How Consumer Engagement Technology Helps Brands Connect with Their Audience

Published on May 30, 2023

Research shows that 89% of consumers are willing to leave a brand over one bad experience. Consumer engagement technology helps brands interact with their audience, providing them with a more personalized experience. Eventually, providing the ability and means to track customer behavior, create targeted campaigns, and analyze information.

By utilizing these tools, brands can create a more meaningful connection with their customers, increasing loyalty and sales.

What is Consumer Engagement Technology?

Consumer engagement technology is a set of tools and strategies that enable brands to interact with their customers more meaningfully. Leveraging real-time analytics, contextual updates, and personalized journeys, brands can create meaningful connections with their customers, increasing loyalty and sales.

At Mantra Labs, our conversational AI-based chatbot – Hitee, not only adjusts its responses based on the sentiment shown by a particular user, it remembers previous conversations and responds accordingly. With over 32 languages supported, it ensures that the customer is at ease during their interaction with the brand.

An effective customer engagement tool would have the following qualities:

Enhances customer experience through automated marketing tools
Seamlessly integrates with existing customer databases
Offers personalized experiences through predictive analytics

Benefits of Consumer Engagement Technology

Increased Brand Awareness

Increasing competition makes it difficult for brands to differentiate themselves and create a unique presence in consumers’ minds. As a result, consumer stickiness becomes a challenge for brands. Tools to improve customer engagement through brand awareness help create a credible, trustworthy, and positive brand association.

Brand awareness helps increase the number of users at the top of the sales funnel and boosts long-term customer relationships.

With mobile marketing gaining immense traction, engagement through lock screens and push notifications are vital tools that meet marketing objectives.

Inmobi’s Glance leverages advertising through their Smart Lock Screen offering, which allows brands to communicate with consumers directly on their mobile lock screens, eliminating the probability of users selecting an app on their mobile phone and that being the app where the brand chooses to advertise.

Consumer-first Brands such as Swiggy, Zomato and Cult.Fit leverage push notifications to maintain their daily touchpoints with users. Well known for its humorous copywriting, Zomato reaches out to consumers 2-3 times a day, based on user preferences and previous purchases, to make recommendations and jog the user’s memory towards ordering food.

Improved Customer Loyalty

Customer loyalty is critical in driving repeat purchases and long-term customer relationships. Customer engagement technology helps brands build loyalty by providing personalized experiences to customers.

Tools such as loyalty programs, rewards, and gamification help brands create a sense of belonging and reward customers for their loyalty. These tools also help brands understand customer preferences and behavior, allowing them to make more personalized experiences.

In India, Amazon recently partnered with ICICI Bank to create co-branded credit cards to provide rewards points to their customers. Further, Mantra Labs worked with Myntra to help create a gamification experience during their End of Reason sale boosting user engagement rates.

Improved Customer Insights

At its core, customer engagement technology helps unify customer data and draw better insights. Data insights from multiple channels and digital touchpoints are pulled together to help form a comprehensive understanding of how your user interacts with your brand. It assists every function of your enterprise in creating better-targeted campaigns.

Customer insights are critical in planning business objectives, understanding the pains and motivations of ideal customer personas, analyzing the impact of marketing campaigns, and more.

Tools such as HotJar and Microsoft Clarity help digital teams understand how a customer interacts or progresses across the customer journey on their website. This allows teams to tweak content, improve UI/UX, and run enhanced campaigns to ensure an optimized user experience.

How to Leverage Consumer Engagement Technology

Brands can leverage Consumer engagement technology in various ways to help brands connect with their audience. They can use automated marketing campaigns to send personalized customer messages based on their interests and preferences. Platforms such as CleverTap and ZenDesk help marketers design and run targeted campaigns.

Rewards and incentives are often used to increase customer loyalty and engagement. Using data and analytics to gain insights into customer behavior and preferences allows brands to tailor their marketing strategies. We recently created an advanced analytics dashboard for our insurance clients to help their sales & marketing teams receive and analyze customer data from multiple sources in one place.

Ultimately, brands can create a more personalized and engaging customer experience by leveraging consumer engagement technology.

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

How Consumer Engagement Technology Helps Brands Connect with Their Audience

What is Consumer Engagement Technology?