Business
How Dynamic Pricing Impacts Product Valuations & Brand Reputation
Source: Finance Derivative
Author: Gediminas Rickevičius, VP of Global Partnerships at Oxylabs
Dynamic pricing, in one form or the other, has been around ever since people have been exchanging things among themselves. After all, in this most basic setting, the price is negotiated between the seller and the buyer and may thus vary depending on circumstances.
Negotiations between buyer and seller are also the defining factor of the market value of any product or service, albeit on a grander scale. Ultimately, the price of the product is constituted as the middle ground where consumers are willing to buy and the supplier is willing to sell.
On the one hand, dynamic pricing is clearly based on the idea that a product’s or service’s market value is not set in stone. On the other hand, with the surge of dynamic pricing strategies, we now witness a more interesting phenomenon – valuations changing due to the unstable character of pricing.
Scarcity and strategic buying
Special offers are the most familiar (slow) form of dynamic pricing that we, as customers, constantly encounter. Another way in which dynamic pricing has made its mark on the markets is as the staple of the travel and hospitality industries, where factors such as dates might change the value of a service.
Buyers accept them both as understandable price fluctuations over time due to the changing scarcity of certain products. However, they are different with respect to the possible effects on valuations.
Studies of online markets also recognize strategic buying as one of the aspects influencing buying decisions in dynamic pricing environments. When buyers are strategic and recognize the dynamic aspect of prices, it is likely that the perceived scarcity of the commodity will have a great effect on valuation.
These phenomena ground the difference between such industries as the restaurant industry on the one side and the travel and hospitality industries on the other. An opportunity to eat a meal that you like at a restaurant is usually not perceived as scarce. While seats on a plane or rooms at a hotel, when tied to particular dates, are finite.
Web scraping remains the main method of figuring out what sort of pricing strategies are likely to bring the best results. Naturally, companies first and foremost utilize web scraping to watch the prices of the competitors to see whether they have some room to outprice them.
However, web scraping might be even more effective when used to monitor the stock of other online stores. Nowadays, companies tend to freely display the availability of each product on their websites. They do it to avoid situations when only after ordering does a customer realize that the product is out of stock.
Scraping these sites to find out if the competitors are running out of stocks allows them to identify the approaching scarcity of particular commodities. Thus, prices can be adjusted to predict the rising valuation of these goods. And as scraping provides information almost as soon as it is reported online, it will also let the retailers immediately shift back to the lower prices when stocks get refilled.
In turn, such a process induces an overall advantage for the consumer. Outside of monopolies and cartel agreements, dynamic pricing creates near-equilibrium in product value across many different companies, making the competition much more fierce.
A dynamic pricing feedback loop
The rapid spread of information, which is one of the defining features of our times, is also one of the main factors affecting valuation in dynamic pricing settings. While price matching is the most common way to implement dynamicity, rising interests and trends might also play a role.
For example, artificial price changes can be used to boost customer awareness of the product or service. Lower prices for limited periods of time might help the provider to reach the high-valuation buyers it would have not with fixed higher prices. Such a step away from automated pricing algorithms might produce increased attention for the company.
On the other hand, if increased interest becomes apparent, dynamic pricing should be undertaken with care as it can lead to unintended consequences. This happens, for example, when customers realize that someone else paid significantly less for the same product.
Disgruntled customers may ask for businesses to cover the differences, leave bad reviews, or, worse, start shopping elsewhere. As a result, dynamic pricing can lead to a negative feedback loop where changing prices might cause effects that end up costing the company more than the additional profit made.
Since low prices were the main selling point, customers felt cheated when seeing the updated pricing, leading to decreased sales. The speed of the information flow first formed certain expectations, then led to their disappointment on account of the pricing algorithm reacting to changing market value too fast. Yet, negative perceptions often last longer than pricing changes, which can negatively affect the brand’s reputation.
Conclusion
Dynamic pricing affects significantly more than the prices of products and services. As all of them have underlying value, which buyers intuitively grasp, dynamic pricing has the potential to affect it. If price flexibility is perceived as understandable or even beneficial from the buyer’s side, it will raise the market value of products and services.
