I wanted to know what a real analyst thought of Iris, so I spoke to Ben Ellis who headed up social listening at BT, Microsoft, Groupon and most recently We Are Social, before joining Brandwatch last week.
How could Iris be used by social analysts?
Ben says that defining a specific job that Iris supports is difficult. “You could easily argue that it helps with every job – It spots successful campaigns, a potential brand crisis, new influencers, customer pain points, unseen consumer insights and more.”
Ben outlines three key ways it can help analysts working with Brandwatch:
1. Speed to insight
Manually analyzing peaks takes time. Clicking through and analyzing a peak while comparing it to the historical data would take a user several hours.
Most users take shortcuts to get around this, Ben says. “They click on the peak and analyze a small subset of data. This could lead to inaccurate results, but it takes them less time.”
He continues: “Still, in a typical week, your brand, products, campaigns and competitors will generate 20 major peaks. I’d say an analyst spends around 3 hours and 20 minutes conducting this analysis – in other words, a whole morning. Iris would take less than two seconds to conduct the same analysis.”
“Iris is much faster than human analysis, providing arguably more reliable results.”
2. Spot previously invisible insights
Often analysts don’t have the time to compare a peak with 20 days of historical data manually. They could miss significant insights.
“They’ll miss the news story trending today, the video released commenting on the issue, the Reddit thread that popped up hours earlier or the new influencer that joined the conversation,” Ben says.
“Iris highlights these anomalies for you by constantly comparing to historical data. It keeps you aware of all the important insights around your brand.”
3. Benchmark your analysis
Ben thinks that some analysts, who have spent years working with data, might turn their noses up at a virtual assistant to help identify important trends. But, he says, a second opinion can always be helpful.
“If they look at a peak and decide that an influencer caused the growth, there’s no harm in checking that against Iris. A second opinion could help solidify their insight or make them aware of something they didn’t see before.
“Iris provides expert users with a crucial second opinion, to make their insights even more reliable.”
How does Iris differ from other AI on the market?
Ben says he’s seen social listening tools cram AI in their platform, but often this isn’t done with the analyst in mind.
“When poorly implemented, these newly AI-powered tools only bring up patterns with little to no context. Some of it can be useful, and some of it has served me well in the past, but as an analyst I found myself and other analysts around me being lost in the mechanics of these tools.”
He says this could include re-programming tools to fit the user’s needs, having to re-categorise mentions for accurate reporting, or having to adjust and correct the tool where it went wrong, hoping that it’ll learn from its mistakes moving forward.
“These implementations just don’t work for analysts. I’ve used so many of these tools, enough to comfortably say that Iris is different. Insights are observations with a reason. Iris surfaces and highlights these observations, by looking at the data, structuring the information within it, identifying the patterns while taking context into account. This is what an analyst would spend hours doing. This is what Iris spends a second on. Iris is different because it works for analysts.”