Over the last few weeks, there’s been lots of talk on Techcrunch about personalization technology, particularly in e-commerce, from Nir Eyal, Hank Nothhaft and Leena Rao et al. And certain parts of these articles have been bugging me, a lot.
I think my biggest issue with those articles is that they don’t really know what personalisation is – there’s not a single, standard, solid definition of personalization. I think the internet community need this to move on effectively. Wikipedia states that personalization “involves using technology to accommodate the differences between individuals.” But I think that’s too broad.
So I’d like to put forward a definition of my own.
Personalization technology enables the dynamic insertion, customization or suggestion of content in any format that is relevant to the individual user, based on the user’s implicit behaviour and preferences, and explicitly given details.
In a nutshell, I think personalization helps any format present or suggest content that is relevant to the individual at that moment in time, based on implicitly and explicitly given data – dynamically.
Personalization Isn’t Recommendations
In 1999, Jeff Bezos and his Amazon employees were sitting in Seattle, thinking hard about how to make more money. And they started to look at collaborative filtering.
“…collaborative filtering… [is] able to help people to discover exactly what they’re looking for, saves them time and improves their lives… We have 6.2 million customers, we should have 6.2 million stores.”
Collaborative filtering is the technology behind simple recommendations which looks at the relationships between users (customers) and items (products). Whilst effective in their own way, “People who bought Product X also bought” and “People like you also looked at” are supremely simple algorithms.
And there are lots of companies peddling these simple product recommendations as “personalisation”.
As Hank says,
“These recommendation engines were once ground-breaking, but they have failed to evolve. And more importantly, our expectations as Web consumers have evolved beyond the simple concepts of “users who purchased item X also purchased item Y.” At best, services that claim personalization based upon these aggregate metrics attempt to triangulate an identity for us as individuals based upon the galaxy of other individuals. They try to pin us down into an archetype, into a box of likes and interests, without recognizing that as humans, what we desire, want and need is in constant flux and ever-evolving.“
Recommendations look primarily at the crowd. But personalization, as the name suggests, needs to look at the individual person.
As he says, there are providers out there who claim to offer “personalization” services, but looking at their websites and implementations, it’s clear to see that they’re faking it. One in particular seems to sell crowd-based recommendations dressed up as “personalization”. And that’s just not right.
What Is Personalization?
Let’s dissect my definition.
“Personalization technology enables the dynamic insertion, customization or suggestion of content” – personalization doesn’t just have to be product recommendations: it can also include inserting any content like images or text (e.g. displaying a golf-orientated banner for a returning golf supplies buyer), or customizing content that is already there (e.g. “Hi Joe, we’ve got some great movie suggestions for you!”).
“…in any format” – it isn’t restricted to the web. It can be implemented for any medium or touchpoint, such as emails, apps, instore kiosks, etc.
“…that is relevant to the individual user, based on the user’s implicit behaviour and preferences, and explicitly given details” – finally, the most important part. Personalization uses both implicit and explicit information, derived in two ways. Firstly, a visitor might explicitly declare some information, such as their gender or date of birth.
Secondly, their behaviour can be mined and processed to help understand affinities and relationships. A good example of this in action would be on a clothes store. If you haven’t given your gender, but in the last 4 clicks you’ve only looked at men’s clothes, it’s pretty safe to say you’re shopping for some men’s clothes – so don’t recommend women’s clothes for this session. If you’re a regular returning visitor and you have bought 4 blue items and one purple item, you can be profiled as having an affinity to the colour blue – but let’s not forget that you like some purple in your wardrobe too.
And it doesn’t have to stop at simple product attributes like colour, size, gender or similar. At Netflix, they have a team of specialists classifying each film with plot details such as “Strong Female Lead” to add more data for real-time and past behaviour profiling. By adding their expert knowledge into the system, Netflix’s personalized recommendation engine becomes smarter as it understands the subtle nuances that users can subconsciously relate to.
And, this leads nicely onto Part 2 – Curation isn’t Personalization.
It will be available to read on Friday 17th February – to get access now, add us to your Google Plus circles and get the password!
Part 2 – Curation Isn’t Personalization.
I’ve read Nir Eyal’s article about ten times, trying to understand what he was saying. And I totally disagree.
“Pinterest will be the first company to nail eCommerce personalization…Pinterest is becoming the web’s personalized mail-order catalog. Each user is presented with a one-of-a-kind visual interface based on their tastes.”
No, it doesn’t. Well, it sort-of does, but in a very roundabout way. Let me explain.
First of all, Pinterest isn’t ecommerce – you can pin anything, not just products, and I doubt that their interface will ever become mainstream for anything else than Pinterest-like sites. It’s just another marketing channel, like Twitter.
Secondly, it’s not personalized.
