How to increase revenue in e-commerce in 2025?

T

he e-commerce market is developing at a dizzying pace, but at the same time, it is becoming increasingly competitive. Consumers expect not only a wide assortment but also fast, personalised, and intuitive shopping experiences. Simultaneously, online store owners must face the pressure to reduce operational costs and improve efficiency. In this article, you will discover effective solutions that will help you optimise business processes and increase the revenue of your e-commerce platform in 2025.

1. Optimising User Experience (UX)

Personalisation
User experience (UX) is more than just the look of a website or intuitive navigation—it is the full spectrum of experiences that a customer undergoes when using an online store. UX optimisation can significantly impact key indicators such as conversion rates, customer loyalty, average order value, or the rate of returning users.

One way to achieve this is through personalisation. Analysing user behaviour on the website, such as clicks, viewed products, or time spent on specific pages, allows for dynamically adapting content to the preferences of the audience. What does this look like in practice? For instance, if a customer is browsing the trekking boots category, instead of seeing a generic banner, they will see a promotion for trekking backpacks or specific boot models that are bestsellers in that category.

Dynamic email marketing campaigns, automatically generated based on user data, encourage purchases through individually tailored offers. Stores can also use AI to optimise dynamic pricing, which allows for maximising revenue depending on demand and competition.

Optimising the purchase path
Optimising the purchase path is not only about shortening it—it's also crucial to eliminate elements that cause frustration and doubts among users. To achieve this, it’s worth implementing a one-click checkout. The "Buy Now" feature allows a customer to finalise a transaction with one click, which eliminates the risk of cart abandonment. Implementing such solutions can significantly increase revenue, especially for impulse purchases.

Cross-selling and upselling
Cross-selling and upselling are sales strategies aimed at increasing the average order value (AOV). Intelligent AI-based product recommendations can not only boost the likelihood of purchase but also encourage the customer to spend more.

Advanced recommendation systems analyse user purchase data to suggest complementary products. For instance, if a customer adds a camera to their cart, the system might also suggest lenses, a bag, or a tripod to encourage further purchases. For the seller, this represents an opportunity to increase revenue.

Segmenting discount offers can also be helpful. Personalised promotions can appear when a customer reaches a specific cart value. For instance: "Add products worth £10 to get free shipping" or "Buy three products from this category and get 10% off." These actions are automatically triggered based on data analysis and predictive algorithms, eliminating the need to manually design upselling strategies.

2. Cost optimisation through automation

Automating logistics and warehouse management
Warehouse Management Systems (WMS) enable efficient allocation of storage space, optimisation of order picking routes, and cost reduction associated with excess inventory. IoT technology allows for real-time tracking of warehouse inventory, helping avoid shortages or product expiration.

Predictive algorithms can analyse historical data and current trends to accurately forecast demand. This helps stores avoid unnecessary storage costs and better plan deliveries.

Automating order and return processing
Integrating ERP (Enterprise Resource Planning) systems with an e-commerce platform allows for full automation of processes such as invoice generation, inventory synchronisation, and shipment planning. Return processes can also be automated—customers gain access to self-service portals where they can register a return, print labels, and track its status. These solutions minimise administrative costs and reduce processing time.

Automating customer service
Chatbots and voicebots replace traditional customer service methods for handling inquiries about order status, product availability, or technical support. Thanks to AI, chatbots can even solve more complex problems, reducing costs associated with maintaining a large customer service team. At the same time, integrating CRM systems enables automatic customer relationship management and personalised communication, increasing their satisfaction.

3. PIM (Product Information Management)

Improving the quality and consistency ofpProduct data
One of the biggest challenges in e-commerce is managing massive amounts of product information, such as descriptions, photos, or technical specifications. A Product Information Management (PIM) system centralises all this data, ensuring consistency across all sales channels, from online stores to external platforms like Amazon.

Consistency of product data has a direct impact on customer trust. Consumers are more likely to make a purchase when they have full knowledge about a product, including details about its features, materials, sizes, colours, or compatibility with other products. For example, a customer buying electronic equipment, such as a laptop, expects detailed technical data, such as battery capacity, available ports, or compatibility with accessories. A lack of this information can lead to purchase abandonment or increased returns. A PIM system eliminates such problems, ensuring that key data is always complete, up-to-date, and easily accessible.

Faster introduction of new products
Speeding up the introduction of new products to the market is a key factor in the competitive e-commerce environment. A PIM system automates processes related to adding and updating product data, significantly speeding up the launch of new offers.

