infoSentience's automated content today is the worst you will ever see it. That will be true if you are reading this article on the day I published it, and will also be true if you are reading it a year later. Why is that? Simple- every day, our content is doing one of two things: (1) staying the same, or (2) getting better. The ‘staying the same’ part is pretty straightforward, as our software will never get tired, make mistakes, need retraining, decide to change jobs, etc. On the other hand, we are constantly making improvements, and each of those improvements establishes a new ‘floor’ that will only get better. I call this the ‘improvement ratchet’ since it only moves in one direction- up! There are three key ways that automated content gets better over time:
Improving Writing Quality
The most straightforward way that our automated content improves is by teaching our software how to write better for any particular use case. One way we learn is through feedback from our clients, as they see the written results and suggest changes or additional storylines. Another set of improvements comes from the iterative dance between the system output and our narrative engineers. When you give software the freedom and intelligence to mix content in new ways you sometimes come across a combination of information that you didn’t fully anticipate. For example, let’s look at this paragraph:
Syracuse has dominated St. Johns (winning 13 out of the last 17 contests) but we’ll soon see if history repeats itself. Syracuse and St. Johns will face off at Key Arena this Sunday at 7:00pm EST. St. Johns has had the upper hand against Syracuse recently, having won their last three games against the Orange.
This paragraph works reasonably well, but the specific combination of the first and third stories aren’t tied together as well as they could be. In this case, the final sentence (about St. Johns dominating recently) would be improved by incorporating the information from the first sentence (about Syracuse having a big advantage overall). The updated version of the last sentence would read like this:
Despite Syracuse’s dominance overall, St. Johns has had the upper hand recently, having won their last three games against the Orange.
This improvement is an example of what we call an ‘Easter Egg’, where we add written intelligence that is targeted to an idiosyncratic combination of events. Our reports contain hundreds of possible events adding up to millions of possibilities. Adding intelligence to these events allows them to combine together properly and avoid repetition. However, there’s no way to build out specific language for all possible interesting combinations in advance. As we read actual examples we come across unique, interesting combinations. We can then add specific writer intelligence that covers these combinations to really make the reporting ‘pop’.
Critically, this intelligence is usually a bit broader than just a simple phrase that appears in only one exact combination of events. In the example above, for instance, we would add intelligence that looks for the contrast between a team’s overall record against an opponent and their recent record and allow that intelligence to work in any such situation. We also need to make good use of our repetition system to make sure that all these Easter Eggs don’t start tripping over themselves by repeating information that was already referenced in the article.
Expanding the Content
Another way that content improves is by quickly expanding into similar use cases. For example, when we started with CBS we only provided weekly recaps for their fantasy baseball and football players. We soon expanded to offering more fantasy content: weekly previews, draft reports, year-end recaps, and more. Because those were successful, they then asked us to provide previews and recaps of real-life football and basketball games. We quickly added soccer, and then expanded the range of content by also providing gambling-focused articles for each of those games.
This same story has played out with many of our other clients. One of the big reasons for this is because automated content is so new that it’s often difficult to grasp just how many use cases it has. After seeing it in action, it’s much easier to imagine how it can help with new reporting tasks.
The other big reason that automated content often quickly expands is because the subsequent use cases are often cheaper to roll out due to economies of scale. There are three main steps to generating automated content:
Each of these steps is usually much easier when rolling out follow-up content. In the case of Step #1, gathering data, it is sometimes the case that literally the exact same data can be used to generate new content. This happened when we expanded from general previews to gambling-focused previews for live sports games, which just emphasized different aspects of the data we were already pulling from CBS. Even if there are additional data streams to set up, it’s usually the case that we can still make use of the original data downloads as well, which typically reduces the amount of set up that needs to take place.
When it comes to Step #2, creating the content, infoSentience’s ‘concept based’ approach pays big dividends. Instead of creating Mad-Lib style templates, infoSentience imparts actual intelligence into its system. That allows the system to be flexible in how it identifies and writes about the most important information in a data set. It also means that it can quickly pivot with regard to things like: the subjects it writes about, the time periods it covers, the length of the articles, the way it adds visualizations, the format of the report, the importance of certain metrics and storylines, and many more. Entire new pieces of content can often be created just by turning an internal ‘dial’ to a new setting.
Finally, for Step #3, there is usually a tremendous amount of overlap when it comes to the delivery process for follow-up content. Typically, we will coordinate closely with our clients to set up an initial system for delivery. This might entail dropping our content into an API ‘box’ that our clients then access, but other times we send out emails ourselves or set up a web site to host the content. We might also set up a timing system to deliver content on demand or at particular intervals. It is often the case that these exact same procedures can be used for follow-up content.
A great example of how all these steps came together is when we expanded to providing soccer content for CBS. In that case, the data pulldown and delivery procedures were identical, requiring no changes at all from CBS. While we did create some soccer-specific content, much of the sports intelligence for soccer was able to make use of the existing sports intelligence we had built into the system.
Better Audience Targeting
Finally, another way that automated content improves is from user feedback. Automated content allows for a level of A/B testing that would be impossible using any other method. I’ve already mentioned that our AI can change what it focuses on, its time periods, length, format, and more. It can also use different phrase options when talking about the same information, and even change the ‘tone’ that it uses. All of these options can be randomized (within bounds) when delivering content on a mass scale. It is a simple task to then cross-check user engagement with each of these variables to determine what the optimal settings are.
It's also possible to allow individual readers to customize their content however they want it. All of the ‘options’ mentioned above can be exposed to end users, allowing them to specify exactly what they want to see. This not only allows users themselves to improve the content they see, but also gives organizations a better understanding of the information that each of their customers really care about.
So much of our time in business and life is spent in a losing fight against entropy. Automated content provides a welcome break from that struggle. Set it up and enjoy great benefits from day one, knowing that the only changes that will ever take place are for the better.
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