ThinkSmart – from university research to commercial product- Deriving value from location analyticsJames L it t le – founder and CTO of ThinkSmartTechnologies

- Senior staff researcher, Dept of Computer Science, Univers ity of Cork, I re land

jamlit t ls j@gmai l .com

Aim today in this presentation Show a real life case study of taking a research project through to a product via spin out company

Describe the product, its development and its impact

Suggest why we did things

Only my opinion and what happened for us

Read further technical details in, “Understanding Passenger Behaviour in an Airport from Locational Wi-Fi Data” by JAMES LITTLE, DARA CURRAN, ROMAN VAN DER KROGT, SAMEER BHADOURIA AND KEVIN O’SULLIVAN

Background Between June 2010 and October 2012 ThinkSmart Techologies, a spin-out of University College Cork, Ireland, developed a product line based on the observations of location of Wi-Fi enabled devices

The value proposition was that the “Patterns of visitor behavior around retail, operations and security could be analysed offline to increase stakeholder value associated with the building”

The Data As a Smart Phone negotiates a Wi-Fi enabled space, its location can be calculated through triangulation and RSSI measurements from the Wi-Fi controller and the Access Points

This produces a set of potentially millions of tuples in active buildings

{Time, Location (x, y, floor), ID}

From this, certain behavioral patterns can be extracted, if analyzed correctly

The Value Proposition

New vs Repeat How many people have been to your

store before? What about to other stores? Is there a pattern?

Loyalty & FrequencyHow often do your customers return? Where do the most loyal go to or come from?

Footfall/ Window ConversionHow many people walked by? How many walked through the door? The full journey from entry to exit.Typical paths customers take.

Dwell timeHow long did your customers shopWhere did they spend time and how long?

Behavior InfluenceWere promotions effective? What deals work, and where?How to optimize your displayadvertising!How to optimize your capacity planning & operations

ReportsCompare day by day/hour by hour orweek by week. Compare by entrance, by store, by destination, and many more.

Analytics needed Two main type of information required

◦ Behavior within areas◦ Movement between areas

Initially, to group and measure devices in an area, we used clustering to identify areas◦ X-Means algorithm

Dwell time, crowding algorithms developed to measure these parameters

Flow between different area

Typical paths◦ Special k-Means with path distance measure

All done on the Amazon cloud based service and presented within Google Maps

Remember: we are still at the startup stage, which means getting noticed

Where are most devices detected throughout the airport

Typical path in Copenhagen Airport from airside to finger C

Operational issues: going the wrong way?

Where do international departing passengers go after security?

Reports

Analytics for Town Restaurant on Laurel St, San Carlos, CA

Important step – getting industry engagement1. Shopping Mall• Bow Valley Mall, Calgary, Canada

2. Outdoors• City of San Carlos, CA

3. Airport• Copenhagen Airport, Denmark

4. Congress Venue • Mobile World Congress 2012 Barcelona

Issues which emerged Privacy – concern that individuals can be identified

Vendor independence

Competition

Exposure to changing underlying technologies◦ Apple removed probing MAC Address, Bluetooth beacons improved

Domain specific or general◦ Retail was the initial driver

Who is the user here?◦ Planners vs marketing – different competency levels

Company issues ThinkSmart had around 10 people, most part-time and with other on-going projects inside and outside the university

Necessary to keep cash flow going and keep costs low

Just keep afloat long enough in the belief that customers will appear

We did invest considerably in going to the US and networking there◦ Ireland has no internal market ◦ UK may have worked too, for end users, but US was the HQ of the mobile Wi-Fi companies

Seek funding and support everywhere

Go to VCs early as they will at least give good general advice, even if you don’t get funds from them

Be prepared to change direction – especially if have built a good team

Acquisition and beyond In Oct 2012, Cisco acquired ThinkSmart Technologies for their expertise and implementation of location analytics based off Cisco’s Mobility Services Engine.

First Irish acquisition

Only four staff transitioned full time from the company and only one moved to US

Improvements made to UI, new architecture

Small company multi-roles disappear and now each is replaced by a large department of sales, marketing, testing, engineering and R&D

Conclusions - Technically Give users what they want and they will, in time, demand more advanced

Quality of the data drives the quality of the results – need to clean data, understand its limitations

◦ E.g. Passers by, devices with few locations, areas of poor coverage

Re-implement certain parts, but don’t lose momentum and customer attention

Number of engineers does not increase quality/time to market ThinkSmart – 4, Cisco - 40

Develop within limits - other technologies are better for certain analytics (video, beacons) counting/locating people in small areas. Consider joint approach

Conclusions - Commercially Get a lot of early attention

◦ Make it highly graphical◦ People will ‘get it’ immediately and can assist in the product roadmap◦ Present wherever your customers are

Accuracy of data means that detailed analysis is often misleading – big turnoff◦ Actual numbers versus trending

Worth putting in features for early adopters – they know what is of value and keeps them on-board

Being in the right place – “drink coffee in Silicon Valley”

Currently As of last week, the product is still being developed under the CMX label

http://www.cisco.com/c/dam/en/us/products/collateral/wireless/mobility-services-engine/at_a_glance_c45-726562.pdf

An even more crowded market, but becoming a commodity where everyone has “analytics” with their Wi-Fi offering

Privacy still an issue

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Myself - now working on extraction of unbiased rules/concepts to tag documents using Association Rules. Application to social and environmental sciences. Part-time Cankaya University