In Buzz Radar’s second year of operation, the team began collaborating with IBM.
“IBM immediately saw the value of what we were doing, and commissioned us to build some dashboards for their marketing team,” says Charlton. “Coincidentally, this was the same time that the IBM Cloud was launching some really interesting features, so we started looking at migrating some of our infrastructure over to them.”
Buzz Radar’s engineering team identified three key advantages that the IBM Cloud offered, compared to its existing infrastructure hosting provider.
“First, building trust and security around our data and insights has always been core to our business,” says Charlton. “We work with huge international brands, and we need them to trust us to integrate their marketing data—which is incredibly commercially sensitive—into our platform. They put our systems through all kinds of security analysis and penetration tests, and since we’ve moved to IBM, we’ve always passed with flying colors. Having IBM in our corner for data security and resilience is very important to our credibility.”
The second advantage is flexibility. “With our former provider, we paid for a fixed number of physical servers monthly, regardless of the demand on our applications,” explains Charlton. “But our business is very seasonal: during big events or campaigns, for example, social media volumes go through the roof. We’re pulling over half a million data points an hour into our servers, and performing very sophisticated analytics. The IBM Cloud lets us scale seamlessly to meet demand, without paying a penny more than we need.”
Finally, Buzz Radar relies on the IBM Cloud to provide access to game-changing cognitive technologies from IBM Watson.
“From the beginning, we could see that IBM Watson would offer us some groundbreaking technologies for which we could see incredibly valuable applications,” says Charlton. “There was also a huge degree of interest from our clients in Watson technologies, but they didn’t have the expertise to integrate them into their own systems—so we saw an opportunity to act as a bridge for them.”
Today, the company is using IBM Watson Natural Language Understanding to provide flexible, multi-language emotion, intent and sentiment analysis for Twitter, Facebook and other social media posts.
“Watson’s natural language capabilities are top-of-the-line,” says Charlton. “Most of our competitors chose to build their own natural-language processing [NLP] analysis engines from open source software—but that’s only practical if you have huge amounts of venture capital funding. It would have taken us several years and millions of dollars to develop a platform that would rival what Watson gives us with a flick of a switch.
“Moreover, because it’s cognitive, the platform just keeps getting better. It already understands entities, and it’s starting to get a grip on category-based slang and even emojis. Instead of having to focus on a building these very specialized tools, we can leave it all to Watson and focus on areas where we can really differentiate ourselves from our larger rivals, like our revolutionary data-visualization engine.”
The company also uses IBM Watson Personality Insights to help marketers determine how best to understand the emotions of its audience, and how to select the right influencers to work with – a critical task. Meanwhile, IBM Watson Assistant helps power the Tombot cognitive assistant that acts as an artificial intelligence-driven data analyst to answer marketers’ queries and provide instant reports, predictions and alerts.
“In the past five years we’ve made massive, rapid strides with our infrastructure and a lot of that has been down to the ease and speed of development IBM technology has afforded us,” says Charlton. “Running on the IBM stack, we can plug new capabilities into our platform in weeks, at a fraction of the time and cost of developing them from scratch. That means we can outcompete larger organizations even though we have a much smaller budget.”