Big Data for a better Customer Experience. Lauren Sager Weinstein Head of Analytics Transport for London

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1 Big Data for a better Customer Experience Lauren Sager Weinstein Head of Analytics Transport for London

2 Our purpose Keep cities working and growing and make life better Plan ahead to meet the challenges of a growing population Unlock economic development and growth Meet the rising expectations of our customers and users 2

3 London s Big Data 10 million people in London by million oyster cards in regular usage million Tube journeys on 28 Nov 2014 More than 50 million bus journeys in week of 22 Sept 2014 Given our number of customers, by its very nature our data is Big... 3

4 Our work in Big Data The first step is to understand the context of our data But we can do even more to turn this into Big Data Analytics when we combine it with other data sources and apply algorithms.... 4

5 Inferring destination for bus trips A customer taps an Oyster card on the reader, which records the location and time Can we infer the exit point? Stop A Stop B Bus events are recorded in the ibus system and we can match this with our Oyster data 5

6 Where is the next tap? From the location of the next tap (if there is one), we can infer where a customer alights If next trip begins at stop X, the current segment is inferred to end at stop A Stop A Stop X Stop B Station Y If next trip begins at station Y, the current segment inferred to end at stop B 6

7 Using our Big Data tools Time ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### LEWISHAM CENTRE LOCKMEAD ROAD THE SQUIRRELS ST MARGARET'S CHURCH / BRANDRAM ROAD BLACKHEATH HOSPITAL BLACKHEATH STATION # ROYAL PARADE MONTPELIER ROW ST GERMANS PLACE STRATHEDEN ROAD / SHOOTERS HILL ROAD BLACKHEATH / ROYAL STANDARD KIRKSIDE ROAD WESTCOMBE PARK STATION # WESTERDALE ROAD SAINSBURY'S AT GREENWICH PENINSULA ODEON CINEMA MILLENNIUM VILLAGE SOUTH MILLENNIUM VILLAGE / OVAL SQUARE NORTH GREENWICH STATION <> BOORD STREET BLACKWALL LANE MORDEN WHARF ROAD BLACKWALL TUNNEL / EAST INDIA DOCK ROAD ANDREW STREET ST LEONARD'S WHARF COVENTRY CROSS ESTATE BROMLEY BY BOW STATION <> GRACE STREET BOW INTERCHANGE MARSHGATE LANE WARTON ROAD STRATFORD HIGH ST STN [DLR] / CARPENTERS ROAD STRATFORD BUS STATION <> # [DLR] We use our analysis to monitor congestion so that we can tailor our bus services where needed We also use this for our bus route groupings so we can design good transfer points 7

8 Customer s Database holds more than 4 million addressees 85 million s sent in 2013 Key sources are: Oyster data Congestion Charge Barclays Cycle Hire Registered cyclists Multi modal message reaches 2m customers every week 83% of customers rate the CRM service update s as useful or very useful Message content is often dynamically tailored 8

9 Automated refunds Revenue collection is important for us and our customers Operational issues or customer mistakes Automated refunds Pattern matching 9

10 Customer Segmentation at Stations B aker S treet - C lus ter S izes 18% 30% 16% % Cluster Description Median Start Time Regular Frequent User Occasional User (Resident) Irregular Frequent User Occasional User (Visitor) Journeys / Day Stations / Day # of Regular Days # of Irregular Days Cluster Sample Size 08: % 12: % 11: % 11: % 10

11 Behaviour Change Trial Communications Leaflet Whiteboard poster Announcement Leytonstone to be played from TfL is investing to improve the capacity and frequency of Tube services but we know that at certain times and places the network can be very busy. The busiest time here is between 08:15 and 08:30. If you are able to travel outside this time you could have a more comfortable journey. 11

12 Proportion of demand between 7am and 10am in each period Proportion of demand between 7am and 10am Bethnal Green Results The results suggests change in passenger behaviour: Demand distribution over the peak period consistent between pre trial and September periods Approximate 5% shift in demand during target time period of 08:15 to 08:45. Total Peak Demand over the trial period relatively unchanged Target Time Period 12

13 What next? Our future aims Many more topics and questions to explore! Integrating ticketing, bus, traffic congestion, and incident data for better performance of the bus and road networks Integrating social media with our customer data for deeper understanding Looking at weather data to see how it affects transport use Using new data mining tools and geo-spatial visualisations to bring data to life

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