When you think about climate change, you might not consider a daily commute to work or a drive around town as big contributing factors. And yet, transport is the fastest growing source of CO2 emissions from fossil fuel, which in turn is the largest contributor to climate change. This is the key insight behind the mission of Spanish carbon-neutral multinational Acciona. The group, which provides sustainable solutions for infrastructure and renewable energy projects across 65 countries, launched an electric scooter sharing service called Acciona Mobility in 2018.

Aiming to contribute to the decarbonization of the transport sector while helping relieve traffic congestion within cities, Acciona Mobility enables users to rent electric scooters powered 100% by energy from renewable sources. Users can find, reserve, and rent the scooters at the tap of their mobile screens via the Acciona Mobility application and pay on the basis of minutes spent riding. The app, which is available free to download online, is also the gateway through which new users can register for the service, which welcomes anyone with a valid driver’s license for motorbikes. After having their right to drive, identity, and card details validated, citizens can start enjoying the service, available 24 hours a day, every day of the year.

“We needed to deploy a reliable infrastructure for a new service before understanding exactly what its scale and demand would be. Our strategy was to adopt a serverless architecture that can grow with us to sustain our long-term vision. This kind of thinking led us directly to Google Cloud.”

—Jose Luis Rosell, CIO Services Division, Acciona

“Acciona Mobility is exciting because it stems from our vision to make people’s lives easier in a sustainable way,” says Jose Luis Rosell, CIO of Acciona’s Services Division. “40% of the pollution near cities comes from private transportation. We want to help solve that problem with zero-carbon, electric, multimodal transportation that’s also convenient for people to use.”

However, Jose Luis says that building the appropriate IT infrastructure to bring the new service to life required external support. “We needed to deploy a reliable infrastructure for a new service before understanding exactly what its scale and demand would be. Our strategy was to adopt a serverless architecture that can grow with us to sustain our long-term vision,” he explains. “This kind of thinking led us directly to Google Cloud.”

Developing the transportation platform of the future

Because Acciona was already a Google Cloud customer, Jose Luis and his team reached the decision to develop the new service on Google Cloud with ease, as he explains: “We had a very positive experience working closely with the Google Cloud team during Acciona’s first project on Google Cloud, and that collaborative mentality is very important for us. We feel Google Cloud is a trustworthy partner that is willing to take risks with us and is flexible enough to lead us through business uncertainty with the right technology,” he says. “So we were confident that its technology and people could help us bring the Acciona Mobility service from theory to reality.”

This time, Acciona partnered with cloud consultancy Altostratus as well, to consult on a selection of Google Cloud products that would help develop the application quickly by making use of managed services. Acciona’s strategy was to launch the new services first in Madrid, where the company is headquartered, and then spread the vision across more cities later, if the idea worked out well in practice. Due to the scale of the project and its data needs, BigQuery was chosen as the primary data warehouse to store all the information related to the service, such as the location of scooters and their availability status. With all its data readily available on BigQuery, Acciona is able to run specific queries that help it to gain insights such as which urban areas have the highest volume of scooters being rented. Knowing this, Acciona can reorganize availability to make sure there are always enough scooters in that specific location.

Integrating it with Pub/Sub and Dataflow, Acciona ensures that the data generated by scooters is ingested and processed in real time so that users searching for scooters nearby, using the Acciona Mobility application on their phones, can always have up-to-date information at hand. Using Google Maps Platform APIs such as the Directions API, Acciona’s mobile app translates the geographical coordinates of scooters into an easy-to-read address displayed beside a visual map. Using Cloud SQL, Acciona automates the storage capacity management of the database as the number of scooters and active users grow.

Meanwhile, Google Cloud Armor protects the service against cyber breaches and distributed denial of service attacks to keep it running uninterrupted. In addition, Google Cloud itself helps to reinforce the cyber security measures with secure-by-default managed services, such as data encryption. “The managed services mean more resilience and more uptime, because we don’t need to worry about maintenance or external threats,“ says Jose Luis of the solutions protecting all information generated by the new service, which as CIO is a topic he holds dearly. “I face cybersecurity issues more confidently as a Google Cloud partner,” he adds.

“Today, we have data coming in every 15 seconds from 10,000 electric scooters around Europe. BigQuery and Cloud SQL-managed services help us handle that data very carefully and with precision so that our users don’t experience any lags when searching for scooters or returning them.”

