The Data Tsunami & Rising Expectations: Why Is Personalization at The Core of Connected Customer Experiences?

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The future of customer experience development is packed with possibilities. Driven by big data, machine learning, and AI, customers will be able to interact with brands via increasingly conversational and intuitive experiences. And as we wrap up the decade, we can expect a world where hyper-personalization is the norm. Companies that excel at personalization, in fact, generate a whopping 40% more revenue than their average competitors.[1]

By tailoring products, services, and content to the unique needs, preferences, and behaviors of each customer, enterprises that win the CX race are in a position to foster greater loyalty and outpace competitors in revenue growth.

That much is clear.

On the flip side, the consequences of not personalizing experiences can be dire. According to analysts at McKinsey, 76% of consumers are disappointed when brands lack personalized interactions. And as the pandemic has shown, customers can be quick to switch their attention when enterprises don’t deliver – over 75% of customers changed their shopping behavior across 2020 and 2021, leaving many brands confused and racing to react.[2] For brands that are slow to adapt, this translates into missed revenue opportunities and reputation erosion.

Leading enterprises avoid these outcomes by tailoring their offerings and outreach to the right individual at the right moment with the right experiences. And most often, these experiences are built on a technological foundation that prioritizes data transparency, greater collaboration, AI integration, and process efficiency.

Traditional Surveys Can’t Keep Pace with Enterprise Decisioning

Surveys have long been a mainstay of marketing and product professionals looking to build data-driven GTM strategies. However, traditional surveys may not be as effective as you think. Over the last two decades, the response rates for surveys have declined from 36% in 1997 to a meager 2% in 2020.[3] Surveys are also a poor way to measure the customer experience for a number of reasons, including,

Delayed data

Traditional surveys are typically conducted after a transaction or interaction has occurred, which means that feedback can be delayed. Today, customers expect organizations to be highly responsive, and delayed surveys may not provide the insights needed to address CX issues at the pace of digital.

Limited insights

Surveys are often limited in the type of data they can capture. Today’s customers leave behind a wealth of data, such as browsing behavior, search queries, and social media interactions, which can provide insights into their needs and preferences. Traditional surveys may not capture this information, leading to inaccurate insights.

Biased results

Surveyors rely primarily on self-reported data, which may not accurately reflect customers’ experiences. Additionally, customers may be more likely to respond to surveys if they had an extremely positive or negative experience, skewing the interpretability of the data.

Survey fatigue

On the web, customers are often bombarded with feedback forms, leading to low response rates, unreliable data, and an inaccurate picture of CX.

To crack CX delivery, enterprises need to reimagine how they create and deliver value. This means pushing past the traditional enterprise model and integrating data sources that paint a more accurate, more dynamic picture of customer behavior.

How Does the Connected Enterprise Drive Real-Time Personalization?

AI and machine learning are undoubtedly essential tools when it comes to building real-time personalization for customers. By analyzing vast amounts of data in real time, these technologies can anticipate customer needs, provide tailored recommendations, and dynamically adjust content to create personalized experiences that foster customer loyalty. But with any AI solution, your results are only as good as your model and data sources.

Within connected enterprises, cognitive operations are the real game changer. By automating most, if not all, of their manual processes and leveraging AI to contextualize data sharing across different departments, ecosystem participants, and customers, these organizations develop immense collaboration and innovation potential.

At the same time, by connecting data feeds between every stakeholder, intelligent enterprises are able to build a dynamic view of their customer interactions on both macro and granular fronts. These value networks can capture a wealth of data that traditional surveys often miss, providing a more comprehensive understanding of customers’ behavior and preferences. In turn, this translates into real-time insights and a high degree of adaptability to customer needs.

A great example is how a global CPG leader deployed an AI-powered operations management platform that reduced their product trace time from 4 days to just 2 hours. This sort of near real-time, granular visibility into their distribution network also made it much easier to manage recall risk, forecast increasingly fragmented demand, and respond to customer queries at lightning speed.

Another essential component of creating a great customer experience is innovation. But great ideas rarely come from a workforce that’s tied up with data entry and spreadsheets. By automating repetitive manual tasks, connected enterprises are able to free up their talent for creative problem-solving and innovation, improving both the customer and employee experience. 

Scaling Great CX Involves a Powerful IT Foundation

CX is on the brink of a revolution. We now live in a world where businesses can anticipate needs before customers even realize them, offering personalized recommendations that are tailored precisely to your preferences. With the rise of smart speakers and voice assistants, personalization is quickly becoming more conversational and integrated into lifestyles. Augmented reality and virtual reality technologies already allow customers to visualize certain products in real-life settings and personalize their experience by changing colors, materials, and more. And with M2M and IoT communication integrated into population centers, personalized digital services will erupt on an unprecedented scale.

But much of the groundwork for these experiences starts today, with deploying the right infrastructure and foundational capabilities to ride, let alone lead, the digital CX growth wave of this decade. The shift from treating technology projects as point solutions to holistically integrating them into the way we do business is already underway. And enterprises that are still relevant in this future will likely be heavily invested in a backbone of AI-assisted operations and streamlined value networks that drive sophisticated product and service delivery and demand sensing in real time.

[1] https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying

[2] https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying

[3] https://bounceinsights.com/the-death-of-traditional-surveys/

Sateesh Seetharamiah
Sateesh Seetharamiah is a pioneer in the field of IoT, AI and Intelligent Automation, and a member of the MIT AutoID Lab - the taskforce that defined interoperability standards for IoT and use cases that eventually became household applications. Sateesh recently introduced the concept of process discovery, as a fundamental capability to enable machine learnability. He has extensive industry knowledge on the importance and impact AI, automation, and data analytics have in driving efficient and secure digital transformations for enterprises.

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