Analyzing Netflix’s earning calls with Natural Language Processing
I will briefly touch on the sentiment categories as many movies and genres were mentioned in the conference calls, and the words may have skewed the sentiment scores. However, the Chief Executives used positive expressions when sharing Netflix’s performance in the calls. Based on the bar charts, many words associated with trust and anticipation emotions were used as well.

If you are interested in sentiment analysis, here is a joy plot that shows how emotions varied in Netflix’s earning call transcripts!

Netflix’s earning call line count from Quarter 1 -4

Netflix earning call sentiment joy plot

Deep dive into Netflix’s content and product strategy

In this section, we will explore insights related to Netflix’s latest content and product strategy.

It’s all about the content!

Netflix has been spending a remarkable amount of money producing and promoting original content. The company aims to increase its subscriber growth by housing original programming that can’t be found anywhere else.

Word correlation network graph

The word correlation network graph illustrates the words that often appear together relative to how often the words appear separately. If you look at the word chunk at the bottom, you will see how certain words are related to the “content” keyword. Based on the graph, we could easily categorize the terms into two groups — Content budget (“budget,” “investment,” “spend”), and originality (“original programming,” “local language”).

Interestingly, one of Netflix’s hit series “Stranger Things” also made it into the network graph.

We could also use other NLP techniques to verify our findings. Below is a bi-gram (2-word) frequency bar chart that shows the terms that are regularly mentioned in the conferences. I highlighted words that may be relevant to Netflix’s content strategy, including “Casa de Papel” (which translates to Netflix’s original series — Money Heist), “Original programming,” “local language,” and “content slate.”

A bi-gram bar chart

Based on the analysis above, we can assume that Netflix’s focus continues to be producing original, only at Netflix content. Furthermore, Netflix may be looking to produce more movies and series in languages other than English to attract more international subscribers.

Netflix’s product strategy — Stream anywhere, anytime from your pocket.

When I was analyzing the sentimental aspects of words used by the Chief Product Officer at the conferences, one particular word caught my attention — “mobile.”

CPO — Word frequency based on emotion bar charts

To get a better understanding, I looked at other phrases that start with the word “mobile.” The results show that the word “mobile” may be referring to a new mobile plan/offering/tier in Netflix.

List of phrases with “mobile” as the first word

The word correlation bar graphs below unveiled some exciting insight regarding Netflix’s product strategy. And I made a few assumptions based on the findings:

Netflix plans to use the mobile phone offering to expand the company’s international footprint, particularly in India. Lower pricing may be one of the features in the mobile plan to attract more subscribers outside of the United States.

Words correlated with “mobile” bar charts.

If we incorporate Netflix’s content strategy (more original productions) and product strategy (mobile phone subscription targeted at the Indian market) and the findings turn out to be valid, we might just see more Netflix-only, Indian movies and series in our Netflix recommendations!

Netflix’s video-streaming war with Disney Plus

In this section, we will look at the competition in the video-streaming industry.

Words correlated with “competition” bar charts.

If you have been following the video-streaming industry, you may already hear of Disney Plus, a video-on-demand streaming service started by Disney. The country “India” stood out in the bar charts above, which in some way is correlated with “Disney.”

The results suggest that Netflix and Disney are interested in penetrating the video streaming market in India. And to win the video-streaming war, Netflix introduced a mobile plan (possibly with lower pricing) to absorb more market share globally, especially in India.

Revealing Netflix’s financial positions with words

A typical earning call informs of the public the company’s financial status, figures, and some metrics that investors are interested in. However, in this section, we will focus on using words only rather than numbers to extract insights from the conferences.

Phrases that start with the word “margin.”

Looking at the words that are used with “margin,” we see a somewhat positive sentiment with words such as “expansion,” “potential,” “growth,” “outperform,” etc. used in the earning calls.

Next, let’s look at some keywords identified using the RAKE (Rapid Automatic Keyword Extraction algorithm) algorithm.

Keywords identified by RAKE method bar chart

Notice that the “price increase” phrase is on the top of the list of keywords extracted from the earning calls.

Count of phrases that start with the word “price.”

Next, let’s look at the language used by the Chief Financial Officer at Netflix to find keywords relevant to pricing or cost.

CFO Word frequency based on emotions bar charts

As expected, words such as “cash,” “money,” and “margin” are regularly used words by the CFO in the conferences. Next, let’s look at how these keywords correlate with each other in the transcripts.

A network graph composed of words

Words correlated with “financials” bar charts.

Based on the findings above, it seems that Netflix has been increasing the price of its subscription plans, and has caused subscribers to stop paying for their Netflix subscription. We can also assume that Netflix’s revenue streams were impacted by the drop of subscriber count to its services. This would explain why Netflix decided to bring in a new mobile phone offering (with lower pricing), to maintain its subscriber base.

Conclusion

Overall, we were able to derive some insights in the earning call transcripts by analyzing the texts only. The results suggest that:
['Feng Lim']
['Investing', 'Data Analysis', 'Business', 'Netflix', 'Data Science']