I was lucky enough to see how analytics and big data remade the game industry while working for Playdom and then Disney building games. And while some in the industry went public on the case that big data and analytics meant they could predict hits, bucking the old adage that “Nobody knows anything” made famous by William Goldman, I would never go so far. A metrics driven culture did vastly improve our chances of making a hit, and while it has its limits, no one in the industry is looking to go back to how it was before.

At the time, not many game studios were tracking their users, and game design was still largely based on gut instinct and user feedback rather than numbers. From the start, Zynga differed from other gaming companies by hiring product managers from finance or consulting backgrounds, who were used to approaching problems analytically. The company promoted data transparency. Everyone had stakes in the metrics, from software engineers, to the product managers, to the CEO. Zynga executives stressed the importance of having both art (the ability to generate great game ideas) and science (the ability to test and find out whether the ideas are any good).

I saw firsthand the business impact having such analytics and big data at my fingertips and at the fingertips of everyone on my team. This data-driven model is not unique to the gaming industry, but is transferable wherever data, big or small, is captured. Here at Ruvixx we’re bringing this same approach to the licensing and brand protection industries. Bring data to everyone and every level of the organization, use data to prioritize tasks and targets, more importantly – make connections across the data to identify opportunities.

The basic metrics are different

The licensing and brand protection industry certainly isn’t games. In games we tracked:

  1. Reach – How many users do you have?
  2. Retention – How many users stick with the game over n days?
  3. Revenue – How much are those users paying to play?

Like many, we at Disney found that day one retention was a good indicator of long-term retention. So around the office the question was always – what’s your retention rate? From there you could gauge how well an experiment went, compare cohorts, and do everything that gets people excited about big data.

Even during my time building online consumer products at Yahoo! And Experian, we tracked the same metrics. Not surprisingly, startups like Kissmetrics and Mixpanel sprouted up to help other game, online, and mobile companies jump start their big data and analytics departments. It wasn’t too difficult to outsource your analytics when your industry used the same basic metrics as the others. However in the licensing and brand protection industry the similarities aren’t as prevalent. That isn’t to say retention is something you don’t want to measure, but licensing deals are typically multi year contracts versus the daily visits of an online users. More pertinent questions for this industry revolve around if you’re getting paid accurately? Is your IP appearing on unlicensed products? Do I have grey market issues?

So what are the basic metrics of the licensing and brand protection industry? Surprisingly, or not surprisingly, I find that is something that’s still coalescing across the industry. This could be for a couple of reasons:

  1. The element of crime for brand protection
  2. Data issues
    1. Siloed data in legacy IT systems
    2. Interoperability between IT systems
    3. Data sharing across business partners
    4. Diversity of potential external data sources

While in the game industry it was pretty easy to drop some code into a game to measure something new and, foregoing any bugs, you were ensured to get back accurate data. It isn’t the case in the licensing and brand protection industries, where brand protection organizations need to take a more a detective-like approach to uncover what products are missing from the royalty report, who distributed this product, who manufactured the product, etc. Traditionally much of this analysis is handled through ad hoc manual analysis, often times manually via Excel spreadsheets, by disparate teams as required. However, the detailed results of such audits are rarely if ever fed back into the data repositories because of the siloed nature of these teams. And this is where Ruvixx has started down the path to analytical nirvana, building a model to layer in the data and make connections.

I liken this model to a crime with a dead body. Forgive me as I was new to this industry before joining Ruvixx and try to apply analogies to everything. But anyhow, you’ll commonly start with a dead body, or a product that looks to have your IP but the owner isn’t a licensee or the distributor is not an authorised partner, or they are but they’ve never paid royalties for it.

  • Body (or body of the crime) = The product that contains the infringing IP or royalty report if a compliance issue.
  • Crime = The type of infringement
  • Suspect = The entity that committed the crime. If it is a typical licensing case it’s the entity that should have signed the contract with you for the IP. In brand protection it might be the manufacturing entity or a distributer.

As in our justice system, to charge anyone with a crime you need to have all three elements but the detective case starts with one or two of them missing. In a brand protection case you might start with a Body (Product) and know the Crime (Infringement), but don’t know who the Suspect (owner entity) is. In a licensing case you might start with the suspicion that a company is infringing on your IP and you just need to find the body.

If you have data in such a model, you can start making connections and sizing the opportunity (as every issue can also be seen as an opportunity). For example, if you were able to connect a product to a brand, Ruvixx could then show you how many unresolved port seizures involved products of the same brand, or entity. You may still not know who the owner of the brand (in the licensing world) or who the unauthorized distributor/partner is (for BP), but you could ascertain the size of the opportunity by comparing to other opportunities you have in Ruvixx. If you’re using Ruvixx Data Services you could size an opportunity by seeing how large a brand’s market footprint is – that is how many sellers carry the brand across the major online marketplaces.

You’ll also be able to start sizing the holes you may have in your data. For instance, you could see how many logo infringements you had last quarter and how many of those were on products you do not know the owner entity of. Given the ability to size these opportunities you could start truly attacking the problem and monitoring your results.

This is where the fun really begins. And who knows, soon we might be able to establish those industry wide metrics that really produce results.