Nir writes: “… items [which Pinterest users] see are curated through people and topics they’ve identified as interesting and what is shown to them improves the more they interact with it. Every time they pin, re-pin, like, or comment on an object, the relevancy of the products displayed on their magic catalog improves.” I don’t think they do.
I asked Pinterest expert Vikki Chowney what she thought of Nir’s comments. “I’ve not heard about this functionality within Pinterest, so either Nir has some inside information, or he’s misunderstood. If Pinterest did adapt based on relevancy, in my opinion you’d miss out of much of the ‘discovery’ within the community you build – which is based on ‘Pinners you follow’. That after all is part of the appeal.”
At any time, someone you follow could pin something so totally irrelevant to you. So let’s agree that no – Pinterest don’t personalize. In one sense, yes – they allow users to say “I want to follow this person or this topic because I’m interested in what they are sharing” – a form of social discovery, customise their feed. But, that’s not personalization. It lets you follow curated feeds and nothing more.
Imagine that Netflix was more like Pinterest. You’re really interested in romantic films, so you might follow a romance specialist. One day they say you should watch The Notebook, the next day they suggest Jack and Jill, starring Adam Sandler. They’re totally different films genres, but loosely can be classified as “romance”. But Jack and Jill is totally irrelevant to you, because you hate comedy films.
However, it is important to allow users to declare new interests, to ensure they don’t get stuck in what we call the Locked Loop, a continual and relentless regime of repetitiveness where users can’t escape from the Filter Bubble. But this can be done through menu navigation – it doesn’t require a long-winded social discovery process.
So now we’ve got that cleared up.
Part 3: The Future of Ecommerce Personalization
I want to borrow some words from Hank’s article on Techcrunch again.
“Groupon knows that targeting by regions increases conversion and sales, but imagine how much they could amplify that effect if they were targeting based on a rich and sophisticated understanding of the individual person that receives each offer?”
Online retailers should be itching to move beyond recommendations into personalization. A better understanding of each individual visitor and customer can help retailers to provide better experiences; to suggest more relevant products and to maximise the chances of a conversion.
Here are just a few examples of where personalization can make a difference and change the face of ecommerce.
Initially, driving product recommendation areas with personalization logic will lead to much more relevant product suggestions. Retailers can use business rules and their own merchandising expertise to curate personalized suggestions – for example: in jewellery, a retailer may know that buyers are more susceptible to upsells; in electronics, BestBuy might know that potential customers usually look at products around their maximum pricepoint, so don’t show more expensive products – instead, suggest similar products that have a higher margin.
I think that crowd-based recommendations have their place in a personalization strategy. Cross-selling is a big part of helping to improve the average order value metric by suggesting relevant products – but there always needs to be an element of personalisation. I think the best way to achieve personalization is by filtering the crowd. For example, if I’ve added some new sneakers to my shopping cart, don’t show me Nike products because you know I’m a slave to the Adidas brand – so show me the most popular Adidas products bought by people like me.
One of my biggest bugbears is searching on a clothes store for a generic term, like “t-shirts”. You know I’m a man – please, only show me men’s clothes.
Any online retailer can now use individual visitor behaviour to provide personalized search results that are optimized to encourage conversions. The clothes store is a great example – there are so many different data-points available which can be used for real-time and past behaviour profiling such as gender, size, preferred colours, particular affinities to brands or designers and so on.
Imagine an online foodstore, where a visitor has previously and explicitly declared they are lactose intolerant. When they then use the onsite search feature, there’s more chance of a conversion if dairy-free foods are surfaced to the top. It’s the simple things like this that create smoother experiences and will improve other metrics such as loyalty.
Like Leena picked up on in her article, personalized email marketing is growing because it’s infinitely more relevant than segmented email marketing.
“Recently, I started to receive emails from Gilt Groupe that suggested similar earring to like those those I had added to my wait-list on the e-commerce site. The company also sends personalized email notifications on sales that are tailored to each customer… And brick and mortar retailers like Saks Fifth Avenue, and many others are also starting to jump on the personalized email bandwagon.”
With personalization, retailers don’t need to spray n’ pray – segment-of-one emails put the right content and product suggestions in front of the right people.
I’m a bit of a social commerce sceptic – but I do see the value in obtaining and using social data in personalization. Knowing a visitor Likes “Levi’s” on Facebook is an instant declaration of interest. But to be done right, it should be part of a more all-encompassing strategy, using the most relevant data in the most relevance places.
What’s Next In Personalization?
Its ok, you can relax. I think we’re only at the beginning of what will be the biggest disruption in the web since social media.
Recommendation engines paved the way; Amazon led the charge. But now it’s up to each and every online retailer to step up to the plate and have the same lofty, but much more obtainable, ambitions as Amazon did in 1999.
“There should be the optimum store for each and every customer.”
But, please, please remember that personalization isn’t crowd-recommendations, or curated content! Let’s learn to crawl, before we start walking and running.