Deep insight in the physical world

Neosperience Store Analytics is the SaaS solution to extract meaningful informations about people visiting stores in an accurate and reliable way

Transcript

1. www.neosperience.com | blog.neosperience.com | info@neosperience.com Neosperience Empathy in Technology Deep insight in the physical world March 12th, 2019
2. Safe Harbor Statement Certain information set forth in this presentation contains “forward-looking information”, including “future oriented financial information” and “financial outlook”, under applicable securities laws (collectively referred to herein as forward-looking statements). Except for statements of historical fact, information contained herein constitutes forward-looking statements and includes, but is not limited to, the (i) projected financial performance of the Company; (ii) completion of, and the use of proceeds from, the sale of the shares being offered hereunder; (iii) the expected development of the Company’s business, projects and joint ventures; (iv) execution of the Company’s vision and growth strategy, including with respect to future M&A activity and global growth; (v) sources and availability of third-party financing for the Company’s projects; (vi) completion of the Company’s projects that are currently underway, in development or otherwise under consideration; (vi) renewal of the Company’s current customer, supplier and other material agreements; and (vii) future liquidity, working capital, and capital requirements. Forward-looking statements are provided to allow potential investors the opportunity to understand management’s beliefs and opinions in respect of the future so that they may use such beliefs and opinions as one factor in evaluating an investment. These statements are not guarantees of future performance and undue reliance should not be placed on them. Such forward-looking statements necessarily involve known and unknown risks and uncertainties, which may cause actual performance and financial results in future periods to differ materially from any projections of future performance or result expressed or implied by such forward-looking statements. Although forward-looking statements contained in this presentation are based upon what management of the Company believes are reasonable assumptions, there can be no assurance that forward-looking statements will prove to be accurate, as actual results and future events could differ materially from those anticipated in such statements. The Company undertakes no obligation to update forward-looking statements if circumstances or management’s estimates or opinions should change except as required by applicable securities laws. The reader is cautioned not to place undue reliance on forward-looking statements.
3. Luca Bianchi Who am I? Chief Technology Officer @ Neosperience Stuff that makes me happy: • Discussing about Software Architectures • Talking about Serverless • Developing on Blockchain technologies • Implementing Neural Networks github.com/aletheia @bianchiluca https://it.linkedin.com/in/lucabianchipavia
4. Janos Tolgyesi Who am I? Team Leader Machine Learning @ Neosperience Stuff that makes me happy: • Connecting the dots • Making things work • Rising the abstraction level github.com/mrtj @jtolgyesi https://www.linkedin.com/in/janostolgyesi/
5. Neosperience Cloud allows to create personalized, relevant experiences that strengthen 
 the relationship with the customer across touchpoints: web, app, platforms, point of sale How Neosperience Cloud delivers digital experience innovation The first digital experience platform to establish empathic relationships with customers that takes into account their uniqueness. A set of application modules condensing multi-disciplinary skills: data scientists, designers, software architects, cognitive, behavioral and social psychologists, to unleash your brand’s potential. Increase customer engagement • Tailor storytelling and call-to-action • Grow the value of the customer • Suggest the most suitable products and services • Accelerate on-boarding and increase conversions • Generate recurring revenues, evolving loyalty into membership • Send personalized notifications • Delight the customer with gamification • Make digital experiences come alive in extended reality • Nudge advocacy 01 Understand Listen to customers
 across channels 02 Engage Deliver relevant
 experiences at scale 03 Grow Transform prospects
 into customers for life
6. Neosperience Cloud Cloud
7. Store Analytics “a Neosperience solution to detect meaningful insight in the real world”
