How e-retailers can predict customer demand before it happens.

Predicting customer demand for a product many months before it is required is key to any retailers’ success. Get it wrong and you are left with a whole load of inventory you can’t shift. In this article, Robin Buxton, Business Relations Director at Evestico, delves into how some of the larger retailers are predicting customer demand.

First we start with data. Retailers need to gather and analyse a wide range of data points related to customer behavior, as well as external factors that could impact demand. Here are some of the key types of data that would need to be considered:

1. Historical Sales Data: One of the most important data points to consider is historical sales data. This includes information on sales volume, sales trends, and seasonal fluctuations. By analysing past sales data, retailers can identify patterns and trends that can be used to predict future demand.
2. Customer Demographics: Another important data point to consider is customer demographics. By analysing information such as age, gender, location, and purchase history, businesses can identify the types of customers who are most likely to buy a particular product. This can be used to inform marketing and advertising strategies, as well as product development.
3. Marketing Data: Marketing data includes information on advertising campaigns, promotions, and other marketing efforts. By analysing the effectiveness of different marketing strategies, businesses can identify which tactics are most effective at driving sales and generating interest in a particular product.
4. External Factors: External factors such as economic conditions, weather patterns, and competitor activity can also impact demand for a particular product. By monitoring these external factors and analysing their impact on sales in the past, businesses can predict how they might impact demand in the future.
5. Social Media and Web Analytics: Social media and web analytics data can also be useful in predicting customer demand. By monitoring social media conversations and online search behavior, businesses can identify emerging trends and topics of interest. This can be used to inform marketing and advertising strategies, as well as product development.
6. Supply Chain Data: Finally, supply chain data can also be useful in predicting customer demand. By analysing data on production capacity, lead times, and supplier performance, businesses can ensure that they have enough inventory on hand to meet demand, while also minimising the risk of stockouts and excess inventory.

What happens if you’re a start-up? Could some of the above requirements be extracted from publicly available date over the internet?

Some of the data required to predict customer demand could be obtained from publicly available sources over the internet. For example, social media and web analytics data can be accessed through tools like Google Analytics, which provides insights into website traffic, user behavior, and search trends.

Similarly, demographic data can be obtained from sources such as the U.S. Census Bureau or other government agencies, which publish information on population demographics, income levels, and other relevant factors.

However, some of the data required to predict customer demand is likely to be proprietary and not publicly available. For example, historical sales data, marketing data, and supply chain data are typically closely guarded by businesses and not available to the general public. To obtain this type of data, businesses would need to invest in advanced analytics tools and technologies that can integrate and analyse data from multiple sources, including internal databases and third-party data providers.

In addition, businesses may need to work with partners and vendors who have access to the data they need. For example, an e-commerce platform or order fulfillment company, may be able to provide valuable data on customer behavior and order patterns, which can be used to inform demand forecasting and other strategic decisions. Speak to Evestico about who best to approach for this

Is it possible to extract the publicly available internet sources of data and present it in a user friendly web dashboard?

In theory, yes. Whilst I cannot provide the actual source code as it would require a specific programming language, frameworks and tools, and also depend on the specific data sources and technologies you wanted to use. However, I can explain to you want is required and manage the process for you through one of our trusted partners.

To develop a web dashboard that extracts and presents the required data from publicly available internet sources, the following steps would typically be involved:

1. Data Collection: The first step is to identify the sources of data that will be used and to develop a process for collecting and storing that data. This may involve using web scraping tools to extract data from websites, APIs to access data from social media platforms or government databases, or other methods.
2. Data Processing: Once the data has been collected, it needs to be processed and cleaned to ensure that it is accurate and usable. This may involve removing duplicates, filtering out irrelevant data, and structuring the data in a way that makes it easy to analyse.
3. Data Analysis: Once the data has been collected and processed, it needs to be analysed to identify patterns and trends. This may involve using statistical analysis tools, machine learning algorithms, or other methods to identify correlations between different data points.
4. Dashboard Design: Once the data has been analysed, the next step is to design the web dashboard that will present the data in a user-friendly format. This may involve using a web development framework, such as Django or React, to create an interactive dashboard that allows users to explore the data in different ways.
5. Dashboard Development: Once the design has been finalised, the next step is to develop the web dashboard itself. This may involve using HTML, CSS, and JavaScript to create the user interface, as well as integrating the data analysis and processing code with the dashboard.
6. Dashboard Deployment: Once the dashboard has been developed, it needs to be deployed to a web server where it can be accessed by users. This may involve using cloud hosting services such as Amazon Web Services or Microsoft Azure, or deploying the dashboard on an internal server.

Overall, developing a web dashboard that extracts and presents the required data from publicly available internet sources requires a combination of data collection, processing, analysis, and web development skills, as well as an understanding of the specific data sources and technologies involved. If you’d like to know more about this particular aspect drop us a line.

Summing up, in order to predict customer demand for a product many months before it is required, e-retailers need to gather and analyse a wide range of data points related to customer behavior and external factors. By leveraging advanced analytics tools and machine learning algorithms, businesses can identify patterns and trends that can be used to predict future demand and optimise their inventory and order fulfillment processes. Nowadays, there are lots new tools being developed to address some of the above requirements, so if you’re a provider of one of these, drop us a line also. We love learning at Evestico.