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What factors influence DAU/MAU? Nature versus nuture

Surprisingly, it can be hard to figure out if you’re at Product/Market Fit or not, and one of the big reasons is that comparable numbers are difficult or impossible to come by. You have to look at comps for products in a similar or equal product category, and sometimes they just aren’t available.

Nature versus nuture
One way to think about this is that products have a nature/nuture element to their metrics. Some product categories, like chat or email, are naturally high-frequency. You use them a lot. Other products, like tax software, might give you value but you only use it once per year. A lot of ecommerce products are in-between, where you might buy gadgets every couple months but not every day. Just because people only use your product once a year doesn’t mean you don’t have product/market fit, as long as you’re building a tax product and not chat.

Here’s a great article on the breakdown of retention versus frequency for a bunch of categories on mobile:http://blog.flurry.com/bid/26376/Mobile-Apps-Models-Money-and-Loyalty

The two extremes are interesting:

  • Medical apps: They may have high retention since if you have a chronic ailment, you may constantly be using an app relevant to your condition, but maybe not every day
  • Books/Games: You read them nonstop for a few days or a week or two, and then once you’ve consumed the content, you never go back

The point I’ll make on this is that due to the nature of certain product categories, there’s a natural range of DAU/MAUs, +1 day and +1 week retention metrics. That’s the “nature” part of the product category. No matter how good your tax software is, you won’t get people to use it every day.

Based on your product execution though, you can maximize the the metrics within the natural range. A really good news product like Flipboard is able to drive 50%+ DAU/MAUs, which are fantastic.

Some product categories cannot get high DAU/MAUs
One key conclusion of this is that it doesn’t make sense to try to compare against Twitter or Facebook’s 50% DAU/MAU unless you are in the same category as them. A lot of social games target 30% DAU/MAU, but we can also see from the Flurry chart that social games are also amongst the highest DAU/MAU categories.

That said, if you are in the same category, then these rival products really tell you how good your metrics could really be, if you executed them in the right way.

Either way, don’t fight your nature :)

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  • http://twitter.com/howardvk Howard Kingston

    Hey Andrew – thanks for the post – awesome! We’ve found DAU/MAU (stickiness) one of the most difficult metrics to increase as we’ve found it hard to directly influence it (it’s not an actionable metric) – seems that there are so many contributing factors. We’re in the social gaming industry.

    What are the key ways you would recommend to increase our DAU/MAU in a significant way?

  • http://www.facebook.com/bhaskar.roy Bhaskar Roy

    Seems like the Flurry data is missing a key app category – Communications. I would think that would have a high DAU/MAU.

  • http://www.facebook.com/andrewhchen Andrew Chen

    Yeah, it’s also missing photos. The diagram is from 2009, unfortunately, so it’s more than a bit old.

  • http://www.facebook.com/andrewhchen Andrew Chen

    That’s a great question about increasing DAU/MAU. If I find an answer I’m sure of, I’ll let you know ;) I’m sure that it’s really a function of the mechanics of your game, and it varies from genre to genre. I’m not deep enough on the game design side to say more than that though.

  • http://twitter.com/webbie Lilia Tovbin

    I am not an expert, but I imagine things like achievements, expert levels based on scores, progress status towards any milestones and dashboards that compare data for you and your circle of friends would improve stickiness in social gaming. Also, perhaps leagues or teams of some sort if the game concept lends itself to it.

  • http://twitter.com/webbie Lilia Tovbin

    Great post Andrew and thanks for the diagram, even though it’s old. It would be interesting to connect loyalty metrics to value per category as well. Your post on virality pointed to a balance to catch against usefulness to user, are there similar considerations for efforts to improve loyalty? Is there such thing as too much?

  • http://www.aginnt.com/ A G

    Interesting graphic. It seems that user trust and credibility generally increases as retention over 90 days increases. Apps with high retention tend to be used to acquire information that needs to be credible. Good stuff.

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