Machine learning offers eCommerce many benefits. Improved client knowledge, the automation of processes and the ability to personalise offers and improve customer experience means that your ROI is fast and ongoing. What would once have been labour intensive, and so ignored in favour of other more manageable goals, is now the work of AI, and is becoming essential to business.
Monetizing your data is the basis for all business success. AI works fast to aggregate your data, improve processes and bolster your business. From making customer recommendations to serving your client’s needs, an investment in AI is an investment in the future of your business.
1. Recommendation engine
Personalisation and recommendation engines are improving customer interactions with eCommerce providers, and driving sales. Artificial intelligence is used to process huge amounts of data and to analyse the online activity of hundreds of millions of users. Using this information, a recommendation engine is able to make product recommendations that are tailored to a specific customer or customer base (auto-segmentation).
To do this, the engine analyses collected big data on the current traffic on your website to determine which sub-pages the client used to reach you. Based on information such as social media data, location and weather data, search requests and other data, the AI engine personalises results to suggest products that might interest the user.
2. Personalisation of website content
Personalised content increases conversions and customer engagement. Machine learning algorithms select the content that is most applicable to a user by finding patterns in data, including images and text.
AI algorithms take into account various factors such as image intensity, activity history, preferences, etc. The results are adapted to the personal preferences of each individual user. This helps ensure that your content reaches the right audience with content that resonates.
3. Dynamic pricing
Machine learning algorithms learn new patterns from data by continuously updating from new information, demands and trends.
Predictive models can help eCommerce retailers to measure, predict and adjust price points based on aggregated data from competitors. The technology can be applied so that optimised pricing can be applied in real-time and based on your actual inventory. This help you maximise your inventory and optimise sales, especially for larger operations.
4. A/B tests using AI
AB testing is an experiment where two or more variants of a webpage are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal. The testing is time-consuming, however, artificial intelligence has improved the process:
- AI can be used to assess the efficacy of your cart, and improve abandonment rates.
- Automatic customer segmentation dependant on their characteristics such as age, gender, expenses, industry and personalisation of content, such as product suggestions that meet their requests.
5. Prediction using Machine Learning
- Real-time prediction of user purchasing in a specific product category so leads can be followed up.
- Predicting whether the user will return and make further purchases.
- Customer lifetime value prediction (CLTV or LTV) – to predict how much money a user will spend. The accurate estimation of the CLTV optimises budgets and improves customer service responses.
- Customer churn prediction helps you reduce cart abandonment and improve your site UX.
6. Image processing
Investment in computer vision technology with visual search possibilities could help you to match customers’ choices with products you sell. This can be done by accessing user information on Pinterest or search history, for example.
Image search is predicted to become increasingly popular in coming years and while the AI is still relatively new, it is an asset that could help online retailers improve stock offerings and move inventory.
7. Smart chatbots to improve customer service
Intelligent chatbots based on NLP and AI can interpret individual users’ questions and respond to them individually. Offering personalised customer response to queries in a timely manner helps to improve your customer relationships and overall business.
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