In the first part of Defining the Context Layer in NLG, we discussed the context layer for the opening, or lede, of our story. The next NLG story element we want to outline is actually the final element in the narrative: the ending. Much like any good fiction writer dreams up the ending of their story and writes to that ending, good NLG leaves the reader with actionable information-and the creator should write to that. Outlining the context layer for the ending works a lot like outlining the beginning, only instead of outlining all viable ledes (the shortest possible summarization of the most important facts hidden within the data), we’ll be listing, prioritizing, and grouping all the potential endings. Potential endings can take any format. That’s a business decision based solely on the purpose of the content. But there are some commonalities for good endings, and that gives us a place to start.
What Does It Mean?
If you’re having trouble thinking up a good ending for your NLG content, remember: all good stories have meaning. In other words, all content has some purpose for its existence, whether it’s to sell something, mark progress, or even provide an update until more information arrives.
Think about the purpose of your NLG content. We read sports content because we’re fans of the sport, the team, or the player, and there’s usually some financial transaction involved with that fandom, whether it’s advertising, merchandise, or tickets.
Spoiler alert: It’s usually advertising.
We read finance content because we want our net worth to increase. We read business intelligence reports because we need to make important decisions for our business, and data makes those decisions easier (most of the time). We read product descriptions of 4K televisions to make those same kinds of important decisions, only for our living rooms.
Much like we grouped our ledes into Records, Trends, and Deltas, there are three major categories we can group into for our endings:
- Call To Action
- Greater Goal
- Next Event
Call to Action
Call to Action is the broadest use of NLG, and it can take many forms. For example, NLG alerts about earthquakes prompt the reader to decide whether or not to take shelter without having to know much about earthquake data. Stories about stock performance prompt us to buy, sell, or hold, by analyzing performance over time or against other securities. Stories about retail products, if written well, help us decide to put that product in our shopping cart.
In some cases, we know enough about what the data is telling us to be suggestive or prescriptive about what the call to action might be. But this gets tricky quickly.
Remember, we’re providing the reader with the information to make a decision, not necessarily making that decision for them. Suggesting available options and predicted outcomes is usually where that prescriptive process ends.