However, the perceived value will drop rapidly if the buyers feel that they are being taken for a ride. Which will be the case when in the vast volume of information available to them they will not find sufficient reasoning for specific price changes. As a result, improperly implemented dynamic pricing can negatively affect brand reputation.
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Business
How to maintain efficiency without sacrificing authenticity with GenAI
Jason Morris, SEO Marketing Manager, Brew Digital
Content creation has been fundamentally and forever changed by GenAI tools. They offer a fast and efficient way of building content, in a matter of seconds, and a majority of businesses have been deploying it in some capacity for the last two years.
In particular, tools like ChatGPT, Copy.ai and Jasper have transformed copywriting, allowing businesses to quickly turn around high-quality content, social media content, blog posts and marketing copy, in a matter of minutes. It has also revolutionised our approach to SEO, helping with everything from optimising keywords to generating content that meets search engine standards.
However, the same capability that makes AI attractive – its speed in generating content – can result in copy that feel generic and impersonal, risking the intuition and creativity that are essential for compelling brand messaging. This is because AI, by nature, operates on probabilistic models that predict the most likely next word or phrase based on patterns in the data it has been trained on.
Large language models (LLMs), are excellent at identifying and replicating patterns, but lack the creativity and intuition that come from human experience. While this can be useful for producing factual or structured content, it often results in formulaic writing that lacks originality and depth. In areas like storytelling or thought leadership, where original insights are key, AI struggles to deliver the same level of nuance and emotional connection that human writers can achieve.
Inauthentic writing does not just risk disengaging brand audiences, either, and many search algorithms are now designed to detect web pages with AI-generated content and diminish its domain authority. This will inevitably lead to diminished visibility and a drop in organic traffic, and is exemplified by Google removing a staggering 40% of indexed web pages for failing to meet its E-E-A-T (Expertise, Experience, Authoritativeness, Trustworthiness) standards. This is a trend we can expect to see continue as GenAI created content becomes even more prevalent.
Thus, the challenge for brands in the age of AI is to harness the power of efficiency without losing the unique voice that sets them apart.
‘Bans’ are counter-intuitive
The first step in striking the balance between efficiency and authenticity is to establish blueprints for how AI can and should be used within a business setting. For companies keen to retain that human flair, limiting AI usage may seem attractive. However, staff will use GenAI for content creation regardless of whether or not their employers tell them not to. In fact, a blanket ban on GenAI will only lead to the bigger challenge of ‘Shadow AI’.
Recent research shows that 78% of knowledge workers use their own AI tools to complete work, yet 52% don’t disclose this to employers. This not only poses significant organisational risks like data breaches, compliance violations, and security threats, but it also means business leaders will never have full transparency over how much, and how often, GenAI is being used to create the messaging that’s being published for customer or public consumption.
So how should GenAI be used?
For businesses that regularly need to create high-volumes of detailed content, GenAI tools like Jasper can provide crucial support for building first drafts, generating ideas, and providing digestible summaries or even detecting mistakes across existing copy. This process of leaving the more time-consuming content creation tasks to AI frees up considerable time for content creators and enables them to focus on strategy and creativity.
Likewise, tools such as MarketMuse can analyse audience behaviour and preferences, audit existing content, suggest SEO optimisation, and identify gaps in coverage. These data-driven insights enable brands to create more tailored and engaging content, improving both reach and conversion rates.
Another significant advantage is consistency. Ensuring a uniform tone and style across various platforms is essential for cultivating a cohesive brand identity. AI tools play a crucial role here, by supporting content so that it aligns with established brand guidelines. By maintaining consistent messaging, businesses can strengthen their connection with their audience and foster long-term trust.
For businesses and content creators looking to use GenAI to create content which will be made public, it’s essential for them to review, refine and personalise outputs extremely carefully. This ensures that the final output remains true to the brand’s voice and offers genuine value to the audience.
GenAI is no substitute for human storytelling
While AI can support efficiency, human insight is essential for crafting content that truly resonates. This is especially true in storytelling and in complex strategies like seo consultancy, where aligning with audience expectations requires a thoughtful, hands-on approach.
Humans are still best equipped to connect with audiences on an emotional level. Storytelling, creativity, and real-life experience are areas where human writers hold a distinct advantage.
