Chatbot Personalization: Good & Bad Examples Show Why It’s Needed to Improve Customer Experience

Last updated on by Ted Vrountas in Marketing Personalization

The more internet technology advances, the more able marketers are to truly personalize the customer experience. Once that included simple tactics, like the inclusion of a prospect’s name or a follow-up email with “recommended” content based on a purchase, but it’s not anymore.

With data growing, marketing automation maturing, and artificial intelligence coming into its own, personalization has risen to the level of 1 to 1 -- meaning one business to one consumer.

At the intersection of all three phenomena are chatbots. Today, these bots are capable of some pretty amazing things, from serving as a personal stylist to booking trips as a virtual travel agent.

As they continue to improve, hyper-personalized messaging experiences blur the line between human and bot, enabling customers to accomplish nearly everything they’d otherwise need to navigate a website or an app for. When you get chatbot personalization right, your customers can get what they want, and they can get it faster.

Is chatbot personalization really a thing?

Chatbots have been on the minds of marketers for a long time. But for some, they seem like sci-fi advertising tactic that we, in the present, will never catch up to. Christi Olson, chatbot expert and Head of Evangelism for Search at Bing, begs to differ:

Gartner predicts that by 2020 people will have more conversations with chatbots than their spouse... The chatbots of the future don’t just respond to questions. They talk. They think. They draw insights from knowledge graphs. They forge emotional relationships with customers.

Larry Kim, CEO of Mobile Monkey, adds:

2019 will be a big year for chat blasting with new APIs from the most popular messaging applications in the world, including Instagram, WhatsApp, Google RCS, and more.
Consumer preferences are already favoring chat over email for communication, and more and more businesses are figuring this out!

At least one March 2018 survey from Bing supports these predictions. When asked if they would interact with brands on a one-to-one basis in the future, 60% of consumers didn’t just say yes -- they said they expected to have their own, branded, personal assistant with which they could build a relationship over the next five years.

For insights into current chatbot usage, we can turn to Salesforce’s report, The 2018 State of Chatbots. In it, the company finds that, while adoption of chatbots isn’t quite mainstream yet, that 15% of all consumers have engaged with a branded chatbot in the last 12 months.

That’s still fairly impressive when you consider that email, the web’s most established communication channel, boasts an engagement rate of 60%.

Furthermore, consumers identify many instances in which chatbots are preferred over apps:

Chatbots vs. apps

For a number of reasons, it’s clear that chatbots are preferred by customers. Let’s take a look at the major factor driving their adoption.

Why chatbot personalization is needed

Chatbots have major potential to close the personalization gap in online channels like website and email, in which personalization is applied at a broad scale: email lists are segmented into groups, websites may serve a gender-specific version of a website relevant to the user -- but none of it is true, one-to-one personalization.

Consider this example, a splash page from H&M:

It’s so impersonal that I’m prompted to choose the country I’m from. And then, after I click US, and hover over “Men,” I’m presented with an overwhelming list of products:

The same goes for Polo Ralph Lauren:

And the same can be said of most websites of major stores that carry a lot of products. They just throw a bunch of categories at you and make you sort through them. Why?

“Most products have a goal of solving a painful problem for a specific group of people,” says ChatbotsLife founder, Stefan Kojourahov. “Great companies learn a lot about their customers and build entire solutions for them. In the process, a lot of testing is done, and a product becomes the result of an ongoing A/B test which is the average of what most customers do. The final result becomes a generic flow, like this:”

But there’s a better way.

Chatbot personalization done right and wrong

That major menu from H&M and Polo Ralph Lauren may be what you land on as a user, but it doesn’t mean it’s the best and fastest solution -- just the best and fastest solution of the previous iterations.

For example, it’s better to be offered the US version of a website from the get-go instead of having to select it yourself, but that still doesn’t constitute impressive personalization. Chatbots, when correctly built and maintained, can offer even better solutions than the best the web has to offer. However, when they miss the mark, it can end up looking like this:

It’s basically a conversational version of the overwhelming menus we saw earlier. Korjourahov says:

This is what happens when you try to condense a website into a bot and rely on averages. When you don’t know why a person is on your site, or using your bot and you end up playing a guessing game and hoping that the shoe fits. Most of the time it does not.

With chatbots, though, hyper-personalization is possible with the help of AB testing that can be done not on groups of users, like email lists or website visitors, but on individuals:

Chatbots have the potential to store the information offered by users, like preferences and past purchases, and turn it into hyper-personalized recommendations.

That would be the goal of bots for H&M and Polo Ralph Lauren. But not all bots are created with the goal of becoming a personal assistant. Some offer dietary advice, fitness plans.

Others accomplish awareness campaigns, like National Geographic’s Einstein bot, which was created simply as a promotion for the network’s show “Genius:”

While all it did was converse with users, ROI shows it was highly effective for keeping them engaged. On average, users chat with the bot for 6-8 minutes, and 50% of them re-engaged.

Similarly, Duolingo, a language learning app, created several bots that users could converse with. But in their case, it was for more than just engaging users. These bots had made-up personalities, like cab driver or police officer, and gave users a way to practice their language skills privately:

While these may not appear to be examples of hyper-personalized marketing, remember that a bot’s goal isn’t always product purchase. Einstein bot’s was brand awareness, Duolingo’s bots were supplementary to their app. And some aren’t even so complex. If 99% of your customers simply want to track their packages with your bot, then all it needs to do is be able to do is track packages. There’s no need to overcomplicate if your customers don’t want anything overly complicated.

Chatbot for conversion rate optimization

Chatbot personalization doesn’t end at messaging apps. Some businesses have, instead of using live chat, incorporated chatbots onto their web pages to help with conversion optimization.

Take this Chatbot from MongoDB, which answers prospects questions and, based on their responses, qualifies them for a sales call:

If they’re qualified, a calendar integration lets visitors choose the time they’re available for a demo, and they’re routed to an available salesperson:

Take another example from Richard McGrath, Founder of All Chat Solutions, who set up an onboarding sequence with a chatbot. It included six questions they needed to answer before they got into using the bot. He says:

So far, 473 people have started the onboarding sequence. Out of 473, 328 (69.6%) have answered the first question:

Then out of those 328, 267 have answered all six questions from the chatbot:

An example like this shows the capability of a chatbot to get valuable information from your visitors. Compare this to a 6-field lead capture form, which almost certainly wouldn’t have the same response rate.

Others have seen similar success from chatbots already, like Jay Baer, who started using them to engage his audience and distribute content. He found that, compared to email, customers were 10X more likely to open the content and 5X more likely to engage with it.

When martech company, Segment, incorporated chatbots into their lead generation and qualification strategies, they saw benefits almost immediately. Just weeks later, the chatbots became the number one source of leads for Segment, and year to date became the biggest growth factor for the company. Regarding ROI, Segment has seen a 5x increase in engagements and 2x boost in conversions.

Where are chatbots now?

Following a study on chatbots from Pegasystems, the company’s spokesperson, Russell Dougan, stated that the “top complaints about chatbots include not enough smarts to effectively answer questions (27 percent), lack of context in the conversation (24 percent), [and] robot-like engagement with few human qualities (14 percent).” Overall, while the future looks bright for chatbots, it seems they’re still only getting started.

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