AI-based, Hardware-agnostic, Predictive Wi-Fi Network Management Software

Project Information

TEApot

Grant agreement ID: 855248

DOI

10.3030/855248

Project closed

EC signature date 23 January 2019

Start date 1 February 2019

End date 31 July 2019

Funded under

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Total cost

€ 71 429,00

EU contribution

€ 50 000,00

Coordinated by

TEA ILETISIM TEKNOLOJILERI SANAYI VE TICARET LIMITED SIRKETI
Türkiye

Periodic Reporting for period 1 - TEApot (AI-based, Hardware-agnostic, Predictive Wi-Fi Network Management Software)

Reporting period: 2019-02-01 to 2019-07-31

Summary of the context and overall objectives of the project

Around 50% of all customer service calls to Internet Service Providers (ISPs) are related to issues with home Wi-Fi. A recent study conducted in UK and USA shows that around 70% of all customers experience problems when streaming or downloading content over Wi-Fi in their homes. Of all customers, 75% expect their ISPs to solve their Wi-Fi related issues, and 54% are willing to pay an extra €4.5 per month if their ISP would provide a premium Wi-Fi service. ISPs respond to around 35.000 Wi-Fi related customer calls resulting in around 3.500 truck rolls per 1 million subscribers per month. Average length of a Wi-Fi related help desk call is 26 minutes. Calls typically cost €0.7 - €1 per minute and truck rolls typically cost between €80 - €150 per visit. Thus, with a modest assumption of €0.7 per minute cost of a call and €80 cost per truck roll, Wi-Fi related customer care cost sums up little over €11 million per year per 1 million customers. Moreover, the customers that call to complain are often only the tip of the iceberg. Many of the dissatisfied customers do not prefer calling for a complaint, instead they silently churn by switching their service to another provider either immediately or when it comes time to renegotiate their service contract. Thus, in addition to the above-mentioned costs, ISPs undertake unnecessary hardware replacement costs, customer churns and lost higher tier upsell opportunities. These findings demonstrate that there is solid need to diagnose and solve Wi-Fi problems remotely.

TEA’s innovative solution, TEApot (predictive optimization technology), comprises embedded (local) and cloud software modules that facilitate predictive maintenance and proactive control of home Wi-Fi networks, thus avoiding customer dissatisfaction. TEApot assures significant cost savings for ISPs by improving QoE of their customers. Moreover, TEApot promises new revenue opportunities for ISPs by its AI-based features such as advanced parental control, per client behavioral analytics, and advanced network setup assistance. The objectives of this feasibility study are summarized as follows:
•Studying technical requirements with potential customers and partners through online and face-to-face meetings before and after field trials. In this study, one of the main objectives is to understand complexity of remaining development activities, such as productization of embedded and cloud controller modules, and the potential impact of these complexities on the overall work plan.
•Analyzing and projecting the scalability of SaaS costs, and validating and/or modifying our cloud infrastructure that relies on Amazon Web Services (AWS).
•Studying data privacy related regulatory issues, such as GDPR, of the target markets.
•Market clearance study for ensuring freedom to operate.
•Studying the market by benchmarking customers and competition, examining trends to validate (and/or modify if necessary) our current knowledge and assumptions.
•Economic comparison study of direct and indirect competition and practices.
•Studying different business models, testing the business model against different customers, and assessing the risk associated with each one.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

•We have prepared a demo setup of our solution, which runs 24/7 in real homes with varying number of mesh APs. 35 homes are connected and managed by the TEApot cloud controller. One of the homes is selected as the demo home, with 4 mesh APs and more than 25 wireless clients with varying Wi-Fi capabilities, mobility and download patterns, which represent numerous real home use cases.
•We have presented our demo to potential customers. For prospective customers, we have given restricted access to our web user interface so that they could themselves go and check-in what is going on in the demo home 24/7. Also, for interested customers, we have prepared plug-and-play mesh networks, which they can install to their homes or to their offices, and start monitoring our TEApot in action right away.
•Through our demo setups, we built customers’ confidence on technical capabilities of our solution, also received to-the-point feedback about our solution, which really helped improve our solution. We also used our demo setups as a means to analyzing and projecting the scalability of SaaS costs.
•We have worked on the GDPR and discussed with prospective customers about their requirements.
•We have analyzed our target markets, in particular, Europe and Turkey. We have tested different business models against different customers.
•We conducted face-to-face meetings with 8 different companies from 4 different countries, particularly, France, Denmark, Korea and Turkey. We have made our demo to these customers, and tested our business models.
•We performed a demo of our solution with a European CPE vendor in International Broadcasting Convention (IBC).
•We have also analyzed small enterprise Wi-Fi market in Turkey. Although technically fit to the requirements of the small business market, we have concluded that our preferred business model is not feasible for the current landscape of small enterprise market.
•We analyzed and tested both technical and business feasibility of our project in home Wi-Fi market. We have made necessary revisions in our preferred business model, and improved and revised technical requirements of our solution in accord with the requirements of our prospective customers.
•It is revealed that Wi-Fi network management is gaining momentum in the market. Both established networking companies and emerging companies like us are presenting their solutions, and trying to reach out to major ISPs. Compared to available products in the market, TEApot stands out as one of the most technically capable product. We have concluded that our preferred business model and technical solution are feasible, and render a scalable business.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

It is observed that managed Wi-Fi, and especially, AI-based managed Wi-Fi is gaining momentum. Throughout the feasibility study, it also became clear that managed Wi-Fi represents the foundation for the connected home. From the customer perspective, their broadband service does not stop at the WAN port, but at their devices. Therefore, without a solid Wi-Fi foundation, there is no connected home and service providers are aware of the need to defend and leverage their turf from the OTT players such as Apple, Google, and Amazon; otherwise, they risk remaining merely dumb pipe providers. As a result, managed Wi-Fi becomes the premise for a future connected home service delivery platform to enable the onboarding, securing, and managing IoT devices that deliver the genuine connected home experience.
ISPs all around the world are looking for feasible, reliable, future-proof standards-based solutions. EasyMesh standard has arrived. However, proprietary solution providers in market are not adopting EasyMesh quickly because they first want to monetize their investment in their proprietary solutions. In other words, they do not want to bear high cost of development and migration for their deployment base which would diminish their profits in the short term. This provides us with an opportunity window, which we would like to make use of. Our study and analysis have demonstrated that both our technical solution and our business model is a good fit for the market. We believe that this project has the potential of quickly scaling up to millions of deployments in 2020.

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