Some people have begun to conflate responsive design with the broader concept of personalisation. It’s understandable. The term 'responsive design' is really easy to blur with the broader, more colloquial definition of the word ‘responsive,’ both on the agency side and the client side (the incorrect idea that the experience should respond to me).
Put simply, responsive design is about adjusting layout based people’s various screen sizes. With that the definition (hopefully) more clear, let’s look not only at responsive design, but some of the other strategies for personalisation.
Personalisation is really just an umbrella concept in which responsive design represents one tactic. Nearly any piece of data that can communicate any context about the user or her environment can be used to personalise an experience. Other tactics include geotargeting (Yelp), autocomplete (Google), collaborative filtering based recommender systems (Amazon, Netflix) and behavioral targeting (interest based advertising) (Bluekai, DoubleClick).
When planning a project that involves personalisation, it's important to separate these types of tactics in order to make informed decisions about which to employ and how far to go.
Responsive design
The data/context is screen size or the size of the browser window. It’s becoming more complicated to deal with high density displays as more of these devices come to market. Responsive design personalises layout more than content and adjusts to the device more so than the person.
Geotargeting
Geotargeting uses location, determined by GPS, ISP or IP address, as the data/context. In a simple execution, Yelp already knows what zipcode you’re in. A more complex application may use the GPS on your mobile to help ad networks deliver coupons when you’re in or near a certain grocery store. There is a lot that can be inferred by knowing a person’s location, and also a lot that you can get wrong, but geotargeting should always be considered when location can add value to an experience.
Autocomplete
Autocompletion is an overlooked but hugely important piece of the personalisation puzzle. When I type something into a Google search box, it personalises what it thinks I want to search for based on what I've searched for in the past. Of course, Google is much more sophisticated than that. They also run a collaborative filtering algorithm to predict what you may search for based on the larger pool of searches. When designing search with autocompletion into our applications, it's crucial to make calculated decisions about how it works. Should we skew the results toward marketing objectives? Skew them toward new content? Keep it straight?
Collaborative filtering
Collaborative filtering based recommender systems (like those on Amazon or Netflix), that employ your browsing/viewing history as the data/context, are getting really good. And they typically avoid the creep factor by telling users why a recommendation is being made. On Amazon, instead of “popular”, “recommended” or “related” they explain that the products listed are “what most people bought after viewing this item.” In fact, most recommended products on Amazon have a “why recommended?” link below them. But these systems rely on complicated algorithms and can take a lot of effort to get right.
Behavioural Targeting
Behavioural targeting also uses your browsing history. But, instead of using data from a single site such as Amazon, it uses data from all the sites you visit and offers little explanation as to why I might see an ad for McDonalds and you might see an ad for Whole Foods. This type of personalisation is, in my opinion, the really creepy kind. It’s facilitated by companies who call it “Interest-Based Advertising” and insist they are helping consumers see “relevant” ads. This is the darker side of personalisation.
Imagine a scenario where a company, say an airline, scrapes your LinkedIn information, cross references your employer and job title with GlassDoor to find out your approximate salary, and sets pricing based on what they determine you would pay. The 'big data' has personalised the market to you, and is likely going to over-charge you for plane tickets. Maybe the company then looks at your Facebook friends or Twitter contacts and finds the people who they decide should fit the same profile, so they can overcharge them too. Some people would applaud that type of personalisation as Robin Hood-like. Some would call it a violation of privacy. Others might call it a business plan.
The need for clarity
As we design experiences, we need to be much more clear about the tactics we plan to implement. We need to make sure that our teams and our clients have a clear understanding of what the project requirements (or wish lists) are, and be equally clear about what personalisation tactics will be ignored.
It will be crucial for those designing these kinds of personalised experiences to be tuned in to what's possible. It will be even more crucial for us to understand our users, be considerate, and be respectful of what they are willing to accept. The one thing we do know is that if we lose a site visitor (or scare them off), it takes an incredible amount of effort to win them back.
