What is predictive analysis and how can it be leveraged in an online store?
Table of contents
- What is predictive analysis?
- Why is it worth analysing data in an online store?
- How to collect data for predictive analysis?
- What data is worth collecting in an online store?
- What challenges can you face during data analysis?
- The sales potential lies in data - take advantage of the predictive analysis
- sales forecasting - predictive analysis allows predicting sales peaks, downtime, increased demand, and other trends. This enables the store to adjust its actions accordingly, avoiding excess or lack of goods at key moments and thereby maximising sales.
- better understanding of customers - analysing user behaviour on the platform enables better personalization of offers, customization of communication, and increased customer satisfaction.
- price adjustment - data collection enables the identification of optimal price levels for different customer segments and flexible response to changing market conditions.
- improving marketing activities - data analysis allows better targeting of marketing campaigns and personalization of customer communication, leading to increased effectiveness of promotions and customer engagement.
- optimising the supply chain - predicting product demand allows for better planning of deliveries and optimization of logistical processes. efficient inventory management - through data analysis, the store can precisely control inventory levels, avoiding both excess and shortages of goods. Increasing the security of the sales platform - data analysis algorithms can detect suspicious user behaviour, helping to prevent fraud and increase the security of online transactions.
- optimising the supply chain - predicting product demand allows for better planning of deliveries and optimization of logistical processes. efficient inventory management - through data analysis, the store can precisely control inventory levels, avoiding both excess and shortages of goods. Increasing the security of the sales platform - data analysis algorithms can detect suspicious user behaviour, helping to prevent fraud and increase the security of online transactions.
- optimising the supply chain - predicting product demand allows for better planning of deliveries and optimization of logistical processes. efficient inventory management - through data analysis, the store can precisely control inventory levels, avoiding both excess and shortages of goods. Increasing the security of the sales platform - data analysis algorithms can detect suspicious user behaviour, helping to prevent fraud and increase the security of online transactions.
- General Data Protection Regulation (GDPR)
- Digital Services Act (DSA)
- AI Act (a European Union regulation that came into force in March 2024 but will be fully enforceable 24 months from that time)
Trends, customer expectations, changing demands - the e-commerce market operates under its own dynamics and constantly evolves. Keeping up with these changes, the ability to predict them, and make informed business decisions are crucial. This is where predictive analysis (or predictive analytics) comes into play, becoming an increasingly common element of strategies for many online stores.
According to "The 2020 Global State of Enterprise Analytics" report, as many as 59% of organizations use predictive analytics tools, and this number is increasing year by year. Joining this group can bring a range of benefits to your online store. Learn more about predictive analysis in eCommerce - read this article!
What is predictive analysis?
Predictive analysis is a type of data analysis that uses historical data to predict future outcomes. The key element of this process is machine learning algorithms (ML), increasingly powered by artificial intelligence (AI).
ML algorithms analyse vast amounts of data, identifying patterns and dependencies. Based on this, they formulate forecasts that enable companies to make better, more informed business decisions. These decisions can include sales forecasting, inventory forecasting, or price optimization. However, with the development of AI, algorithms are becoming increasingly advanced, predicting more complex events and behaviours.
Predictive analysis has become an indispensable tool for many businesses, such as online stores, allowing them not only to understand the present but also to prepare for the future with greater certainty and flexibility (which is particularly important in times when technology and the market are constantly changing). Curious about what data you can use in your eCommerce? Read on!
Why is it worth analysing data in an online store?
Imagine introducing a new offer to the market and being able to predict which products will become "bestseller" and which will linger on the shelves. Similarly, you can assess which customers will become your regular buyers and which may abandon their purchases and abandon their cart. These are examples of the possibilities offered by predictive analysis.
Regularly gathering data in your e-store allows for their versatile use in many areas, including:
Additionally, predictive analysis also allows:
As you can see, predictive analysis is an extensive tool that can help you not only increase sales but also improve customer satisfaction and optimise the effort associated with daily e-store management. In short, well-selected and analysed data can open doors to new opportunities for your e-business.
How to collect data for predictive analysis?
When conducting business online, opportunities to obtain valuable data arise at almost every step. This is both an opportunity to increase the efficiency of the online store and a challenge - why?
