TVadSync at the SPROCKIT Startup Suite – CES 2018

SPROCKIT Startup Suite

We’re delighted to announce that TVadSync will be taking part in the SPROCKIT Startup Suite at CES this January 9-10. The event will cut through the CES noise and connect Fortune 500 executives, venture capitalists and influencers to the hottest market-ready media, entertainment and technology startups. Check out the agenda below:

Tuesday, January 9

  • 4:30 – 6:30 p.m. – Libations and Lively Conversation. Join us for a drink before beginning your night.

Wednesday, January 10

  • 9:00 – 11:30 a.m. – Innovation in the Media, Entertainment and Technology field. Hear from Dave Knox on Innovation, followed by a book signing and discussions on video advertising and the artificial intelligence between content discovery.

  • 2:00 – 4:30 p.m. – Trends in the Media, Entertainment and Technology field. Hear from Michael Tchong on UberTrends, followed by discussions on the audio/video workflow and the blurring lines between digital and physical worlds.

  • 4:30 – 6:30 p.m. – Closing Reception C-Suite and Startup Founder Connections Reception

Going to CES and interested in stopping by? Then sign up here!

TVadSync team up with Centro

We recently announced the expansion of our offering across the US, and our team-up with Centro. Their Basis platform provides us with best-in-market attributes when it comes to real-time retargeting, and helps further cement our position as the leader in this area.

Check out more details here.

Attribution Modelling: Why you’re doing it wrong

Medals

Once upon a time, I loved my Nokia 3210. I had never owned anything like it. Being able to text people? Instant connectivity on the move? Snake?! I got so used to (and dependent on) my precious little best friend that I felt that nothing could improve on it. It was everything I’d ever need from a phone. Fast forward a few years, and I’ve an iPhone in my hand. Enough said.

The same sort of thinking can often lead even smart marketers into a world of myopia, where what’s ‘worked’ before will always be enough, or at least will do just fine for now. One of these areas is attribution modelling.

A big problem with traditional attribution models such as last touch, first touch, linear, position based, and time decay is that they rely on a predetermined, subjective weighting being placed on a touchpoint. Additionally, these models often only consider one consumer path at a time, without any consideration for the performance of key variables in any of the other paths. Mostly the only paths that are considered are those that led to a conversion, without considering those that didn’t lead to a conversion. This is where short-sightedness kicks in.

Consider an example. 100 users interact with 3 advertisements (A, B & C) in various ways, but only one user converts.  That user’s path to conversion looks like this: A > B > C > Conversion.  Let’s apply some traditional models.

First Touch: A (100%), B (0%), C (0%)

Last Touch: A (0%), B (0%), C (100%)

Linear: A (33.3%), B (33.3%), C (33.3%)

Time Decay: A (10%), B (30%), C (60%)

A quick look at these and C is the clear winner, with A performing quite well, right?  As an astute marketer your instinct may be to redirect budget from B, to C and A proportionately.  But, you’re not seeing the whole picture.  You’re not considering the paths that weren’t successful.

Now suppose I told you that in the other 99 user’s journeys A featured in 99 of them, C was in 50 of them, whilst B was only in 5.  Do you still think C and A performed best?  Would you still move budget from B?

A and C were involved in so many users’ journeys not leading to conversion. Their hit rate is really low compared to B, but without considering all paths, you wouldn’t know this.

Tip: when considering platforms and providers of attribution solutions, find out what their methodology looks like. Do they use an algorithm that can consider millions of these data points at a time, and apply a data driven performance index to each touchpoint? Or are they reliant on subjective weighting and blinkered analysis? In other words, are they an iPhone of attribution modelling or a poor old Nokia? If they’re the latter, you need an upgrade.

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