Add Big Data To Twitter's 'Everyday Moments' And You Could Go From Real-Time To Next-Time

Interesting move for Twitter to launch its 'everyday moments' tool for UK marketers. It kind of sits in the back of my head as a second screen tool for people when they're not second screening. Or perhaps that should be extending second screening beyond the sofa, where you're second screening your life?

Allowing brands to see what people are talking about hits a couple of sweet spots in digital marketing, namely working in real-time to tap in to and place a brand within timely conversations. It could be used to interact with people as well as, perhaps, judge how well a campaign is going. It will offer maps of activity and so if you want to judge how people in Manchester have responded to a city-specific campaign, you could do so. If you wanted to see which areas were responding the most enthusiastically to a nationwide campaign with an uplift in conversations around eating an x chocolate bar, again, that would appear highly possible.

It needn't all be now and previously, though. Throw in big data and I reckon you could add a future tense and add next-time to real-time.

Using big data collection and interrogation techniques, it's quite easy to see how you could not only react in real-time but to also project forwards and anticipate that a person who goes to a cycling club is going to be meeting up at a regular time with like-minded friends. A good opportunity for a local pub offering soft drinks and a lunch deal to promote a post to as many of them as possible a day or two before when a route is being decided upon? Or, maybe a couple of days before, a promoted post for whatever kit might be required would hit the spot -- thinner shorts for the summer or warmer gloves for the winter?

The same logic could be applied to after events. A brand might have noticed it's been unusually hot and so our cyclist might have found a scorching ride meant they wished they had a larger water bottle, a cap, summer shorts and thinner gloves. Not a bad time to promote a post at the end of their ride - whose route they may well have posted - to highlight some summer riding gear? This, of course, could all have been planned in advance, knowing the ride would take place and the weather would be unseasonably warm.

The same could be applied to any interest or hobby. Bored train passengers might be in a more receptive mood than usual to respond to a promoted post for a music streaming service or to download an ebook. Guys going out for a night out might well be more tempted to respond to a post suggesting they download Uber so they're set up for the taxi ride home. If these 'everyday moment' follow a pattern, maybe it's book club night at a local bar or a weekly commute to see friends and loved ones, then big data would enable markets to not just tap in to real-time but also future-time opportunities.

Promoted posts for books, wine clubs, snacks, the latest fashion could all be sent in advance or during the event as well as after. Anticipation could be key. A book club member who doesn't like the book they're reading - as their tweets reveal - could well have another promoted to them in the hope it becomes book of the week and guarantees another twenty sales. Back, to the weather, if it's turning cold, might be a good time to promote a coat, scarf and umbrella? If it's not been acted on, maybe it's a good idea to try again when that someone's on the bus home or in the taxi they booked via the app you suggested they download the month before.

There will obviously be a lot of dross in there. But amidst the chat about making a ham sandwich and feeding the cat, there just may be some surprising nuggets that can be gleaned directly or indirectly. Someone who makes ham sandwiches every day might well respond to an invite for a change to chicken and a person who feeds their cat at in the afternoon might not be interested in cat treats but, other tweets might suggest, they work from home and so would be a good target for home office related tweets.

So, 'everyday moments' is a really intriguing move, in my opinion. All I'd add to the equation is that real-time needn't stop at being real-time.

Those current conversations can actually be fed in to big data interrogation systems that could provide a new genre - 'next-time'. 

Now that really would be something worth tapping in to.

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