To this we must add the spontaneity of these figures who quickly make real connections with their community.
In short, they are key when it comes to sending a personalized message.
But precisely because of the strength and popularity they have achieved, they have been losing credibility given that there are figures who only dedicate themselves to advertising and that drives away those we want to attract.
However, trends come and go and the one that has arrived is that of nanoinfluencers , a figure that has the same role as an influencer only that due to their small number of followers, they have the probability of generating more credibility and affinity with the community.
The thing about these figures is that their followers can see them and feel them as peers, not as a public figure who is being quoted by big brands.
And that is exactly what has caused certain brands to set their sights on them.
Why is it important for marketers to target people in a specific way?
I answer with another question: how did you feel when you saw your name on the Coca Cola can?
Surely with an indescribable feeling of exclusivity.
In Spain alone there are 269,281 men called Daniel (according to the INE ).
Personalization in marketing - Coca-Cola example
But despite the number of people with the same name as you (Daniel or whatever), the experience of having a can of Coca Cola with your name on it is fantastic even if the sensation only lasts a few seconds or minutes.
This is where the importance of individualizing our marketing strategies lies. In these times of tough competition, customer experience is what truly counts.
Micromoments, as Google calls them, are what will ultimately make our customers remember us.
In fact, we can see it in some well-known brands and companies, such as:
Target
A department store took a risk a few years ago and created an algorithm that guesses when a customer is pregnant.
I feel it's worth remembering Target's wonderful way of personalizing their strategies.
Andrew Pole, the person responsible for creating the algorithm, had recently been a statistician at the company when colleagues asked him if it was possible to create an algorithm that could predict customers' pregnancies even before they did.
As explained in The New York Times article , parents are the holy grail of retailers, yet most of them don't buy toys at, say, Target.
If they wanted to do it, they would go to a toy store and the same if they wanted supplies for the pantry.
However, Pole (the creator of the algorithm) believed that the behavior of an American father could change just before the birth of a child.
So Target's idea was to get there before other retailers (competition) knew the customer's status.
The intention of the store was to create personal messages in the second trimester of pregnancy, which is when women start buying all kinds of new things related to their pregnancy and the birth of the baby.
I quote Pole's quote from the New York Times verbatim:
As soon as we get them buying diapers from us, they're going to start buying everything else too. If you're rushing through the store, looking for bottles, and you pass orange juice, you'll grab a carton. Oh, and there's that new DVD I want. Soon, you'll be buying cereal and paper towels from us, and keep coming back.
— Andrew Pole of Target for the NYT
Pole believed that as soon as the store got them to buy diapers, customers would start buying everything else.
This is how they created the great algorithm that predicted the pregnancy of a teenager who had not yet reached the age of majority.
Target knew about it, even before the pregnant woman's parents found out.
Target's algorithm was based on Bayes' theorem, a theory that relies on random probabilities, so the retailer could know the probability of certain things happening.
The case of the pregnant teenager came to light because, according to the algorithm created by Andrew Pole, pregnant women have a purchasing pattern.
For this reason, this teenager received discount coupons for baby products in her email.
Target certainly had to be included in these examples because it has always made an effort to learn personal details about its customers, who are also identified with an ID.
This is personalization in data-driven marketing, as I mentioned at the beginning.
Sainsbury's, British supermarket
Do you remember proximity marketing?
Well, this British supermarket used it to promote offers inside and outside the store.
Even to send messages to customers who were buying from the competition and generate offers that would allow them to return to Saunsbury's.
Their campaign was so great that it won the Mobile and Marketing Award at last year's Marketing Week event.
AMA: Chilean organic food brand
It is a Chilean organic food brand that has been creating a solid campaign with the help of nanoinfluencers.
You can visit their website or view the profiles of the figures who are supporting the brand's micro strategy.
Here are the screenshots:
@gabiquevedo
Personalization in Marketing - AMA Example
@fernibytheuniverse
Personalization in Marketing - Nanoinfuencers AMA
@ohmnatural
Personalization in marketing - AMA example nanoinfluencer
If you go to the home page of one of these profiles, you will notice that none of these nano-influencers have more than 5,000 followers, but their likes are few, organic and true.
A strategy that only a few people can access, but due to the characteristics o philippines code number mobile f these figures, they can create much closer and more sincere relationships with their audience.
Netflix
He runs about 250 A/B tests a year, that's almost daily.
It uses hybrid filters in its product recommendation engine. As I mentioned earlier, this means that a user in Argentina will not be shown the same content as a user in Colombia, for example. In addition to being guided by age, tastes, preferences, etc.
With the help of AI and machine learning, Netflix can learn: search history, rating data, connection time, date, and the device from which you connect.
This will help Netflix, for example, recommend similar content. If a user is interested in series based on real stories, the algorithm will make an effort to recommend more of the same content. Or if a user logs on very late during the night, Netflix will show content that the user has left halfway through on the home page instead of new content.
Certainly proof that proper data analysis can lead to organic and exponential brand growth.
Uber and Spotify
They have formed an alliance that shows their interest in taking personalization and experience to the next level.
Youtube video thumbnail
This means that Uber users can create a playlist that they want to listen to during their trip, also sharing their favorite songs with other Uber users.
Once the journey to the final destination begins, the playlist starts