Today s presenter. Paul Tyndall Vice President, Strategic Projects

Transcription

1 Today s presenter Paul Tyndall Vice President, Strategic Projects 2

2 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/webinars 3

3 Today s agenda The unique characteristics of mobility analytics that make it a highly valuable and exciting new resource for generating consumer insights whether you have customer data or not. Privacy compliant use cases to illustrate the types of insights that can be pulled from the growing mountain of mobility data points. How mobility analytics can help businesses, retailers, shopping centers, municipalities and others better understand their consumers without the need for implementing costly loyalty programs. See what affect the competition is having on your business and discover if you re cannibalizing your sales Environics Analytics

4 Why mobility 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/loyalty Competitive analysis on trade areas, visitor profiles Analyze current visitors to prospective sites This helps organizations better understand their trade areas and visitor profiles and how these change by time of day and day of week Environics Analytics

5 About Mobility data It is anonymized, permission-based data collected from locationenabled mobile devices Analysts can identify devices observed within a defined area and, in many cases, infer the likely home/work neighborhood of those devices. Quality of sources and captured data are evolving quickly, as are best practices on use and interpretation. Billions of data points are being generated daily. Most have not fully realized its abilities to enhance customer understanding and there is much confusion in the market. The challenge is to bring order to chaos. So, what does the data actually look like? Environics Analytics

6 Tracking the path of a single device ONE DEVICE Environics Analytics

7 Each device tells a different story 10 devices Environics Analytics

8 Bringing order to chaos Tracking 1,000 devices Dealing with so much data, you need a strategy to turn this seemingly random noise into meaningful consumer insights. We have identified several key business analytical challenges where this data can be applied today. As the technology matures, more will likely become possible. We are including in our processes best practices relating to privacy Environics Analytics

9 Key Steps in the mobility analytics process Define Study Define a two-dimensional boundary ( geofence ) around your point of interest such as a shopping center, branch or arena on a map Determine timeframe for study (e.g. most recent 12 months) Extract Data Extract raw mobility data points observed within geofences during period of study Transform Filter, aggregate and geocode Append small area datasets such as demographics, financial, psychographics Analyze results and generate insights Environics Analytics

10 Sample Geofence > mobile visit data Geofenced area (in purple) represents where mobile visitor data will be pulled. Each dot represents the first point where a device was observed during a visit Environics Analytics

11 Data transformation and Enhancements Digest and transform raw data to drive real insights From Raw Data Unstructured observations Device ID Latitude Longitude Unix Timestamp 86cb3277ce29b9a42fd3e7b1de71acbc38e b3bd936a898a1a67e10eb0b afd7531f19058e46ddbd675d689efc9ec3cc3b2d d88f4ed325c f2a3d51c18b8e17472b afd7531f19058e46ddbd675d689efc9ec3cc3b2d Each row represents an observation; a device can be seen more than once. To Value-Added Data Structured Device-Level DeviceID Home Country Home Postal Code Home - Long Home - Lat PRIZM5 Work Postal Code Work - Long Work - Lat Home Distance to Geofence Work Distance to Geofence 0001bcdb baa6cf87bdace2a6c9ce5f CAN V1W4T V3T0G f797be471217f41b7a4ddb4ad7712fd9fd1c CAN V0H1T V4T1N fb5fdf45e9f2f5d317aff2a9a e3b CAN T5K1X V4N5S f f5aae259d59375eea21b8 CAN V2A5Z V1Y8P b4c4150f8ecce1b71c7e0ba227eeaa08 CAN V1W4N V1Y7N Each row represents a unique device; geofence observation details appended. Total Unique Visits Environics Analytics

12 How do we gain insight? Integration with detailed small area data Latitude and Longitude Market Sizing Consumer Behavior and Mindsets Media Use Postal Geography Detailed Demographics and Change Over Time Segmentation Financial Environics Analytics

13 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 Optimize merchandizing and marketing; right-size large networks CPG Find best locations for consumer activations and brand events Municipalities Differentiate resident and non-resident service usage; 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 Compare trade areas with competitors, create work-based trade areas, evaluate sponsorships Restaurant Know visitor profile, evaluate share of visits by market Automotive Evaluate cross-shopping activity Know which postal codes are in market Environics Analytics

14 Competitive visitors Your locations versus your competitive locations Environics Analytics

15 Demographics comparison MY credit union A bank Lifestage average Household Income Size of household Dwelling Education Occupation Diversity Environics Analytics

16 Competitive cross-shopping quantified North Plaza Visitors by Centre Count % only Visited north plaza 8, Also visited south mall 1, Also visited east centre 2, Also Visited both others Total 13, ,719 total Visitors at North Plaza 27% Visitor Overlap with East Center North Plaza had the least loyal visitors with 36% visiting other locations In contrast, East Center has the highest loyalty with only 21% cross-shopping Visitor Overlap at competitor locations South Mall Visitors by Centre Count % Also Visited north plaza 1, only visited south mall 9, Also visited east centre 2, Also Visited both others Total 13, East Centre Visitors by Centre Count % Also Visited north plaza 2, Also visited south mall 2, only Visited east centre 22, Also Visited both others Total 28, Environics Analytics

