Yet, many enterprises are struggling to deliver on this promise, falling victim to what experts call the “customer journey black hole.”  

This phenomenon occurs when customer data, context, or information are lost between interactions, creating fragmented and frustrating experiences that can cost brands both loyalty and revenue. 

Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, describes the black hole as the exact opposite of what brands intend to deliver.  

“Your data – whether it’s for an inquiry, a product question, or a complaint – is going in one channel and then getting lost. That’s the black hole side of it,” he explains.  

And the impact of this journey black hole is severe. It’s not just that the customer is unhappy. Your actual business numbers are getting impacted. 

Why It Happens: Silos, Channels, and Technology Gaps 

The consequences of a fragmented customer journey are stark. A study by PwC found that 92% of customers would abandon a brand after two or more negative interactions. 

In other words, every dropped call, unresolved query, or repeated customer statement represents a potential lost sale, diminished brand trust, and decreased customer lifetime value. 

The causes of the black hole are multifaceted. Enterprises often operate in silos, with marketing, product, billing, and support teams holding pieces of the customer puzzle in separate systems that do not communicate with each other.  

Misaligned channels exacerbate the problem, as customer preferences differ by demographic, time of the day, their stage in the journey etc. For example a younger consumer may prefer messaging apps, while older generations often prefer phone calls. 

Technology gaps, such as disconnected AI solutions and multiple vendor systems, along with operational inefficiencies, further worsen the situation.  

And in today’s world, if the hand-off from AI to humans loses context and customers have to repeat themselves, then this black hole can be pretty deep.  

The Cost of Ignoring the Black Hole 

The impact extends beyond customer experience metrics. Anand points out that “closing these gaps is just not about improving customer experience. It’s about protecting the brand value and the value of the businesses that these brands are in.”  

Fragmented customer interactions can cost enterprises significantly in lost sales, operational inefficiencies, and increased churn, underscoring why addressing the black hole is not just a CX priority, but a business imperative. 

When customer data is lost or mismanaged, brands risk reduced sales, operational inefficiency, and reputational damage, as negative experiences can spread quickly in the age of social media. 

Compliance and privacy requirements can also unintentionally introduce friction, making it more challenging to provide smooth, personalized interactions. Ignoring the black hole may result in a slow erosion of customer trust, which competitors can capitalize on. 

Closing the Gap: Unified CX and AI Solutions 

Addressing the black hole requires a holistic approach.  

Enterprises need to unify their data and orchestrate interactions across all touchpoints to streamline operations. This includes ensuring that every channel communicates with the others, maintaining context between interactions, and empowering human agents with the right information at the right time.  

AI can help, but only if fed with contextual & comprehensive data and deployed thoughtfully, maintaining continuity between automated and human-led experiences. 

The payoff is significant. Brands that achieve seamless, data-driven experiences can reduce churn, increase engagement, and extract actionable insights from end-to-end journey analytics. In a competitive marketplace, providing frictionless experiences is no longer optional – it’s a differentiator.  

As Anand warns, “It sounds simple, but the customer has high expectations. And if their expectations aren’t met, they will leave. The black hole is real, and brands cannot afford to ignore it.” 

As more enterprises confront the customer journey black hole, many are turning to unified CX strategies that bring together communications, data, and AI.  

Companies like Tata Communications are helping organizations reimagine this space – integrating CPaaS, CCaaS and CXP technologies to create more connected, context-rich interactions.  

It’s a shift from managing channels to orchestrating journeys – a move that could define the next era of customer experience. 

To explore how your organization can overcome the customer journey black hole and create seamless, unified experiences, contact Tata Communications to learn more about their integrated CX platform capabilities.

Why Traditional Customer Experience KPIs Metrics Aren’t Enough 

Many organisations are still leaning on traditional metrics like Net Promoter Score (NPS), Customer Satisfaction (CSAT) or First Contact Resolution (FCR) as their primary customer experience KPIs. But in an era of real-time and predictive operations, these indicators increasingly miss the moment when you could intervene and influence. Instead of measuring solely speed and volume, you should be tracking trust, outcomes and lifetime value. That means you need a CX measurement framework not simply anchored in “how did we do” but in “what’s going to happen next”. 

What Predictive CX Looks Like 

Predictive customer experience means using real-time insights, AI-driven modelling, customer journey analytics and automation triggers to anticipate needs, identify friction before it surfaces, and act in the moment.  

“Organisations that forecast needs, reduce effort proactively and engage before a customer raises a ticket gain a significant advantage over their competitors.”

So, what benchmarks define ‘real-time readiness’? Below are three categories every CX leader should be keeping their eye on: 

Friction-avoidance

Average Customer Effort Score (CES) trending lower (i.e., less effort) compared to peers, and a high ratio of issues resolved before they require a customer-initiated interaction. For automation-enabled contact centres, fewer inbound contacts per thousand customers in predictive models signals maturity. 

Predictive action adoption 

Percentage of customer interactions where CX AI tools triggered a next-best-action or intervention, versus purely reactive responses. For example, what proportion of service cases were flagged by predictive routing or agent assist automation?  

Business-outcome orientation 

Conversion, retention or revenue increases tied to CX automation and predictive interventions. For instance, tracking how many customers avoid churn due to predictive outreach, or how many upsell opportunities were initiated by predictive insight rather than campaign alone. The shift from “ticket closed” to “customer retained or expanded” marks genuine progress. 

Assessing Your Position 

Start by mapping your current set of customer experience metrics and overlaying them with these questions: 

• What share of your issues are discovered proactively versus via customer complaint? 

• How much of your customer journey analytics feed into automated triggers or next-best-action flows? 

• When you implement AI in customer experience, how do you measure its actual impact on retention, lifetime value or cost-to-serve? 

Then, benchmark against industry indicators. Top performers usually maintain CSAT in the high-70s, CES (on a 1-5 scale) around 1.5, and above industry retention rates significantly better than average. Use those numbers as directional, then identify your gaps. The impact isn’t abstract – companies leading in CX grow revenue 41% faster than peers. 

How to Scale Your Predictive CX AI

Align data and architecture: Without a unified data backbone and real-time access, predictive CX won’t scale. Many organisations are now re-architecting with vendor-neutral layers to power AI-driven experiences.  

Define measurement frameworks tied to outcomes: Shift from “calls answered in 30 seconds” to “calls prevented, or revenue preserved via predictive intervention.” 

Pilot and scale automation and AI use cases: Identify lean use-cases (for example, churn-risk triggers or next-best-action in onboarding). Track the ratio of automated/predictive interactions versus baseline. 

Iterate on the journey analytics: Deploy customer journey analytics to detect drop-off points, then layer in predictive signals to intervene. 

Monitor, optimise and embed learning loops: Predictive models degrade over time if not retrained. Embed feedback loops so your automation and AI refine themselves. This transforms CX automation from a cost-cutting exercise into a catalyst for growth. 

Turning Insight into Competitive Advantage 

With customer expectations sky-high, the brands that are winning are those operating with foresight, not hindsight. Leveraging CX AI and establishing clear benchmarks allows you to move from reactive service to proactive experience design.  

Ready to move from reacting to customer issues to predicting them? Read our Ultimate Guide to Automation & AI in CX