Deep Insights Smart Decisions Motionlogic

Transcription

1 Deep Insights Smart Decisions Motionlogic

2 About Motionlogic Big Data business of Deutsche Telekom 100% subsidiary Analytics of people movement behavior and demographic indicators Using anonymized network signaling and CRM data Able to deliver real-time as well as historic statistical insights Compliant with telecommunications and data protection regulations Launch of services in Poland in November 2014 jointly with T- Systems Poland 2

3 Motionlogic combines outdoor and indoor movement data to generate business insights OUTDOOR ANALYTICS INDOOR ANALYTICS Movement Data Sociodemographic data & GEO INFORMATION Valuable business insights

4 VALUABLE INSIGHTS BASED ON MOBILE NETWORK ANALYSIS Generating insights about locations 300 calls per hour 200 calls per hour 400 calls per hour based on anonymous network data and extrapolation 1,200 people per hour 1,600 people per hour 800 people per hour 4

5 Access via web dashboard X 5

6 Use Cases PUBLIC SECTOR City planning Detection of most growing regions Support of emergency services RETAIL Support of disease research Pedestrian frequency Demographic segmentation Conversion rates Marketing campaign evaluation Lost customers to competitors Competitor benchmarking OUT-OF-HOME ADVERTISING Location benchmarking Pedestrian/Car frequency at billboards Socio-demographic segmentation per location and time period TOURISM Detection of travel routes of roamers Travel trends Ranking of important tourist destinations Verification of campaigns by tourism board TRANSPORT Real-time speeds on roads Travel volume on roads Traffic jam detection Commuter traffic optimization Public transport statistics Train occupancy First- and last mile analysis REAL ESTATE Anchor effect analysis Shoppers dwell time in mall/on floor levels Location benchmarking Demographic segmentation Geographic segmentation 6 Competitive benchmarking

7 Successful trial on 85 store locations 85 locations Calibration counting Trend comparison Conversion rate by POS type Tag 10:00-11:00-12:00-13:00-14:00-15:00-16:00-17:00-18:00-19:00-11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 Mo Di Mi Do Fr Sa Ø Ø Frequency by POS type Customer Question Use of footfall for location analytics SOLUTION Process 85 locations in Germany Manual calibration at 4 locations Result Supplied outdoor frequencies and conversion rates for the selected locations Identified trends per store and location type Trial successfully concluded, results presented to CEO: positive evaluation Next Steps Currently in negotiation of a commercial contract for an integrated solution

8 Store Visitors / Hour Key performance drivers identified Project Results Germany / Retail Chain R² = 0, Footfall / Hour Results for inner city stores; footfall for pedestrians with a minimum speed Benchmark project from Germany shows strong correlation between specific footfall and the number of store visitors Tests to identify best performance drivers were conducted Clustering of stores into different groups Test of data for different catchment areas Split of data along different attributes As a result, approximately 59% of store performance can be attributed to a specific footfall (coefficient of determination at 0,5868) 8

9 Footfall / Hour Analysis of opening hours Analysis of Opening Hours in Zagreb High footfall in the morning Opening Hours 9:00 to 21:00 (Mo-Fr) Low footfall at night Footfall data suggests that more visitors could be attracted if opening hours were extended in the morning or shifted to 8:00h to 20:00h Footfall data for 1 km radius 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Time 9

10 Successful trial on 11 billboard locations Non-standard posters Custom visibility areas Calibration counting Counts per movement direction Daily footfall within visibility Customer Question Demo UI with results How many people view new billboards? SOLUTION Process 11 locations in Berlin and Hamburg (oversized blow-up posters where standardized metrics are not applicable) Manual calibration & blind tests at 3 locations in Berlin Analyzed footfall counts Result Supplied calibrated outdoor frequencies for specified locations Daily transit counts with +/- 12% difference to manual counts Trial successfully concluded Next Steps Integration of lessons learned Potential trials / implementation in NatCo markets

11 Successful trial on a moving train Specific route and train Custom algorithm Passenger counts for the particular train Customer Question Ho many people take a certain train? SOLUTION Process Regional train route: Berlin Brandenburg Manual control counts on 2 days Developed special algorithm for counting groups of people moving along a railway route, separate from car passengers on parallel road Result Supplied train passenger counts for a particular train (7 days, historic counts) Difference from manual control counts 1-3% Trial results accepted, further trials recommended Next Steps Identification of commercial potential for passenger counts Trial of further transportation specific solutions for the train company in 2014

12 Indoor Analytics technology CUSTOMER OPT-OUT Data COLLECTION & ANONYMIZATION Data AGGREGATION & STORAGE DATA ANALYSIS & VISUALIZATION e.g. via guest WiFi on existing local WiFi infrastructure on a secure local server / cloud in a single dashboard for all your stores Every minute a WiFi enabled smartphone sends out its MACaddress, a unique device ID Secure Data Transport via SSL/TLS at all process steps WiFi access points with Indoor Analytics software collect this data from smartphones and anonymize it (hash+salt process) The anonymous data about customer movements in-store is saved on a local server no sharing of your guest data! The analytics system pulls the data, calculates KPIs and displays them in an interactive web dashboard 1

13 Indoor analytics detailed data on indoor movements VISITOR COUNT DWELL TIME ROUTE ANALYSIS TRENDS OVER TIME SEGMENTATION BENCHMARKING 1

14 Indoor analytics detailed data on indoor movements 1

15 Indoor analytics detailed data on indoor movements 1

16 Motionlogic combines outdoor and indoor movement data to generate business insights OUTDOOR ANALYTICS INDOOR ANALYTICS Movement Data Sociodemographic data & GEO INFORMATION Valuable business insights

17 Thank You!