The data you can obtain are extremely diverse both in terms of content and form. In reality, you will rarely have access to already structured data ready for analysis (e.g., structured in tables). Most information will be unstructured, which means that they will require proper preparation and processing before they can be analysed. An example of this could be product reviews - each customer can describe the product in their own way. This means that initially it will be necessary to extract relevant information from these texts (e.g., overall product rating or compliance with the description). Only after proper processing of such data can they be introduced into some structure and subjected to analysis.
To prepare the data, external tools are often used, e.g., GoodData, Sisense, SAS, Microsoft R Open, Radius, and Microsoft Azure Machine Learning Studio. With their help, it is possible to collect, process, and analyse information from various sources, both structured and unstructured. They allow for data visualisation, building predictive models, and identifying trends, which is crucial for effectively conducting predictive analysis.
What data is worth collecting in an online store?
The first step in preparing for predictive analysis is to clearly define the business goals that the online store wants to achieve through data analysis. It will depend on these goals what data needs to be collected. For example, the goal may be to increase conversion in the online store by better understanding customer behaviours on the site or forecasting sales trends to better manage inventory. To facilitate your choice, we have prepared tables where you can check what data will best meet your expectations.
| Type of Data | Examples | Example Use Cases |
|---|---|---|
| Customer Demographic Data | Age, gender, geographic location, marital status | Offer Personalization |
| Transactional Data | Browsed products, time spent on site, cart abandonment | User interface optimization |
| Behavioral Data | Browsed products, time spent on site, cart abandonment | User interface optimization |
| Preference Data | Preferred product categories, brands, delivery methods | Offer personalization |
| Data on Interaction with Advertisements | Clicks, conversions, online ad viewsy | Optimizing advertising budget |
| Social Media Data | Interactions on social media platforms | Building customer engagement |
| Review and Opinion Data | Customer reviews on products | Customer satisfaction assessment and identification of areas for improvement |
| Seasonal Data | Shopping trends at different times of the year, holidays | Planning seasonal promotions |
| Competitive Data | Competitor price analysis, product availability, promotions | Competitive monitoring and adjusting pricing strategies |
| Logistics Data | Delivery times, shipping costs, delivery system efficiency | Optimizing delivery processes |
| Inventory Data | Inventory level, inventory turnover, product popularity | Forecasting demand, minimizing inventory surplus and shortages |
| Cost Data | Operating costs, product margins, advertising costs | Optimizing expenses and controlling operating costs |
What challenges can you face during data analysis?
Undertaking predictive analysis, you must be aware that behind the accuracy of the results lies many details that need to be perfected. The challenge may lie in the data itself. Firstly, especially in the case of smaller online stores, the problem may be too small a amount of data. Lack of sufficient data may hinder the construction of reliable predictive models and lead to conclusions inconsistent with reality. Similarly, poor data quality can have similar consequences - they may be incomplete, contain errors, or be inconsistent.
It is also important to adhere to legal regulations regarding the protection of personal data. Awareness of the risks associated with data privacy is growing year by year, and users are becoming increasingly cautious about sharing information online. Consequently, legal regulations on data protection are constantly tightening, and non-compliance can result in serious financial consequences. It is particularly important to adhere to regulations such as:
Additionally, popular analytics tools, such as Google Analytics, are increasingly introducing restrictions on data access (e.g., data sampling, data thresholds) to protect the privacy of their users.
Considering all these challenges - predictive analysis is a process that requires continuous monitoring and adaptation to changing conditions. Both tracking changes and trends in data and complying with legal regulations regarding the protection of personal data are crucial for the effectiveness of predictive analysis and maintaining customer trust.
The sales potential lies in data - take advantage of the predictive analysis
Are you looking for a way to improve customer satisfaction, optimize operations, or increase profits? The answer to your search may be predictive analysis. By combining historical data with machine learning algorithms, you can more effectively predict sales, and moreover - get to know your customers better and outpace the competition. However, you must keep in mind that although predictive analysis brings many benefits, it is also associated with certain challenges, such as data availability and quality, data protection, and continuous process monitoring. In the article, we discuss these and other difficulties, as well as share practical tips on how to overcome them.
Unlock the potential of predictive analysis and pave the way for a whole new level of eCommerce success!
