Publication date
17/09/2025
Update date
19/09/2025
Description
In a world where immediacy is becoming increasingly important, predictive commerce has become a key tool for anticipating consumer behaviors, optimizing decisions, and offering personalized experiences. It's no longer just about reacting to the customer's needs, it's about predicting what they want before they even know it.
In this article we are going to explain what predictive commerce is and the importance of open data in it, including real examples.
What is predictive commerce?
Predictive commerce is a strategy based on data analysis to anticipate consumers' purchasing decisions. It uses artificial intelligence algorithms and statistical models to identify patterns of behavior, preferences, and key moments in the consumption cycle. Thanks to this, companies can know relevant information about which products will be most in demand, when and where a purchase will be made or which customers are most likely to purchase a certain brand.
This is of great importance in a market like the current one, where there is a saturation of products and competition. Predictive commerce allows companies to adjust inventories, prices, marketing campaigns or logistics in real time, becoming a great competitive advantage.
The role of open data in predictive commerce
These models are fed by large volumes of data: purchase history, web browsing, location or comments on social networks, among others. But the more accurate and diverse the data, the more fine-tuned the predictions will be. This is where open data plays a fundamental role, as it allows new variables to be taken into account when defining consumer behavior. Among other things, open data can help us:
- Enrich prediction models with external information such as demographic data, urban mobility or economic indicators.
- Detect regional patterns that influence consumption, such as the impact of climate on the sale of certain seasonal products.
- Design more inclusive strategies by incorporating public data on the habits and needs of different social groups.
The following table shows examples of datasets available in datos.gob.es that can be used for these tasks, at a national level, although many autonomous communities and city councils also publish this type of data along with others also of interest.
| Dataset | Example | Possible use |
|---|---|---|
| Municipal register by age and sex | National Institute of Statistics (INE) | Segment populations by territory, age, and gender. It is useful for customizing campaigns based on the majority population of each municipality or forecasting demand by demographic profile. |
| Household Budget Survey | National Institute of Statistics (INE) | It offers information on the average expenditure per household in different categories. It can help anticipate consumption patterns by socioeconomic level. |
| Consumer Price Index (CPI) | National Institute of Statistics (INE) | It disaggregates the CPI by territory, measuring how the prices of goods and services vary in each Spanish province. It is useful for adjusting prices and market penetration strategies. |
| Real-time weather warnings | Ministry for the Ecological Transition and Demographic Challenge | Alert of adverse weather phenomena. It allows correlating weather with product sales (clothing, beverages, heating, etc.). |
| Education and Digital Literacy Statistics | National Institute of Statistics (INE) | Provides information on internet usage in the last 3 months. It allows digital gaps to be identified and communication or training strategies to be adapted. |
| Facts about tourist stays | National Institute of Statistics (INE) | It reports on the average stay of tourists by autonomous community. It helps to anticipate demand in areas with high seasonal influx, such as local products or tourist services. |
| Number of prescriptions and pharmaceutical expenditure | General Mutual Society for Civil Servants of the State (MUFACE) | It offers information on the consumption of medicines by province and age subgroups. It facilitates the estimation of sales of other related medical and parapharmacy products by estimating how many users will go to the pharmacy. |
Real-world use cases
For years, we have already found companies that are using this type of data to optimize their business strategies. Let's look at some examples:
- Using weather data to optimize stock in large supermarkets
Walmart department stores use AI algorithms that incorporate weather data (such as heat waves, storms, or temperature changes) along with historical sales data, events, and digital trends, to forecast demand at a granular level and optimize inventories. This allows the replenishment of critical products to be automatically adjusted according to anticipated weather patterns. In addition, Walmart mentions that its system considers "future data" such as macroweather weather patterns, economic trends, and local demographics to anticipate demand and potential supply chain disruptions.
Tesco also uses public weather data in its predictive models. This allows you to anticipate buying patterns, such as that for every 10°C increase in temperature, barbecue sales increase by up to 300%. In addition, Tesco receives local weather forecasts up to three times a day, connecting them with data on 18 million products and the type of customers in each store. This information is shared with your suppliers to adjust shipments and improve logistics efficiency.
- Using demographic data to decide the location of premises
For years, Starbucks has turned to predictive analytics to plan its expansion. The company uses geospatial intelligence platforms, developed with GIS technology, to combine multiple sources of information – including open demographic and socioeconomic data such as population density, income level, mobility patterns, public transport or the type of nearby businesses – along with its own sales history. Thanks to this integration, you can predict which locations have the greatest potential for success, avoiding competition between stores and ensuring that each new store is located in the most suitable environment.
Domino's Pizza also used similar models to analyse whether opening a new location in one London neighbourhood would be successful and how it would affect other nearby locations, considering buying patterns and local demographics.
This approach makes it possible to predict customer flows and maximize profitability through more informed location decisions.
- Socioeconomic data for pricing based on demographics
An interesting example can be found in SDG Group, an international consulting firm specialising in advanced analytics for retail. The company has developed solutions that allow prices and promotions to be adjusted taking into account the demographic and socioeconomic characteristics of each area – such as the consumer base, location or the size of the point of sale. Thanks to these models, it is possible to estimate the elasticity of demand and design dynamic pricing strategies adapted to the real context of each area, optimizing both profitability and the shopping experience.
The future of predictive commerce
The rise of predictive commerce has been fueled by the advancement of artificial intelligence and the availability of data, both open and private. From choosing the ideal place to open a store to efficiently managing inventory, public data combined with advanced analytics allows you to anticipate consumer behaviors and needs with increasing accuracy.
However, there are still important challenges to be faced: the heterogeneity of data sources, which in many cases lack common standards; the need for robust technologies and infrastructures that allow open information to be integrated with companies' internal systems; and, finally, the challenge of ensuring ethical and transparent use, which respects people's privacy and avoids the generation of bias in models.
Overcoming these challenges will be key for predictive commerce to unfold its full potential and become a strategic tool for companies of all sizes. On this path, open data will play a fundamental role as a driver of innovation, transparency and competitiveness in the trade of the future..
