In recent years, AI has evolved to become one of the most powerful tools in the hands of e-commerce companies. AI is able to predict behaviours, increase sales, optimise marketing activities and improve relationship skills and fidelity.
Artificial intelligence is driven by customer and business data. Starting from this incredible amount of information, technology is able to multiply e-commerce opportunities and possibilities.
What are the advantages of AI for e-commerce?
- Customised Marketing Actions
Artificial intelligence allows you to customise content based on the user. Big data analysis of purchase history and other customer interactions allow you to send personalised messages and content.
- Greater loyalty
Personalising messages means increasing the sense of engagement and, therefore, the value of the relationship. Omni-channel personalisation, according to McKinsey research, can generate a potential 10-15% increase in revenue.
- Seamless automation
Artificial intelligence plays a crucial role in automating repetitive e-commerce activities. AI lets you automate, for example, product recommendations, loyalty discounts, entry level support, and more.
- Efficient sales process
AI helps create a more efficient sales process by collecting customer data, automating follow-up requests on abandoned carts, and interacting with chat bots.
Predictive analytics: today’s data for tomorrow’s results
Predict the future. We’re not talking about magic, we’re talking about predictive analysis. Predictive analytics analyses customer data to provide results and advice. To do this, it uses methods and tools such as data mining, data modelling, machine learning and artificial intelligence algorithms.
In a competitive and dynamic market such as e-commerce, predictive analytics allows you to understand what consumers want in advance, to offer engaging and personalised shopping experiences.
The starting point is that customers are not all the same, they have different behaviours, tastes and preferences. For example, technology allows you to analyse clicks, purchase history, and product preferences in real time, to segment your audience into groups and subgroups. Thanks to all this information, e-commerce can send personalised messages to stimulate an action: complete a purchase, recover an abandoned cart, etc.
Predictive analysis can also do something else: it can help e-commerce to define the optimal price of a product, through an analysis of customer opinions. This valuable information helps companies understand the highest price customers are willing to pay for each product.
In all these cases, the ultimate goal of predictive marketing is to increase sales and conversions, through targeted and personalised campaigns.