From Personalized To Predictive: How To Harness The Next Big Boom In Experience
Founder of global innovation firm: why the shift from marketers to machines is smart for the entire customer lifecycle
Peter Sena II, Founder of Global Innovation & Design Firm Digital Surgeons, shares why leveraging machines for customer experience is smarter than we think.
Marketers have been trying to master personalizationsince the moment the gesture driven interfaces and small screens of mobile devices rendered desktop customizations useless.
Who’s my audience? How do I personalize the creative? How do I personalize the offer? How do I figure out how to engage consumers in a more meaningful way? What can I show that’s going to drive purchase?
Amazon wrote the book on personalization, using the comprehensive shopping data they have about each consumer to personalize everything from their homepage to the offers they receive—all with the goal to drive purchase behavior and increase loyalty.
But just as personalization made customization look trite, smart(er) AI powered by machine learning and natural language processing (NLP) engines is poised to replace the personalized with the predicted in our user experience.
Empowered by machine learning and natural language processing we can distill the unprecedented amount of consumer behavioral data now available to form predictive analytics that can begin to make sense of today’s fragmented consumer journey. When paired with advances in automation technology, these predictive analytics allow us to deliver tailored consumer experiences in real-time that put the personalized to shame.
The Devil In In The Defaults
People overwhelming use default settings. Sure, there’s always a small percentage of power users, but most people take what is given to them. With that in mind, it’s not about customization or even merely personalization, it’s about the ability to have a predictive layer in your application that leads to perfect personalization.
If we understand where and how our users are interacting with our experiences, we can make our messages, and how they are delivered, that much more relevant to their environment— after all the medium is the message.
Understanding a user’s context also has interesting implications for supply and demand, and improved pricing. As Economics 101 tells us, the fairest price occurs at the point where consumer demand matches product supply — supply increases, prices go down; buyers demand more, the price goes up.
Demand pricing informed by digital inputs has been around forever, just think of the countless hotel and airline booking sites that price offers in real time based on market supply and demand.
Or Uber’s surge pricing that raises the price of rides to match driver supply to rider demand. When there are more riders than drivers, increasing the price puts more drivers on the road to reduce customer wait times and improve its customer service.
Uber’s head of economic research recently disclosed that riders are more likely to pay for surge pricing if their battery is low. Uber claims that they don’t use this information to gauge riders with low battery life, but it is an interesting glimpse into the ability of our digital products, informed by digital inputs, to understand the context from which consumers are making each purchase and how we can price offers based on predicted demand.
While context is important, a message is truly predictive when it’s personalized based on intent — this allows you to not only understand what, when, where, and how, but why? To determine intent, strengthen your inputs and data warehouse by supplementing click-stream digital analytics with machine learning and natural language processing.
The step from the personalized to predictive occurs when your user data is processed through a machine-learning library like Google’s TensorFlow to determine trends, or a natural language processing platform like IBM’s Watson that can actually generate consumer profiles.
If I’m a clothing store, I may warn a customer it’s going to rain, or simply tailor my sales messaging to emphasize they are missing their last chance to buy a pair of pants on sale that they keep browsing past.
It is important to note that predictive tech can help eliminate the paralyzing paradox of choice that can occur now that the world of decision sits at our fingertips.
Predictive technology is even making a big splash in the B2B software industry, Salesforce’s CEO Marc Benioff spoke at the Forbes CIO summit in March and shared his belief that software that can analyze data and recommend the best course of action is the next wave of opportunity in his industry.
This isn’t a hollow proclamation, Salesforce has walked the walk and acquired a number of machine learning and data startups in the last several years, including PredictionIO, MinHash, and Tempo AI. Just two year ago, it spent nearly $400 millions to buy RelateIQ, an intelligent email client, calendar, and work dashboard that can automatically determine which salesperson has the best relationship with a client or predict when you need to start filing an upcoming project.
Less Marketer, More Cyborg
Automated predictive technology can and will enable us to provide the ideal consumer experience at each touchpoint.