—Jose Luis Rosell, CIO Services Division, Acciona

Expanding an 100% sustainable transport solution throughout Spain

Within two months of the decision to deploy Acciona Mobility on Google Cloud, the service was ready for launch, filling the streets of Madrid with 500 electric scooters that run 100% on renewable energy. By 2020, the number of scooters has grown to 10,000 and the service has scaled to multiple cities in and outside of Spain, including Lisbon, Milan, and Rome. To date, more than 3.5 million intracity rides have used the Acciona Mobility app, reducing CO2 emissions by more than 1,000 tons.

“Today, we have data coming in every 15 seconds from 10,000 electric scooters around Europe. BigQuery and Cloud SQL-managed services help us handle that data very carefully and with precision so that our users don’t experience any lags when searching for scooters or returning them,” says Jose Luis of the fast-growing scale of the project.

“Google Cloud enables us to keep our services always available and to scale quickly to respond to its growing demand. Our ratings have been very positive as a result. More importantly, our vision of transportation as a cleaner, more sustainable commodity is really spreading and resonating with the public.”

—Jose Luis Rosell, CIO Services Division, Acciona

Set to continue expanding, Acciona is using Google Kubernetes Engine to make sure the Acciona Mobility application has the computing capacity it needs to continue scaling to reach new markets and serve more users. On that note, Google operations tools (formerly Stackdriver) are also being deployed to help monitor, troubleshoot, and improve the performance of the application as it scales.

“Google Cloud enables us to keep our services always available and to scale quickly to respond to its growing demand. Our ratings have been very positive as a result. More importantly, our vision of transportation as a cleaner, more sustainable commodity is really spreading and resonating with the public,” he concludes.

Editor’s note: We invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. This original blog post was published by OnlineSales.ai. Please enjoy this updated entry from our partner.

Tata’s online healthcare entity, Tata 1mg, aims to revolutionize how a consumer finds the right healthcare professional for their needs. The platform offers e-pharmacy services, diagnostics, and health content and has more than 200,000 active brands in its rapidly expanding list of suppliers, sellers and manufacturers. Several of these brands would need regular involvement from the marketplace team in order to set up and run ad campaigns. As one of the largest platforms, Tata 1mg approached OnlineSales.ai to address their growing needs for an efficient Retail Media Monetisation suite to help scale up their existing monetisation efforts. 

Tata 1mg needed more data-driven insights and customizability for their monetisation efforts

Tata 1mg saw several challenges that they needed to address.

1. Manual effort: Tata 1mg marketplace team’s monetization process was largely manual and required heavy time and resource investment to activate advertisers, which subsequently ate into their overall ad revenues.
2. Non-scalable framework: Given the fast-growing number of advertisers on their marketplace – well into the hundreds of thousands – it became increasingly clear that their existing monetization framework was not scalable.
3. Inadequate reporting & analytics: The existing framework facilitated only simple reporting, and brands were not able to draw the deep insights they needed to make data-led decisions and refine their advertising ROIs.
4. Steep learning curve: Several brands lacked dedicated advertising experts who could bring the experience and skills needed to plan successful ad campaigns and optimize budgets. The involved learning curve led to increased advertiser-churn.
5. Lack of personalized offerings: The manual nature of Tata 1mg’s monetization framework left little room for customizability, and account managers could offer very little in terms of tailored ad-buying experiences to different types of advertisers.

All of these issues led to regular revenue leakage. Tata 1mg realized the need for a central platform that addressed the above-mentioned issues and would also be able to cater to new ad channels and the requirements thereof. Developing such a solution in house was evaluated, but was found to be expensive and would require a lot of time to build.

OnlineSales.ai’s AI/ML-powered solution on Google Cloud offered the automation and flexibility to support modern omnichannel advertising needs

Therefore, Tata 1mg looked to OnlineSales.ai Monetize, an AI/ML-powered retail media monetisation suite on Google Cloud, to help address their needs for a cloud-based monetisation platform that offered the flexibility to support omnichannel advertising models, had automation built in to reduce manual tasks, provided detailed intelligence and analytics, and delivered quick, reliable scalability.

1. White labeled self-serve platform: OnlineSales.ai’s retail media platform was implemented within a short 4 weeks. After thorough testing and implementation, the platform was successfully launched, and enabled sellers to run ads on a fully self-serve platform. This required zero involvement from the Tata team.
2. Unified omni channel buying: The team at OnlineSales.ai worked closely with the platform’s core teams to activate and mobilize various advertising products through a self-serve platform, while simultaneously addressing any unique requirements they had.
3. Omnichannel analytics with AI-led suggestions: Sellers are now also able to generate detailed reports that provide them critical insights into campaigns, and help them make better use of their budgets – enhancing their experience and interest in advertising on the platform.
4. Personalized buying experiences: The tailored UX offered by OnlineSales.ai’s Monetize helped Tata engage brands of all levels in technical aptitude to use the platform easily. This also allowed admins to configure what types of inventories were available to different brands or advertiser segments.

