20 Aug 2018
by Victoria Kuleshova
Tapping into AI solutions potentialIt is becoming commonplace for entrepreneurs to acquire AI solutions and integrate them into their business processes. According to a survey by the Economist Intelligence Unit, 75% of execs plan to actively implement AI in their companies by 2019. Frankly, there is hardly a business domain that has not yet experienced the impact of Artificial Intelligence and Machine Learning. The entertainment industry is no exception. Some businesses, however, cannot make head or tail of it, thus demonstrating complete ignorance of analytical models. And while some of them lag behind the peer group, others enjoy prime mover advantage in their market sector. What, one may wonder, are the key drivers enabling their success? Let’s take a sneak peek at how you can capitalize on AI-fueled techniques in your marketing campaign. To add more practical value to this article, I will share some hands-on insights on how Gallantra provides its AI-driven solutions for businesses like a big-time Broadway performance company.
Understanding data analytics in marketingArtificial Intelligence, namely its application aspects like Natural Language Processing and Machine Learning enable to orchestrate your disparate business data into manageable information for further BI’s analytical purposes. The insights gained from data mining and analysis can help entertainment companies in multiple ways – from improving customer experience to estimating box office performance to defining pricing policies. Data analytics is evolving and has gone a long way from imprecise human-aided routines to accurate AI-powered tools and methods. Now Prescriptive Analytics can give you market foresight on every single behavioral data and pattern. Here below you can see how the development of analytical models look like.
Implementing data architecture strategiesNo matter what analytical tools you are going to employ within your marketing campaign, the following stages are essential to get a holistic picture of business performance and to anticipate further deployment activities.
- Data acquisition.
- Data organization (in terms of distributed database).
- Data analysis (including ML, NLP, Big Data processing and other techniques).
- Data delivery (automated exposure of relevant insights of analyzed data).
Despite the fact that a data analytics process is way more complicated and may have many interim steps, these four are the cornerstones of any tiny or large-scale business flow analysis.
Leveraging leaders’ success with AII guess you’ve had enough of the theoretical part, so let’s get down to some practical examples. When it comes to applying AI advantages in the entertainment industry, we may take on the findings of most prominent market leaders. For instance, Netflix enjoys over 130 million subscribers worldwide as of July 2018. Well then. For the uninitiated, it sounds like an abstract fact, and nothing more. So can an extended audience pool be utilized for measuring and predicting business performance? Definitely so. It allows them to gather immense feedback data for marketing analysis. Given these insights, Netflix marketers can easily predict the upcoming performance success and customize ongoing shows and movies to best fit to their customer needs and expectations. The words of Joris Evers, Director of Global Communications at Netflix, saying that they have 33 million of different versions of Netflix has become proverbial.
Building robust data modelsLikewise, Gallantra has implemented a range of AI solutions to optimize the predictive potential for the performance industry. Advanced NLP and ML algorithms dovetailed with existing business capabilities have led to the fruitful results in terms of qualifying relevant data and estimating better performance outputs. Given the above-mentioned classification, the streamlining process boils down to the following:
- Acquiring meaningful data.
- Organizing the retrieved data.
- Analyzing data via ML algorithms.
- Delivering the improved results.