The most successful e-commerce businesses today leverage personalization to boost their conversion rate, increase customer loyalty and drive up average order value. Gartner said it: “by 2020, smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15%.” And the folks at Accenture have seen this trend too: 43% of U.S. consumers are more likely to make purchases with companies that personalize experiences. In short, if you run an e-commerce shop, you should pay close attention to personalization.
How does personalization work?
Broadly speaking, personalization is the practice of showing targeted content to different segments of your audience. It’s a conversion optimization tactic that has been in use in email marketing for some time. Its use has recently picked up in websites. There are two different types of personalization:
- “Rules-based” or “prescriptive:” a manual approach where you create certain rules to target different segments of your audience. For example, if a customer is logged into your system, you probably have some information about their previous purchases at hand (their size, color preferences, location). You can use this data to personalize your site, i.e. if visitor is based in London, highlight free delivery to London on the home page the next time they visit; or if a visitor has purchased a black pair of shoes before, display the newest styles in black when they land on your site again.
- Automated: a process that uses machine learning algorithms to automatically create personalized experiences for different audience groups. To follow up on the example above, automated personalization would leverage an algorithm to display products in your inventory that match the visitor’s purchase history or products viewed.
Which personalization type is right for you?
The type of personalization you choose should depend on your budget, the amount of data you have available and skill level. If you’re new to this, you may want to start with rules-based, as it requires less data and a lower up-front investment in time and resources. Keep this in mind when selecting a tool.
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