Like Google Analytics, but for real life.
Finalist 2020 Swiss Innovation Challenge
Built in 94 Days
The Livealytics solutions provides IoT tracking & measurement and analytics as a service for retail, live-marketing, trade shows, retail spaces and smart buildings. The GDPR ready solution is capable of transforming measured data points into benchmarked insights to improve live customer experiences, optimise operational costs and increase sales.
Axelra relaunched the digital product of livealytics and, together with the livealytics team, took it to the next level, in terms of the business model and in sales. The new product is deployed on the cloud, and its onboarding, setup, measurement, and reporting processes are fully automated. Projects can be configured in 90 seconds, and as the mobile connectivity is built in the IoT sensor, no technical expertise, training, or on-site staff is needed.
The Co-Creation between Livealytics and the tech venture builder Axelra started in June 2019. On the 19th of December, the product relaunch was officially launched on the market and few days after, the first have been onboarded successfully.
«What if you could measure and improve live marketing, gather insights and benchmark your activities based on real data? This is Livealytics - Plug and Play»
Marc Degen, Member of the Board
- app.livealytics.com
- Product & UX
- Tech Delivary Infra, Fe, Be, Int
Livealytics was developed out of direct customer needs of Promotiontools AG with the aim to verify the impact of promotional campaign projects. Up to this point, numerous projects with many customers had already been successfully implemented. With the collaboration with Axelra, the intention was to take the product to the next level and make it fit for scaling.
The Co-Creation between Livealytics and the tech venture builder Axelra started in June 2019.
The core team of 3 people and another 3 subject matter experts supported Livealytics and built a React.js Front-end with custom UI and innovative UX, a microservice-based, scalable and serverless architecture on AWS, role-based subsystems for handling different permission layers, an AWS IoT Core infrasructure and an AWS Time Series Forcast for predicting outcome of future marketing campaigns based on previous interactions.
The Beta Version went live on the 30th of August 2019 and on 19th of December, the product relaunch was officially launched on the market and few days after, the first have been onboarded successfully.
We handed over to Marc and Simon in December 2019 and are supporting their growing team and journey.
«Axelra has an excellent team of top Tech Venture Builder. They are always open to new ideas, very accommodating to change, and most importantly – generously share their wealth of knowledge and experience. In 2019 we were able to get our Analytics as a Service platform off the ground in record time, thanks to them. I would highly recommend them to anyone looking for a tech partner to accelerate their MVP launch!»
Simon Tuck, Co-Founder Livealytics AG
Livealytics turns the data to actionable insights at any location. Besides the reach, visibility, Bounce Rate and Engagement, you’ll understand the interaction and conversion rate of your traffic. But we go further with the data and provide with the KPI a comparable standard to evaluate effectiveness and derive recommendations for real-time optimization of fairs and event activities. Moreover the new benchmark provides a comparable scoring across locations based on their activity type, industry category and their performance and profitability.
In order to achieve this, livealytics provides an analytics dashboard for measuring and benchmarking live marketing promotion activities, trade shows and retail spaces. Since for marketing agencies and brands it is difficult to evaluate the effectiveness of their offline marketing investments, this added knowledge can help them to better plan their marketing strategy.
In order to measure Wi-Fi signals, livealytics uses custom GL-iNet MiFi 4G Smart Routers. These devices are able to passively listen to emitted Wi-Fi signals and differentiate between different signal strengths. The signal strengths combined with the Dwell Time algorithm are then used to classify visitors into different zones of interest in the product area. The overall goal is to count the number of (unique) MAC addresses and to cluster them among five circular surfaces. Each circular area can be viewed as a zone of interest, whose radius depends on the available area during the product demonstration.
Gathered data is used in a second step to predict the outcome of marketing activities with numerical metrics. The main idea is to be able to predict how different marketing activities would perform in a similar context based on historical data.
The overall approach consists of (i) selecting the features used to train the ML model and (ii) let the ML algorithm run in the cloud and output predictions.
The first process is about finding a subset of features from the dataset, which contribute the most to the target prediction variable and influence the outcome of the model most once it gets trained. The implementation of the second part for forecasting the outcome of marketing activities relies on AWS Time Series Forecasting. This service uses machine learning to deliver accurate forecasts based on historical series of data.
The Livealytics platform is entirely cloud-based through Amazon Web Services (AWS). Incorporating an intuitive dashboard, various measurements are generated and can be configured. In the background, IoT sensors are configured through AWS IoT Core. With this setup, the user only needs to plug the sensors in and any further configuration is not required but customization is possible. When a measurement has terminated, the data is processed and aggregated in the cloud to be displayed in the dashboard.
The system is built on the AWS stack, with AWS Lambda, AWS Cognito, and AWS RDS. AWS IoT Core with its Device Shadow allows users to manage the IoT devices remotely. The amount of people within the sensor’s range are measured by passively listening to Wi-Fi scanning signals constantly being emitted by smartphones. The frontend was built with React Native and apllied libraries such as Redux and Redux-Saga to manage the application state. The build pipeline is based on AWS Amplify and enables continuous deployment.
