1.
Empowering Businesses
with Data
Lightning Talk
Adam Fletcher

2.
$whoami
PhD in Molecular and Structural Biology
10 years experience in biological research
Almost 1 year at Equal Experts on EvolvE
Research Areas
Detected DNA abnormalities from blood
Predicting resistance to therapy in cancer patients
Working with clients to understand and better use their data
afletcher@equalexperts.com
LinkedIn: adamfletcheruk

3.
Introduction
What is Machine Learning?
A system which can perform tasks without being
explicitly programmed.
Instead it relies on patterns of data and inference.
Why Should Businesses Care?
Understanding why certain patterns are present to take
specific actions
Personalising experiences for users by predicting their needs
Understand public perception on Social Media

4.
What Can Data Analytics Reveal?
How Can Machine Learning Help?
Talk Aims
Using statistics, understanding variations in a
process that impact productivity.
Lead to restructuring processes based on
results.
Understanding of how users interact with a site.
Tailoring their experience leading to greater
user retention and satisfaction.

5.
Data Analytics in Business
The Cost of Variation
● UK-based events planning company
● Franchised, operate through standard pricing
● Organisation and staffing coordinated in branch
Aims and Objectives
● Different areas coordinate the events differently
● What is the impact of this variation
● What happens if we reduce this variation

6.
Data Analytics: Variation in Consultations
Initial Consultations
2+ Appointments:
● East Lancs,
W.Yorks
● Hamp and W.
Sussex
● Manchester North
● S Wales
● Greater Glasgow
● Merseyside
● W. Lancs

7.
Data Analytics: Variation in Consultations
Length of Appointments
Which are Good
/Bad Results?
*
* ** *
*
* * **
H1: Detailed planning
means team can proceed
quickly
H2: Length of Time in
appointment has no
impact on delivering
event

8.
Data Analytics: Variation in Consultations
Hypothesis Testing
Time between 1st Consultation and Event (Days) Region Expected Observed Correct
Central and SW Scotland SLOW FAST N
Manchester North SLOW NEUTRAL N
Essex SLOW SLOW Y
Northern Ireland SLOW FAST N
West London SLOW NEUTRAL N
Cumbria FAST FAST Y
Durham FAST NEUTRAL N
South Yorkshire FAST SLOW N
Central Yorkshire FAST SLOW N
East Yorkshire FAST NEUTRAL N

9.
Data Analytics: Variation in Consultations
Impact of Variation
As-Is Model:
Total Time = 111,897 hrs
Sample Size = 58,161 events
100,000 Events/Year = 192,391 hrs
New Model: One 90-min
Consultation
Total Meeting Time
= 100,000 x 1.5 Hrs
= 150,000 Hrs
Difference = 42,391 hrs per year
Assume 50% Model Uptake:
21,955 hrs to reclaim
Approx 14,000 additional
consultations

10.
Data Analytics: Variation in Event Staffing
Requirements at an Event
Capabilities Requested
● 2 Drivers
● 1 Event Conductor
● 2 Event Setup
Staff Members Needed
● 2 Drivers
● 1 Event Conductor
● 2 Event Setup
Do you need 5 staff members to fulfil these 5 capabilities?

11.
Data Analytics: Variation in Event Staffing
Impact of Variation
As-Is Model: Proposed Model:
Task Conductor Staff 1 Staff 2 Staff 3 Staff 4
Pre-Use Check - 0.5 0.5 - -
Pick up Clients - 1 1 - -
Travel to Event 1 1 1 1 1
Event 2.5 2.5 2.5 2.5 2.5
Return Trip 1 1 1 1 1
Clean Vehicle - 1 1 - -
Sum 4.5 7 7 4.5 4.5
Task Conductor Staff 1 Staff 2
Pre-Use Check - 0.5 0.5
Pick up Clients - 1 -
Travel to Event 1 1 1
Event 2.5 2.5 2.5
Return Trip 1 1 1
Clean Vehicle - 1 1
Sum 4.5 7 6

12.
Data Analytics: Variation in Event Staffing
Impact of Variation
As-Is Model:
Total Staffing Time: 27.5 Hrs
95,000 Events a Year = 2.6M hrs
New Model:
Total Staffing Time: 17.5 Hrs
95,000 Events a Year = 1.6M Hrs
Assuming 40% uptake
= 400,000 Hours gained
Time for an additional
23,000 Events
Conclusion:
Simple Data Analytics and
Statistics can make a
difference

13.
Machine Learning in Business
Understanding Users
● EU-based job application website
● Want to personalise experience for users
Aims and Objectives
● Understand how different people use the site
● Personalise their jobs based on their needs

14.
Machine Learning: Understanding Users
Unsupervised Machine Learning
● Applicants asked a series of True/False questions
● Approach used: k-means clustering
● Responses were grouped into 5 clusters (k=5)
● Common trends were seen in the clusters
● Personas were designed to explain the series of needs
applicants will face

15.
Machine Learning: Understanding Users
Data Analysis
● Blue cells are low values for each question
● Red cells are high values for each question
● Clusters have trends of similarly answered
questions
Raw Clustering Data

16.
Machine Learning: Understanding Users
Personas

17.
Machine Learning: Understanding Users
Impact of the Personas
● Characterising users allows customised job search order
● Alter the information required to list a job
● Detailed understanding of how people use the site
Results
● Higher user retention and engagement in platform
● More companies paying to promote jobs
● More new users through recommendations

18.
What Can Data Analytics Reveal?
How Can Machine Learning Help?
Conclusions
● You can examine inefficiencies in
your business
● Measure the impact of changing
business processes
● Better understanding of users
● Increased satisfaction promotes
confidence for paid users

19.
Any Questions?
and thanks
for listening