5 Ways to Boost Your Personalization Power
…and one department that you wouldn’t expect to be the hero.
When my title changed to the “Product Manager for Personalization” at my last job, my LinkedIn inbox was flooded with recruiters looking to build out their algorithms and website’s personalization capabilities. However, when I would talk to each recruiter regarding their goals for the role, I always got a different answer. Usually it had to do with on-site recommendations, email recommendations, and preference-management tools, but sometimes it was about login credential management or even data mining. Despite being one of the hottest areas in retail, it seemed like no one really agreed on what “personalization” really was. I think that Wikipedia defines it well (as it often does):
“Personalization, also known as customization, consists of tailoring a service or a product to accommodate specific individuals, as opposed to general groups.”
However, I offer a simpler version:
Personalization is any way a website makes a user’s experience unique and more useful.
Over the past 5 years “curation” has become common jargon and e-tail companies that have built entire models around it — often subscription services such as Trunk Club, Peaches & Petals, and Loot Crate. Subscription services, however, aren’t the only ones making big moves in personalization. We see major players like Amazon and Google thriving in the personalization game. For example, right now you could go to any page on Amazon and you’ll likely see between 6 and 15 exposed recommendation features at a given time (no, seriously. Go look right now). So the question begs to be asked:
If companies with vast resources like Google & Amazon are so serious about personalization, how long is it until they can do it better than the startups (or until they just buy them out)?
It’s a fair question, and with the vast amounts of dedicated resources available and data that these (and even mid-size) companies are able to analyze and infer from, it’s hard to argue against the potential inevitability that the big guys will win. That is, until you examine the cracks in the foundation caused by their achilles heel:
Scale.
To solve a complex problem the usual response from big tech companies is to throw a gaggle of engineers at it, which can be effective…to an extent. See, to offset some of the problems that came with their rapid growth (decelerated innovation, increased bureaucracy, and a lack of product focus to name a few) these large companies often create a competitive environment, pitting teams against each other to create a superior product around specific problems, target markets, or areas of expertise (which certainly can yield impressive results). A difficulty that arises from this, though, is tracking your infrastructure. With such frequent iteration, it becomes a huge deal to make changes to your underlying systems, such as data warehouses, analytics platforms, and for retail businesses, their product catalogs/taxonomy, because mapping dependencies to other services is a nightmare.
Product catalog & taxonomy at first glance seems like a pretty stable area of the business — I mean, how often are we going to change how we measure pants? Rarely. However, the context around your catalog changes all the time.
Think about how often terminology in the fashion industry changes, or how there are seemingly endless points of view regarding something as simple as a vest: Is it a jacket? Is it an accessory? Is it a top? Is it suiting? Is it casual? The answer could be yes to any or all of these at a given time. Because of this, big businesses are forced to create layers and layers of filtering, mapping and attribution software on top of archaic catalogs and taxonomy structures — after all, it’s much safer to do that than risk breaking the whole website and potentially losing millions of dollars for a change that users won’t even directly see. Even if you wanted to take that leap, it’s difficult to pitch such a risky move to a numbers-driven exec for something as hard to measure — and frankly, unsexy — as catalog structure. Tracking impact on conversion would be a nightmare, and even if you could find a good way to do it, so many other factors are at play on search & product pages that attribution numbers would almost certainly come with an asterisk.
This is a problem that most of the little guys don’t face. When a retail company is still considered a “startup” or even just a small business, the focus is on building the best possible experience. They have cleaner catalogs, simpler data pipelines, and less internal dependencies to worry about. With smaller catalogs to manage and more focused customer bases providing data, reasons for iteration are often more obvious. Even beyond that, these smaller, more niche sites have a key advantage: they speak the same language as nearly all of their customers. Nastygal speaks Millennial Twenty-Something Fashionista; and they can focus on speaking it fluently because they don’t need to learn other languages. Massive retailers like Nordstrom or Macys have to learn to speak the languages of mothers, grandfathers, high-school girls, ten-year-old boys, athletes, and many more. This usually results in a streamlined, yet less informative catalog versus a hyper-focused, more insightful one.
So how can your company become personalization juggernauts? No matter where your company falls on the size and scope spectrum, I have a five suggestions that should help you get a real edge:
1: Stop looking at Personalization as a consumer of data rather than a creator. Too often, our need for labels within companies limit our thinking. For example, if you think of Personalization as just recommendations, you will see it as the consumer of the catalog rather than the co-creator, and you probably won’t give them authority to make upstream changes. When you remove this mental barrier you may see opportunities to combine teams that would never have worked together before.
2: Open up your catalog to your users. In today’s (hash)tag-based social media landscape, we see that users are willing to help provide context to images, products, places, and experiences. Many of us think that product reviews are a good enough version of this. However, opening up your catalog to users by allowing them to tag & describe products in their own terms not only makes your catalog more robust, but it also allows you to authentically speak languages that you haven’t yet mastered. This helps you have a more useful on-site search experience, you’ll rank better in web search, and you may even discover unseen trends in your own customer base.
3: Spend more time on building out your customer profiles. There seems to be a perception in e-retail that customers are very secretive and resist any attempts to self-describe online. While this may be true for older generations, today’s millennial customers see the value in providing useful info for a better shopping experience (for a great example of this, look at the profiles in Sephora’s online community). In reality, most often the barrier to customers building out profiles is the time required to do so, not the desire for anonymity. And now that it’s become commonplace for users to connect social media accounts to fill in many of these blanks on profiles, it’s easier than ever for customers to help themselves get a more personalized experience online with minimal exposure to the time-barrier. Imagine how much better you could recommend clothing if a customer uploaded their measurements into their profile and noted their fit preferences. Suddenly, exposing unknown (and perhaps more lucrative) brands in search results is more effective, because users can have confidence that their garment will be flattering on their body. The applications are endless; you just need to allow for it.
4: Add human behavior experts to your data science teams. When creating recommendation algorithms for your products, having team members that are able to understand the context at the micro (behavioral psychologists) and macro (sociologists) levels could make the difference between useful recommendations and wild guesses.
5: Leverage your online marketing team’s data to inform your knowledge on-site. As a marketer, there’s a treasure trove of behavior data you can ethically access which could lead to the ideal on-site experience for visitors. Many display ad networks and social platforms with advertising allow for cookies, pixels, etc. Often this data is used in a vacuum, kept separate from website/customer data and compared at a high level as a performance baseline. This application is reactive, and not proactive. Imagine the amount of improvement you could make to the user experience if you could connect the dots between even half of your customers and their off-site behavior. Powerful stuff.
*Disclaimer: Data leakage & breaches are to be taken seriously. Don’t get cavalier with linking data if you can’t guarantee that you’ll be the only audience.
If you have basic personalization services on your website and can execute well on even three of those suggestions, you should be in good shape. Got any other suggestions? I’d love to hear them in the comments below!
