In this post we are going to look at a hypothetical scenario and outline a strategy with which a media company can increase and retain it’s digital content viewers. In this post,

  • first, we will establish the media company’s background
    • the content it produces
    • the content categories it’s trying to improve it’s viewership
  • we will lay the foundation for the proposed improvements
    • discuss the improvements
      • UI/UX improvements
      • Interactivity based improvements
      • A sci-fi level but still technically feasible proposal
    • establish a time frame for implementing the improvements
  • Lastly, we will provide estimates for the return on investment.

The media company

This media company has one of the most popular television channels in the country and over the years has attracted a steady stream of viewers to it’s television programming.

The content it produces includes popular sporting events, soap operas, international shows and reality shows (more of these in recent years). Over the years, it’s sports content has attracted a steady stream of viewers leading to consistent advertising (ad) revenue from it. The rest of the content however had seen a bit of a down-turn in viewership as the viewers were tuning in to competing TV channels that provide digital streaming content online.

To improve digital online viewership the media company recently launched apps for both smartphones and tablets. The apps launch saw an increase in viewership for that quarter, making the external stakeholders happy/satisfied, however as the post app launch euphoria subsided, so did the viewership. This is a cause of concern for the company executives and they have decided to hire external help (us) to have a look at improving this.

Problem

“Improve digital content viewership”

However wide the scope maybe, it’s good that we have a one line problem definition and a starting point. However, the company has hired us on a project basis, hence we have a limited time to propose a strategy to solve the problem. Therefore the first thing we would do is to narrow down the problem to a smaller, more solvable scope. With that in mind, let’s see if we can decompose the problem i.e. break it down into smaller sub-problems. Let’s have another look at the content the company airs,

The content it produces includes some popular sporting events in the country, soap operas, international shows and reality shows (more of these in recent years)

One of our goals would be to increase viewer retention and one way in which I could think of is captivating the viewer via some form of content interactivity. Soap operas and international shows, present very limited opportunity for adding any interactivity. The sporting events already have a steady stream of viewers and revenue so we will leave them out for this project. The reality shows however can facilitate some interactivity and for this project we will focus on increasing viewer retention for that content.

Target demographic

I think, to maximise the potential for success of this strategy it would help if we narrowed down the problem even further. Viewers from different age groups watch reality TV, however instead of targeting everyone, we should just focus on a particular age group for this project. To do so, first let’s outline some assumptions (without any data to back it up, right now),

  1. Reality TV is only popular among teenagers to young adults
  2. All reality TV is accessible from the age of 15
  3. Viewers up to the age of 30 in the country use Instagram a lot
  4. We have a very capable market research team

Now, we have some assumptions to build a target demographic for this strategic initiative, so… the strategy we propose here to increase and maintain viewer numbers, for anyone who’s an Instagram users (probably) and aged between 15 to 30 years.

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Proposed improvements

Let’s outline our objective in one line so there’s some context for the rest of the post.

Our goal is to prevent the decline in viewer numbers and ensure the amount of people consuming digital content on their tablet increases and remains steady (+/- 5% every week).

In other words, we need to devise a strategy by which more and more 15-30 year old people watch reality TV on their tablets and keep coming back for it every week i.e. increase retention. The proposed improvements are as follows,

Phase 1. Optimise tablet app layout

In the age of machine learning and artificial intelligence, this may seem a bit too basic. However it’s amazing how, we often overlook the simplest improvements which may lead to substantial growth. We can start this phase by collecting a few user stories for reality TV  consumption, a few user stories are,

As a young working dad, I get distracted by the kids, so I can’t always finish watching an episode in one sitting, hence I want to be able to continue what I started watching from the point I left off. 

As a school kid with limited time, I want to start watching my favorite show with a single click during my bus ride home from school.

As an art student at a university with lots of assignments and the curiosity about my favorite genre, I want to know about the latest shows in that genre.

The above are a few examples of what the viewers may expect from the app, hence a review of the app’s User Interface (UI) with the User Experience (UX) team will help identify whether or not the app satisfies those needs.

Teams involved

  1. UI/UX: conduct surveys, review other competing/popular apps UI and establish a current state of the art in digital entertainment content delivery. Then prepare an outline to implement how the tablet app can best meet those needs
  2. Front-end engineering: They will be responsible for all the UI changes to the tablet app
  3. Backend engineering: In the event the survey finds that the current tablet app is lacking features that require fetching content from the server

Time-frame

We propose approximately 1- 3 months for this project to reach completion. This is of course, relative to the availability of the involved team members.

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Phase 2. Streaming video interactivity

Our target demographic are 15 to 30 year old (s) that use Instagram i.e. the Instagram generation. Let’s think about what Instagram is? It’s a social platform where you share pictures, scroll through pictures and potentially get “likes” for what you share. Getting “likes” is gratifying and entails approval. Based on those characteristics, I would conclude that we are trying to engage a set of viewers who have a very short attention span. They want something quick and seek instant gratification, therefore it’s a big challenge to deliver content that will hold their attention for a set time i.e. episode’s running time.

