Shopping is becoming more and more accessible, however, the decisions are more complex.
In many cases, impulse purchases, which we decide in a matter of seconds and which, through AI-based offline recommendation systems, help to generate the conversion of these impulses into increased sales.
This ‘knowledge’ about consumption habits typically comes from what the audience has previously purchased or viewed, the date and time frame in which they have done so, the environment and the impact of other external factors that may influence when they do so.
AI-based recommendation technologies collect and analyse this data to deliver personalised and tailored advertising in real time.
These tools require massive volumes of data, and the bigger the data, the better and more accurate the predictions we can make.
This is what is known as “Deep Learning”: a branch of artificial intelligence that processes massive amounts of data to create behavioural patterns.
Artificial Intelligence anticipates consumer purchases
AI-based purchase predictions
We have all made a purchase driven by suggestions and recommendations based on our behavioural data, in the OFF and ON world. Netflix recommendations, offers in a supermarket or shopping suggestions on platforms like Amazon.
And now the engines are getting smarter: they use deep learning tools applied at the point of sale to help optimise your time at the point of sale, through advertising with personalised products and services.
Retail businesses, where the last mile plays a fundamental role as it is where most impulse purchases are made, with the consequent increase in sales.
Learning algorithms develop this improvement, with personalised suggestions at the point of sale that generate and encourage the conversion of these impulse purchases.
Coupled with real-time analysis, optimising the planning and content of our communication to the point of prediction is possible.
This allows us to be one step ahead of our customers, that is, to think about the customer of the future, to capture their attention with personalised products and services that we know in advance will interest them and that they will want to buy.
A recommendation engine based on Deep Learning
Deep Learning connects the data we have extracted and analysed, allowing us to understand the Customer Journey of consumers by behavioural groups.
If we know the type of audience in your business during a certain time of the day, the recommendation mechanisms will associate advertising to their tastes, interests or needs during that time.
Ladorian’s Prime Time Algorithm simulates our way of thinking, analysing countless data sets and information that will produce logical and objective decisions in our in-store marketing strategy.
The essence of our in-store recommendation model is the ability to create proactive, high performance rules, which for a group of people is unachievable.
Static advertising is a thing of the past; if your in-store audience is different throughout the day, shouldn’t you adapt your in-store communication?
The use of intelligent recommendations strengthens your retailer’s or brand’s relationship with consumers by increasing retail sales, improving conversion rates and generating revenue growth.
Programmatic advertising through digital signage systems that automate impacts in real time to ensure effectiveness.
The right communication is the key to achieving this by making use of the data that your point of sale generates every day. ⇨ With Ladorian and the use of our algorithms based on Artificial Intelligence we will make your advertising impacts really improve your sales.
Ask our professionals for more information!