Making the move to Mobile Analytics. Services to Identify Consumer Travel Patterns Any Time, Any Day, Any Where

Transcription

1 Making the move to Mobile Analytics Services to Identify Consumer Travel Patterns Any Time, Any Day, Any Where

2 Today s Presenter Michele Sexsmith Senior Vice President & Practice Leader Retail, Media & Real Estate Environics Analytics Michele Sexsmith Senior Vice President & Practice Leader Retail, Media & Real Estate 2

3 housekeeping Listen only mode for attendees Questions at the end. Use the Webex Q&A Feature in your Interface Technical difficulties? Presentation deck will be available environicsanalytics.com/webcasts 3

4 Agenda 1. About mobile analytics 2. Applications 3. Case studies 4. Data sources and evaluation process 5. Questions 4

5 Mobility data: what it Is and what it does Anonymized, permission based data collected from locationenabled mobile devices Users can identify devices observed within a defined area, and in many cases, infer the likely home location of those devices Quality of sources and captured data are evolving quickly, as are best practices on use and interpretation 5

6 Key Business Questions Trade Area Sizing Before/After Work My Store Other Store Home Profile Analysis Competitive Impact My store Competitor 6

7 Our mobility data partners We ve engaged two well known mobility data providers and continue to evaluate others. Partners Uber Media and BI Spatial Partners INRIX and esite Uber Media: Data supplier. One of the largest proprietary datasets of global mobile consumer activity BI Spatial: Technology partner. Experts in processing spatial Big Data for real estate applications with a number of spatial filters to assess likely home geography INRIX: Data supplier. Also a global company, collects and provides movement data pertaining to road use, driver services and some smartphones esite: Technology partner, has built INRIX data into its web based Trailblazer platform Product Data derived from smartphones through mobile ad exchange technology and first party apps Because it s smartphone enabled, it s an ideal source for analyzing foot traffic patterns in shopping malls/ plazas, urban areas and large stores Data output is flexible and customizable Product Data derived from in car navigation (2013 MY and newer) and GPS systems and mobile devices Helpful for capturing vehicle oriented traffic flows Fixed data output within easy online tool (no path data in standard tool) 7

8 Why mobile analytics? new insights regardless of your existing data Limited/No Customer Data Visitor profiling Trade area validation Sister store impact analysis Time of day/week analysis High Quality Customer Data Cross shopping/co tenant analysis Competitive analysis on trade areas, visitor profiles Analyze current visitors to prospective sites 8

9 A new set of solutions Before Trade Areas for a Location Drive times/rings often chosen based on gut feel Sometimes informed by intercept survey work Sometimes informed by customer data Limitations: No real world data incorporated Does not indicate growth opportunities Survey work expensive, captures one point in time Sparse customer data in many industries, and only helpful for current locations 9

10 A New Set of Solutions Now Before Trade Areas Now Overlay actual visitor mobility patterns Better understand visitor draw from the local area Flag the gaps for additional learning Assess differences by time of day or day of week Update to understand competitive effects, other market changes All Tracked Visitors, Past Year 10

11 DAYPART ANALYSIS Airport Rd & Queen St East, Brampton All Trip Origins (Past 1 year) Airport Rd & Queen St East, Brampton Saturday Afternoons Airport Rd & Queen St East, Brampton Weekday Mornings 11

12 INRIX data: Visualize in Trailblazer Select target area and daypart Visualize visitor origin hotspots Convert to polygon format to export and use in ENVISION 12

13 Integrate with detailed small area data - see the opportunity WHO THEY ARE PRIZM Analysis WHAT THEY DO WHERE THEY SHOP 13

14 Time of day analysis Need: Understand visitor differences by time of day to optimize local online spend 14

15 QSR Early Morning Visitors (Midnight to 6 AM) 50% within a 20 minute drive High Volume PRIZM Premier Clusters 24% 10% 15

16 QSR 16

17 QSR Midday Visitors (12 Noon to 3 PM) 50% within a 30 minute drive High Volume PRIZM Premier Clusters 6% 5% 5% 17

18 Use insights to bridge offline and online media preferences Mobile Use Early morning visitors Midday visitors Heavy Heavy Online Use Heavy One Stop Shop Heavy Purposeful Searching Ad Placement What they do online Paid search Competitive restaurants Recipes Entertainment Sports Local (eg., Maps, Yelp, Groupon) News Finance Weather Local (e.g. Maps, Yelp, Groupon) Sources: PRIZM Premier, GFK MRI 18

