‘Know your customers inside out’ is a paradigm emblematic of customer-centric & customer-focused business operations today. As a significant component offering valuable customer insights, machine learning powered customer journey empowers organizations to address pain points, uncover opportunities and create enriching customer experiences.

But no two customer journeys are look-alikes today. Reaping insights from customer journey means overcoming hurdles caused by disjointed data sources, touch points and channels by using heterogenous data, wading through complex journeys and infusing machine learning power into the journey.  Whether it is a complex or a unique customer journey, machine learning algorithms can take cue from signals and customer actions to guide businesses in promoting great customer experiences.

There are 3 ways to revolutionize customer journey with machine learning.

Personalization connecting customer persona and customer journey

Personalization starts with the two-fold exercise here. One is creating customer personas at a micro-level. Behaviors, needs and goals, motivations, demography, habits, quotes, channels, personality, pain points, profile are critical elements leveraged for creating micro-level customer personas. The other is mapping out the created customer personas’ journey. This helps understand what customers want to accomplish, what channels and touchpoints they are using, and the pain points they are facing along this journey.

Promoting personalization leveraging micro-level customer personas necessitates processing of heterogeneous data from various channels and sources that help nail down the context through emotion, attitude, buying habits, need, location, sentiment among others.

Clustering algorithms come good in finding patterns in customer data and connecting a specific behavior to the right customer persona in real time. Machine learning models can be leveraged at every touchpoint to generate the next best action aimed at creating personalized customer experience. Every touchpoint in this customer journey offers opportunities for promoting personalization. Customer actions and behaviors in the past and the context can be leveraged in terms of promoting personalized customer experiences through messages, promotions and recommendations.

Predicting customer intent models along customer journey

Every point along the customer journey offers signals to take proactive action. Like the pages visited by customers to their thoughts shared on social platforms, customer signals are there to be tapped along this customer journey. With signals, modelling predictive intents in terms of predicting what the customer or the prospect wants even before the customer realizes it helps roll out targeted marketing, sell more and promote enriching customer experiences.

And what forms would custom intents take?

Sample this one. Mark is pursuing his graduation in Information technology. As the journey begins from his mobile, Mark lands on YouTube to seek knowledge on the new technology subject that matters to him to secure graduation – hunting for good video training materials. Mark also uses various online sources to seek valuable information on the new technology subject.

There is implicit intent hidden in his actions – gained from the total of behavioral instances pertaining to Mark. Predicting customer intent in this case can be beneficial for an institution offering training on this new technology that Mark wants to learn, in terms of unlocking a potential customer by promoting the training course. Big data, natural language processing and machine learning algorithms make up for a good combination to unearth customer intent signals.

Reducing customer churn & improving customer acquisition

Let’s say a customer hasn’t interacted with a company for some time after having had a smooth tie with the company in terms of interactions and transactions.

Was that a customer complaint gone wrong or was the customer not happy with the pricing?

Customer journey analytics, driven by machine learning, that takes data all along the journey, from touchpoints to channels to interactions and transactions, can help take measures to unearth and reduce friction, understand factors leading to churn and roll out fitting customer experience initiatives to reduce customer churn

A Telecom company wants to attract new customers. Customer journey infused with machine learning offers insight into the most telling behaviors unearthed from reams of interactions, guide organizations to use effective acquisition channels, leverage micro segmentation and better targeting to improve customer acquisition rates.

Tracing customer footsteps and using 3 ways to revolutionize customer journey with machine learning, organizations are embedding machine models in customer journeys to offer personalized experiences and enhance customer trust.