Summer arrives, and with it the Sales, with the peak in consumption that this entails in certain sectors such as fashion. Sales soar especially at these times, and this poses a great challenge from a logistical point of view. One of the main problems faced by retail supply companies is the control of their stock levels or available inventory, and at times with demand fluctuations that are sometimes so unpredictable, the problem can be exacerbated. Having sufficient stock to meet consumer demand is essential for the proper functioning of companies, and failing to do so can lead to a significant loss of customers and a huge future risk for the company.
Fortunately, as with many other aspects, technology has made things easier and nowadays we also have techniques that allow us to anticipate peak consumption, predicting demand on specific dates to ensure we have adequate stock. These are predictive models that use statistics and machine learning techniques to cross-reference data which, although they may not seem to have much in common at first glance, allow us to understand how people behave when certain factors are present. This is the case of Big Data, a predictive technology that allows suppliers and retailers to have a complete view of the landscape and thus forecast the needs of end consumers and how they will behave in certain situations.. These tools, based on macro data, allow for the analysis of large quantities of information, both internal and external, to filter and analyse it. This helps in understanding consumer motivations, enabling business decisions that forecast demand.
In conclusion, we can say that Big Data is data analysis taken to the highest level. These are data that rest on a series of characteristics that have come to be known as the «5 V» and which, in summary, are:
- Variety Big Data analyses data of all kinds, both structured and unstructured, textual and audiovisual.
- Volume The more data you have, the more reliable the predictions will be. That's why it's important to collect, capture, store, process, and interpret the largest possible volume of information.
- Speed It is necessary to be agile in data collection, and to capture all the information generated in real-time on social networks, for example.
- Value It is important to be clear about the value that data brings to the end consumer, establishing strategies to use that information which allows us to offer the ideal product for that consumer.
- Truthfulness The quality and reliability of the data need to be checked to ascertain if they can effectively be used in a particular strategy.
Once this data is available, artificial intelligence technologies will make it possible to analyse how events will unfold in the future given a series of factors. In this case, it will allow us to predict how consumers will behave during the summer period.
Advantages of applying Big Data
Big Data allows for the adjustment of production processes and the necessary stock to meet future demand, thereby enabling companies to approach operational excellence as closely as possible. Employing predictive technologies allows for finding the right balance in stock, avoiding overproduction while also preventing shortages.
But moreover, predictive technologies also allow decisions to be made in the present that will yield benefits in the future. From the perspective of summer sales, for example, having a prediction of what might happen allows companies to implement marketing strategies or actions that improve that prediction and maximise data, increasing sales. In this way, information is not used passively, but rather advantage is taken of the Insights that have been obtained in order to achieve a better future. Therefore, one of the biggest advantages of Big Data is that it allows not only to avoid actions that could jeopardise the company, but also to boost results and maximise effectiveness, thereby increasing profits. This, in turn, translates into greater competitiveness. In fact, according to the consultancy McKinsey, companies that use these types of technologies to understand their consumers outperform their competitors by 85% in sales growth and by 25% in gross margin.
Furthermore, The great advantage of current Big Data infrastructures is that they allow very complex models to be built., which allow for the exploration of a vast number of variables and therefore enable the identification of those that truly impact the hypothetical future we are predicting. Consequently, as long as correct and quality data are used, the predictions will always be reliable and will contribute to making good forecasts.
How do you create a predictive strategy with Big Data?
As we have been saying, good data management can lead to a greater understanding of consumer habits and, therefore, better personalisation of offers during the summer season. However, this requires correct data monitoring and an appropriate strategy. For this purpose, data obtained in different ways can be used:
- Shopping basket analysis: The information can be used to understand which products are typically bought together, to detect microscopic but revealing changes in trends. Thanks to this analysis, it can be predicted how each person approaches the buying cycle and decisions can be made about the assortment of certain products, the placement of items, or how cross-selling of those items can be optimised.
- Product Group Analysis: The cause-and-effect relationships between sales and the profits derived from them can be compared to design more effective promotions that better adapt to consumer purchasing patterns and societal trends.
- Customer segmentation Using purchasing data, the market can also be divided into customer groups with similar habits, common interests, and similar consumption patterns. This allows for the creation of a very precise and well-studied customer profile, enabling practically personalised commercial actions that will have a better impact on the consumer with less effort and investment.
And so, the Big Data undoubtedly stands as one of the most useful tools that companies have for planning and managing their sales periods and seasonal collections., in order to avoid stock imbalances and maximise your profits. A new way of doing things that should be taken into consideration and implemented to fully exploit our business with all the advantages that technology provides.
