Using Analytics to Understand Consumers, Neighborhoods and Advertising
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Customer analytics vary by industry – what’s important for retail clothing stores isn’t always important for a telecom client, etc. Join Precisely Demographics product managers Andy Peloe and Dylan Conrad as they describe how Precisely has done the work of aggregating diverse types of data so that you can more easily answer important questions for your business as well as your customers. This webinar will help you: - Partner with your clients and choose the right data variables and granularity - More accurately identify target customer markets based on dynamic demographics - Understand retail performance and site selection based on neighborhood profiles - Use measured analytics to tell a story about a neighborhood - Identify trends to support investment strategies -Accurately assign risk
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Using Analytics to Understand Consumers, Neighborhoods and Advertising
- 2. Today’s Presenters Dr. Andy Peloe Senior Product Manager Demographics Dylan Conrad Product Manager Consumer/Context
- 3. Introducing Precisely • The merger of Pitney Bowes Software and Data and Syncsort • Precisely offers powerful data integration and optimization software alongside best-in- class location intelligence, data enrichment, customer information management and engagement solutions. • 12,000 customers • 90 of the fortune 100 • Customers in more that 100 countries • 2,000+ employees Portfolio: • Integrate • Verify • Locate • Enrich • Engage Headquartered in Pearl River, NY with offices across North America, EMEA, Asia Pacific to support our global customers and partners.
- 4. Better data, better decisions, better outcomes Precisely Connect Precisely Ironstream Precisely Assure Precisely Syncsort Integrate Precisely Spectrum Quality Precisely Trillium Precisely Spectrum Context Verify Precisely Spectrum Spatial Precisely Spectrum Geocoding Precisely MapInfo Confirm Locate Precisely Streets Precisely Boundaries Precisely Points of Interest Precisely Addresses Precisely Demographics Enrich Precisely EngageOne Communicate Precisely EngageOne Compose Precisely EngageOne Digital Self Service Precisely EngageOne Enrichment Engage
- 5. Clarify who your customers are, what they need, and where potential markets exist with this robust collection of data designed to help you understand people and the places they live, work and do business in. Portfolio includes: • Base Demographics • Detailed Demographics • Demographic Estimates and Projections • Segmentation and Geodemographics • Crime Index • Context Demographics • Consumer Data Insights • Consumer IQ Precisely Demographics
- 6. Clarify who your customers are, what they need, and where potential markets exist with this robust collection of data designed to help you understand people and the places they live, work and do business in. Portfolio includes: • Base Demographics • Detailed Demographics • Demographic Estimates and Projections • Segmentation and Geodemographics • Crime Index • Context Demographics • Consumer Data Insights • Consumer IQ Precisely Demographics
- 7. Using Dynamic Demographics for Retail Site Selection
- 8. CAMEO USA Category CAMEO USA Type 1 1A High Society Families 1B Upper Crust Households 1C Asset Rich Families 1D Elite Suburbs 1E Moguls And Mansions 2 2A Skyscraping Nouveau Riche 2B Subtopia 2C Cosmopolitan Suburbia 2D Old Money 3 3A High Flying Families 3B Urban Movers And Shakers 3C Middle Class Managers 3D Professional Urban Families 3E Affluent Established Suburbia 3F Escape To The Country 4 4A Big City Startups 4B Middle Age, Middle Class 4C Urban Success 4D Urbane Melting Pot 4E Settled In The Suburbs 4F Rural Empty Nesters 5 5A Big City Hipsters 5B School Run Families 5C Small Town Suburbia 5D Settled In The City 5E Close To Retirement, Out Of Town 5F Mature Suburbs 5G Comfortable In Retirement 6 6A Studying In The City CAMEO USA Category CAMEO USA Type 6B Suburban Sharers 6C Big Family Values 6D Diverse Urban Mix 6E Settled And Single 6F Established Traditional Neighbourhoods 6G Retirement Communities 7 7A Flown The Nest 7B Struggling Scholars 7C Fledgling Urban Families 7D Coastal Chic 7E Downtown Tenants 7F Maturing In Middle America 7G Retiring Renters 8 8A New Kids On The Block 8B Urban Endeavours 8C Bohemian Broods 8D Blue Collar Bourgeoisie 8E Provincial Fusion 8F Golden Oldies 9 9A Urban Start-Ups 9B Cramped City Families 9C Big City Small Wallet 9D Small Town Family Struggle 9E Low Income Melting Pot 10 10A Stretched Family Start-Ups 10B Struggling Young Families 10C Hard Up Households 10D Big Town Austerity 10E Homeowners In Hardship XX Unclassified / No Population Data Precisely Geodemographics
- 10. Create a Profile for a Location • Retail Site Location • Want to know potential visitors socio-economic or lifestyle profile • Overlay a geodemographic system • Estimate a catchment radius • Example profile: Aspiring Consumers Prosperous Families Exclusive Society • BUT this is the resident (night time) population only • Populations are dynamic and mobile 1. American Aristocracy 2. Exclusive Society 3. Prosperous Families 4. Enterprising Households 5. Comfortable Communities 6. Aspiring Consumers 7. Dynamic Neighborhoods 8. Diverse Communities 9. Stretched Tenants 10. Strained Society
