Mobile Network Experience Analyzer

Overview

Mobile Network Experience Analyzer (MNEA) measures real user-experience of mobile networks, by running patented network tests on smartphones. For business owners, it is like an NPS with billions of crowdsourced real-time data points. For marketers, it is a data-driven marketing tool. And for engineers, it is a magic box that makes your complex optimization operation seem like a piece of cake.

Pin-point network anomaly detection

Automatic prioritization of anomalies

What differentiates us from others is our deep experience of network optimization at SoftBank Corp in Japan, as well as Sprint Corp in the USA.
We aim to enrich societies by helping to improve network accessibility across the globe, by automated algorithms, and delivering it to you on a beautiful, modern and easy-to-use dashboard. The main indicators we use are the followings:

1. Network connection rate
2. Throughput (up & down)
3. Number of people in the area
4. RF (RSRQ, RSSI, RSSNR, etc)
5. And more

Problem we are solving

Today, many carriers are suffering from lack of data, knowledge and resource to analyze mobile networks. RF data bought from data providers can be very noisy, field definitions vague and complex, making it hard to utilize without prior experience. Data from BTS has low resolution as it’s not based on user’s device side measurements, is restricted to connected devices (i.e. can’t detect non-connected devices), and competitor analysis is not possible. While driving tests can give you deep insights, it is very expensive and highly labor-intensive; thus cost-inefficient and slow. Driving tests can’t get you measurements of your competitors either, therefore, hard to make prioritization of areas to optimize, ultimately leading to lower user-satisfactions and churns.

Our solution

Our solution not only allows you to easily understand and improve the world’s mobile network in real-time, for any carrier, but also greatly reduce the cost and work complexity of your mobile network optimizations.

1.Real-time visualization of network quality

From each smartphone, network connection is measured as either “Connected”, “Time-out” or “No-connection”, together with GPS measurements. These record level measurements are aggregated by area to calculate connection rates in each region.

Blue and orange boxes have good and bad connection rates, respectively. Clicking on each of the colored boxes will give you more details.

The left picture is a popup showing aggregated details of the clicked box. From this we can see the connection rate, upload and download throughput, indoor ratio of the devices, as well as RF information.

The picture on the right shows the time-series trend of connection rates within the region of the clicked box. The line-plots are connection rates of each carrier (i.e. A, B and C), while the bar plots are their log volume. On this graph, values are aggregated by date for simplicity, however, it can be changed to any resolution based on your needs (i.e. per hour, minute, week, month, etc).

2.Anomaly detection & prioritization using machine learning

When deciding priorities of areas to improve, just looking at the current network quality is not sufficient. It is critical to consider the ROI; how persistent the network anomaly is happening, how severe it is relative to your competitors, as well as how many people are affected by it. Our proprietary machine learning algorithm does it all for you.

Enabling this feature will allow you to see the exact location of all the significant anomalies all over the world (i.v. anomaly clusters), shown as colored circles on the picture above. These anomaly clusters are automatically ranked on the top right corner, based on their severity, persistency and the number of people affected.

Contact

For more details, contact us now at:

mnea@agoop-business-support.zendesk.com