Using Business Intelligence in Film Marketing
And the importance of data-driven decision making
Recently, I participated in MIPCOM film market. It takes place in Cannes in southern France, exactly at the place where the prestigious festival of Cannes is held.
Unlike the festival, which is full of celebrities and glamour, film critics, film premieres and endless photoshoots and media interviews , MIPCOM is a practical event, where people come to meet and close deals (or spy and gossip on each other, which is also fun).
Producers, distributors, financiers, business developers, analysts, regulators — all gather for 3.5 days of pinpointed meetings.
Not too far from Cannes, in Amsterdam, the best engineers gather in IBC to showcase their latest editing software, rendering farms, cloud environments, lenses, cameras and microphones. They don’t care much about the content of the films or programs in which their technology is used as much as they care about resolution, sharpness and speed.
And why did I dedicate a whole paragraph to describe the difference between the three events?
Because the film industry is composed of inherent contradictions. It’s an industry where pure art meets high technology and cold business decisions.
Not that’s it’s the only sector which embodies such a wide spectrum of characteristics, but in my eyes, the film industry is the most extreme in that sense.
What I mean to say is that those contradictions (engineers at one end, business people at the other end and actors, directors and scriptwriters in the middle) makes it more complex for everyone to communicate with each other efficiently.
Take for example the following obvious simple question:
Do they like my film?
- The technical person will not bother to measure it.
- An actor will probably measure it by the applause she gets from the audience in a venue, plus the likes and followers on social media. He will be reluctant to use statistics or browse tables of box office sales.
- The business person (producer or distributor) will look for revenue data.
It’s not always clear cut: there are many producers and distributors who came from art or still do art from time to time. Some Hollywood actors became successful producers for example. And producer Marek Rozenbaum won prizes for his acting in The Death of Cinema and My Father Too.
Therefore, it’s not rare to find producers and distributors who relate to the business making in a more emotional way. They believe in gut feelings rather than raw data.
And that’s what I want to write about in this post: how the use of data can improve decision making in the film business and why it’s not contradictory to intuition.
The Use of Data in the Film Business
The field, in large, is called Audience Analysis. It includes tools that measure engagement and identify popular and unpopular films, clips or segments across different audiences (age groups, economic levels etc.).
The data is used by production and distribution companies in order to reassess their efforts on the go or to predict the success of certain projects, by trying them out on focus groups.
Audience Analysis is far wider than the film world: it is used by marketers, brand managers and ad agencies, which are using audience analysis on a daily basis. Audience analysis puts an emphasis on culture and language (both oral and audio-visual). Approaching your audience while relating to humor, values and slang, helps in reaching out to them and make them love your brand and product.
There are many companies which provide audience analysis (and market research, another sub-field), such as Audiense, Brandwatch, Dstillery, Infegy Atlas and many more.
In the film industry we have Nielsen with Nielsen One and Media Logiq with ITVR. Those are enterprise level tools, used by large companies.
As for the small and medium size businesses or indies, the following article provides and practical guide on how to gather data from available sources on different platforms (Google, Facebook, Vimeo etc.) and how to use pixel tracking in order to get insights about audiences. Naturally, that method requires a lot of manual work. But since there are no software solutions for indie filmmaker or SMB production houses, gathering data for, different sources seems like a viable solution.
Why Use Audience Analysis?
As I wrote before, a large portion of the film industry rejects using analytical tools altogether. They prefer to follow their passion and will never start a project only because a research proved that “that’s what the audience wants.”
Fair enough. But producers and distributors, although artists themselves, must at least know where they stand. And even if they decide to follow their hearts, they need to be aware of their chances of recouping their investment.
Audience and data analysis is therefore important for all stages: when deciding to go for a project and when evaluating its success or where to put more efforts in its marketing.
Our Take
Screenable was built with the above statement in mind.
Film screeners are the videos that are sent between buyers and sellers, between producers and distributors, between commissions editors and programmers. Screeners are the spark that can ignite a deal, a projection, a commissioning or an investment. They can be either a completed movie or an idea, edited and shortened as a rough cut, trailer, promo, assembly or even raw footage.
Some screeners are tracked for overall usage (not following a specific viewers), some are not tracked at all and are just sent with passwords.
That's problematic, but as astounding as it might seem, untracked screeners are the majority of the screeners out there. Sending the fruit of a work that took 3–4 years to produce and not knowing who watched it and how? It's not less than crazy.
Track 'em
With Screenable, we haven't invented film screeners as a concept, nor did we invent their tracking. Screeners platform exist for some years and in my other article you can read a deep comparative analysis of them.
My point, and this is where I come full circle with the rest of this article, that we put a large emphasis on data and insights.
Data and Insights
All screeners solutions put an emphasis on security. Of course, it exist on Screenable too. Actually, security and anti-piracy have become trivial today.
That is why we decided to concentrate and put our best efforts on data and insights.
And that is where things come together and connect with the title of this article, which speaks about Business Intelligence.
If you send a screener to a streaming platform and you notice on Screenable that it has been watched 3 times from start to end, then you have invaluable information, that might later help you in the negotiation (maybe you can insist on the price for example?).
This is one good reason to cherish viewership data.
But that is the easy part…
Because how would you interpret a less distinct viewership pattern?
How would you interpret one viewing fro start to end, another one of the first 30 seconds and a third one, which was skipping between different parts of the screener, all with the same user?
Do they like your screener? Do they hesitate? Maybe it's something else?
This is where insights come to the rescue
Based on past experience (in machine learning terms it's called "datasets") and with the help of a few smart algorithms, Screenable tries to assist in drawing conclusions from what might seem at first sight as chaotic results.
And when you have multiple screeners for various projects, the need for insights becomes critical, because every production house or a distribution business needs to know where to put efforts. Only a small fraction of production ideas are realized at the end. The same goes with finished films, unfortunately. Only those who evoke interest and excitements get deals. And it's better to know it in advance.
Intuition vs. Data
Intuition does not contradict using data. Actually, data helps in decision making, based on the combination of information and experience, which create the gut feeling. I definitely do not preach to rely blindly on data, like pilots do when there's no visibility. I do urge decision makers, even if they are deterred by numbers, to use data as an assistant.