17 Competitive shopper profiling Trade Area Pop 12+ North Plaza Visitors South Mall Visitors East Centre Visitors LS SG SESI Target Group Name Count % Count % Index Count % Index Count % Index Upscale Families 76, F8 U1 2 Urbane Villagers 10, F8 S1 4 Suburban Success 4, F8 S1 6 Kids & Careers 10, F9 U1 8 Boomerang City 18, F8 E1 9 Satellite Burbs 2, F1 S2 16 Pets & PCs 23, F2 E1 17 Exurban Wonderland 7, Comfortable Families 60, F1 S2 18 Management Material 5, F5 S4 22 Aging in Suburbia 21, F1 U4 31 New World Symphony 4, F2 S2 37 Trucks & Trades 21, F3 R1 40 Wide Open Spaces 3, F7 S3 42 Home Sweet Rows 2, Young Aspirationals 40, Y2 U3 12 Street Scenes 17, Y1 U3 38 Grads & Pads 3, Y2 S2 50 Suburban Scramble 2, Y2 U6 52 Striving Startups 9, Y1 U6 56 Single City Jazz 7, Downscale Locals 46, F5 U6 63 Lunch at Tim's 24, M3 U5 66 Sunset Towers 7, Y2 U6 68 Low-Rise Renters 15, Later Years 8, M1 U2 28 Metro Multiculturals 3, M3 U5 29 Silver Linings 2, M1 S4 39 Our Time 3, Low Market Presence 16, Total 248, , , All locations attract lower than average rates of Upscale Families, the target segment for North Plaza North Plaza attracts more from Young Aspirational segments while East Centre attracts more from Comfortable Families South Mall skews more heavily toward Downscale Locals 2018 Environics Analytics

18 Share of Visits Even if you have a good sense of the activity at your own locations, it can be challenging to understand the overall activity in your market US With mobility analytics you can create an apples to apples measure of your relative share of total category visits in market US THEM THEM US This can help identify over and underperforming locations at a neighborhood level Environics Analytics

19 Evaluate sponsorship opportunities Sponsorships can represent a significant portion of your marketing budget but the effectiveness of those investments can be very challenging to measure Mobility analytics allows you to understand who actually attended events and whether their profiles match your targets Environics Analytics

20 Foot Traffic Profiling Do visitors to your office areas match your target profiles? Environics Analytics

21 Trade areas: then Before Trade areas for a location Drive times/rings often based on gut feel Sometimes informed by intercept survey work Occasionally informed by customer data Limitations Don t incorporate any real world data Don t indicate potential growth opportunities Survey work is expensive and captures one point in time Environics Analytics

22 Trade areas: now Before Now Trade areas informed by mobile analytics offer greater precision to: Allow you to overlay actual visitor mobility patterns Better understand your visitor draw from the local area Flag gaps for additional learning Assess differences by time of day or day of week Easily update to understand competitive effects and other market changes All observed Visitors, Past Year Environics Analytics

23 density by time of day Aggregated mobility metrics for use in modeling 9AM 24 9PM 2018 Environics Analytics

24 Time of day ANALYSIS See how your visitor profiles change over the course of the day All Trips Early morning midday Environics Analytics

25 Travel and Tourism Mobility data provide unique insights into your visitors Visitor data are notoriously difficult to collect in the tourism sector. Leverage mobile device-based data to identify: o Who is visiting? o Where are they coming from? o Which specific attractions/destinations are they visiting? o How frequently are they visiting? Profile and identify characteristics of visitors overall or differentiate by: o Types of attractions visited o Distance travelled o Frequency of visits o Seasonality Environics Analytics

26 Tourist attraction affinities Visited Springfield (35%) Visited Attraction(s) (84%) Did Not Visit an Attraction (16%) City Zoo (13%) Art Gallery (18%) Amusement Park (22%) Stadium (12%) Park (8.5%) Concert Hall (11%) Environics Analytics

27 Envision5: our New Mobility ANALYTICS Tool 2018 Environics Analytics

28 Step 1: Define a Geofence 2018 Environics Analytics

29 Step 2: Define a Date / Time Range 2018 Environics Analytics

30 Mobility Visitor Dataset saved to My Data > Customers 2018 Environics Analytics

31 Leverage envision5 with or without a customer file The Mobility Analytics tool was designed to allow you to use all the existing reporting, mapping and analytics functions you are used to using in ENVISION5 Automatically append our comprehensive datasets to mobility visitor records to drive deep insights 2018 Environics Analytics

32 Questions? 33