To solve this problem, we propose to create and release a companion app for the smartphone. Once a viewer starts watching an episode on their tablet, they get periodic notifications on their smart phone asking them to answer some multiple choice questions that best dictate their opinion. An example of such a question would be,

Should Ben have agreed to Sheila’s conditions?

a) yes she knew what was involved, b) no she had no idea what she was doing, c) Ben should leave 

All the viewer responses would be collected, tallied and displayed on screen starting with the one with the most votes at the end of the episode. A customised message depending on how close their answers were to the most voted answer would be sent to the viewer as well. The viewer can then share the messages and the results on social media.

Teams involved

  1. Content/editorial team: they will help us identify the best content to apply this new strategy on. As a trial and a proof of concept, we will use this strategy on a show with the highest rate of viewer drop-offs i.e. those who only watch the first 5-15 mins of a 40 min episode before switching off.
  2. UI/UX & front-end engineering team: They will be responsible for designing the interface for the companion smartphone app.
  3. Backend engineering & Data team: They will be responsible for creating the companion smartphone app and work with the data team to establish a framework to collect viewer answers.
  4. The finance team: Responsible for preparing the pre and post companion app launch advertising budget.
  5. Marketing team: They would be tasked with creating a “buzz” for the new, upcoming companion app, along the lines of “your opinion matters” or “watch, answer and know if other follow your chain of thought”. The key here is to use language that empowers and makes viewers realise just how important they are. We must be careful here, as the language used should make the viewers feel that others follow their chain of thought as opposed to them being a part of the herd.

Time-frame

This is a much bigger project and we anticipate anywhere between 6-9 months. Ideally, once a reality show’s season ends, this project would start off with a few viewer surveys, followed by UX surveys. The project due date would be before the launch of the next season of the show. The marketing campaign for the new season of the show will heavily feature the “exciting new, upcoming companion app where your voice (viewers) matters” or something along the lines.

Phase 3: Shopping re-imagined

Phase 1 and 2 are very technically feasible, for this phase while technically feasible, is a comparatively larger and an almost “sci-fi” level advanced phase. However given my background in computer vision research, I know this is technically possible.

For this one, I propose, the advancement is in people being able to shop for items they see their favorite stars using on screen. Since we are dealing with the reality TV format, the objects in these shows are real-world objects that people can shop for. While viewing a show on their tablet, the viewers can click on the shirt or shoes that they see their beloved stars wear on screen and that should take them to the retailers site where they can purchase it.

How does image processing work?

Let me try to explain and please be patient with me on this one, because you will have to do some reading before you find the answer. 

To know that, first let’s establish what a video is? a video is a collection of frames played together in a sequence such that it simulates motion. A solution to identify or detect the presence of an object in those frames would be something that would analyse each frame before it’s presented on screen.

You may have seen people’s faces being detected and annotated (circled) on a smartphone. For that to work, the face detector algorithm analyses each frame of the video before it’s displayed and gets the coordinates (if any) for the face, relative to the image and annotates (draw circle) it. A frame is an image and what the face detector is doing to the image, is processing it to detect the location of the face. Hence the term, image processing.

Let’s think about this for a second, if the image processing algorithm, can detect faces, it can detect a shirt too? right? I know from my academic research in human victim detection in 2014 that image processing works “reasonably well” for objects with static shape  as opposed to the human body which is well articulated. Therefore, we could have an algorithm in place that could detect say shoes, tennis rackets or cups which would process each frame for the reality TV show video as we broadcast it. As it’s being broadcast, the viewers who are watching it on their tablet have the capability to buy e.g. the shoes worn by their favorite star.

p.s. The challenge with detecting objects in a video would come from partial occlusion of the objects but that’s a topic for another post.

Time-frame & Returns

This project has many moving parts that pose a number of challenges and may almost end-up being a very loooooong drawn out project. Some of it’s challenges are,

  1. Convincing third-party vendors: this would be one of the first major hurdles before we even start thinking about the implementation related technical challenges. The sales team would needs to work very and convince the vendors who’s products we aim present for shopping
  2. External stake-holders and investors: convincing them would be difficult given the large ivestment and up-front costs of this this project
  3. Infrastructure and development: we not only need to find a team capable of setting this up but we also need to factor in the on-going maintenance costs of such a team

Right now, I anticipate this project can take anywhere between 2 to 3.5 years.

Return on Investment

We anticipate an increase in ad-based revenue approximately 2 months after the completion of Phase 1 of this strategy.

We anticipate a significant increase in viewer numbers, along with a major reduction in viewer drop-offs a month after completion of Phase 2 of this strategy. For starters we will limit our goal for Phase 2 to increasing viewer numbers and retention, as opposed to increases revenues.

Phase 3 if and when finished will almost double the annual revenues, I mean think about it? We could take some X% of profit for each item purchased from the vendor while watching the TV show.

I have kept this part as brief as I could on purpose, as it’s relative to the team in the media company. I could have included numbers such as a 50-70% increase in ad revenue after phase 1 but those would just be arbitrary numbers. A full cost-benefit analysis would involve having a sit down with upper management, discussing the available talent on hand and running costs of the company.

 

Summary

There you go, in this post, I discussed a strategy through which a media company may increase viewer numbers, retention and revenues.

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