There are plenty of stories about personalization projects that become unwieldy monsters with lots of effort, lots of technology and results that aren't necessarily more impressive than what a modest A/B testing program can achieve. It seems clear that, in the spirit of much of today's digital enterprise, smaller, more agile, more organic efforts that gradually deepen the breadth and depth of segmentation and the corresponding personalization are more likely to succeed than big bang efforts. On the plus side, when personalization works, it works. We measure many sites, and in almost every case where a digital property significantly improves its performance, some form of segmentation and personalization is the driver.
Beware, though, of falling prey to the "me-too" effect when thinking about personalization. Everyone is so familiar with the product recommendation strategies ("people who bought/viewed this item also bought/viewed this") on giant internet sites that people tend to think of that type of basket-based recommendation engine as synonymous with personalization and not just as an example of one style of personalization. For enterprises without large product sets, thinking of personalization in terms of a product recommendation engine can make it hard to see how personalization is relevant. Every experience can be varied and made more personally relevant - not just product selection. I think there's irony in viewing personalization with a one-size-fits-all lens!
If personalization is the thing we're mostly struggling to do, the biggest concern I hear about getting personalization done is finding the people to do it. I don't talk to many digital managers who aren't having a hard time finding (and keeping!) talent. As with any white-hot industry, the issue of human capital is "what keeps us awake at night."
When you have a mismatch between demand and supply, price goes up. That's obviously a good thing for us practitioners, but it puts a strain on managers seeking to hire and retain talent - especially when they find themselves restricted by corporate HR policies that don't appropriately match job titles to market salaries and don't create reasonable career paths for in-demand analysts and data scientists.
So if you can't buy them, the thinking often turns to making them and, once made, keeping them. When it comes to training analysts, I hear an almost overwhelming consensus that résumé skills are terrible predictors of analytic chops. Teams that consider themselves successful in hiring and training analysts almost all seem to focus on finding curious, logical thinkers - not on any particular degree or academic achievement.
Of course there's nothing worse than finding great young analysts, honing their skills, and then having them walk out the door just when they are starting to really contribute. But it's mostly not salary that drives attrition. The number one source of job satisfaction (and retention) that analysts cite when talking about why they stay or leave is whether the work they do makes an impact on the business. Talk about a vicious circle! If your organization isn't great at using and operationalizing analytics, you're likely to lose the very people who might make a difference.
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