19 Sister Store analysis Need: See how nearby retail stores share customers and trade areas 19

20 Electronics Retailer: Ajax Store Shoppers Retailer 20

21 Electronics Retailer: Ajax and Oshawa Store Shoppers Overlap area Mainly shop the local store Mainly shop the local store Retailer 21

22 Electronics Retailer: Ajax and Oshawa Store Shoppers Overlap Shoppers Oshawa Shoppers Ajax Shoppers (70% TA) Mainly shop the local store Trade Area Size: 291K HHs Overlap area Trade Area Size 71K HHs Trade Area Size 249K HHs Mainly shop the local store Retailer 22

23 competitive analysis Need: See how nearby locations compete for trade areas and shoppers 23

24 Competitive Analysis Identify overlap in trade area for more consumer insight 24

25 Foot Traffic or cross-shopping analysis Need: Understand who s present in different areas of a Property 25

26 cross-shopping or Foot traffic analysis Evaluate high density areas for foot traffic analytics Identify 1 hour before, 1 hour after, for crossshop analytics of specific anchors within retail mall Foot Traffic Heat Map Shopping Mall Avg HH Income: $95K Avg HH Income: $160K 26 Environics Analytics Mobile Analytics Overview

27 A note on privacy All permission based, but important to respect individual privacy in use of data, and watch the line between informative and creepy Device ID is NOT MAC ID and cannot be connected 27

28 Mobile analytics: a bricks-and-mortar game changer Optimize marketing displays to known audience Understand profile trends by time of day/week Identify gaps in existing customer profile Cannibalization and competitive analysis Define and refine trade areas Overlay PRIZM identify key visitor lifestyles Optimize flyer distribution Infer demographics, spend and behaviours Evaluate investment/ divestment decisions Embed in network optimization models Optimize staffing Identify new locations or tenants Align products/services to consumer needs Know consumer paths to and at site 28

29 Applications across many industries Grocery Evaluate likely customer profile of new locations, and cross shops in current ones Out of Home Identify audience for properties by time of day/day of week Fuel Retail & C Store Know consumer path, optimize merch. and marketing; right size large networks CPG Find best locations for consumer activations and brand events Municipalities Align services to needs of non resident population; attract new businesses Not For Profit Evaluate canvassing areas; select locations for fundraising events Gaming Evaluate trade areas and competitive impact; understand casual visitor profile Banks and Credit Unions Right size large networks and grow smaller networks based on consumer pathing Restaurant Know visitor profile and size competitive threats Automotive Evaluate cross shopping activity Know which postal codes are in market 29

30 Source Comparison Sources and Use Cases Primary Source Trade Area Insight Cross shopping and foot traffic analysis Output and Access Type of Output Uber Media and BI Spatial Mobile ad platforms through smartphones Visitor likely home location or trip origins Very strong Trade Area polygons Visitor points INRIX and Trailblazer Mix of vehicle GPS and service chip and some smartphones Currently trip origins only Currently not available Software as a service heat maps and trade area polygons Customizable Output High Moderate ENVISION friendly output Yes Yes Cost structure Project based Project or license option 30

31 201 8 Nosehill Natural Environment Park Sample Deliverables: Uber Media Data Trade Area Starting at $1,500 Visitor analytics Starting at $3,500 Fully Loaded starting at $6,500 Foot Traffic Heat Map Starting at $2,000 Division No. 6 Ã Ã Ã Ã Ã Northland Centre ì 1 Ã ì 1 Ã ì 1 Ã ì 1 Ã ì 1 Ã Calgary 31

32 Sample Deliverables: Inrix Data Trailblazer software Unlimited Use in Canada and/or US $36K USD per country + customization costs Ad-hoc Trade Area Starting at $900 per location Web based, easy to use, designed for deploying site models Data assets/models are pre loaded into the tool Heat maps or hex based polygons to load into ENVISION 32

33 Thank you! Michele Sexsmith Senior Vice President & Practice Leader Presentation deck will be available environicsanalytics.com/webcasts Visit us at RECON! Booth N

34 Uber media examples of contributing apps Other Words with Friends Solitaire TextNow Panel Flashlight AroundMe TuneIn Radio 34 Environics Analytics Mobile Analytics Overview