- 11. Create a Dynamic Profile for a Location • Again, we can use a geodemographic system to help us understand audience profiles • To uncover population mobility can use mobile trace data (weekday mornings, 2 weeks in Feb) • How do inflows of population change the potential audience? • Population flows into the location are drawn from all over city and include many different geodemographic groups • These inflows of population change the location audience Inflows of Population: Mobile TraceOrigins of Flows to One Destination
- 12. Create a Dynamic Profile for a Location Inflows of Population: Mobile TraceOrigins of Flows to One Destination Chart 1: Resident profile Chart 2: Resident profile plus inflow
- 13. Population Pre- and Post-Covid Lockdown Presentation name13 Map 2: Pre-Covid 19 lockdown week-day morning population distribution Map 2: Post-Covid 19 lockdown week-day morning population distribution 3,800 and over 1,900 – 2,600 2,600 – 3,800 1,200 – 1,900 1,200 or less CAMEO USA: Enterprising Households Population Distribution Based on Mobile Device
- 14. Other factors that impact location-based decisions
- 15. Precisely CrimeIndex Burglary Score Murder ScoreAuto-Theft Score Larceny Score 250 and over 180 – 225 225 – 250 100 – 180 100 or less Composite Crime Score
- 16. Contextual Data to Support Investment Strategies
- 17. Measured and modelled analytics, linked to specific geographic boundaries tell a story about a neighborhood
- 18. Locally relevant boundaries such as neighborhoods, postcodes, and administrative areas that define where customers live and spend their time and money. Portfolio includes: • Community Boundaries • Postcode and Administrative Boundaries • Risk Boundaries • Telco Boundaries • World Boundaries Precisely Boundaries
- 19. • Defines areas populated by people with shared beliefs, needs, and experiences • Includes densely populated metropolitan areas, suburban residential enclaves, and remote rural locations • Captures the socially relevant geographies where people spend time reflected by the ways that people naturally think about and relate to location Community Boundaries
- 20. Precisely is the leading source of neighborhood boundary content on the market. Industry-leading neighborhood solution • Precisely offers the most extensive and granular neighborhood coverage available Detailed product structure and segmentation • Content is intelligently structured with no overlaps or ambiguous boundaries, and includes multiple levels of neighborhoods Relects change over time • Neighborhood Boundaries is released quarterly to introduce product updates and inform where change and coverage expansion has occurred, with emphasis on the top 500 markets across the U.S. Macro-neighborhoods (Downtown Manhattan) Neighborhoods (SoHo) Sub-neighborhoods (Alphabet City) Neighborhood Boundaries
- 21. Context Neighborhood Boundaries Name: Castro OBJ_ID: 194444 Type: Neighborhood Metro: San Francisco, CA Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather
- 22. Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather Context Demographics OBJ_ID: 194444 Total Population: 2,584 Pop Age 20-24: 98 STEM Workers: 361 Renters: 845 Homeowners: 401
- 23. Context Real Estate OBJ_ID: 194444 Home Sales last month: 7 Average Sale Price: $1,867,283 Average 1st Mortgage: $1,229,998 Average Tax: $15,980 Average Sq Ft: 1,650 Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather
- 24. Context School Rankings Status: Operational Student to Teacher: 22.8 National Rank: 2016 State Rank: 533 Free/Reduced Lunch: 167 Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather
- 25. Context Walkability OBJ_ID: 194444 Overall Score: 4.5 Amenity Score: 4.5 Leisure Score: 4.7 Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather
- 26. Context Commuter Score OBJ_ID: 194444 Biking Score: 4.5 Driving Score: 4.9 Public Transit Score: 4.9 Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather
- 27. Context Weather OBJ_ID: 194444 Winter Avg Temp: 51 Spring Avg Temp: 58 Summer Avg Temp: 68 Autumn Avg Temp: 63 Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather
- 28. Context Crime Index OBJ_ID: 194444 Crime Index: High Violent Crime Index: Average Burglary Index: Above Avg. Auto Theft Index: High Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather • Coming Soon!: • Context CrimeIndex • Context Segmentation
- 29. Products include : • Context Demographics • Context Real Estate • Context School Rankings • Context GreatSchools • Context Walkability • Context Commuter Score • Context Weather • Coming Soon!: • Context CrimeIndex • Context Segmentation Context Segmentation OBJ_ID: 194444 Enterprising Households: 697 Aspiring Households: 347 Dispersed Communities: 202
- 30. Using Analytics to Understand Neighborhoods and Consumers Dynamic Demographics • Classify and label groups of people based on where they live • Data easily linked to an address via the PreciselyID • Apply mobile trace data to understand daytime population and changes over time Contextual Demographics • People live in neighborhoods (not block groups) • Summarized and modelled data make it easy to answer questions about a geography • Consider factors beyond the location of your target audience
- 31. Q&A