By blending AI’s efficiency with the creativity and empathy that only humans can bring, brands can produce content that is not only high-quality but also deeply engaging. AI tools, therefore, should complement human creativity, not replace it. Striking this balance allows businesses to maximise efficiency without sacrificing authenticity, creating content that resonates and builds long-term connections with audiences.
Business
Artificial intelligence will revolutionise the manufacturing industry: here’s how
By Grace Nam, Strategic Solutions Manager at Laserfiche
Much of the manufacturing industry continues to rely on traditional ways of handling data and documentation, with scattered technology and complex processes acting as barriers to progress and hybrid working. This siloed information coupled with rising costs, logistical uncertainty, and doubts over the supply of essential raw materials is making manufacturing stagnant.
The answer is digitalised smart systems and processes. Innovations such as AI-powered cloud document management systems (DMS) can control and organise critical data, creating a single source of truth to streamline processes and provide multi-layer protection against missing documentation to comply with regulations. Let’s look at some of the core ways that AI will revolutionise manufacturing: minimising errors, empowering employees, and attracting talent.
Harmonising processes, freeing up time
First, by digitalising data and investing in AI integrations, manufacturing leaders can reduce margin for error. While the integration of IOT may appear to garner similar results, the real impact of AI is incredibly different.
IOT focuses on supporting machines and network enablement. On the other hand, AI supports the functionalities that are traditionally confined to the realm of human responsibility and intelligence. While IOT connects physical objects to manufacturing networks, AI harmonises the entire process and structures unstructured data.
The value of AI lies in its scalability. AI can reduce tedious data entry for employees and eliminate delays, having the potential to transform employees’ lives by reclaiming time for valuable projects and saving resources.
Creating more efficient process cycles
AI also enables manufacturing leaders to enhance process automation, making the more time-consuming elements of the manufacturing process rule-based and decision-driven. For instance, in a recent SME study that surveyed over 300 manufacturing professionals, one-third of respondents reported experiencing work delays a few times a week across various operational processes.
As is the case with minimising errors, the role of AI is to free up human employees for other tasks, not replace them. AI and ML are invaluable tools to save countless hours wasted by employees fulfilling administrative tasks, reclaiming significant amounts of time that can be redirected to supporting customers. Manufacturing companies can implement processes driven by AI and ML to streamline areas of the business and integrate processes that often experience delays, as well as augmenting high-cost processes involved with compliance documentation to create better process cycles.
AI-powered decision making
With endless data at their fingertips, leaders can utilise AI to gain insights and power decision-making. This technology gives organisations the ability to centralise customer information and order history and gather product information that involves hundreds or even thousands of parts, where each part carries its own unique identification number.
Another example could be analysing the data in relation to how suppliers perform, enabling the manufacturing organisation to better understand what to expect and prepare for potential pitfalls in advance.
AI-powered technologies are also being implemented to resolve interoperability issues, enabling computer systems and software to exchange and make use of information across platforms. Allowing data to be shared between different software and technologies will help in streamlining processes.
By utilising process automation and enhancing data processing speed, we can expect organisations to see an increase in operational efficiency. These improved systems will reduce costs while improving scalability and flexibility, enabling the streamlined sharing of data across the business.
Looking ahead: the future of manufacturing
AI-powered solutions improve the working lives of almost everyone in the sector, helping them make better informed decisions at speed, while process automation solutions empower remote employees who are integral to the success of future workforces. This innovation is vital for businesses to attract, and retain, new generations of talent.
AI and machine learning are offering the manufacturing industry the opportunity to unlock new levels of efficiency and create a solid foundation for future growth and innovation. From sales and supply chain management to quality checks and inventory control, AI is streamlining complex processes, predicting potential issues, and ensuring timely delivery of projects.
It’s not just about keeping up with technological trends and jumping on the bandwagon. AI proves its worth for manufacturers aiming to make critical decisions promptly, effectively address high-cost functionalities, streamline operations, ensure accuracy of compliance documentation, enhance scope for innovation, boost ROI, and improve sustainability. By bringing together human intuition with the speed and scale of AI technologies, manufacturers can remain competitive – and continue to evolve – for years to come.
