How Machine Learning Boosts Customer Experience

Improving the customer experience is the mantra for survival in today’s highly competitive business environment. More and more businesses have identified machine learning as a reliable tool towards this end.
Machine learning is in essence software coded differently to traditional software. Rather than a long list of if-then-else statements typical of traditional software, machine learning predicts what humans would do given a specific set of inputs.
Currently, marketers and others leverage machine learning to further customer experience through improved personalization, enhancing the computer vision, improving natural language, greater decision support, through analytics optimization, and augmented analytics.

1. Machine Learning Aids Personalisation

Today’s highly pampered customers prefer and even demand personalized engagement and experiences. Machine Learning facilitates it to the hilt. Data and analytics allow marketers to understand customer preferences. Using machine learning in combination with new data sources from the Internet of Things (IoT,) telematics, geolocation beacons, and social data improve the insights.
Several marketers now apply machine learning based algorithms to understand the nuances of their customer’s preferences and engage them on their terms. Marketers use such algorithms to develop highly relevant marketing campaigns, such as a matching audience profile with highly targeted video content. These steps improve the call-to-action.
Customers receive tailored offers rather than irrelevant non-contextual offers. Such non-contextualized offers have a very low probability of conversion.
Segmentation gets better. For instance, insurance companies do not have to go by general assumptions or time-honored conventions to offer the highest automobile insurance premiums to a 16-to-25-year-old male. They can factor in everything specifically related to the customer, and tailor the premium based on individual rather than class factors.
The creation of such relevant content is the godsend at a time when over 90% of online users in the U.S. and Europe feels advertising is more intrusive today compared to two years ago.

2. Machine Learning Facilitates Computer Vision

Machine Learning technology detects everything and anything, from objects and people to complex scenes within the images and videos. Applying the technology to enhance the quality of digital assets is a sure-shot way to win the customer’s heart.
One big success story is Twitter’s Magic Pony, which leverages machine learning technology to make pixelated images sharper, and enhances the quality of video captured on mobile phones in poor lighting conditions. Apart from delighting the customer, the spin-off benefit of Twitter is lower data usage, and by extension improved streaming abilities.

3. Machine Learning Aids Natural Language Processing

The next big thing revolutionizing human interactions with computers is speech recognition technology. The ability of computers to recognize human speech and act on it not only spares the hassles of keyboard typing but also unlocks a host of new possibilities. While speech recognition technology has been around for a while, the application of advanced machine learning technologies has made the system highly accurate, with error rates far lower than humans. Google’s Cloud Speech API now recognizes over 80 languages and variants, with a high level of accuracy.
Marketers can, and are leveraging advanced linguistic data and cognitive technologies spawned by speech recognition capabilities to create highly engaging content, targeted at the customer. In a sense, it furthers the cause of personalization in a big way.
Marketers benefit from natural language capabilities in myriad other ways also. A case in point is the intuitive new tool launched by Relative Insight, a UK based start-up. The tool converts natural language into data, offering marketers a wealth of information to connect with specific audiences instantly and deeply.

4. Machine Learning Improves Decision Support

Machine learning allows the marketers to predict the future. The “machine” becomes capable enough to predict the customer’s likely course of action, based on the data at his disposal, and his present behavior. The market is now flooded with several digital tools and services which provide advanced recommendations on this front.
On the anvil is “copyless paste,” where machine learning will save users time by proactively offering to share information between apps. Marketers will leverage the concept further to offer proactive product suggestions. Integration with other systems also offers the scope for proactive and automatic delivery.

5. Machine Learning Facilitates Analytical Optimization

Businesses leverage the immense analytics opportunities offered by machine data to fine-tune their operations, deliver new business models, and offer new products and services in tune with customer demand. The insights gained, predict not just how a customer may behave or act, but also how the competition may move in the future.
One sector where machine learning algorithms are already in widespread use is the financial sector. Financial services companies use various machine learning algorithms such as random forest and gradient boosted models for a host of applications, from predicting the probability of being ranked at the top of aggregator portals to predict midterm cancellation rates on policies, and more. These applications have a direct bearing on customer satisfaction. For example, banks and financial institutions predict volumes for credit card lines, to adjust rates and terms, and thereby attract the right type and volume of customers for the specific product.

6. Machine Learning Facilitates Augmented Analytics

The scope of machine learning improves with the development of technology. Neural networks support better classification and forecasting, decision trees support more complex rule and relationship-based customer experience programs. All these improve the organization’s ability to support complex decisions, forecasts, and optimizations.
Augmented analytics, which co-opts these latest and emerging technologies, combines various elements of the ecosystem, such as data preparation, business intelligence, predictive analytics and machine learning capabilities into a single, automatic and seamless process. Enterprises would be able to cleanse their data easily, to uncover latent insights and patterns.
Today’s huge data create millions of variable combinations impossible to process manually or even with traditional tools. Augmented analytics, powered by machine learning, deliver quicker insights, reducing customer frustration.
What exists now is just the tip of the iceberg. The future holds a world of possibilities. A case in point is the fragmented nature of the machine learning ecosystem being all set for a big churn. Increased competition, the hyper-fast paced changes in technology, and the proliferation of big data at an alarming frequency force many open source machine learning libraries, algorithms and frameworks to join forces and deliver a better deal to their customers. The lower-level personalization commonplace today will make way for a more robust collaborative filtering, delivering a much higher degree of personalization and contextualization than present levels.
Side-by-side, the machine learning ecosystem is becoming increasingly easier to use, and more affordable. Hitherto, only enterprises with large analytics teams could really afford to play around with machine learning. The advent of various solutions delivered in a cloud-based subscription model makes the power of machine learning available to the masses, including start-ups, freelancers, and even individuals.
Marketers and brands can leverage the improved ecosystem to generate a better picture of their customers’ true context, and serve them better. Simply put, customers will get better food, movie, music, travel, product and purchase recommendations.

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About the Author

Vinod Saratchandran

Vinod has conceptualized and delivered niche mobility products that cater to various domains including logistics, media & non-profits. He leads, mentors & coaches a team of Project Coordinators & Analysts at Fingent.