WiFi Analytics Can Do More than Just Heat Maps - Here's how

Oct 6, 20171 like277 views

WiFi analytics platform could generate heatmaps – Of course, data in backend systems can easily do so. But, what’s puzzling is how customers emphasize the need for heat maps when it’s not the most powerful capability of a location analytics platform.

WiFi Analytics Can
Do More than Just
Heat Maps
Here’s how

2COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
What are heat maps
Typically, heatmaps provide a visual
summary of where visitors are at a
particular location. The colors that you
see on a heat map correspond to foot
traffic in a location. For example, the
red areas could represent the zones
with most foot traffic, the blue areas
could be the ones that receive less
footfall and the green areas could be
the ones which were barely visited.

3COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
The heat map myth
We’ve come across many customers who’ve
asked us if our WiFi analytics platform could
generate heatmaps – Of course, data in our
backend systems can easily do so. But,
what’s puzzling is how customers emphasize
the need for heat maps when it’s not the
most powerful capability of a location
analytics platform.

4COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
Why heat maps are overestimated
Location based Wi-Fi analytics is a fairly
new technology
Customers are not fully aware of analytics
capabilities of Wi-Fi based location
technology.
Location technology platform providers
are peddling limited capability solutions
without informing their customers.

5COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
Why heat maps are inadequate
In many cases, heat maps can be very
misleading. They provide only a vague idea of
where customers spend their time.
Correlation between customer behavior and the
information a heat map provides is surprisingly
low.
They’re useful starting point, but interpreting
the data correctly requires direct observation
where heat maps are incapable .
Heat maps don’t even require location
technology; they can be generated equally well
by plugging a piece of software into your CCTV
system.

6COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
In other words, deploying a location
analytics platform to generate heat maps
is like buying a $2,000 computer and then
using it only to play Tetris.

7COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
WiFi Analytics: Capabilities Beyond Heat Maps
Accurately track behavioral insights throughout
the customer journey at location
The number of visitors and number of passerby's
can be measured in real time
Know the visitor flow or path across the venue
Know the dwell time in each area
Know if it’s a first time visitor or a returning
customer and his visit frequency
Time of visit – what time does a visitor usually
walk in?

8COPYRIGHT © 2017, JULY SYSTEMS, INC. ALL RIGHTS RESERVED
Do better than heat maps.
Get actionable Insights
Heat maps look pretty and are useful for
purposes such as identifying bottlenecks or
corners of a store where customers rarely
enter, but they are really the lowest rung
on the location analytics ladder.
A reliable Wi-Fi analytics platform such as
Proximity MX has the potential to enable
you to gain actionable customer insights.
From identify a customer being present
outside your business premises to the
moment he hands over his credit card to
make a purchase.

9
About July Systems
Founded in 2001, we are pioneers in mobile experience
and engagement platforms. Through a process of
continuous innovation we have stayed ahead of the
curve in a fast changing mobile landscape. We’re now
on a mission to digitize physical spaces through our
latest product Proximity MX, a SaaS tool that provides
wifi analytics and mobile customer engagements for
physical spaces.
Connect with us:
www.julysystems.com | Facebook | Twitter | LinkedIn

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Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

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