LinesA Walk among the Outdoors

A triumphant tale of putting IOT to work for Out-of-home media owners and advertisers.

Building Digital Infrastructure for the Physical World

In addition to engineering intricate and sophisticated financial products, we build and deploy digital infrastructure for the physical world. Which looks unsexy, until it doesn’t.

This is the tale of one such undertaking.

Hashbrown Systems

had developed an IOT device called iRED. The purpose of which was to sample traffic data and create a first party data program for OOH Media Owners. Apart from the fact, that we were able to collect unprecedented volume and variety of data for our client. The whole exercise enabled new revenue streams and augmented sales for media owners.

The Client

Leafberry Advertising is an outdoor advertising company based out of Ludhiana with sole rights to a city that is sometimes called the Manchester of India.

A comparable city would be Newark, Fresno or Guadalajara - just to provide a context.

You may learn more about the company here east

Leafberry Box

Objective

Objective

Objective

Photo by Vladislav Reshetnyak on Pexels

Data as a service to the advertiser.

Data about circulation, points of interest, impressions.

Hard numbers that shall help advertisers with optimum allocation and attain significantly higher return on investment through budgetary allocation to

Out-of-Home

Hashbrown Systems

The higher objective was to quantify consumer data in a meaningful way by deploying scientific methods.

Methods & Technology

Methods & Technology

IOT, Sensors, Cloud computing, APIs, Deep Learning and Vision, whole lots of Statistics and no lesser amount of lady luck.

Identification of points of confluence of traffic streams.

Collection of Traffic data samples by employing Stratified Random Sampling Techniques to ensure statistical validity and Goodness of Fit.

Loci Identification & Traffic Data Collection

Voronoi

Voronoi Diagram for Ludhiana

The city was partitioned into regions based on specific set of statistical parameters for device installations and data sampling.

Location and duration for traffic data sampling

Gravity model in conjunction with unsupervised machine learning algorithms were deployed to divide the city.

Advanced Trip Generation/Attraction Models were utilized to identify specific points for camera(s) installation in each sector.

Goodness of fit test was used on sample traffic data to ascertain how well it represented the population dataset.

Operational Predicaments

Operational Predicaments

  • Freshly Assembled

    Operational Predicaments
  • Outside View

    Operational Predicaments
  • Night Analysis

    Operational Predicaments
  • Device in the Sunshine

    Operational Predicaments
  • Standing tall in the rain

    Operational Predicaments
  • After a hard day’s night

    Operational Predicaments

Out of home is out there. It is a physical medium and is subjected to vagaries of nature - rain, heat, heavy winds, or a power outage. What we had was a heat resistant, weather hardened device with built in redundancies and a backup plan, but what about a crow sitting on the device and moving the angle to views less desirable and less data centric.

Thankfully we did account for it all. As we have spent years perfecting the device to meet the end of data extraction.

Despite a few hitches……we came through in fine style.

From Have Not to Have More

The benefits Leafberry Advertisements incurred is for them to share, but the results are for us to see.

Now we had a true (within a measurable margin of error of course) assessment of demography and the market

Key Data Points

  • Traffic Metrics (see sample report)
  • Points of Interest for Contextual Planning
  • Costs and ROI
  • Impressions
  • Hourly Traffic
  • Daily Traffic
  • Type Wise Count. - Cars, Buses, Trucks, et al
  • Weekday and Weekend Trends

Unintended Consequences of doing things the right way

If we skip steps, we miss lessons. Hence, we had started small, we built along the way and were able to multiply that into something bigger.

When you look at the final report of a location (sample attached), the insights are invaluable for both media owners as well as the advertisers and brands. The numbers improve media allocation and create a meaningful ROI for brands. Most importantly, for the media owners, the numbers make their economies superior. Out-of-home is a fragmented market consisting brands, target geography and more so. What was needed was an efficient emissary to serve as the go-between the two by deploying geospatial intelligence.

Hashbrown Systems mapped the media, the thousands of points of interest and the underlying data to create an insightful mechanism that intermediates between the brand and the buyer effectively

Click here to view all the data in action
Leafberry

Photo by Robin Schreiner on Pexels

Postscript

Postscript

I wish I could tell you that we did it with comfort and in luxury. I wish I could tell you that…. But Ooh is no fairy tale world.

This was an ambitious program that involved Statistics, Computational Mathematics, Cloud Computing, IOT, development of Hardware that would withstand the vagaries of mother nature and capriciousness of the birds. We worked with Deep Learning, Computer Vision, and enormous volume of data with over 30,000 video samples just for the city of Ludhiana.

We are grateful to US Advertising for laying the groundwork towards building the technology and to Leafberry Advertising for taking the process to the market.

Bridging the consumer advertiser gap and building economies of scale.

Hashbrown Systems

Hashbrown Systems is a team of programmers, developers, designers, analysts, mathematicians, and scientists. We conceive and design intelligent solutions for small & medium enterprises across multiple industries including financial services, media, retail, and consumer goods.

We build Out-of-home Business Application and Platform Services for planning, Audit, Media and Brand Management & Performance Analytics. We deploy Data Analytics, Machine Learning and Geospatial analysis to create set of location intelligence tools that support planning, execution, auditing and provide valuable insights before, after and most importantly during a campaign.

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