Customer engagement is the life force of brands. Mumbai-based Locobuzz enables brands to forge strong relationships with their customers and increase their lifetime value using artificial intelligence, machine learning, big data and analytics.
We got in touch with the founders of Locobuzz — Vishal Agarwal (co-founder and CEO), Shubhi Agarwal (co-founder and COO), Nitin Agarwal (co-founder and CTO) — to get insights into their startup story, use of AI and ML in their offerings, growth plans and more.
Incorporated in 2015, the team always believed in the power of analytics. The trio started Locobuzz with simple plain analytics on unstructured data, focused on processing large volumes of data in real-time and further augmenting it with sentiment analysis. Locobuzz is a portmanteau of Location-based buzz.
The startup started connecting with customers across a spectrum of digital channels to help brands increase efficiency across business processes and create a seamless experience for customers — all using analytics.
Locobuzz CX Platform is designed to work on a standalone basis or as an integrated solution bringing together various departments and functional verticals like product teams, marketing teams, customer experience, CRM and sales teams. Explaining how multiple teams can leverage Locubuzz’s insight, founders shared:
- The marketing teams can derive inputs that help design campaigns, monitor brand chatter, identify influencers and benchmark with the competitors.
- The customer experience teams can engage with customers, resolve queries, identify patterns, and streamline processes to improve customer satisfaction scores.
- The sales teams can scope out leads and other engagement opportunities.
AI At Locobuzz
With over 50 members in the tech team consisting of engineers, data scientists, and product developers, Locobuzz’s AI engine can provide rich business and customer insights on a seamless unified platform. The platform can understand 30+ global languages, process data and engage in real-time.
From streamlining customer experience to forging long-lasting relationships, Locobuzz has catered to hospitality, telecom, automotive, BFSI, aviation etc.
AI and ML have been leveraged for:
Campaign Analytics and Product Analytics: Locobuzz was able to help an automobile brand monitor the launch of a new car through AI, sentiment analytics and real-time analytics. The car initially targeted males of a certain age-group. When the car launched, Locobuzz gathered data insights proving — contrary to the assumption — women have shown more interest in the car and were generating maximum engagement for the brand. The brand then redesigned and relaunched its campaign with woman-oriented messaging and became a success.
Chatbots used for (IoT) Smart Home Management: Locobuzz technology platform can be integrated with all popular messaging apps like WhatsApp, Facebook and Twitter Messenger, Telegram, Website and more. In this case, Locobuzz converse (chatbot platform) worked with a popular messaging app in Japan – LINE Messenger and the purpose of the bot was to interact with the smart home. By controlling the IoT hub through LINE, we can manage every aspect of the smart home – temperature, mood/regular lighting, locking and unlocking of doors (and other aspects of visitor management), etc. The user can control the smart home, send for assistance to a live agent if required through the bot, etc.
AI For Customer Behaviour
The founders of Locobuzz believe empathy is the actual key to success. The way to quantify this success is to channel engagement efforts in a timely and honest manner to relay to the customer that measures have been taken to understand their concerns.
AI and ML are specially trained to understand the details of these microscopic needs that often get lost in unilateral measures of sentiment-score upliftment. They can be smart by prioritising complaints and queries based on emotions, engagement pattern, influence, etc.
The idea is to make sure your customer is genuinely happy. This is why we focus most of our efforts on constantly building every tiny aspect of actionable intelligence, the founders said.
Keeping Data Secure
For enterprises, data security and data isolation are some of the biggest challenges when dealing with third-party platforms. Locobuzz follows a multi-tenant architecture keeping each client’s data separate.
Founders said Locobuzz undergoes a vulnerability assessment and penetration tests every six months through a leading third-party security firm in India to bolster the security side and proactively plug any vulnerabilities if found.
Locobuzz has built a scalable architecture that automatically scales to cater to higher data volume and self corrects itself in case of anomaly. “We process millions of data points in near real-time every day and enrich them via different micro-services. The enrichment micro-services are plug-and-play services that can be hot pulled or pushed into the system without hampering the working of the system,” the founders said.
Locobuzz has been able to bring about the growth of brands across verticals. With the use of machine learning and NLP, they have built data models for language detection, intent detection, deviation alerts, emotions, etc. “We also have now created a fully cognitive chatbot platform,” added the founders.
Locobuzz has evolved into a fully integrated, unified customer experience platform in a short period. So far, it is working with more than 100 reputed clients across geographies.
Locobuzz recognises the role of machine learning and is investing heavily in it. The founders believe 2021 is the year of automation. They are looking to invest in natural language processing to provide actionable consumer insights, topic analysis, and consumer behaviour analysis. The startup is also looking to explore business opportunities outside India in geographies such as the US and UK.
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Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.