OnlineSales.ai makes use of Google Cloud Dataflow to facilitate computations on real-time streaming. Microservices are deployed on the Google Kubernetes Engine (GKE) platform due to the solution’s well-known ability to handle scale.

In addition, Dataproc is used by the company to run batch processing apps while Cloud Load Balancing helps manage traffic across different regions; all at scale. Onlinesales.ai also uses Memorystore as a database for their ad servers in order to have very low latency, and deploys BigQuery to run analytical applications and facilitate powerful reporting. The bulk of their office applications are deployed on different VMs on Compute Engine, and the company also makes use of Cloud Storage for data storage and transfers between applications.

Learn more about OnlineSales.ai available on the Google Cloud Marketplace.

^{About OnlineSales.ai}

^{OnlineSales.ai, offers a fully white labeled retail media platform which helps retailers unlock additional revenue by activating advertising real estate on their platform. With its AI/ML-powered solution, OnlineSales.ai allows retailers to turn brands of all sizes into advertisers – at scale. Its self-serve platform simplifies the ad buying process, and enhances outcomes with smart automation, boosting ad retailers’ ad revenues by multiples.}

In 2006, online travel agency redBus introduced internet bus ticketing in India, unifying tens of thousands of bus schedules into a single booking operation. (Think of it as Expedia for bus booking.) Using BigQuery, redBus crunches terabytes of booking and inventory data in mere seconds and at a fraction of the cost of other big data services. BigQuery also helps engineers fix glitches quickly, minimize lost sales, and improve customer service.

Challenge

Executives at the Bangalore-based redBus needed a powerful tool to analyze booking and inventory data across their system of hundreds of bus operators serving more than 10,000 routes. They considered using clusters of Hadoop servers to process the data but decided the system would take too much time to set up and would require a specialized staff to maintain it. It also would not provide the lightning-fast analysis they needed.

“It would have taken at least a couple of hours to analyze anything,” says Pradeep Kumar, a technical architect at redBus. “Crunching very large data sets would have been a day’s job. We needed something more powerful to get the real-time analysis we were looking for.”

Solution

Kumar and his colleagues learned about BigQuery and realized it was the right match for their data processing needs. The web-based service, which enables companies to analyze massive datasets using Google’s data processing infrastructure, is easy to set up and manage since its simple, SQL-like query language doesn’t require complex technology or specialized personnel. It also has low overhead costs.

The redBus team uses BigQuery as part of an intricate data collection and analysis process. Applications hosted on a range of servers continually pump information related to customer searches, seat inventory, and bookings into a centralized data collection system. Engineers upload the data to BigQuery, which provides answers to complex queries within seconds. For example, BigQuery helps redBus staff:

• Learn how many times customers searched for seats and found none or very few available, indicating more seats should be added to a route
• Investigate decreases in bookings and notify engineers if a technical problem is the cause
• Identify server problems by quickly analyzing data related to server activity

Results

BigQuery provides near real-time data analysis capabilities at 20% of the cost of maintaining a complex Hadoop infrastructure. Queries that would have required a day to analyze on a Hadoop framework take less than 30 seconds using Google’s web-based service.

“We explored several data analytics solutions. Nothing comes remotely close to the sheer power of Google BigQuery,” Kumar says. “It made large-scale data collection and crunching possible with little effort, which has translated to a significant business advantage.”

Google Cloud Platform results

• Analyzes data sets as large as 2 terabytes in less than 30 seconds using a simple, SQL-like language
• Saves time analyzing technical problems and customer booking trends
• Spends 80% less than they would have on a Hadoop infrastructure and avoids setting up and maintaining a complex infrastructure in-house
• Strengthens the company by improving customer service and engineering quality

The fast insights gained through BigQuery are also making redBus a stronger company. By minimizing the time it takes staff members to solve technical problems, BigQuery has helped improve customer service and reduce lost sales.

“Getting to the root of problems used to be really time-consuming,” Kumar says. “By the time we figured it out, customers might have given up. Now if there are booking problems, BigQuery helps us understand the reason right away. Choosing Google BigQuery was the right decision for our company.”