Key Benefits Summary (TLDR)
Mist’s Predictive AI and cloud‐based services include several benefits that are points of difference from its competition:
Best in class reliability to lower customer’s downtime costs (imagine, for example, the cost of a network failure while teaching at Duke)
Quick and often automated service and repair to lower maintenance costs and service disruptions
Lower labor costs from the ease of network configuration and operations
o Mist capabilities span both wireless access points and wired switches
o Mist facilitates connections to external networks from different vendors by diagnosing
Scalability of features and configurations to accommodate network growth
Benefits in Detail
1. Predictive Intelligence: machine learning is a fantastic tool for predicting outcomes as a function of data (e.g., CPU, memory utilization, bytes transferred, traffic, and literally 1000s of other measures). The cloud can collect data continuously across many installations, configurations, and usage patterns to improve predictions. These forecasting models are used in several ways.
a. Systems Reliability. Networks sometimes are plagued by anomalies. For example, there may be a 35% failure rate from 10‐11 AM on a particular device on given bandwidth as a result of timeout signals. Normally, a technician would have to collect this information and store it on a device. Instead, Mist automatically captures the information, organizes it, and uses its predictive models to recommend fixes, such as which cable needs to be replaced. In some cases, Mist can identify and apply the fix proactively, for example by reconfiguring port mode settings. This results not only in better network performance and reliability but a large reduction in service costs.
b. Integrations. Not only does Mist enable engineers to find problems with the
deployment of Juniper’s equipment, but it also enables customers to determine whether problems are caused by vendor solutions that feed into the network. The more data collected; the better able AI is to spot these problems.
Natural Language Processing: Mist is the Alexa of network maintenance and design (only they call it Marvis). For example, engineers or users can ask “what’s wrong with access on the second floor?” or “why is my user’s video chat application experience bad?” The result is a faster and simpler and more widely accessible interface.
Cloud‐Based: Finally, the product is cloud‐based. That has several advantages. First, data are collected across customers and applications. This means more information to train AI to solve problems. Second, bug fixes can be deployed in real‐time, meaning once a problem has been identified in one location, it can be deployed in another related setting. Third, various network services (eg analytics) can be turned on depending on a user’s needs and budget, and off and sold as a service. Fourth, tools can be developed and deployed to update the switches and routers to interact with a customer’s legacy software and hardware as needed.
What are the key benefits to be communicated to the target and why are they the most effective? What problems does the target have and how does the solution help them. Why would they listen to the message? Why would they buy from you?
Our target is to the colleges with an increasing student population, such as Western Governors University, which had a 21% increase population over five years. The reason why we want to choose the target is those kinds of colleges grow fast, they have a high demand of a reliability, scalability service to accommodate network growth. Also, …..
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