Business
The role of leadership during digital transformation projects in the financial services sector
Stephen Foreshew-Cain is CEO of Scott Logic, the specialist software consultancy
Until around a decade ago, the UK financial services and banking industries were dominated by a core group of major players who controlled almost the entire market. They built up their offering over a period of decades – even centuries in some cases – and invested in robust, weighty technology platforms pre-millennium that were seemingly built to last and which most hoped could be relied on for years to come.
These businesses still hold significant market share, but in recent years their position has come under attack from the emergence of several notable challenger banks. This change has slowly but surely driven the need for digital transformation within the established financial services market and seen many firms consider modernising their offering. However, the success of these programmes depends on much more than buying the latest off-the-shelf package, and there are several elements that dictate how effective this change really is. Leadership is one of those factors; but what’s driving this need for digital transformation and why is having the right management in place so critical to success?
Modernisation pressures
Perhaps no other sector has felt the pressure to modernise itself as much as financial services, where growing security challenges – and the rise of the aforementioned emerging brands – have made this a priority. These newer digitally native organisations are setting the standards for customer experiences and operational efficiency, and the more long-standing institutions are battling to keep up. The challengers have demonstrated how a digital-first approach can offer a superior customer experience and deliver greater operational efficiency. These banks have built their infrastructure from the ground up and have leveraged big data, embraced cloud-native architectures, and developed a focus on user-centric design and experiences. And their streamlined processes, fast decision-making, and commitment to innovation have won them a growing share of the market.
In contrast, many of the more established financial groups are built on platforms that were developed decades ago. While this may sound staggering, it’s understandable. It’s only in recent years that the cloud has matured sufficiently to offer a viable migration path to these institutions, with more benefits than were previously available.
In addition, there is an inherent resistance to change amongst more long-standing institutions – again, for understandable reasons; much of their success is based on caution and reducing risk. One wrong move or misstep can naturally set off a domino effect that has ramifications around the world, as seen in the global financial crisis in 2008. Convincing these organisations to go through the process of upgrading their systems to replace a platform with another more modern one, that does something very similar, is challenging. Hierarchical organisational structures can often hinder rapid decision-making and the swift adoption of innovative and emerging technologies.
Despite these challenges, now is the time for change. External market forces, improved customer experience, cyber-resiliency, and the ability to meet evolving regulatory requirements means that migration from legacy is no longer a ‘nice to have’. The pace of evolution in modern technology means that the longer organisations wait, the rate at which challengers outstrip them will only accelerate. Migration projects are as challenging as they ever were; however, it is now possible to employ migration strategies that allow a phased process, making them easier to manage from a risk mitigation perspective. Any remaining reasons to delay are now massively outweighed by the reasons to proceed with migration – frankly, it’s now imperative.
The pivotal role of leadership
That’s not to say that all modernisation programmes are a success; research from McKinsey indicates that over 70% of digital transformation projects fall short of their goals. There is a range of factors to this, but a common recurring theme is ineffective leadership. Without leaders capable of reimagining these structures and securing buy-in from the workforce, digital transformation initiatives are always likely to encounter issues.
However, in the context of digital transformation, leadership is more than just guiding an organisation through technology adoption. These individuals are responsible for setting the tone and direction for change, building a sense of urgency, and ensuring that the everyone understands the purpose behind the transformation. Remembering the quote attributed to Peter Drucker, culture eats strategy for lunch. The best managers will be able to set a clear vision for change and convey palpably how it will benefit customers and colleagues. They will also be adept at fostering a more agile culture, encouraging collaboration and a sense of continuous learning across teams. To horribly torture Drucker’s famous statement, if strategy is lunch, leaders need to make sure culture is the breakfast of champions.
There’s a form of culture that’s more than “the way we do things here”. It’s something that’s baked in; the structural risk aversion that often critically undermines the change programmes organisations set out to deliver. It doesn’t matter how many digital experts an institution hires to lead the way into a digital future if outdated governance approaches fetter initiatives before they can get out of the starting blocks. Leaders need to be given the scope to propose new approaches to investing in infrastructure – ones that not only balance investment in legacy modernisation with investment in innovation, but which allow the two to work in symbiosis.
Fundamentally, culture should not be allowed to eat innovation for lunch; leaders need to be free to keep pace with innovation beyond the ‘walls’ of their institution – free indeed to conceive of those walls breaking down as the banking ecosystem of the future takes shape.