8. People number is a KPI used to estimate ROI Why count people in store? • Understanding the number of people is considered a good way to estimate the average return of a given store • The daily income of a store divided by the overall number of people detected gives a ROI • Understanding high traffic stores can led to strategic decisions • Low traffic or lower ROI can be closed or moved
9. Commonly used devices are flawed and lack accuracy People counting is broken IR sensors are cheap but inaccurate • no unique counting • no information about what happens in store • false positives laser / thermal counters are expensive and do not provide relevant information except counting • unique counting • no information about what happens in store • lesser false positives
10. Detect relevant insights about your customers in stores using cameras Introducing Neosperience Store Analytics Neosperience Store Analytics is the SaaS solution to extract meaningful informations about people visiting stores in an accurate and reliable way • Uses both standard cameras and dedicated hardware with a cost effective profile • Dedicated Hardware is projected to optimise costs, heat management and reliability • Stream acquisition is achieved in cloud • Allows for multiple people counting, detects unique visits • Enables advanced insights extraction
11. A serverless solution based on Neosperience Cloud Store Analytics • Almost fully serverless • Idle costs for Kinesis Video Streams • Rekognition was too expensive for our use case, had to provision ML model directly • Model customisation capabilities through Sagemaker (marketplace / custom built) • KVS does not trigger Lambda (polling through lambda orchestration function triggered by cloud watch) • Face obfuscation to make our solution GDPR compliant (faces deleted within 24h)
12. Initial rollout in Milano
 4 cameras / 3 months / 12 hours per day video / 5 fps Product case study: Deborah Deborah Milano (Sodalis Group) started a progressive rollout of StoreAnalytics in their Stores in Milano. Data in numbers (referred to Milano area only): • ~ 240.000 minutes of video (~ 1 TB) • ~ 70 million frames • ~ 350 million times detected a person • ~ 20 GB detection data in JSON
13. Mapping people presence within a given area of interest Results: people heatmaps, trajectories, insight • Being able to recognise people and track their movements in front of a camera leds to interesting results not only related to people counting • Store managers can obtain a clear view of the preferred areas inside a store • And event the overall amount of people that do not enter the store • Store Analytics over delivered about store understanding, delivering a different but more meaningful metric
14. Trajectory clustering
15. Average number of persons per day in the store and in the storefront Store Analytics POC insights 0.00 2.00 4.00 6.00 8.00 20190102 20190103 20190104 20190105 20190106 20190107 20190108 20190109 20190110 20190111 20190112 20190113 20190114 20190115 20190116 20190117 20190118 20190119 20190120 20190121 20190122 20190123 20190124 20190125 20190126 20190127 20190128 20190129 20190130 20190131 20190201 20190202 Persons present per day in Deborah CityLife Store Persons present per day in Deborah CityLife Camera view 0.00% 8.00% 16.00% 24.00% 32.00% 20190102 20190103 20190104 20190105 20190106 20190107 20190108 20190109 20190110 20190111 20190112 20190113 20190114 20190115 20190116 20190117 20190118 20190119 20190120 20190121 20190122 20190123 20190124 20190125 20190126 20190127 20190128 20190129 20190130 20190131 20190201 20190202 percent_in_store
16. Average number of persons per hour in the store and in the storefront Store Analytics POC insights Number of total persons 0 2 4 6 8 10 12 14 9 10 11 12 13 14 15 16 17 18 19 20 Number of total persons 0 2 4 6 8 10 12 14 Hour 9 10 11 12 13 14 15 16 17 18 19 20 Monday Tuesday Wednesday Thursday Friday Saturday Sunday
17. More features and many improvements in the Summer ’19 Release Store Analytics 2.0 • Person detection at the edge (Intel Movidius / Google Coral / Nvidia Jetsons) • Time series database to store data as stream (better performances and scalability thank DynamoDB, support for temporal queries) • Trajectory reconstruction improvements with recurrent neural networks • Pose detection and behavioural analysis to better track people and detect different behaviours towards psychographics analysis • Replace Video Streams Processor with Kinesis Video Streams Inference Template - KIT 
 (https://amzn.to/2JaK9i2)
18. Thank you.
19. www.neosperience.com | blog.neosperience.com | info@neosperience.com