When we talk about personalization, we think of Amazon and Netflix. These two big names have set the industry standards on personalization. For customization, you can look at Facebook and WordPress. Facebook has given back (some) control to users, and WordPress.org is well-loved for its highly customizable interface.
Customization and personalization both give users the opportunity to change their digital environment to suit their tastes and preferences, but they’re not the same and shouldn’t be used interchangeably.
Consumer-input vs. consumer-facing
For customization, the input comes from the consumer. They pick through options and select the ones they want and turn off what they’d rather not have. This applies to websites, tools and platforms. Aside from Facebook and WordPress, pretty much every app on our phones and computers can be customized.
Customization requires interaction for the customization to start, while personalization makes intelligent recommendations using big data to entice more interaction and therefore more insight to make the personalization even more refined.
Personalization removes the burden on the user, and technology has progressed so much that customers no longer want to input anything, not even their email. You can sign into most apps using your Facebook or Google account and you’re done.
Personalization is consumer-facing and in the hands of the business or service provider. Consumers can focus on their needs instead of having to tweak things for their use. In fact, less than 5% bother to customize defaults.
While everyone expects a customized experience, the majority of people skip customization setups. Personalization can digest vast amounts of data to detect patterns of behavior so it can make recommendations that match those patterns, without interrupting the consumer in their journey before they’ve even started.
Customer benefit and customer experience
Customization is driven by customer benefits while personalization is more about the customer experience.
For example, Netflix asks you to select shows and movies you like after signup so they can recommend similar shows you might like. And Netflix’s customization interface is quick and easy. Tap a few squares. Done.
People also diligently customize their Facebook for privacy and post visibility, not minding the navigations and dropdowns and needing to click “Save” for changes to apply.
If you want to your customers to engage in customization of your product or service, you have to incentivize that behavior with the customer benefits. What’s in it for them? WordPress users customize to make their websites look good. That’s a pretty big incentive.
Product recommendations are more for a business’s benefit, so use personalization to understand, help, and delight customers — to deliver a positive customer experience.
Case in point, even if you skip Netflix’s customer-input selections for customization, the company still uses powerful personalization algorithms to fill your feed with shows you might like based on what you watched.
Spotify, without input, uses personalization data for your “daily mixes” and “discover weekly.” It will even use powerful technology to detect your running rhythm and suggest songs that match your tempo.
AirBnB and restaurant apps utilize your location and previous searches to personalize recommendations.
Amazon doesn’t require any customization, but waits for your searches to show you recommendations and “frequently bought together” products.
Amazon’s recommendation engine drives 35% of their revenue, but this was back in 2013. We can presume it’s worth more than that now, with the improvements in machine learning.
AI’s role in the shift from customization to personalization
Before today’s technology, companies used segmentation to organize their target customers into groups to deliver segmented messaging to each group. Customization then helped segmentation, with input from the customers deciding which segment they belong to.
But now AI technology makes complex data algorithms accessible in seconds. Using dynamic, real-time data, insight for each customer is now accurate, with zero disruption for the customer.
Tailored and individualized experiences are easier to deliver through reliable data from previous response, purchase, navigation, and search behaviors.
Personalization powered by machine learning combines the benefits of segmentation and customization in gathering insights about customers, all done behind the scenes: their geographic locations, past preferences, search and input history, and many other indicators that empower your team to effectively personalize useful experiences for the customer.
What is useful and not intrusive? Products frequently bought together, what you might like, what others bought, what’s trending. Low in stock and restock alerts. Automatically apply discounts your customer can use. Product summary hovers. Dynamic text or image replacement. These are subtle and expected – but they’re still engaging and very effective.