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Can AI predict our intentions better and faster than us?

NLP and AI are now predicting our needs better than we can. Here is how

Connected devices that are authorized by humans to collect data – can do this at many touch points. The combination of artificial intelligence (AI) and natural language processing (NLP) coupled with the Google ecosystem, have surprised many people. But Google is not alone: Customers using chatbots are equally shocked at how accurate machines become at predicting our needs. Well, our needs are often deduced from our intention: in the digital marketing world, “customer intent” is a big word – in fact, it is the foundation of search marketing.

AI and ad serving – Let’s use an ordinary example:

You visit a news site and an advert pops up. To your surprise, it suggests a product or service which is EXACTLY what you need right now. You think to yourself: “But if I visited this site a month ago, I would not have needed what the ad suggests – so how do Google Ads know my needs so well, better than me?”.

The same question can arise: Well, how did the chatbot know to serve me with such a precise recommendation, how did Facebook Ads know that I needed to see such a relevant ad? Sure, you agree that it makes sense for Linkedin to show a relevant ad based on your career, but these other platforms, who are unaware of your role, just HOW do they snuff out such accurate predictions?

AI travels a long path – and can tie into digital marketing ecosystems in many ways. For example remarketing is one of the biggest vehicles to use AI and serve ads that are spot on to a customers’ needs. It is not that complicated in many cases. Here is what may have happened:

  1. Thousands of consumers buy certain products in sequence – details of their credit cards, identify them.
  2. Patterns are established based on large data piles: customers who buy “X” usually buys “Y” too.
  3. The CRM of many stores have your email – which is used to set up remarketing campaign OR
  4. If you simply looked at certain items online, you also hopped onto a behavioral remarketing list, as if you bought those products physically in store (as per 3 above).

What happened? AI used the data of others who went through the same purchase journey to show you the way, in anticipation of your own needs.

Social media, AI and predicting user intent:

Scrape the rate of change in sentiment across social platforms – add the time and velocity of the changing event and you end up with an accurate prediction of what will happen if any trend is allowed to continue. Such is the effectiveness of this, that certain people (hint) used this to win an election in the worlds biggest economy. Now we have social platforms such as Linkedin Learning (from Microsoft) that makes use of AI for the better of humanity, to predict what skills they need to learn next.

Why voice is a game changer:

Eventually, voice will accumulate more impulses than text. If all of this data is analysed in relation to specific events, in cases where NLP is active, we find that the systems connected to the data, deliver accurate predictions and a delightful user experience thanks to high relevance. Voice can gather data without us intentionally giving information. So once voice recording is functioning in the background to make life easier for humans – provided that it is trusted we would have entered a new quadrant – and that, is where we are right now.

How AI will power segmentation:

Already, large e-commerce data provides a new basis for segmentation. Over 600 segments have been identified in some cases, based on a variety of variables that are measured. As AI improve and integration speeds up, the CRM systems that will drive programmatic media buying – will tap into NLP and other AI features to create segments and deliver highly relevant messaging on the fly. Yes, this is already happening, but it is only getting started.

Final scoop:

AI research is speeding up, with statistics and data surprising people on a daily basis. We are not quite there yet – as the cases of this happening is still the exception rather than the norm. Once consumers understand that they can trust the ecosystems that manage their data – and businesses trust the validity of programmatic, we will live through consumer experiences where everything is relevant.




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