Pricing is the anchor of a business’s profit strategy, as it directly influences the company’s revenue generation and profitability. It still remains the primary decision-making factor for consumers on whether or not to opt for a good or service.
Secondly, proper pricing helps cover costs, ensuring that each sale contributes positively to the business’s margins. Moreover, pricing can be used strategically to gain a competitive edge, attract a target market, and even shape consumer perceptions about product quality. A well-calibrated pricing strategy not only drives revenue but can also boost profits through cost management, customer retention, and market positioning, making it a vital element in a business’s overall success.
Manual pricing, once a standard practice in business, has fallen short in meeting the demands of today’s fast-paced, data-driven markets.
Limitations of the Conventional Pricing Methods
Manual or Rule-Based pricing, though familiar, is fraught with several issues, like:
Inefficiency: Human pricing teams struggle to process the massive volumes of data required to make informed decisions. This results in slower reaction times to changing market dynamics.
Inaccuracy: Factors such as fatigue, personal judgement, and cognitive limitations can hinder the precision of pricing decisions.
Limited Data Utilisation: Humans are unable to efficiently analyse vast amounts of historical data, customer behaviour, and market trends, making it difficult to identify optimal pricing strategies.
Inflexibility: Manual pricing strategies are often static and can’t adapt quickly to real-time market changes, such as shifts in demand or competitor actions.
Missed Opportunities: Human teams may miss out on opportunities to maximise revenue through dynamic pricing adjustments because they can’t practically monitor market conditions 24/7.
As technology continues to evolve, rule based pricing is likely to become a relic of the past, replaced by data-driven, automated pricing solutions that benefit both businesses and consumers alike.
Advocation of Tech in Pricing Decisions
Majority of the businesses rely on pricing strategies to entice their target customers and plan their revenue strategies as pricing is what ultimately drives all their profits.
This means that before setting every price, category managers are required to take into account dozens of pricing and nonpricing factors like stock levels, weather, sales data, vendors’ terms, etc.
All of this translates into billions of data points that need to be processed, analysed, turned into insights and recommendations, and then finally – applied.
As you also remember, all of these actions need to be done in near-real-time.
That’s exactly where technology comes into the picture — and in the majority of the cases, it’s machine learning. Now, how does this work? Well, machines and algorithms basically eat this historical data, including competitive and internal data, and analyze the different outcomes that happened in all of those situations, and accordingly recommend prices.
This intervention of technology in pricing for the travel industry is even more essential to keep up with the very liquid demand and supply trends of the market. An extremely agile system is needed to price correctly, every time, that eliminates any possibilities of manual errors in deciding on the prices for their product/services. A human mind, no matter how intelligent, can not process or even begin to comprehend every small and big element that plays a role in pricing – and give it the same weightage.
Here, reinforcement learning through deep learning and machine learning is enormously important to make the system even smarter and efficient.
To convince you further, let’s take a look at the examples of Uber and Lyft, the pioneers in ride-hailing platforms, to understand how significant technology and data is in pricing to drive your business to success.
A Brief Study into Uber and Lyft
Uber and Lyft have transformed the transportation industry by offering convenient, on-demand ridesharing services to millions of users worldwide. At the core of their business models lies the sophisticated use of technology, artificial intelligence (AI), and machine learning to dynamically price their services.
Uber and Lyft start by gathering vast amounts of data from various sources, all of which is real-time data. They continuously collect and update this information, which serves as the foundation for their pricing algorithms. This body of knowledge consists of millions of data points, some of which are:
Route and Traffic Data
User Profile and History
Rider to Driver Ratio – Dynamic Pricing Algorithms
Competitor Pricing and Market Positioning
Seasonal, Weather and Geographic Factors
User Feedback and Rating Systems
Both organisations utilize GPS and mapping technology to calculate the estimated duration of a ride based on the chosen route, currenttraffic conditions, and anticipated roadblocks.
If you are a frequent user of Uber or Lyft, you may have noticed certain coupons and discounts that are specific to only your account. If you are wondering why, the AI systems in place behind these services take into account user profiles, including ride history, payment methods, location, and much more. Some studies even claim that Uber also taken into account – the kind of phone you are using, it’s model, your battery % and locality of the region while calculating the correct price for your ride.
One of the fundamental components of their pricing strategy is the assessment of supply and demand in specific geographic areas. This is done in real-time and allows the platforms to make immediate pricing adjustments.
For instance, during rush hours or special events, demand is high, leading to surge pricing. Conversely, during low-demand periods, prices may drop to the incentivize riders<p.</p
Both companies employ sophisticated machine learning algorithms to dynamically set prices. These algorithms weigh various factors and make immediate adjustments to the pricing structure.
Uber and Lyft keep a close eye on their competitors’ pricing strategies. If a rival lowers their prices or introduces new promotions, Uber and Lyft can respond with similar or more attractive offers to maintain their market share. Studies have been performed which claim that there is a difference before and after a user switches between different competitors (other ride-hailing apps) to compare the prices.
AI also factors in events and weather conditions. For instance, when there’s a big concert or sporting event, or when it starts raining, demand for rides may spike. The algorithms account for this and trigger surge pricing to encourage more drivers to get on the road. This is how they manage demand as well as supply for their services.
Uber and Lyft adjust pricing to account for seasonal variations and regional differences. In some cities, for example, rides might cost more during the winter due to increased demand and inclement weather.
User ratings and feedback are valuable in pricing decisions. High-rated drivers may have the flexibility to charge a premium, while low-rated drivers might see fewer ride requests.
Why Viaje.AI?
The above listed are just the fundamentally major factors which are considered while pricing for the services. And as you already know, these factors are never static, but always keep changing by-the-minute. So, the prices also need to be optimized and updated accordingly. These billions of optimisations and real-time updates are not possible for a human mind – but only a machine or stellar technology to perform.
What Uber and Lyft’s dynamic pricing technology is to the ride-hailing industry, Viaje.AI is to the Airline and Luxury Bus Industry. Viaje uses extensive factors and billions of price optimisations every minute to understand the recurring market, demographic and seasonal changes and prices according to the current trends.
Viaje’s sophisticated algorithms make sure to price for the travel industry which eliminates the need for any and all manual interventions and completely uses data backed methods to always price right.
Viaje studies several data points and historical patterns every second and uses it’s learnings to deliver fair prices for both the business and their end customers.
Viaje has a proven track record of boosting the overall revenues by 25-30% across all it’s clients and improving the brand image of their clients only through pricing correctly.
At Sciative, we understand the importance of technology and have a modern approach to pricing solutions. Our stellar product, Viaje.AI, unleashes the richness and power of data, deep learning and artificial intelligence to ensure precision in its pricing.
See how our AI-powered dynamic pricing tool VIAJE.ai , can help you with that
Book a free demo of our automated retail solutions or contact our optimization experts at info@sciative.com.
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