ALVA has chosen Google Cloud as its primary cloud services provider and is leveraging its best-in-class AI-optimised infrastructure, and data cloud and AI technology to drive targeted innovation across four key pillars – advancing targeted R&D efforts, empowering customer service teams with personalised customer insights, improving productivity of sales staff, and supercharging marketing efforts through the generation of locally-relevant content.
Purbaja Pantja, Chief Executive Officer, ALVA, said, “Electrification is transforming the mobility sector, and we have a goal to make one in every three electric two-wheelers in Indonesia an ALVA vehicle within the next three to five years. Google Cloud’s data cloud and AI technology are critical to helping us derive data-backed insights that will inform R&D and experiences at every touchpoint in the customer journey, ultimately accelerating the adoption and demand for electric vehicles (EVs).”
To lay the foundation for a data-backed approach to customer experience, ALVA uses Google Cloud’s industry-leading unified data platform, BigQuery, to integrate structured and unstructured data across the organisation, such as data from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and IoT sensors installed on its two-wheelers; as well as data from other services within the broader Google ecosystem that ALVA is currently using, such as Google Ads, Google Maps, and Android Auto. Google Cloud’s open approach to data enables ALVA’s databases to be connected with those of their external partners, bringing all relevant data into a single platform for visibility.
The ease of using Google Cloud’s data cloud solutions has further enabled ALVA’s business intelligence teams to reduce time spent developing plumbing code to access data in a meaningful way from 80 percent to approximately 25 percent.
ALVA can also integrate its data platform with AI models on Google Cloud’s enterprise AI platform, Vertex AI, to analyse and extract valuable insights to improve overall experiences for their customers. This includes the ability to extract data and insights from company-wide data, regardless of format or where it resides within the organisation, to answer questions about the business.
One key insight distilled from its unified data platform on Google Cloud that has informed R&D at ALVA is riders’ EV charging anxiety.
Prospective buyers often ask if ALVA’s electric two-wheelers can travel up to distances of 120 kilometers with one charge. However, data revealed that their customers typically only drive 60 to 100 kilometers a day. Delving into this, ALVA surveyed customers about their concerns and findings uncovered that riders were actually worried about the availability of charging stations on their route rather than the maximum range of the EV.
“By integrating data silos across the organisation in BigQuery, our R&D team can use AI to analyse common rider behavior and how to address them in ways that not only delight riders but also advance our competitive advantage,” said Purbaja.
For instance, integrating Google Maps for navigation directly into the vehicle’s dashboard can improve rider safety as they no longer have to constantly shift their focus to external devices like mobile phones.
Features may also be integrated with the My ALVA App, which is already leveraging Firestore, Google Cloud’s serverless database, to prevent unintended data loss; Google Identity Management, which secures user identities and data on the app; as well as Firebase, Google’s mobile and web app development platform designed to reduce the time and effort required to build and maintain backend infrastructure, allowing developers to focus on creating exceptional user experiences.
Another use case that ALVA is exploring involves empowering customer service teams with contextual clues on riders that support richer, more fulfilling conversations. Currently in proof of concept, the AI-powered support system generates an automated summary of a rider that draws relevant insights from data on BigQuery. These could include data on riding behavior derived from IoT sensors installed on ALVA’s EVs, such as speed, acceleration, braking, gyroscope orientation, and distance traveled. These may then be analysed for possible connections to other information, such as vehicle battery levels or health, vehicle servicing schedules, or if the rider is a new customer that may not fully understand all the features of their vehicle.
With AI summarising and synthesising information across different systems, customer service teams have greater context on why a customer might be calling. This enables them to deliver an added personal touch that is crucial in strengthening the customer connection. For example, if a customer is calling during their usual commuting hours halfway through the distance they typically travel, an agent may deduce that the enquiry may be a priority request involving a vehicle malfunction – even before speaking with the customer.
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