Marketing Plan And Chill?
By: Michael Taylor
Tags: Growth, Startup, Growth Hacking, PPC Marketing

1) Persona Development

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If we want to solve a problem, it helps to know a little about the people we’re trying to solve it for. The goal of researching personas is to understand how your potential users think and act. That way you’ll know the appropriate time, place to reach them, with a message they’ll actually be receptive to. Most people wing this part, to be honest, and assume they know who the customer is. They may be right, but personas form the bedrock for every decision you make in marketing — wouldn’t it help to spend at least a couple hours digging through data to make sure?

Netflix Research

- 81m Netflix Subscribers, 47m in the U.S.

- 70% binge watch shows, 30% share accounts

- 79% of Millenials, 38% of GenX and 26% of Baby Boomers use Netflix

- Average Netflix user watches 7.7 hours of content per week

- 5,532 titles in the Netflix library

- Typical subscriber only looks at 10–20 titles on a couple of rows of recommendations

- “The user either finds something of interest [within the first 60 or 90 seconds] or the risk of the user abandoning our service increases substantially,” — Neil Hunt, CPO @ Netflix

- Netflix saves over $1 billion a year through stronger retention due to their AI recommendation engine.

Sources: (1), (2), (3), (4), (5)

I picked all these facts up with a few minutes Googling. So what do they tell us? Well it tells the market is huge — 81m subscribers (that’s not counting the other streaming services And Chill could cater for). It’ll only get bigger as the millennials grow up and age out the baby boomers who aren’t using Netflix. It’s also a big part of people’s lives — 70% binge watch shows and 7.7 hours of content watched per week. It also gives us a figure we could use in our marketing — with 5,532 titles how will you possibly choose from all of them? Finally it gives us a sense of urgency — if the typical user only looks at 10–20 rows and gives the task only 60–90 seconds, we know And Chill’s recommendations have to fit within that window.

Next we’ll look at Facebook data. Not everyone knows this, but if you set up a Facebook ads account, there’s a tool called ‘audience insights’ where you can filter for demographic and likes / interest data to get an idea of your audience size. Here’s what I got from playing around for a little while:

Netflix Fans:

- 25m-30m like Netflix in the US

- 71% are below age of 35

- Pretty much 50:50 split on gender

- Major cities: Chicago + Los Angeles + New York = 16%

- 99% use some mobile device to log into Facebook

- 25% on Android (23% more than avg pop), 22% use iPhone

- 32% less likely to be married

- 39% more likely to be in relationship

- 64% under $75k household income

- 69% work in sales / admin, (+20%)

Does that quell any assumptions you had? For me I didn’t expect Netflix fans to be less likely to be married. I also expected much more on iPhone than Android. It’s pretty stark how dominant mobile is in this space — And Chill is doing the right thing by starting mobile first. The household income one was a surprise — I saw video streaming as something my more privileged friends were into. You’ll probably have different reactions to this… or maybe you guessed all of this exactly right (congrats!). But as you go through the rest of the data in this plan, keep track of how many times your assumptions were wrong — it’ll give you a good sense of why this is a valuable exercise.

Next we’re going to drill down into specific personas. Now this is more of an art than a science, but to arrive at these groups I followed this procedure:

1. Filter for an attribute Netflix fans are more likely to have (vs the gen pop)

2. Filter for an attribute this subset is more likely to have

3. Keep going in this fashion until I hit a niche audience of ~300k people

Doing this I arrived at 3 pretty interesting segments, all of which represent a substantial (but niche enough) ‘type’ of Netflix user. To give you a feel for the process, I’ll run through the first one:

1. Filter for ‘in a relationship’ — 15% of Netflix fans are.

2. Filter for 18–34 year olds — representing 52% of this group.

3. Filter for Food/Retail/Sales jobs — that’s 23% of what’s left.

4. Filter for College educated — another 69% of the audience.

5. Filter for Women only — that’s 69% of this group.

6. Record what the likes / interests / demographics of this group are.

Millenial Couples // 375k

+Relationship (15%), +18–34 (52%), +Food/Retail/Sales (23%), +College (69%), +Women (69%)

— Likes: Yandy.com, Hair Addiction, Bustle, Buzzfeed, Poems porn, Victoria’s Secret PINK, Forever 21, Teen Mom, FreshTrends.com, Brilliant Earth, Pretty Little Liars, Starbucks, Taco Bell.

— Movies: Easy A, Dear John, Pitch Perfect, The Last Song, Disney Alice

— Actors: Taylor Lautner, Channing Tatum, Marilyn Monroe

— Lives: Chicago, Illinois (5%)

— Drives: Economy/Compact (37%, +17%)

— Income: below $40k or above $250k

GenX with Kids // 275k

+Kids (28%), +25–44 (65%), +Married (64%), +Medical/Care (40%), +Women (81%), +College (72%) = 275k

— Leah Messer Fanpage, Flintstone’s vitamins, Trending on Wominista, Yonique by Natasha, Screaming Owl, Black Friday, Garanimals, Disney Baby, Elf on the Shelf Ideas, Sprout, Old Navy, Olive Garden.

— Movies: 50 Shades of Gray

— Actors: Channing Tatum, Candace Cameron Bure

— Lives: San Antonio Texas (4%, +68%)

— Drives: SUV (47%)

— Income: below $50k or above $150k

Blue Collar Single Men // 375k

+Men (48%), +18–34 (78%), +Single (50%), +Production (10%)

— Likes: LED Concepts, New Era Cap, Need for Speed, CARiD, Champs Sports, Playboy, Red Bull, Monster Energey, Miller Lite, Rockstar games, Call of Duty

— Movies: Fast & Furious, Jackass, Ted

— Actor: Megan Fox

— Drives: Pickup Truck (20%, +15%)

— Lives: Milwaukee, Wisconsin (2%, +73%)

Of course there are a number of different personas here, but I selected the above showed a significant bias in the data vs the general population i.e. Netflix users were much more likely to have these traits than average. This is part science, part art, so play around with different segments until something in the data jumps out at you.

Usually those who create personas without data end up with a carbon copy of themselves (or close friends). To see the value of starting with data instead, try filtering for your own persona to see how many people there actually are that have similar demographics to you. Chances are, even with a pretty broad definition, the number will be low (for me it was < 1%!).

Sometimes it’s more useful to define what your audience is not, rather than define what it is. I call this an ‘anti-persona’. You can find one by following the exact reverse of the personas above (filter for traits that are less common amongst Netflix users). In our case, it would be:

Mature Male Managers // 125k

+45–65, +Men, +Admin/Management/Sales

— Likes: History Channel, George Takei

— Movies: Star Trek

— Actor: George Takei

— Drives: Sports Car

— Lives: Philadelphia, Pennsylvania