offering?
20 What are SME-advocated LEAD INDICATORS for each given client set and/or offering?
22 What types of INTERNAL STRUCTURED data would be most useful to sellers?
23 What types of EXTERNAL STRUCTURED data would be most useful to sellers?
24 What types of INTERNAL UNSTRUCTURED data would be most useful to sellers?
25 What types of EXTERNAL UNSTRUCTURED data would be most useful to sellers?
32 What client engagement capacity do sellers currently have?
33 When do sellers currently engage clients?
35 When should we use the different channel components of DemandGen engine at different times/stage?
38 Is there any client feedback to indicate WHEN they might prefer/not prefer to be engaged?
41 Which sales resource channels are available to a given sales team?
42 Do we have a centralised structure for account/seller alignment?
43 Do sales management know which of the available sales resources are the optimal ones for a given cohort/campaign?
45 To what extent are channel or external partners integrated within engagement model?
49 What methods of engagement are available to sellers - traditional or digital?
50 Do sellers know which is optimal engagement strategy for a given client?
60 Do sellers have personal contact information freely available?
64 Can any of the support teams (BOM or STS) help resolve data quality issues?
71 How available, accurate and useable is Customer Contact data for the purpose of client engagement?
77 How many total ALTERNATE DATA SOURCES do sellers have to choose from?
79 Which data sources are used for what purpose?
81 IS client engagement driven by a simple, standardised and transparent business process?
82 How many alternate directions/methods do sellers have to 'instruct' client engagement?
83 Is there clarity on how sellers alternate between strategic/planned engagement and tactical/campaign engagement?
90 Do sellers have an effective TERRITORY MANAGEMENT planning framework?
92 How easily can sellers interpret all of the available data available for purpose of sales engagement?
93 In what broad categories do the sellers want to 'see' their client and territory data to drive engagement?
108 Where are sellers currently spending time in their BAU working day?
109 How much time do sellers have available to engage in a territory management process?
110 How much time are sellers spending on data related activities that could be optimized?
111 How often are sellers engaging with the clients within their territories?
112 What are the mimimum acceptable levels for data timliness for different TYPES of data?
117 Do sellers have the skills to engage clients given specifics of their market/product mix?
119 Do sellers and managers have the required Territory Management skills?
7. Effectiveness of Data?
8. Singularity of Data?
9. Simplicity of Process?
10. Ease of Interpretation
11. Time Considerations
12. End User Skills
1. Where to start?
2. Who to engage?
3. What to discuss?
4. When to engage?
5. Which sales resource?
6. How to engage?
Problem
Assessment
Analytical
Operational
Engagement
Data transformations will
impact first by uncovering
systems inefficiencies
3
2
1

11. Page  11
Framework
Pillar Practice # WHY - Questions of interest?
121 What are the Key performance Questions are for the sales teams?
122 Is there clarity on what the Key performance Questions are for the sales teams?
124 What are the top BUSINESS PROBLEMS by brand mission and by stakeholder type?
125 How do you RINGFENCE the productivity saving from data-driven work?
126 Is there a structured and engaged process to solicit end-user requirements from clients, sellers and partners?
127 Have we CAPTURED all the various input and feedback into concept centrix matrix?
128 What lessons can be learnt from anti-log past experiences within sales?
15. Data Governance 132 Are there any ways to leverage support teams or develop new processes to improve data quality?
16. Data Acquisition 134 What is the list of data sources required to acquire for required artefacts
17. Data Integration 137 How are we integrating the various data sources required?
138 Do we have SME modelling available based on codification of Lead Indicators analysis?
139 Do we have End-User modelling available based on codification of Lead Indicators analysis?
140 Do we have any additional formal Analytical Modelling available to assist client selection criteria?
142 What types of analytical models drive most value?
148 Is there a correlation between digital eminence and results?
149 How do we integrate data driven modelling (historic regressions) with lead indicator-type approaches?
151 Do we have a High Tier (L1) view of Territory available for sellers and managers?
152 Do we have a Medium Tier (L2) view of Client Clusters available for sellers and managers?
153 Do we have a Medium Tier (L3) view of Client 360 available for sellers and managers?
154 Do we have a Low Tier (L4) view of data available for sellers and managers?
155 What methods do sellers drive BAU to support targets?
156 How many lists are sellers BAU provided with?
157 Is there a process to evaluate and manage lists from all possible sources into a single 'funnel' system?
158 To what extent are sales teams execution activities in line with KPQs?
159 How many different business priorities exist in reality across the sales teams
160 How aligned or discordant is the sales team with the KPQs or strategic goals?
161 To what extent does urgency exist within sales to structured territory management?
162 Do management buy-in and appreciate the end-to-end process and behaviour requirements?
163 To what extent are managers driving seller behaviour around Territory Management?
164 Within what timeframe are sellers expected to execute against engagement tartgets?
175 Have we created a GAMIFICATION approach to drive seller adoption and engagement with new process?
176 Who is executive Sponsor and how bought in are they to support and drive implementation?
180 What is process to manage roadblocks to transformation as they are unearthed?
181 Is there an effective way to evaluate end-user adoption and engagement with new processes?
182 is the sales process enabled with activity track to commence territory management structure?
187 What yield targets have been set for KPQ deliverables?
188 Has business modelled opportunity cost of non-performance?
189 Is there a 'balanced scorecard' view of the end-to-end sales process to determine where value is being created or inhibited?
191 Do we have a view of how individual clients have responded and what we've learned?
192 Do we have a view of how market cluster have reposnded and what we've learned?
194 Is there a methodology to quantify and evaluate all benefits accruing to implementation of data-driven sales practices?
195 What are the baseline performance levels for KPQs and other programme benefits?
Results
Management
13. Establish KPQs
14. Implement Lean Startup
18. Data Analysis
19. Data Delivery
20. Assess Strategic Alignment
21. Create Urgency
22. Transform Practice
23. Map DAER Engagement
24. Use 6 Step Scorecard
Implementation
Stakeholder
Development
Data
Management
Change
Management
25. Evaluate Market Feedback
26. Evaluate Business Benefits
Data process tools
are organisational
transformation
initiatives not tool
deployments
5