How Text Analytics Rescued Me from a #ShowHole!
My wife and I recently found ourselves in the uncomfortable condition commonly known as a “show hole.” Are you familiar with this?
A show hole refers to the state of loss and aimlessness one experiences after completing—often via binge-watching—the last episode in a beloved TV series without having a successor program queued up. The term was popularized by an Amazon Fire campaign a couple years ago, and you’ll find it hashtagged all over social media these days by people desperately in need of relief.
The show hole is an interesting phenomenon that speaks to how dramatically audience consumption habits have changed with the advent of the DVR, streaming and on-demand services like Netflix, Hulu, Amazon, etc. But what’s curious to me is how such a clearly great need continues to go relatively unmet.
Of course, subscribers to on-demand services have help in the way of recommendations algorithms. Netflix, in particular, has famously invested extensively in developing predictive analytics to suggest other shows to watch.
Still, the preponderance of cries for help on social media would seem to indicate that for many people these solutions have fallen short.
Indeed, it appears that people tend to prefer recommendations from other people, which introduces a different set of problems.
The Problem with Recommendations, Ratings and Reviews from People
In my own disappointing search for what to watch next, I found #showhole aplenty on Twitter, but the platform doesn’t lend itself well to discussion, so most of those who tweet about it get left hanging. Usually it’s just a “Help! I’m in a #showhole!” message from someone after finishing a series, but hardly anyone tweets a reply with suggestions.
Note: Because Twitter isn’t well-suited to this kind of interaction, your standard social media monitoring tool—most of them rely on Twitter data—wouldn’t be effective for the type of analysis we’ll cover today.
I did, however, find a ton of recommendation activity occurring on Facebook, Reddit and a variety of other discussion boards and community-based sites, including surprising places like babycenter.com—a support community for pregnant women and new moms—replete with threads where members actually recommend series’ for other members to try next.
This Yelp-like model for getting out of a show hole has its own limitations, though. How do I know I’ll like what you like? Is it enough to assume that since we’re both new moms that we’ll enjoy the same shows? Or if we both enjoyed one program, that I’ll enjoy whatever else you’ve watched? Similarly, if I ask you on a scale of 1-10 to rate a show, how would that information be useful to me if we don’t have the same tastes? Remember also that we’re looking across genres. Our tastes in dramas might be similar, while our tastes in comedies could be worlds apart.
In short, we have all of these people providing recommendations online, but the recommendations really aren’t any more helpful to the prospective viewer than star-based ratings and reviews. I.e., the show hole sufferer is forced to audition each of these programs until he/she finds one that fits—a time-consuming and potentially frustrating process!
How Text Analytics Can Make These Recommendations Useful
As I pondered the recommendations I saw online, it occurred to me that if I could apply text analytics to identify preference patterns based on recommendations from a broad enough swath of people, I might arrive at a recommendation suited to the unique tastes of my wife and I that we could then invest time in with a high confidence level.
Happily, I discovered that when suggesting new shows to watch via social media, people tend to provide more than one recommendation, and these recommendations usually are not limited to a single genre. This means we have sufficient preference data at the individual level, which, if aggregated from enough people, could form the basis for a predictive model.
In a very short time period, I was able to scrape (collect) several thousand recommendations across a variety of sources online. It’s worth noting that just about every single network that the average American has access to was represented in this data. This is important because someone who uses HBO GO, for example, is obviously more likely to watch and recommend programming from that network than someone does not subscribe to it.
We then layered predictive analytics atop the data using OdinText to see whether text analytics could solve my show hole dilemma. Specifically, I wanted to see what other shows are most frequently co-occurring with shows that my wife and I like in these online recommendations. (OdinText has a few ways it can help in cases like this, including the co-occurrence visualization covered in a recent post on this blog by my colleague, Gosia Skorek, here.)
It’s also important to emphasize here that we accomplished this analysis without asking a single question of anyone, although this type of data could be very nicely augmented with survey data.
OdinText Rescues Tom and His Wife from Their Show Hole!
This data was more challenging than I expected, but OdinText enabled us to find a model that delivered!
Below you’ll find examples of preference clouds based on the co-occurrence of mentions harvested from several thousand recommendations across discussion boards and other social media (excluding Twitter).
Essentially, you’re seeing OdinText’s recommendation for what you should watch next based on the series you’ve just completed.
In our case, my wife and I had completed the most recent episode of “The Walking Dead” on AMC—now on hiatus through February—and, as you can see, OdinText recommended we watch “Goliath” on Amazon.
Not only had I never heard of this series, but when I looked it up I was skeptical that we’d enjoy it because my wife and I are not particularly fond of legal dramas.
It turned out that OdinText’s prediction was spot on; we’re both hooked on “Goliath”!
I'll probably check out "Drunk History" on Comedy Central next...
Attention Show Hole Sufferers: Let OdinText Get You Out!
I think this exercise demonstrates the versatility of the OdinText platform. With a little creativity, OdinText can not only provide breakthrough consumer insights, but solve problems of all stripes.
Here are a few more examples. You’ll note that quite often the suggestions cut across networks. Even though obviously someone recommending something on HBO will be more likely to have seen and to recommend other shows on that network, the model often makes suggestions that are quite surprising, cutting across networks and time. Here are just a few:
Above we have OdinText’s recommendations for anyone who likes “Luke Cage.” (I haven’t seen it and typically am not a fan of super hero stuff, but I ran it as I saw in the data that the show was very popular) “Luke Cage” fans might also like “Daredevil,” “Stranger Things” (which I did love), and “The Flash.” The first three here are all on Netflix, the last one is on CW.
You don’t have to be a premium channel streaming snob to benefit here. If you like the popular sitcom “Big Bang Theory” on CBS, you may well also like their new “2 Broke Girls”, and “Last Man Standing” or “Modern Family” on ABC.
Some of the best shows, in my opinion, are often also ironically less popular and less frequently mentioned. Two such shows are HBO’s “Deadwood,” for which OdinText recommended one good fit—“Poldark,” a BBC series--and Netflix’s “Peaky Blinders,” for which OdinText suggests trying “Downton Abbey.”
I was honestly so impressed with OdinText’s recommendations that I’m entertaining building a suggestion app based on this model. (And unlike Netflix, I didn’t need dozens of developers and millions of dollars to get the right answer.)
I may also refine the underlying model a bit, as well as update the underlying data in a few months when there are enough new series being mentioned to make doing so worthwhile.
In the meantime, I feel obliged to offer immediate assistance to those poor souls in the throes of a show hole today!
If you’re stuck in a show hole, post the title of your recent favorite series in the comment section of today’s blog. OdinText will tell the first 10 people who respond what to watch next. Then come back and tell us how OdinText did.
I look forward to your comments!
Ps. See firsthand how OdinText can help you learn what really matters to your customers and predict real behavior. Contact us for a demo using your own data here!
About Tom H. C. Anderson
Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose eponymous, patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR and the ARF. He was named one of the "Four under 40" market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson.