Crack The Algorithm: An AI driven Strategy to Boost Visibility and Leads in Real Estate Portals
A Menhir Technologies Case Study for Real Estate Companies.
Some companies promote their services and products on marketplaces where they compete for attention against other professionals. These online markets host many advertisers and the competition can get really fearce. If you are not in the top positions of the feed, you barely get any attention and visibility.
For real estate companies, this challenge is particularly daunting, as the digital marketplace teems with listings and agencies competing for the attention of prospective buyers. Securing a prime position in search results can significantly enhance lead generation and client acquisition, which is why it is critical to understand and leverage the intricate algorithms that govern these rankings.
At Menhir we’ve been working on strategies based on AI algorithms and data analysis to improve the visibility of our clients in one of the main real estate web portals in Spain.
How?
Long story short: We trained a Machine Learning model to predict and mimic the actual marketplace feed and cracked it using our xAI tools developed at Menhir to understand and explain the model’s behaviour.
To crack the code of the positioning algorithm, we divided our approach into three key phases common to any machine learning project:
- Collecting the data.
- Training a model to crack the code.
- Ensuring explainability.
Each phase played a crucial role in developing a robust strategy to enhance our clients’ online presence in the most important real state portals in Spain.
Collecting the data
As the first phase of any project involving Artificial Intelligence, obtaining a good quality dataset is the most important task.
Models are only as good as the quality of the data they are trained on. Or as they say: Garbage In, Garbage Out.
Most marketplaces are public and open to internet users. However, they do not usually provide their own data openly on a large scale through APIs or other services (or they have too elevated costs). This is not the first time we’ve faced a similar challenge at Menhir. Using automated web data mining techniques, we collect information from public websites quickly and efficiently without the need for manual intervention.
By automating the data collection pipeline, we can consistently gather up-to-date data with minimal manual intervention, allowing the team to focus on analysis and strategy development. This robust infrastructure is crucial for delivering high-quality, actionable insights to our clients, setting the stage for the subsequent phases of model training and explainability.
Training a model to Crack the Code
With a solid dataset in hand, we train a model to decipher the marketplace’s ranking algorithm. We use advanced Machine Learning algorithms to process the data and uncover the hidden rules governing search rankings. The goal is to create a predictive model that simulates the algorithm’s behavior, being able to infer the positioning of an asset given their publication variables.
Having an accurate model allows us to test different values of the predictor variables and strategies, enabling us to identify the most influential factors and find the optimal thresholds that have the greatest impact on product visibility and positioning. This is further addressed in the model’s explainablity which is the third phase.
Ensuring explainability
Understanding how our model makes its predictions is just as important as the predictions themselves. At Menhir we employ explainable AI techniques developed internally (xAI Menhir) to break down the model’s decision-making process, ensuring that we can clearly explain why certain listings performed better than others. This transparency is crucial for two reasons:
- It helps us refine our strategies more effectively.
- It allows us to communicate our findings and recommendations to our clients in a straightforward, actionable way.
The explainability of artificial intelligence models is a promising and intriguing area of AI research. It aims to make complex algorithms more transparent and understandable, enabling users to trust and effectively utilize AI systems in critical decision-making processes.
By opening and studying the model’s predictions, we can discover which variables most affect the positioning. This way we can “hack” the algorithm and generate optimized ads that quickly rise to the highest positions in the ranking. In other words, we use the model’s predictions to generate actionable insights that drive profits.
Repeat!
Marketplaces are constantly updating their products, and along with them, their internal positioning algorithms. Monitoring and tracking the ranking behavior allows us to always stay ahead of the market and anticipate changes, while repeating the analysis ensures that the strategies remain effective. By continuously gathering data and retraining models, we can adapt to new trends and maintain optimal visibility at any time.
Furthermore, keeping track of marketplaces’ rankings offers numerous other benefits. It enables competitor monitoring and facilitates the identification of seasonal trends within time series data, which allow marketing campaigns to be further optimized.
Conclusion
The use of AI-based strategies for real estate portals has shown great potential that can be extrapolated to many other areas such as e-commerce, hospitality or digital marketing.
Ranking algorithms can be very complex and sofisticated, but with the right set of features and trained models it is certainly possible to approximate them and discover their hidden rules.
Menhir’s solution not only focuses on model development and prediction, but covers the entire lifecycle of an AI application. From data collection via our own automated pipelines, through analysis and model training, to the deployment in production of a modern, interactive UI that allows the user to observe the results and explainability of the model, as well as take action to improve the visibility of their ads.
Menhir AI is a software company based in Madrid, Spain, specialized in the development of Machine Learning models for finance and real estate. Explore career opportunities and join our innovative team. Check out open roles!
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