The correct collection and processing of data is the path that has been marked out for some time and that allows us to create a totally personal marketing strategy.
In fact, thanks to the generation of data everywhere within the digital environment, brands can develop 3 specific actions to improve the personalization of our marketing strategies:
We absorb.
We understand.
We interpret
We see specific and successful cases such as Amazon, Spotify, Uber, Netflix, etc.
We will talk about all of them later.
According to statistics provided by Salesforce Research, in its report “The State of the Connected Customer,” 61% of respondents say that technology is redefining their behavior.
The report makes it clear that:
Half of respondents say they are likely to switch brands if they do not anticipate their needs.
75% of consumers expect brands to deliver consistent experiences, regardless of the channel they engage through; social media, website, mobile devices, etc.
Customers expect every touchpoint with businesses to be immediate, personalized and proactive.
So, I would answer that the "blame" or rather the responsibility in this matter is shared 50-50.
A technology that has an environment conducive to providing unique experiences and a consumer who is increasingly moulded to it and, therefore, becoming more demanding.
Now, once the cycle of reflection is closed, we can get down to business.
What is personalization in marketing?
Attributing a theoretical concept to it is complicated because we can fall short with the definition.
But in its simplest sense, personalization in marketing is about data-driven actions to improve the experience of users, consumers and customers.
Marketing personalization is everything that allows you to break down barriers so that instead of speaking to a target audience, you speak to Daniel Pizarro, a 45-year-old man, residing in Lima, married with 2 children and another on the way, a contractor who is always looking for discounts to improve his profit margin and product quality to always keep his customers satisfied.
As complicated, ironic and long-winded as it may sound, consumers no longer want to be part of an audience; they want to be seen as what they are: people with real lives and problems.
But they also want brands to understand them and treat them as such. Is that complicated?
Not at all!
Because that's what marketing personalization is for, a theory that seeks to interact with an infinite number of individual audiences.
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What are the new trends in personalization in marketing?
It's amazing how the correct management of data can allow us to generate totally individual campaigns, starting with the most beloved one right now:
Product recommendation engines
Imagine the world as it was before: you went to the store near your house and you met “Don José” the owner of the business who has run it for years and who also saw you grow.
Like every Thursday night, you go for the same products to fill your pantry and “Don José” as a good salesman already knows what you like and recommends what to take.
Well now, “Don José” is an algorithm that understands, through machine philippines code number mobile learning, your purchasing behaviors, therefore, every time you add a product to the cart, it has more material to understand what you like and what you might like.
Product recommendation engines can be found in virtually any online store.
They know very well about cross-selling and upselling , just like “Don José”.
Product recommendation engines are a type of information filtering system that attempts to predict a user's preference or rating for a product.
There are several types of recommendation engines, here are the most commonly used ones:
1.- Collaborative filtering
It is a type of filtering that allows you to group several user profiles together and show them the same information.
It's like a kind of database segmentation to match users with the same tastes and create a general profile.
This grouping can reveal new information that will allow for improved marketing actions for each segment in the future.
This is the type of filtering that we can see in some e-commerce sites.
2.- Content-based filtering
It is a filtering based on the keywords used by the user and the items that the user may like.
Based on this information, algorithms analyze the elements and offer more personalized content on future visits.
3.- Filtering based on demographic profile
This type of filtering does not require user history to work on recommendations; instead, suggestions are made b