Home Analysis Delivery Infrastructure ThinkAnalytics exploits Recommendations Engine’s data to offer new analytics platform

ThinkAnalytics exploits Recommendations Engine’s data to offer new analytics platform

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By Barry Flynn, Contributing Editor

ThinkAnalytics, whose content and recommendations engine services over 150 million subscribers worldwide across more than 60 video service providers, is unveiling a new advanced data analytics platform at the CES show, which opens in Las Vegas on January 6.

For Peter Docherty, ThinkAnalytics’ Founder and CTO, the move represents a natural development for the company, since it leverages existing data assets: “We obviously are experts in the programme metadata side of things, we’ve got a good understanding of customer behaviour, we’ve got all the data on what they’re watching, recording, and purchasing, and we’ve got subscriber data about what products and packages they have. So without even considering any other data, the engine actually has a lot of data which is quite fundamental to […] understanding what’s going on from a business perspective and a marketing perspective.”

However, argues Docherty, not every customer is currently able to take advantage of such data, even when it’s available to them. “They’ve got a lot of data but they haven’t got a lot of insight and they don’t know what to do with it in order to take any actions that are going to affect the business,” he says. “To say that we know what billion recommendations were made and we know what hundreds of millions of programmes were watched – that data is there, but what does it mean?”

The company’s new Big Data analytics platform is designed to address that gap, and plugs straight into its existing Recommendations Engine, being available either on-premise, or as a software-as-a-service (SaaS) product hosted in the cloud.

Docherty stresses that while “there’s a huge amount of analysis and insight you can get out of the data the recommendations engine has on its own,” the new analytics platform is designed to easily ingest data-sets from other sources the customer might have access to, such as network statistics – drawing conclusions, for instance, about the levels of network congestion that can cause customers to abandon viewing on-demand content.

Such insights can be generated on-the-fly if required. Docherty notes that ThinkAnalytics’ background, before recommendations, was actually as a real-time data-mining company. “We understand analysis, and what challenges there are whether doing it in batches or in real-time. You can see it from both perspectives. You might want to react very quickly to something that’s happening, or you might want to look back and say, ‘right, we’re seeing these patterns and we’re seeing them over days and months, so we’re actually going to change something from a business or operational perspective – because we now understand better what’s happening.’”

Docherty believes his most likely prospects for the new Big Data product will be existing customers: “All the customers we have on the recommendation engine could benefit from this additional insight, so they’re certainly the initial targets. We think they’re going to like this product and get a lot of benefit from it.”

Indeed, he reveals, “the customers that are thinking about this the most, we’ve already been talking to them for quite a long time about the kinds of things that they would like to see in this kind of product.”

Besides, as he points out, “if you didn’t have the engine and were trying to do this from scratch, you would have to go and put a whole programme in place to go and get all this data – the viewing data and the metadata and the subscriber data – in order to create the equivalent big data analysis tool.”

Instead, by adding the Big Data product to the existing recommendations infrastructure, “you can get going really fast and you can get a lot of value out of the system, very quickly,” while still being able to “add other data, or develop other KPIs and metrics and reports that you want to have” in the future.


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