Imagine you’re going on a ski trip, and for the sake of this story let’s say it’s to Vail and that the trip will be in February. You haven’t been skiing in a while, so you need to buy some new gear, namely a new ski jacket. You start Googling and find that there are a gazillion options out there – heavy down, lightweight shells, water resistant, waterproof, …the list goes on. Some of them seem to be made for warmer spring skiing. Some seem to be made for hardcore backcountry skiing. How do you decide what to go with? Ski jackets can get pretty expensive, and you don’t exactly want to be paying for features that you’ll never need.

Wouldn’t it be nice if all of the jackets that weren’t appropriate for Vail were filtered out? Even more specifically, wouldn’t it be great if all of the jackets that weren’t appropriate for Vail in February were filtered out? You could check the average temperatures and snowfalls for Vail and cross reference those with each of the jackets and what they’re made for, but that would definitely take some time. It’d be way better if the reviews just knew what you were looking for.

Well believe it or not this is entirely possible using contextual awareness, and here’s how it could work. First, the platform (we’ll call it a ‘platform’ just so that we have a name for this smart thing that knows what you want) would need to know that you’re going skiing at Vail in February. Well, chances are that you’ll post about it on social media at some point in time. The platform could take that info and use it as a starting point. Then, when you start searching for gear, especially a ski jacket, the platform will reasonably guess that you’re looking for a jacket for your trip to Vail.

With just this information, the platform will look up those average temps and snowfall numbers for you, using them to filter out all jackets that aren’t going to suit your needs. For instance, jackets that are rated down to -100° are irrelevant and thus filtered out since there’s only a 0.0000001% chance that you’ll experience anywhere close to that temp on your trip.

Then, just like it did with you, the platform will look at other people’s social media posts and figure out who else skied at Vail, ideally in February, and then it will see if any of those people also wrote gear reviews of ski jackets soon after their trips. The jackets that received the high ratings will be the ones that you’ll want to consider, so accordingly, your search will come up with those chosen and vetted jackets instead of the gazillion potential ones that you started with.

To take this a step further, imagine you get to Vail but forgot your gloves. Such a platform would use the GPS from your phone to know that you’re in Vail, it’ll look up the weather forecast for the week and show you not only the best gloves for the weather you’ll be seeing, but since it’ll know that you ordered your jacket, you’ll be presented with gloves that will work well with the particular jacket you decided to go with and since the platform knows your location it can even tell you the closest store in Vail to pick them up at.

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Of course this is just a hypothetical story. For now. Using real-time contextual awareness, such as a person’s physical location or what they’ve been talking about on social media, will be hugely important for improving the gear industry’s recommendation systems. With 30,000+ new outdoor products coming into the market each year, there’s gear being created for almost every type of outdoor experience, but the hard part is filtering out the 29,999 products that don’t apply when you’re looking for that perfect piece of personalized gear. Hopefully by using better machine learning and artificial intelligence systems combined with contextual awareness we can start doing a better job of recommending the perfect outdoor gear so that no one ever ends up with the wrong gear ever again.