For example, when a store wants to introduce a new product category, a PIM system enables the simultaneous update of information in all required formats and languages, as well as across channels, minimising the time needed to prepare the offer. This also means faster revenue generation from new products.

4. Advanced data analytics

Centralising data from different sources
One challenge is managing massive amounts of data from different sources, such as websites, mobile apps, social media, marketplaces, or CRM systems. These data are often scattered, making them difficult to utilise effectively. The solution is to implement a Customer Data Platform (CDP) or Data Management Platform (DMP), which enable centralisation of all information in one place.

A CDP allows for collecting user data in real time, integrating it with other systems, and creating comprehensive customer profiles. For example, an online store can combine data on purchase history, website behaviour, responses to marketing campaigns, and customer service interactions to create a detailed picture of each user. This makes it possible to create precise customer segments, from groups interested in specific product categories to those who regularly shop at certain times of the year.

Forecasting trends and customer behaviour
Advanced data analytics allows not only for analysing the past but also for predicting the future. Using machine learning algorithms, online stores can predict changes in demand, seasonal product interest, or customer preferences.

For example, algorithms can analyse historical sales data, market trends, weather conditions, or special events to forecast which products will be in high demand in the coming weeks. A store selling clothing might order more coats before winter or reduce promotions on products that will sell well regardless.

Return On Ad Spend (ROAS) analysis
Return on Ad Spend (ROAS) is one of the most critical indicators for online stores running marketing campaigns. Advanced analytical tools, such as Google Analytics 4, BI platforms, or dedicated advertising management systems, enable precise monitoring of campaign effectiveness in real time.

These tools help stores identify which marketing channels generate the most conversions and which areas require optimisation. For example, analysis might reveal that social media campaigns yield a lower return on investment than search engine ads. Based on this, it’s possible to reallocate budgets to more effective channels.

5. Loyalty and subscription models

Investing in customer loyalty
Loyalty programmes reward customers for their purchases and engagement. By offering points, discounts, or exclusive benefits for regular shopping, stores can effectively increase purchase frequency and cart value. Loyal customers not only spend more but are also more likely to recommend the store to others, bringing in new consumers.

A successful loyalty programme example might involve earning points for purchases, which can then be exchanged for discounts or free products. Customers feel appreciated, increasing their attachment to the brand. Including personalised benefits tailored to customer preferences further enhances their satisfaction and engagement.

Revenue stability through subscriptions
The subscription model involves regular delivery of products or services in exchange for a recurring fee. Subscriptions are particularly popular in industries such as cosmetics, food, health, or technology. Regular payments provide stores with predictable revenue and financial stability.

Customers appreciate subscriptions for their convenience and time savings. Regular deliveries of favourite products eliminate the need to remember to reorder them, and additional benefits, such as subscriber discounts or exclusive access to new products, encourage them to use this form of shopping.

Both loyalty programmes and subscriptions motivate customers to interact more frequently with the brand, which translates into Customer Lifetime Value (CLV), the revenue a customer generates for a company over their lifetime.

6. Cybersecurity as a long-term investment

Securing customer data
Cybersecurity is becoming a key element of e-commerce business strategy. In 2024, there was a significant increase in data breach incidents in the e-commerce sector, highlighting the need for advanced protection measures.

Companies are implementing technologies like tokenisation and end-to-end encryption to secure customer data during transactions. For instance, adopting PCI DSS (Payment Card Industry Data Security Standard) reduces the risk of breaches by establishing stringent requirements for payment data processing.

Real-time monitoring
Reducing operational risks in e-commerce means not only protecting customer data but also safeguarding the store’s smooth operation and infrastructure. Real-time monitoring systems are among the most effective ways to prevent threats that could disrupt the store’s functionality.

These systems continuously track network traffic, analyse user behaviour, and identify unusual activities. For instance, detecting increased traffic from a single IP address could indicate a DDoS attack. The system can automatically block suspicious IPs or restrict access to certain resources to protect the website.

Summary

Technology is an essential element of success in e-commerce. Investments in UX optimisation, process automation, advanced analytics, and cybersecurity can not only increase sales but also significantly reduce operational costs. For online stores looking to remain competitive, implementing thoughtful and advanced digital solutions is key.

If you need support in planning your 2025 e-commerce strategy or developing your platform, feel free to contact us.

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