A well-oiled bot built with confidence matching, neural networks, and deep learning will soon be able to predict and deliver better consumer experiences. And when the bot isn’t as well-oiled as you hoped, a human customer service rep that takes over will be starting informed by deep insights about the consumer’s pain points and how they have made it through the journey thus far.
The purpose of data isn’t to collect it and assign it buzzwords, it should be used to solve real consumer problems, sometimes before they even happen.
This week I was at Starbucks trying to get a coffee and I couldn’t log into the app. How personalized the in-app experience was no longer mattered, if anything it just made me that much more upset that my rewards were out of reach.
I let Starbucks know with 140 characters of fury.
Okay admittedly I was polite, but that didn’t help matters because I received no response. I’m annoyed now and this interaction is going to leave a bad taste in my mouth (despite the delicious iced coffee).
In the grand scheme of things, this is isn’t a huge deal, and it hasn’t kept me from drinking Starbucks everyday. But I’ve got a decent sized Twitter following, and if a certain percentage of them see the tweet, and that Starbucks didn’t respond, it is not hubris on my part to think I could affect purchase behavior.
The experience economy has arrived and the hundreds, thousands, or in Starbucks case, millions of these touchpoints that regularly occur don’t shape your brand, they are your brand. Brands now stand in the center of the colosseum, subject to the will and favor of the angry connected mob. It’s impossible for any human marketer armed with social listening to stand their ground alone, but with the help of machines, they’ve got a chance.
Starbucks could have improved that touchpoint with smart error reporting that notified their technology team that my username had received an error message. Then, it’s just a matter of automating an apology.
At the end of the day, it’s all about customer lifetime value. It’s about improving the relationships and messages that we create, about giving people what they want. And when consumers have complaints, concerns or issues, it’s about addressing and appeasing them with a gift.
The idea of marketing customer service “cyborgs” fascinates me because since it’s half-human/half-machine. Theoretically, its got all the efficiencies and effectiveness of a machine, but also all the empathy of the human condition.
What if there was a distinct hand-off pre-baked into the process? A consumer could begin chatting to a brand bot through Facebook messenger but once the bot—using natural language processing—determines that the consumer has a high lifetime value, a human actually picks up the dialogue.
This would allow us to scale the one-to-one customer service conversation that the consumer craves. A human experience, but all the grunt work is provided by a smart AI that understands how to analyze inputs to determine the outputs that will drive positive outcomes.
And of course, the brand with the most positive consumer experience wins. Amazon was first to market, but I’m willing to bet that Google Home will ultimately overtake Alexa for market share because we all use Google Search, GMail, Calendar, Apps, and maps. Home will have a better understanding of the end-user and the context to provide predictive—not personalized—experiences.
As for executing personalized messaging, virtual assistant Amy, from Dennis Mortensen and X.ai, has proven that the technology is there to create intelligent agents, not bots, that can complete tasks from end-to-end. Providing seamless service, Amy coordinates and schedules meetings without any oversight. Once the agent is CC’d on a relevant email, it takes care of the rest.
Operator is an iPhone app that gives users recommendations based on their personal taste. Users text Operator a request and the app connects them with a (actually human) expert that asks follow up questions. Not surprisingly, the app was co-founded by Robin Chan and Uber co-founder Garrett Camp, who understand the “uberification” of today’s consumers who crave “one tap” solutions to the problems that occur in micro-moments.
Looking for the perfect shirt for a special night out with your significant other? No problem, just message Operator and let their experts guide you every step of the way. This is intended to eliminate the choice paralysis that can occur when shopping online and faced with an infinite number of choices.
A hybrid Operator/Amy customer service cyborg would bring brands that much closer to “uber-fying” their customer experience and create the next level in marketing automation.
Fashion brands could connect their top customers with a personal shopping intelligent agent that knows exactly their taste in style, sports brands could connect athletes to AI coaches that understand their strengths and weaknesses on the field of play.
A machine could start, or finish the customer journey and reduce the number of steps required from both the consumer and their support representative. The more we can use data to inform and educate, the more we can create valuable customer service relationships.
Humans aren’t going to be replaced any time soon, but maybe it’s time we let machines do even more of the work.
Lead image via Pexels
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