Alteryx Telco Use Cases

Alteryx

Sample Use Cases

Telco

© 2012 Alteryx, Inc. 2

Table of Contents

Reducing Backhaul Expenses ........................................................................... 3

Populations in a Coverage .............................................................................. 7

Identifying Signal Strength Issues .................................................................... 11

Smoothing Coverage Graphics ........................................................................ 15

Match Customer Profiles to Devices ................................................................. 17


Reducing Backhaul Expenses

Business Question

How can my company reduce its backhaul expenses?

Background Information

A significant portion (35%) of a wireless carrier’s operating expenses is tied up in

backhaul.

Traffic from cell towers is transmitted via a multitude of circuit types (DS-1, DS-3,

and OC-3) depending on how much traffic a particular cell site experiences. There are

certain thresholds that are put in place when you switch from one circuit type to

another. For example, when you have a concentration of approximately 8 DS-1's (each

one is 1.5 megabits per second), then it normally makes fiscal sense to switch to a

DS-3 (45 mbps). There are also the same types of calculations moving to an OC-3 (155

mbps), but that office has to have the network equipment to support that

connectivity since those data streams are transmitted by fiber optics.

Answer Provided

This analytic app can be used by engineers to make sure that they have the most

efficient backhaul chosen for the cell tower locations.

It defines where that circuit is honing back into a Wire Center, and whether it crosses

a Wire Center boundary. If backhaul does cross a boundary, that is a more expensive

proposition for the carrier. The types of transport that defines this are inter-office

transport, or intra-office transport. Inter is more expensive, and the ILEC will gladly

provision these types of circuits since it represents more revenue for them.

Actionable Results

• As engineers or planners identify areas where circuits are incorrectly honed back

into a Wire Center, they can ensure the provisioning organizations are made aware

of these anomalies in order to implement less expensive alternatives.

• As indicated by the output below, the central office that is the shortest distance

away from the tower is not always the optimal office to backhaul to. If the

engineering or provisioning teams have incorrectly applied this methodology, they

will be crossing Wire Center boundary lines and incurring additional network costs.

This information can provide the basis for better provisioning.


Output

The engineer will see both a visual map output, as well as the choice of

XLSX/CSV/TXT outputs for the tower locations and information relating to the Wire

Centers.

The following pages are a representative sample of the report output provided by the

analytic application.

Analysis of the Overall Footprint for the Area


Representation of a Sample of an Individual Tower

Alteryx Data Utilized

The data from GeoResults can be used for this analysis to accurately describe the

Wire Center boundary areas.

• National Wire Center Boundaries

• National Central Office Buildings

• TomTom Map Layers


Customer Data Utilized

The typical source of data for this analysis comes from the OSS (Operational Support

System) which can exist in any variety of storage methodologies.

Key Alteryx Tools Utilized

• Spatial Match

• Spatial Info

How it works

You will need to input the following in the Cell Tower Analysis application.

Cell Tower Location - This will include the following core components:

• Tower Name

• Tower Latitude

• Tower Longitude

Central Office Locations - This includes the Wire Center polygons which define where

that central office (where the backhaul circuit terminates to) is located.

Example of Input Format used by Analyst


Populations in a Coverage

Business Questions

How much of the population of an area is covered by our network? What market areas

are being missed by our network coverage? Are we missing population centers that we

could cover?


Wireless carriers need to determine how many people are covered by their network.

This information is important as it is a crucial requirement in reporting to the FCC and

financial markets. Valuations are based on the types of customers the carriers can

obtain, and those populations under their coverage layers. Being able to provide this

information rapidly and accurately allows the carriers to meet analyst needs

effectively, and make strategic decisions on where capital expenditures should occur.

Marketing is also able to use the data driven through this analysis to show their

coverage of populations in their network. This data can also be compared to existing

competitors to help gauge necessary improvements. Understanding the Demographic

and Behavioral profile of the customers within covered networks can assist in

identifying the most effective promotional campaign strategies.

Companies also need to find the population density within a wireless carrier’s

coverage area based on the quality of coverage. Identifying the varying decibel drop

ranges (e.g., Best Coverage at 50db to -60db, Better Coverage at -61db to -80db) that

will define coverage contours is significant. From this information engineering

managers are able to determine where networks need bolstering or where problems

may lay in current coverage.

Answers Provided

• Reports required for regulatory and financial purposes can be generated.

• Strategic decisions can be made with the resulting data to assist in identifying areas

for network expansion.

• Marketing departments can utilize this information to help compare coverage

populations with competitors.

• Network engineers can be provided with information that can be used to plan for

network upgrade.

Actionable Results

• Management will be able to suggest reallocation of capital expenditures based on

population shifts or shifts with customer profiles.


• Management will be able to meet reporting needs in the financial markets more

accurately and efficiently.

Output

The map shows signal strength in the covered area.

The report includes population demographics for covered area.



• A demographic data set is required to report out population values


Raw grd/grc data from native propagation analysis tools such as Atoll and Planet EV

are required.


• Allocate Input

• Input

How it works

You will need to perform the following actions in the Population Analysis application.

1. Choose the grd/grc file that you will be reading and performing the Population analysis, as

well as the output location.

Sample first tab of application


2. Select the demographic variables that you would like to calculate within the coverage

area.

Sample 2nd tab of application


Identifying Signal Strength Issues

Business Question

How can we determine where we have signal strength issues within our network?


Wireless carriers are continually monitoring their coverage areas to maintain an

understanding of where they may have poor signal strength or poor signal quality.

Poor signal strength or quality results in dropped calls for the customer which

negatively impacts customer retention.

