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mDataBetter Decisions from Big DataChris [email protected] of mDataJan2014
ContentsmData Overview
HotspotDiscover
Case Studies
03/05/2023 3
Growing Mobile Market
Monetise Big Data Assets
Drive Internal benefits
Europe US Global Acquisition
Mobile Data Monetisation
Big Data Monetisation
4
Testing value in 20092014 Big Data essential for Big Decisions
2009: Value of
Data
2010: Prove value with
Coca-Cola
2011: One man and his laptop
2012: mData testing begins
2013: Big Data enables Better
Decisions
2014: Big Data essential for Big Decision
s
03/05/2023 5
SINGLE BIG DATA PLATFORM
Discover
Hotspot
2014 Focus: UK Leading Mobile Analytics using Innovation and Collaboration to drive market forwards
CORE PRODUCTS MONETISE DATA ENABLE INTERNAL ANALYTICS
Smart Cities
Advertising
Market Research
Network
Brand
Propositions
Hotspot ExampleManchester mCommerce Posters
7
Hot Spot Example: Manchester outdoor hotspots examplesCase study specificationsThe case study includes all mobile web users in ManchesterWe defined a round area of 5km radius centred at the intersection of St. Peter’s Square, Mosley Street and Dickinson Street.Over 390k unique Orange mobile web users were captured, among them there are 56k unique users who visited commerce sitesTime period 14/10/13 – 20/10/13
8
Hotspot data visualisationEach data point represents one cell on the map
There are three parts of a cell1. The small circle
signify the actual mast on which the specific cell sits
2. A mid circle indicating the area where there is higher density of users captured by the cell
3. The larger circle covers the majority of users captured by the cell
Hotspot ExampleWaterloo Train Station
03/05/2023 10
Heatmap of users – outside of London (1 hour after Waterloo)
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
03/05/2023 11
Heatmap of users – outside of London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
03/05/2023 12
Heatmap of users – outside of London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
03/05/2023 13
Heatmap of users – outside of London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
03/05/2023 14
Heatmap of users – outside of London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
03/05/2023 15
Heatmap of users – outside of London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
03/05/2023 16
Heatmap of users – London (1 hour pre and post Waterloo)
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 17
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
Heatmap of users – London
03/05/2023 18
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 19
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 20
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 21
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 22
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 23
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 24
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 25
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 26
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
03/05/2023 27
Heatmap of users – London
T-60min – T-50minT-50min – T-40minT-40min – T-30minT-30min – T-20minT-20min – T-10min
T-10min – T
T – T+10minT+10min – T+20min
T+20min – T+30min
T+30min – T+40min
T+40min – T+50min
T+50min – T+60min
Discover
Discover Example: mData has the capability to segment customer bases using mobile network data, influence marketing strategy and measure ad effectiveness
Who are my customers?Who are my competitors?
How do I reach my Customers?
03/05/2023Orange UK PAYM smartphone usage 30
Hig
h U
sers
Low
Use
rs
7.9% 8.3% 3.8%
5.4%
12.9% 4.6%
5.8%
7.7% 12.6%
17.7%
13.3%
Mobile Maxers
Photo Sharers
Music Lovers
Mainstream Socialisers Sports
Fanatics
ExtremeGamers
Met Checker
sInfrequent
UsersSole
Searchers
ActiveCulturals
Shoppers
High Low
Web Segmentation
Our top users account for 1/5 of smartphone customers but over 1/2 of all data usage
03/05/2023EE TEMPLATE FOOTER 31
78% 21% 24 41
Photo Sharers
880MB £££8.3%
Music Lovers
54% 42% 24 52
900MB £££3.8%
Mobile Maxers
52% 44% 28 139
1.7GB £££7.9%
Segment Profiles
03/05/2023EE TEMPLATE FOOTER 32
Shoppers
77% 19% 20 46
580MB ££5.4%
Mainstream Socialisers
51% 41% 23 56
650MB ££12.9%
Active Culturals
80% 18% 19 49
500MB ££4.6%
Case Studies
Case Study 1: Role of Mobile in RetailMap
Shopping Centres
Measure Footfall
Mobile Usage
Catchment area extends North and West
22% of customers use Facebook
Instagram 12%
Amazon 7%
Groupon 2%
Asos 1%
Optimise Store Placement
Understanding Shopper
Behaviour
35
AIM:To understand mobile behaviour during the Olympic Games using EE network data
Case Study 2: Olympics Study: 27th July to 12th August 2012
Key findings1. 637k unique customers
identified in Olympic Park
2. On Average 29% of customers used mobile web while in the park vs. 11% control
3. Social Networking sites had a higher usage while outside the park
4. News related sites had an increase in traffic while in the park
Case Study 3: Tottenham Court Road Retail Study
Key Points• 5 areas were analysed using Mobile Network Information
• West of Tottenham Court Road Station (mainly Oxford street)• East of Tottenham Court Road Station• South of Tottenham Court Road Station• North of Tottenham Court Road Station• Piccadilly Circus (not shown on map)
• The data was analysed over a period of 30 days from 8th April to 6th May
• Example insight looks at customer profiles and mobile web usage for the 5 different areas looking purely oat weekday footfall in this pack. Although weekend information is available.
Time of Day Analysis – Weekday only
Key Points• For each area there are some different profiles of behaviour
• There are 3 peaks in footfall in all areas in the morning, afternoon and evening• The South has its largest footfall in the evening – due to it covering Soho and Covent garden areas• To the East there is a bias for during the day time (indicating mainly workers)• To the West there is a large lunch time and evening spike
1. North 2. South
3. East 4. West
Summary• 2013: Big Data enables Better Decisions• 2014: Big Data essential for Big Decisions
• Mobile Data has huge potential
•mData aim is to lead Mobile Analytics in 2014Head of [email protected]
