IFIP Decision Support Science - Jun 2016

1. Page  1
Introducing data driven practices into sales environments:
examining the impact of data visualisation on user engagement and sales results
Barry Mageea, David Sammonb,
Tadhg Nagleb and Paidi O’Raghallaighb
aIBM Europe Digital Sales, IBM Technology Campus, Dublin, Ireland;
bBusiness Information Systems, University College Cork, Cork, Ireland
Authors
1. What happens when you attempt to
introduce data-driven practices into a
complex sales environment?
2. What factors are critical for technical and
behavioural consideration?
3. What role does visualization play in overall
outcomes?
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2. Page  2
your (data) business process
Proces
s
is
critical
your REAL (data) business process

3. Page  3
Context
What’s the setting and what is the problem to be solved?
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
What?
• Create a secondary engagement cycle based
on more ‘complete’ view of client ‘needs’
How?
• Aggregate data, create 360° views of client to
better identify customers with ‘needs’ to be met
When?
• Create an ongoing process to drive more
effective territory management.
12% clients
engaged annually
...
and not even
the “right” ones!

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Methodology
How did we examine the impact of the programme?
Six Stages of Action Design Research
Cycle 1 2 3
Name Spreadsheet SMART Data Aggregation SMART Visual SMART
Time Apr 2012 – Oct 2012 Sep 2012 – Jun 2014 Apr 2014 – Feb 2015
Dual Solving & Research 3 Complete Cycles
1. Action Design Research
2. Deep Dive on Results specifics - DAER
Stage Question What are we looking to determine?
Deployment Are we functional? Is system built to requirements with end users set-up correctly for effective use? % of KDD in use*?
Adoption Do we have buy-in on the concept? Are end-users sufficiently interested to try out the system, tool or process? Do we have ‘mind-share’?
Engagement Do we have ongoing engagement? Are end-users engaged with system in the manner required to drive business results? Is it embedded?
Results Have we impacted? Are we having an impact on business results due to adoption and engagement with the data-process
*KDD from FAYYAD, U. & PIATETSKY-SHAPIRO, G. 1996. From data mining to knowledge discovery in databases.

5. Page  5
1. Data aggregation creates process value
• Reps spent 1 hour on research per client
• 14 different categories of sales ‘research’
• 38 different tools and sources regularily in use
• Reps spent 1.5 hours on data collation per client
• Large variances in seller use of data in ‘work of sales’
2. Tolerance for data accuracy is very low
• Sellers rejected artefact at anything > 3% error
Findings
Cycle 1: Spreadsheet SMART
Apr 2012 – Oct 2012
Evaluation
A. Deployment Cycle 1 Cycle 2 Cycle 3
% of KDD in Use 20%
# Data Sources 1
B. Adoption
% End-User Log-Ins 6%
C. Engagement
Engagements/Wk 0
Territory Penetration 15%
D.Results
Call Conversion 0%
New Pipeline ($) $0
Time Saved/Rep/Client - 5 Mins
What did we do?
• Attempt to shift workload from sales
to sales support – sales proposal
creation
• Sellers reluctant to ‘let-go’ and very
low tolerance for ‘quality errors’
meant little workload transferred
• Sellers showed interest in an
ancillary output of process which
showed overview of client
inventory.
There’s too much
data!

6. Page  6
3. Visualization drives ‘discovery’
• Previously unseen ‘trade-offs’ are surfaced
4. The right data delivery process is critical
• Sellers push back on excel – ‘6/10/100’ line of sight rule
5. Time sensitivity of information is important
• Different data categories had varying ‘age’ tolerance
6. There is over-confidence in ‘effectiveness’
• Sellers hadn’t engaged clients in over 6 months in 80% of
cases where they rejected investigation as unwarranted
Findings
Cycle 2: Data Aggregation SMART
Sep 2012 – Jun 2014
Evaluation
A. Deployment Cycle 1 Cycle 2 Cycle 3
% of KDD in Use 20% 60%
# Data Sources 1 12
B. Adoption
% End-User Log-Ins 6% 20%
C. Engagement
Engagements/Wk 0 23
Territory Penetration 15% 26%
D.Results
Call Conversion 0% 14%
New Pipeline ($) $0 $410,000
Time Saved/Rep/Client - 5 Mins + 9 Mins
What did we do?
• Aggregated multiple datasets
into a central set of views for
sellers.
• Created visual ‘HeatMaps’ to
allows sellers see and determine
‘valuable’ clients for engagement.
• Created client engagement
planning and execution
management process – who did
you call and when?
I can’t interpret the
data!

7. Page  7
7. Visualization is an org change agent
• ‘uncovered’ organizational & behavioral issues from
customer to product selection, time prioritisation to skills
8. The role of analytics is secondary*
• data aggregation is lower hanging, higher outcome work
• Limited impact of *Propensity models with ‘thin’ data
• Limited adoption of Propensity models without ‘context’
9. multiplicity drives irrational behaviour
• Success will be limited in data-driven projects until single-
version of the truth issues tackled as affects urgency.
Findings
Cycle 3: Visual SMART
Apr 2014 – Feb 2015
Evaluation
A. Deployment Cycle 1 Cycle 2 Cycle 3
% of KDD in Use 20% 60% 80%
# Data Sources 1 12 16
B. Adoption
% End-User Log-Ins 6% 20% 52%
C. Engagement
Engagements/Wk 0 23 57
Territory Penetration 15% 26% 29%
D.Results
Call Conversion 0% 14% 20%
New Pipeline ($) $0 $410,000 $1,700,000
Time Saved/Rep/Client - 5 Mins + 9 Mins +24 Mins
What did we do?
• Creation of infographic style 360°
view of customer - Client-On-A-
Page.
• Streamlined delivery process and
integrated with the Opportunity
Management system
• Focus on agile approaches –
value-mapping, feature evaluation
and iterative artefacts.
I’m too busy!

8. Page  8
8
Benefits Framework for SMART Analytics Programme – Cycle 3
Time
Process
Effectiveness
Client
Engagement
Sales
Effectiveness
Financial
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%
20% Lead conversion vs 4% Lead Development avg
56% WinRate vs 50% bau avg
$23k Avg winvs $21k bau avg
80% of New Opps no Renewal in Qtr
> developing new engagement cycle
> spreading forecast risk
$1.7m NetNew Pipe
• o/w $1100k Won
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
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

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# Artefact/Deliverable Question Raised
1 Territory Management Programme Who do I call next?
2 Client-On-A-Page What do I discuss?
3 Challenge to ‘Fuzzy Logic’ Why should I not follow an existing course of action?
4 Time and Productivity Where will data driven activities impact my business?
5 Critical Success Factors How should a data transformation be implemented?
# Artefact/Deliverable Question Raised
1 Importance of leading with
visualization in data-driven projects
How to best implement a data driven transformation?
2 Visualization as a catalyst for
change
How to use data to drive organisational change?
Contributions
What was delivered by programme?
Contributions to Practice
Contributions to Research

10. Page  10
Conclusions
Closing remarks and observations
Data process tools
are organisational
transformation
initiatives not tool
deployments
1
Data transformations
will impact first by
uncovering systems
inefficiencies
2
Visualization
artefacts are
relatively easy to
implement and
drive value for early
wins in
transformation
efforts – low
hanging fruit 3
You MUST tackle the presence of
existing legacy systems and issue of
multiplicity as it has material impact
on sales behaviour.
4