They also want to know how their coverage area compares to that of their projected

coverage and to the strength of their competitors' coverage areas. To analyze their

coverage areas, wireless carriers often 'drive test' roads to measure the coverage,

capacity and quality of services of their radio network. Drive testing is a method of

measuring and assessing the coverage, capacity and Quality of Service (QoS) of a

mobile radio network.

The technique consists of using a motor vehicle containing mobile radio network air

interface measurement equipment that can detect and record a wide variety of the

physical and virtual parameters of mobile cellular service in a given geographical

area.

Drive testing collects an enormous amount of data and when using traditional tools,

network engineers have experienced difficulty with processing the vast amounts data

and analyzing the results in a timely manner using traditional tools such as GIS.

Answer Provided

Utilizing this application with the accumulated data will easily identify the actual

strength of signal in a given area within the network.

Actionable Results

• Identification of areas where actual signal strength varies from the propagation models

built with Planet EV or Atoll will allow for "tuning" of those models.

• Drive time feeds can be readily used by Marketing and Product teams to assure that the

coverage to support their campaigns and devices are in place. It will determine the types

of services that can be offered and the expected customer experience.


Output

The following are sample outputs from this analysis:

The first is a map that shows the signal strength in the defined area by the grid sizes

(miles) as set by the user.

Defined signal strength by chosen Grid size

The second show the population densities in the corresponding grid areas.

Defined population densities within grid areas



Map datasets and the demographic variables.


The included file would need to have signal strength, as well as the latitude and

longitude of the drive test readings.


• Spatial Match

• Spatial Info

• Trade Area

• Allocate Append

How it works

The analyst uploads the data file from the Drive Test process and identifies the

corresponding fields in the file to

• Field identifying signal strength (-db drops)

• Fields identifying latitude and longitude of reading location

The analyst then inputs:

• Desired size of the grids for analysis and mapping (in miles)

• Name of the area for the title of the map

Example of Input Format used by Analyst


Where to find the analytic app

Drive Test Data Analysis

http://www.alteryx.com/module-exchange-details/103


Smoothing Coverage Graphics

Business Questions

How can we make our RF coverage data files more user-friendly with our other

systems?


Often times when producing RF coverage files, they are too exact and are not very

palatable for applying them to Marketing and customer facing applications. Polygons

constructed from this data tend to be more complex than needed and can result in

slowing down the processes running against them. When these represented polygons

go through a ‘smoothing’ process, it makes the polygons less complex, and allows for

other internal teams to use those shapes for publishing covered areas on company

websites, etc.

Industry tools that create coverage polygons from these grd/grc files do not usually

take smoothing into consideration when generating from those files, leaving analysts

with data that is difficult to utilize. Having less complex polygons to represent those

coverage areas, while at the same time remaining as lossless as possible, helps to

introduce efficiencies when utilizing those files. This can save time for downstream

organizations and processes.

Answer Provided

This process creates less complex polygon objects that still accurately represent

coverage areas.

Actionable Results

• Better coverage graphics are available for Marketing departments

• More efficient use of the polygons by downstream organizations that may not be utilizing

Alteryx

• Smaller polygon file sizes to increase processing, and reduce memory consumption needs

Output

The actual output would be the spatial objects created after the smoothing effect.

As an example, the images below show the original coverage on the left and the

smoothed coverage on the right.


Comparison of data files before/after 'smoothing'

Alteryx Data utilized

There are no Alteryx datasets necessary for this processing.

Customer Data utilized

Native grd/grc data from propagation analysis tools such as Atoll and Planet EV.


• Generalize

• Smooth

• Polygon Split

• Spatial Process

How it works

Choose a sample coverage file that represents a raw output type. These would include

grd/grc file types that are from source propagation tools.

As shown in the module below that is downloadable, the points in the data file are

‘generalized’ and ‘smoothed’ multiple times depending on the amount of smoothing

desired.

Where to find the analytic app

Smoothing Examples

http://www.alteryx.com/module-exchange-details/646


Match Customer Profiles to Devices

Business Question

What kind of customer uses a specific device?


The usage of devices on a carrier network will vary greatly by the type of consumer

using that device. The Handset Profile analytic app looks at specific areas that are

user defined, and will then return core demographics of the user base in that area.

Being able to identify customer types in a given area, and then matching them to

devices allows for penetration analysis. For example, when the Marketing team runs

campaigns to for particular devices, the Engineering team can be assured that

capacity in the area can support sales of the device - given the populations of those

"appeal to" demographics in the area.

Answer Provided

Demographic and behavioral profiles can be attached to groups of users of specific

devices.

Actionable Results

• Marketing departments can tailor promotions in areas with predominant demographic

profiles to specific customer groups.

• Finance teams can verify that the pricing models for the plans that have been created are

profitable for varying customer types based on their usage patterns.

• Engineering teams can validate that capacity in given regions is supporting network

growth based on the customer type populations in a given area.


Output

The output file includes information about the device (note: these will have to be a

chosen set of key points describing the particular handset model). It also includes

summary demographic information about the customer types based on the addresses

from the inputted customer file.


Demographic data is used to form the basis of National Averages.


Data will need to be used from BSS/OSS systems that include information on device

usage. Minutes of Use (MOU), Data Utilized, and SMS/MMS (Short Messaging Services


and Multimedia Messaging Services) reporting needs to be included. Additionally, the

demographic data associated with either the Account or Device holder needs to be

included. This will determine whether we are describing the demographic variables of

either the Household or the individual Subscriber of the device.


• Allocate Append

How it works

You will need to input the following in the Handset Profile module.

• Customer Input File – Includes address information for the customer segmented by the

device type to be analyzed.

• Device Type - The Input file customer file will need to reflect the device that is to be

analyzed.

Data & Analytics

Alteryx Telco Use Cases