The struggle to find the best deals on energy, insurance, and other essential services is real. But what if there was a better way?
In this exclusive interview, we dive into how AI-powered comparison services are revolutionizing the way we choose services, using data and machine learning to make smarter and personalized recommendations.
Today we’re surfing with Daniele Bianchi, Managing Director at Supermoney SpA.
Comparison services, like Supermoney, start by analyzing the primary consumption-based services, such as electricity and gas. The first step in the comparison process is understanding a user's consumption habits. By determining how much energy a user typically consumes, his/her consumption habits and location, we can identify the most relevant and cost-effective tariffs available. For users looking to see how much they might save with another provider, this basic comparison is essential.
Data analytics is central to Supermoney’s strategy, serving as the foundation of both our comparison services and overall operations. On one hand, it plays a critical role in enabling accurate and personalized comparisons for users, such as identifying the best energy tariffs based on their consumption patterns and location. On the other hand, data analytics drives innovation across processes like customer acquisition, engagement, and retention.
Supermoney analyzes every interaction—calls, inquiries, and customer journeys. This comprehensive data helps us better understand our customers’ needs, behaviors, and preferences. For example, we can analyze variations in pricing to recommend better offers or highlight potential savings that customers might otherwise overlook.
A common challenge we address is raising awareness about energy costs. Most people know their total bill but not the specific costs, such as per-kilowatt rates or fixed monthly fees. By leveraging data, we empower users to make informed decisions, often revealing savings far greater than they expected—turning what might seem like a minor inconvenience into meaningful financial benefits over time.
AI and machine learning can revolutionize the customer experience in the comparison platform industry by enabling advanced personalization, which is at the core of what we do. Each customer has unique consumption patterns—even two neighbors with similar energy usage might consume at different times or follow distinct habits. AI allows us to analyze and adapt to these individual behaviors in ways that traditional methods cannot.
For example, machine learning can dynamically analyze past bills, predict future consumption, and provide tailored recommendations based on changing market conditions. This capability is crucial for creating accurate, real-time comparisons that account for fluctuations in tariffs, helping customers make smarter decisions over time.
Furthermore, AI can monitor wholesale market trends and alert customers when a shift—such as a drop in variable rates—makes switching plans more advantageous. Handling this level of complexity manually for millions of customers would be impossible, but AI enables us to scale these insights efficiently.
Additionally, machine learning can segment customers based on their behaviors and needs, delivering personalized offers and uncovering opportunities that might otherwise go unnoticed. AI-powered bots also enhance engagement by automating customer interactions and providing instant, data-driven assistance, ensuring a seamless and highly customized user experience.
Last but not least, AI-driven customer agents can support both consumers and phone agents during the whole customer journey having a significant impact on conversion rate of leads and resources’ productivity.
Technology, particularly AI and automation, is a game-changer for businesses, especially smaller organizations, in achieving growth and enhancing customer engagement. For companies like ours, with a lean team managing the scale of operations we handle, would be virtually impossible without automation.
AI and automation allow us to scale efficiently without the need for significant upfront investments in staff. By leveraging machine learning and automated processes, we can handle high volumes of work, enabling us to focus on testing and implementing new ideas with minimal cost and risk, and if a strategy proves successful, we can scale it seamlessly limiting operational disruptions.
AI also empowers us to remain agile, experiment with innovative models, and quickly adapt to changing market demands. This flexibility and efficiency are critical for sustained growth and creating meaningful, scalable customer engagement.
One of the biggest challenges companies like Supermoney face in integrating technology is dealing with legacy systems. This is a common issue for nearly all businesses, regardless of size. Legacy systems often perform critical functions, but integrating them with modern technologies can be complex and time-consuming, requiring careful planning to ensure compatibility and seamless operation.
Another major challenge is managing setup costs. Smaller companies need to be highly strategic with investments in technology. This makes cost efficiency and scalability key considerations in our approach.
Lastly, there’s the challenge of building the right skills within the team. Adopting new technologies often requires specialized expertise, which may not be readily available within the organization. Bridging this knowledge gap is essential for successful implementation and long-term growth.
In my opinion, one of the most impactful emerging trends in the coming years will undoubtedly be AI, as many already predict. However, the real question lies in whether the global infrastructure can keep pace with the rapidly increasing demand driven by AI and related technologies.
AI, especially generative AI and virtual assistants, has significantly democratized access to powerful tools that were previously out of reach for most organizations. This has led to an exponential growth in demand for cloud computing and virtual storage. As more devices become interconnected and reliant on these services, the strain on data centers and the underlying raw materials required to build and maintain them will only increase.
A critical challenge will be whether major players in the market can sustain this demand without hitting resource limitations, and how this can impact in the long term the cost of service offered by public cloud providers.
This scenario prompts us to consider sustainability and efficiency as essential components of technological development. Emerging technologies that optimize resource use may become just as impactful as AI itself in shaping the future.
My advice is to focus on three key principles: flexibility, strategic prioritization, and partnership.
First, design flexible architectures. With the increasing specialization of providers and solutions, companies will inevitably need to integrate multiple technologies. Building systems that are adaptable and able to interact seamlessly with diverse solutions is crucial for staying innovative while maintaining control over data security. Flexibility is also key to continuously optimize processes and reduce time to market.
Second, choose your battles wisely. It’s impossible to oversee every aspect of your tech stack directly, but it’s equally risky to relinquish control entirely. Leaders need to identify the areas they want to manage in-house and those they’re willing to delegate to trusted third parties. This requires careful decision-making about which parts of the IT landscape represent a key success factor and thus requires to be managed in-house.
Finally, treat technological decisions as strategic decisions. Gone are the days when choosing a data center or software solution was solely the CTO’s responsibility, based on cost and service level. Today, these decisions have company-wide implications and should align with overall business goals. Every technology decision should be approached as an opportunity for business development and long-term growth, emphasizing collaboration with providers who share your values and vision.
By keeping these principles in mind, companies can foster innovation while ensuring that data privacy and security remain uncompromised.
Daniele Bianchi is Managing Director at Supermoney SpA. He has a strong background in both management consulting and C-level leadership, with 11 years at top firms like McKinsey & Company and 8 years driving performance transformations at major Italian companies like Nexi, RCS, and GEDI. Daniele’s expertise lies in digital, strategy, business development, and M&A, exemplified by their leadership in multi-billion euro deals like the sale of ICBPI and the takeover of RCS. As CEO of GEDI Digital and IT, he spearheaded significant growth, achieving 2.5x revenue, 5x EBITDA, and 4x subscriber increases. Currently, Daniele serves as General Manager of Supermoney SpA, a leading utilities price comparison company.