How Giggin’ Plans to Make Packed Concerts the Norm

A beautifully packed house (Photo: Daniel Nouri)

Giggin’ says live concert platforms are looking at attendance demand backwards. But can predictive modeling and better data help venues – and artists – better project their live audiences and boost profits?

Major airlines figured out how to fill their planes using sophisticated forecasting and pricing strategies – with travelers rarely riding half-empty flights these days. So why can’t music venues figure out how to reliably fill their venues?

But wait: concert revenues have been surging thanks to a seemingly endless stream of sold-out arena, stadium, and amphitheater gigs from established superstars. Even after the post-COVID calm-down, superstars from Taylor to Blackpink to Oasis are spiking ticket prices and smashing attendance records.

But what about acts playing smaller and mid-sized venues like theaters, jazz clubs, bars, nightclubs, auditoriums, and less-conventional music spaces? Suddenly, filling a venue becomes trickier, though this often-overlooked subcategory could enjoy a revenue renaissance thanks to smart AI and predictive analytics.

Phrases like ‘clustered demand intent,’ ‘turnout forecasting,’ and ‘demand signals’ sound foreign to most venue owners, promoters, and artists. But according to Giggin’ founder Cheryl Kwong, this is exactly the vocabulary the concert space needs to augment its non-superstar income. Just recently, DMN joined forces with Giggin’ to expand this concept.

According to Kwong, existing booking apps are doing demand wrong by using incomplete datasets and post-purchase patterns instead of predictive analytics. That shifts bookings towards safer, more reliable acts but ignores fast-emerging artists that can fill houses.

(And hey, I’ll take a ‘safe’ yacht rock artist any night at my local club. But what about a fast-rising local act that’s getting crazy buzz, too?)

“Our data enables venues to diversify bookings beyond traditional or ‘safe’ artists,” Kwong told Digital Music News. “Existing platforms capture downstream demand after fans decide, while Giggin’ analyzes early signals before decisions are made.”

Of course, sophisticated predictive data doesn’t guarantee sellouts, but it dramatically improves the chances of a capacity crowd – while lessening the risk of empty halls. But how does this all work?

Giggin’ is a data-intensive play that leverages recent advances in AI, predictive analytics, and big data to reduce risk. Thousands of factors are weighted and boiled into an artist’s demand score, including location, venue suitability, and fan engagement metrics. In the end, prognosticating demand becomes a booking superpower, and hopefully, a major revenue booster.

“The overriding goal is simply to allow promoters and venues to predict likely attendance demand before booking acts, and lessen unsold tickets and missed opportunities in the process,” Kwong continued.

But Kwong also stressed that this isn’t your garden variety predictive modeling play.

When most people think of predictive modeling, they imagine an existing dataset that informs future predictions. The Giggin’ approach, however, takes this concept to an entirely new level.

Kwong white-boarded the difference like this. In the traditional predictive model, the scheme looks something like this:

• • Historical data → model → prediction

But the more sophisticated model employed by Giggin’ looks more like this:

• • Fan intent signals → interpretation → booking+routing+ pricing decisions → live event outcomes → new signals → repeat

As part of that approach, Giggin’ taps into fans themselves, whose participation comprises a critical piece of the observed behavioral dataset.

On the Giggin’ app itself, Kwong noted that fans can find better gigs more reliably, from artists they love or might love. Instead of simply scrolling through available gigs on a platform like BandsInTown, the more data-focused approach uses tracked preferences and social data to generate smart show suggestions. But tying that into the broader data ecosystem, fan choices also feed the predictive attendance model.

And that brings us to the driving motivation for this app: to ingest critical data tied to fan intent signals and feed the updated sequence outlined above. For fans, the app’s utility is high, but for Giggin’ it offers a critical signal-capture mechanism for a modeling mechanism that has never been built before.

“Without it, the intelligence layer is limited to demand that has already materialized,” Kwong clarified.

Make sense? Right now, the concepts are mostly foreign to the concert-booking space, though Giggin’ envisions a future in which its app and data intelligence are an indispensable fixture in the live concert space. And beyond venues and promoters, music agencies may also start taking note – and repping more artists with strong predictive attendance data instead of previous sellouts.

But Kwong also pointed to other powerful upsides beyond simple attendance. Filling a space is a big win, but doing it with optimized pricing and well-budgeted marketing is even better. At the show itself, the Giggin’ predictive data machine can also help to slot opening artists and even offer guidance on setlists.

“It’s not just the booking, it’s the entire gig we’re influencing,” Kwong said.

But to make it all work, Giggin’ is taking its early-stage proof of concept and optimizing it with on-the-ground data.

For example, real-time data from shows helps to build a more accurate artist demand score. And data submitted by venues also ensures the predictive engine receives verified, high-quality data. Further fueling the virtuous feedback loop are APIs from ticketing services and social platforms, which supplement data breadth and depth.

In the end, the goal is simple: pack houses, lower booking risk, and make concerts way better for fans and artists. Let’s see if Giggin’ can pull it off.