The approaches to personalization number almost as high as the number of businesses out there. Organizations vary wildly in the ways they optimize and personalize experiences for every customer, product and expectation. 

And the differences aren't just industry-to-industry. Take a look at retail -- what I'd argue is the pioneering industry when it comes to digital personalization -- the differences are remarkable.

Where a brand falls on the personalization spectrum depends on technology adoption, brand and commerce priorities, digital maturity and executive support. But in my conversations with brand leaders, marketers, consumers and data scientists, I've come to recognize three distinct approaches to personalization which we'll break down into personas: 1. The Reliable Recommender, 2. The Loyalist Lover and 3. The Progressive Personalizer.

So where does your brand fit?

The Reliable Recommender

Thanks no doubt to Amazon’s “because you bought this” experience, product recommendations have become the norm in the e-commerce world. And consumers expect them. Like an effective sales associate, product recommendations offer shoppers helpful and timely suggestions -- and in turn, boost conversions and average order value. 

Forrester and's 2011 “State of Retailing Online” study cited personalization as the driving force behind increased average order values and cost savings. And the big winner in their eyes at the time, was these then-innovative recommendation engines. Their advice: invest in one, it’ll pay for itself (and then some) in short order.

Some people didn't need to be convinced of the value of recommendations. Along the way -- probably five or six years ago -- they stopped manually curating product recommendations, threw away the spreadsheet and invested in a recommendations engine. These are the Reliable Recommenders (RR).

Some ambitious retailers built their own recommendations engines, but most licensed one from a technology vendor. It became trendy to talk about “right product, right person, right place/time” -- mostly based on what others have viewed or purchased when considering the same product. The common approach was called collaborative filtering.

While recommendation engines have evolved -- incorporating algorithm innovations and merchandiser-friendly UIs -- unfortunately, like so many things, there is no one-size-fits-all recommendations strategy.

The Reliable Recommender failed to realize this. And while they’ve seen some reliable lift from their set-and-forget approach to recommendations, without some care, feeding and optimization, their cross-sells plateaued or declined. 

Don’t get me wrong: Reliable Recommenders put in place and execute a really critical piece of their site’s personalization strategy (and made their companies a decent amount of incremental revenue). But they fail to fully exploit recommendations:

  • Do they test different algorithmic approaches?
  • Do they test simple things like number of recs that should be displayed, or location of recs on a page?
  • Are they smart about the types of recommendations that should appear on their product description page vs. those they showed in the cart?
  • Are they utilizing recommendations everywhere they have the potential to have impact, like email?

In short, are recommendations performing as well as they could be?

What distinguishes the Reliable Recommender? For them, recommendations are synonymous with personalization. Invest in a recommendations engine and you’re done. This thinking is stuck in the mid-2000s.

We know now that while a recommendations engine plays a key role in digital commerce personalization, it’s only a piece of the strategy. It needs to be integrated into the larger customer experience picture (think contextual, think mobile). 

The Loyalist Lover

You're probably familiar with the Loyalist Lover approach: you know that I am Kevin and, as a result, you (likely) know who I am, what I’m about and how to best connect the dots for me within your brand experience. To get to this point, I probably signed up for your brand’s loyalty program and now authenticate when I come to your site.

In this scenario, the brand reserves its love solely for loyal customers. And that’s fantastic. A personalization program that leverages and deepens the relationship between customer and brand is always a winner. It shows trust, alignment and the potential for new and incremental conversion opportunities which (usually) evolve fairly organically. 

The drawbacks? First, you might be missing out on a new or emerging audience (ostensibly anonymous) who could convert and -- over time -- evolve into cherished advocates. But, just as importantly, you set the bar sky-high to please those existing loyalists you love so much.

Because you know I'm Kevin, I expect much more: more bells and whistles that are spot-on, more personalized recommendations and content alignments and a more me-centric experience every time. And if you miss, you risk alienating me in the short- and long-term.

From where I sit as Kevin the Consumer, I know that you know I’m 35, male and a marathon runner from Chicago. So why show me a woman swimming laps in California? You clearly don’t get me. Are you learning and growing with me? 

Learning Opportunities

What’s the context of my current, real-time experience? If I’m on my smart phone and near your store, invite me in. It’s a simple example, but it brings together the critical pieces -- behavioral, contextual and historical -- for a powerful experience that acknowledges I’m more than a loyalty card number. 

The takeaway is simple: if your brand is in the Loyalty Lover camp, you’re doing great. But, like the Reliable Recommender, it's time to expand how you define personalization. Remember, your goal is to continually improve the customer experience and get more measurable results from your personalization efforts.

So keep pushing, being responsive and delivering relevance at scale in real time using context, past and present behaviors and historical data, starting with those loyalists. As you plug away, you’ll see countless opportunities to learn more, both from these all-important advocates and from the seemingly enigmatic newcomers.

The Progressive Personalizer

The final category knows they have a lot to learn. They see what the big guys are doing, and they see what the smaller, edgier, long tail companies are testing out. Fundamentality, they see themselves as innovators (many like to think of themselves as much technology company as retailer), standing on the brink of delivering incredible customer experiences to every visitor, every time.

In many ways this involves a best in class approach -- a marriage of powerful technology with equally powerful marketing, creative and insights that, together, push the envelope for all of us. 

Achieving Progressive Personalizer status is extremely aspirational, even for the best of the best. Yet it, too, carries risks. It requires a culture of experimentation that starts from the top, trickles through every department and, ultimately, permeates every consumer through every stage of every brand journey.

It’s not recommendations on a product detail page, and it’s not recognizing a familiar face -- it’s all of those things, and lots more. It’s an extremely dynamic relevance delivery system that’s always learning and makes extensive use of new and existing data. We’re talking “always-on” and predictive machine-learning based approaches. We’re talking predictive. And we’re talking omnichannel and cross-device.

"Good enough” isn’t in the Progressive Personalizer’s vernacular. These companies build out their own algorithms, technology platforms, data science programs and, equally, making significant investments in the people and processes that power it all. Progressive Personalizers place a big bet on personalization, and do everything possible to drive it -- and the notion of the Internet of Me and the power of branded, relevant experiences -- forward. 

The one drawback to being a Progressive Personalizer, is the risk of getting caught up in innovation for innovation’s sake. Marketers need to prove the value of their efforts, so keeping an eye on how personalization is moving the needle revenue-wise is key to securing investment and continuing the innovation cycle.

My advice for this approach: Don’t take your eye off personalization performance, and don’t forget about the low-hanging fruit. Cross-sell recommendations are a great example -- maybe not the sexiest strategy in 2015, but again, they work and will fuel personalization growth and gain you support for your more aggressive and longer term initiatives.

Not Sure Where Your Brand Falls? 

Each of these approaches is powerful, compelling, conversion-driving and has the capacity to deliver huge wins, provided you find the right balance.

Recommendations work -- you've probably already seen personalization payoffs. If your brand favors loyalists, we know that authenticated visitors are more invested in the journey and offer greater opportunities to optimize in a deeper, more meaningful way, right away. And if you’re an always-on personalization powerhouse then you’re likely winning from these and countless other best uses, especially as we move to a consumer-centric Internet of Me era.

That shift signals greater, more pronounced differences in the way we engage and deliver customer value, affinity and long-term loyalty going forward. We will have to innovate and elevate the way we recommend, reward and deliver relevance accordingly. This is true, no matter what category your brand falls under, no matter what industry. Being “always on” is becoming less and less the ideal and more and more the mandate. 

Creative Commons Creative Commons Attribution-No Derivative Works 2.0 Generic LicenseTitle image by  Nick Harris1