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Humanize The User Experience With Real-Time Recommendations

October 29, 2019
6 min reading time

Gone are the days of mulling over a purchase for hours on end. Thanks to technology, the market now moves at record speed.

Within minutes, shoppers can now perform extensive research, choose a product, and head to the check-out cart.
This is great for shoppers, especially those with little time to make a decision. But for website owners, this means less time to engage their site visitors.

There are two ways visitors can find products on your site. The first and more direct way to find a product on your site is for visitors to use your search bar. The second and equally important way is by displaying an array of products that you think your visitors might like.

On the occasion that your visitor already has something specific in mind, typing into a search bar works great. But sometimes visitors are just browsing and don’t necessarily know what they want or they are shopping for someone else.

You need to provide good customer experience by allowing your visitor to flawlessly transition between both discovery modes. Because the same shopper is likely to be in both of these modes, perhaps at the same time, your site needs to be set up to easily allow for both types of shopping.

You have to capture the shopper by both search and other discovery tools, like personalized recommendations.

But why would a shopper need to have both types of discovery at the same time? Here’s a real-life example: think about the last time you went to get ice-cream.

You’re craving your favorite flavor and you walk into the ice-cream store fully intent on ordering mint chocolate chip. If you went up to the counter and ordered mint chocolate chip right away, without looking at the menu or the display case, that would be the equivalent of a conversion through search.

This time, however, you’re immediately met with a chalkboard of 50 delicious-sounding flavors, and when you glance down at the display, they look even better than they sound. The smell of espresso beans hits your nose and you can’t help but make an impulse decision to go with the espresso ice-cream instead.

You made a split-second decision in real-time. You had your mind set, but once you started to discover and explore your other options, you realized you wanted to try something new. This type of decision happens almost instantly, when you are taking in all the sights, sounds, smells, and emotions of your current environment.

But how do discoveries through your senses translate to the digital landscape? How do you create great customer experiences on your website equivalent to the experience in front of an ice cream display?

Sticking with this example, if you are the ice cream clerk, you need to surface your “espresso ice cream”, aka the best product or service, for each visitor. And online, your “chalkboard” or “display case” doesn’t even have to present the same flavors to everyone. You can show the very best options to your visitors before they move on. That’s why the right recommendation engine is essential in making your customers happy.

Interacting with customers at a personal level like this is an incredible power, but also a significant challenge. To create moments like these digitally, websites must collect and analyze a massive stream of signals for not just a single user, but for all users. And this data can’t just be analyzed once, or every once in a while. Websites must continue to collect and analyze this data up to the very present moment, in order to most effectively predict what people want.

Many companies may set static rules or use lookalike audiences from analyzing historical data, but this isn’t enough. This signal analysis is most effective with a dedicated Machine Learning system in place to constantly learn and act on changing shopper intent, in real-time.

Do you know how to create “real-time” experiences?

You are right: you need to try to pinpoint what customers want. But be careful about the way you try to do this, as many methods are way off target. Be wary of these tactics:

  1. Don’t trade in great customer experience for sensationalist clickbait — you are showing advertisements masquerading as content. While increased customer clickthrus to the area may make you some money, you are sending customers away from your site.
    This is like the ice cream store displaying all of their 32 flavors, with directions to their competing store down the street!
  2. Don’t make assumptions about what your customers are expecting. If you try to hard-code rules around showing certain types of content together, you are missing out on a substantial section of your audience. I’m looking at ice cream, but don’t just show me the ice cream menu if you sell cookies too. Maybe I want an ice cream sandwich!
  3. Don’t rely solely on old information. If I buy espresso ice cream over and over again, but then one day come into the store with my 8-year old son, are you only going to show me coffee-flavored ice cream?

None of these current strategies help to create real-time experiences, humanize the user experience, or capitalize on the changing needs and wants of their audience. Instead, they use a limited section of outdated data to group everyone into the same basic category. In turn, this can easily decrease customer satisfaction, as the customer experience strategy feels fabricated and repetitive.

Today, people expect your brand to speak to who they are at the present moment.

The newest generation in the workforce grew up with the internet. Younger consumers expect rapid, in-the-moment updates from social media like Twitter, and they consider news stories “old” after 24 hours. Even if you remember a time before in-the-moment updates, you, too, have grown accustomed to a newer, faster way of consuming content and making purchases.

That’s why real-time analytics is the future of marketing. According to a 2018 study from Harvard Business Review Analytic Services, the ability to transform data into real-time recommendations improves customer experience by 85% and boosts decision-making speeds by 58%.

The research goes on to say that while most companies recognize the importance of real-time analytics, only 16% report that they’ve effectively implemented these strategies. However, the companies who have mastered it are extremely hard to ignore. Customers landing on your site feel as if they’re understood, increasing the likeliness of customer loyalty and popularity.

Companies create the most memorable customer experiences through personal connections.

Take Spotify, for example. They could just recommend playlists based on your five favorite bands, but instead, they use several extensive algorithms that consider the time of day, your schedule, your preferred tempo, and the adjectives that describe your favorite genre.

But we believe your company doesn’t have to be an 800-pound tech gorilla to create recommendation systems that are customer-centric.

Just like Google, Amazon, and Facebook, my company LiftIgniter uses a wide variety of data to create personalized, up-to-date experiences for consumers.

For example, our client NTT Docomo recently released the My Daiz app in Japan to provide their own “digital assistant”. Powered by LiftIgniter, My Daiz considers over 50 different personalized factors, like your location, the current weather, and the time, in order to provide in-the-moment services. Instead of answering prompted questions like Siri or Alexa, this app anticipates your needs and acts accordingly.

As technology continues to push forward, the most successful companies will give customers a digital experience that makes them feel seen and understood in a personal way. So, consider the user: personalize the customer experience, and give them something best suited to their present moment.

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