Building an accurate understanding of consumers based on real-world signals
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Building an accurate understanding of consumers based on real-world signals
- 1. Building an accurate understanding of consumers based on real-world signals
- 2. About Near 2 Enabling you to understand consumers better with real-world data, and engage with them. SaaS products for real-world data enrichment & data-driven marketing World's largest source of intelligence on People and Places Bring massive data into an AI-powered unified platform to understand consumer behavior 1.6 Billion Users 44 Countries GDPR compliant | No PII data | Consent driven incoming data Scale/Data Privacy-led design 70 Million+ Places Processing petabyte of data on monthly basis
- 3. Corporate Overview 3 Marquee Investors San Francisco New York Bangalore Tokyo Singapore Sydney Office HQ 2012 Established Year USD $134mn to date Capital Trusted by London Product / Tech / Core Biz France
- 4. 4 Agenda Near - Improving Return on Investment Probabilistic Mapping of People and Places Reaching Out to Consumers Based on Real-world Signals Classifying Staypoints and Waypoints Curating and Analyzing Audience Profiles Store Footfall Attribution
- 5. 5 Near - Improving Return on Investment Staypoints / Waypoints Identify if the consumer is at a brick & Mortar store or walking/commuting/flying while emitting location pings from the device Audience profile Create bespoke audiences using ML/AI algorithms, which learn from both online and offline signals Ping-to-POI assignment Accurate assignment of people visits to brick & mortar stores to understand offline behavior Store footfalls attribution Measure campaign efficacy based on store footfalls using large scale geofenced polygon database Improved ROI
- 6. 6 Near - Improving Return on Investment Staypoints Waypoints ● Footfalls ● Offline user behavior ● User profiles ● Insights ○ Dwell time ○ Distance travelled ○ Brand affinity ○ Brand propensity ● Historical visit behavior ● Home location ● Work location ● OOH advertising ● Commute patterns ● Commute time ● Navigation search
- 7. 7 How Near Extracts Staypoints? Location Pings from a consumer Broken Sessions Staypoints Staypoints Place Matrix Retrieve endpoint of a session (staypoints) by overlaying places Time interval between pings Distance between pings Speed Dwell time at a place Post applying below conditions, algorithm extracts multiple sessions for the user
- 8. 8 How do we probabilistically map people to places? Place Matrix Place Metadata Historical Visit App History Transaction Wifi Signal Bayesian Inference Model Framework Staypoints Ping-to-POI Mapping Example of Distance-based Mapping Probabilistic Mapping of people to places using online and offline consumer behavior
- 9. 9 How Near Segments Audience Profiles? We have successfully surpassed the Gender/Age Group on-targeting benchmarks in USA and extending to other markets such as ANZ, SEA, MEA and APAC Near ML / AI Platform Ping-to-POI Mapping Place DB Historical Visits Device Apps / Usage Online Behavior Device / Signature Offline / Online Data store Entity Resolution Supervised Learning Ground Truth Labeling Semi-supervised Learning Natural Language Understanding Reinforcement Learning Transactions People Properties Place Properties Cloud Engineering MLOps Feature Engineering Jobs Orchestration Container Orchestration SQL/NoSQL Databases Gender Age Group Profiles Interests Brand Affinity Affluence Ethnicity Home Location Work Location Household Place Boundary Brand Category Store Hours Gender Distribution Age Group Dist. Profile Dist. Footfalls Dwell Time Distance Travelled Frequent Visitors Attribute Store Consumer attribute Store Places attribute Store Nielsen Digital Ad Ratings
- 10. 10 How Near does Store Footfall Attribution? Digital Campaign Footfalls Exposed Group (from Digital Campaign) Control Group (ActAlike) Footfalls are measured for pre, during and post-campaigns 1. Footfalls are measured with the most accurate information from location pings and places DB 2. Near Places DB is one of the largest source of Building Place Boundaries and we are adding more every day 3. Location Pings are curated for staypoints and removing any anomalies in the data 4. We use distributed technologies to couple the two sources over large scale (several TBs every day) 5. Time series model estimates / offsets any missing data on a given day Attribution Footfalls during COVID-19 Digital Campaign and Offline Attribution
- 11. 11 Use Case: Marketers / Advertisers can now target super-refined segments With Allspark, you can analyze and reach out to consumers based on Gender & Age Group Profiles Interests Events Weather Income / Affluence Home Location Brands Affinity Stores Proximity Curate Activate Measure
- 12. Allspark is SaaS Platform for curating and activating audiences through your choice of a DSP and measure campaign effectiveness Simplified marketing powered by the world’s first AI audience assistant Allspark Carbon is an data enrichment platform where we augment the customer 360 view by using ML/AI approaches for matching user attributes both in offline and online world The best software platform for data enrichment CARBONTM Curate, Activate, MeasureData Enrichment
- 13. 13 Get Campaign Insights Daily Offline Attribution Report Delivering Performance Deliver Ads Programmatically Activate Campaigns using Custom Creatives Reaching Audience Estimate Campaign Reach Curate Bespoke Audiences Media Planning Pre-Sales Market Research Reports Export Audiences To DSP Advanced End-of-campaign Report Activation MeasurementAudience Curation Allspark Supports the entire Marketing Campaign Lifecycle based on Real-world Behavior
- 14. 14 360o understanding of your audience at an ID level Get your First-Party Data Enriched in Real-time
- 15. 15 Q & A
- 16. THANK YOU Connect with us at near.co From inception, the Near Platform has followed a privacy-led design. The Platform never stores or deals with PII (Personally Identifiable Information) and all incoming data streams are consensual. We are GDPR compliant, and the platform has built-in processes to forget and purge user data on requests. Read the complete privacy policy at near.co/privacy