In the world we live in today, it is not difficult to realize that technology has become a fundamental part of our lives, and one of those consequences is the emergence of new professions or computer terms. One of these concepts that has recently gained great relevance is “Data Science”, which is an interdisciplinary field that analyzes various sources of data, structured or unstructured, to extract conclusions and useful information that will serve for a company’s decision-making. Although the concept was first used in 1974, it is in recent years that it has gained popularity due to the importance that data has acquired, going so far as to say that these are the oil of today, since the details that are deduced from them have a higher value if “refineries” such as Google or Amazon analyze them and transform them into information.
It may be confused with Business Intelligence (BI), another of the relevant terms in this area, but the difference is that the latter helps to interpret past data (such as a company’s historical commercial performance) and is mainly used for descriptive reports or analysis; while Data Science analyzes past data, such as trends or patterns to make future predictions, being used mainly for predictive analysis or prescriptive analysis. This is why its management, for example, could be very useful when carrying out a feasibility analysis to find out if the project being promoted is viable and profitable, since its capacity can predict its future acceptability. In short, with this information, brands gain an advantage when competing.
Its application in retail
Retail is an industry that grows year after year and this growth brings with it the need to constantly innovate due to the digital revolution, which has changed the way we interact with consumers. Large companies representing various commercial fields seek to take advantage of the beneficial value of data with Data Science and thus make profitable decisions for their businesses. Considering this, we can list the main use cases of data science in retail:
- Motores de recomendación: estos demostraron ser de gran utilidad como herramientas para la predicción del comportamiento de los clientes en los sitios web. Proporcionar recomendaciones de productos o servicios a sus usuarios debido a sus comportamientos, permite a los minoristas aumentar las ventas y dictar tendencias.
- Análisis de la canasta de mercado: puede considerarse como una herramienta tradicional de análisis de datos en el retail. Este proceso depende principalmente de la organización de una cantidad considerable de datos recopilados a través de las transacciones de los clientes. Esta información es útil para implementar estrategias de venta cruzada, realizar recomendaciones a los clientes sobre productos relacionados y promocionarlos, por ejemplo, colocándolos muy cerca unos de otros en un sitio web, catálogo o en la misma estantería de un local físico. Por lo tanto, con este análisis se pueden predecir las decisiones y elecciones futuras de los clientes a gran escala, lo que conlleva a la mejora de las estrategias de desarrollo y técnicas de marketing.
- Analítica de garantía: este paso involucra la extracción de datos y textos para una mayor identificación de los patrones de reclamos y las áreas problemáticas. Así, los datos se transforman en planes, información y recomendaciones en tiempo real viables a través del análisis de segmentación.
- Optimización de precios: los datos obtenidos de las fuentes multicanal definen la flexibilidad de los precios, teniendo en cuenta la ubicación, la actitud de compra individual de un cliente, el condimento y los precios de los competidores. Utilizando el modelo de una optimización en tiempo real, los minoristas tienen la oportunidad de atraer a los clientes, retener la atención y realizar esquemas de precios personales.
- La gestión del inventario: mediante el ajuste y desarrollo constante de parámetros y valores, el algoritmo define las estrategias de inventario óptimas. Los analistas detectan los patrones de alta demanda y desarrollan tácticas para las tendencias de ventas emergentes, optimizan la entrega y administran el stock implementando los datos recibidos. Por ejemplo, si una empresa consigue anticipar sus pedidos, consecuentemente, conseguirá avisar a sus abastecedores para que estos programen el plazo de entrega. Con este tipo de proyecciones acertadas, es posible negociar mejores precios en la compra de mercancías y también planear inventarios de tiendas, además de la organización del transporte.
- Localización de nuevas tiendas: los analistas exploran los datos de los clientes en línea, prestando gran atención al factor demográfico. Las coincidencias en el código postal y la ubicación dan una base para entender el potencial del mercado. Esto, junto a la ubicación de otras tiendas y del análisis de la misma red del retail, ayuda a averiguar en qué zona es mejor abrir una nueva tienda.
- Customer Sentiment Analysis: Customer brand sentiment analysis can be performed using data received from social media and online service feedback. Analysts perform sentiment analysis on the basis of natural language processing, text analysis to extract the definition of positive, neutral or negative sentiments.
- Marketing: Marketing mechanisms rely on data to gather insights and form priority sets for customers, taking into account seasonality, relevance and trends.
- Lifetime Value Prediction: Application of statistical methodology helps to identify the customer’s purchasing pattern till he stops making purchases. With this, the retailer is expected to understand its customer, improve services and define priorities.
- Fraud detection: The only efficient way to protect a company’s reputation is to stay one step ahead of fraudsters. Big Data platforms provide continuous activity monitoring and ensure detection of fraudulent activity. This mechanism improves the retailer’s ability to protect the customer and its business.
In conclusion, we can say that there is currently a significant volume of data that is not being exploited, but Data Science has been demonstrating that the management of this data can provide really useful information both in real time and in anticipating trends. With this boom, the professions of the future have emerged, such as the data scientist, who is the person in charge of extracting knowledge from Big Data and who is a mix of computer scientist, statistician, mathematician and programmer.
Retailers know that it is essential to use this data, and that is why they are already taking advantage of its benefits to generate added value for their customers, achieve better margins for their organizations and thus maintain their leadership positions in the market.
