Human vs. AI: What are the Pros and Cons of Price Prediction Algorithm?
With the advancement of technology, more businesses are able to cut through processes that, once upon a time, took longer and were done manually. Nowadays, companies are relying on price prediction algorithm more than ever to capitalise on revenue and profit. But is this always the right way? Does it replace the hard skills of human capabilities forever? More importantly, is AI actually more reliable?
Like many pricing tactics, a lack of planning and research before implementation will cause a business to employ price inconsistencies. This will, in turn, affect a company’s value proposition if it chooses to use data interpretation and pricing analysis selectively.
In this article, we discuss the balance between relying on automated processes and monitoring market trends or patterns manually. At Taylor Wells advisory, our consulting work shows that entrusting decision-makings skills to pricing teams and professionals outweigh the reliance on machines and artificial intelligence.
Working with over 100 clients, we believe in setting value-based pricing that justifies your offer and product portfolio. We also argue that AI will not replace the decision-making and analytical skills of humans.
What is a price prediction algorithm? Real-time smart pricing strategy tool and analysis
A pricing algorithm is a machine learning automation that operates with artificial intelligence to address supply-demand elasticity. It is meant to optimise prices using data in a real-time setup. This is a common practice in industries like eCommerce, insurance, tourism, energy, and advertising.
Problem/challenges: Although a pricing algorithm is designed to speed up the process of accurate price changes automatically, it has its own challenges. Even if it solves problems like manually setting prices through automation.
In fact, it leads businesses to change their pricing quite too often which is always a case of hit and miss. These unpredictable costs and timing, in turn, confuse customers and further test their loyalty and trust.
For profit-driven businesses, price algorithms are commendable. As demand increases, the price of that commodity surges almost automatically. This makes it convenient for companies to continue trading seamlessly, inputting pricing data that adjusts according to current supply and demand trends.
Pros of Price Prediction Algorithm – Real-time smart pricing strategy tool and analysis
We’ve talked about how car insurance pricing models have tried to change the game by implementing personalised pricing. Breaking away from the traditional risk modelling, it monitored risky driving behaviour by using a tracking device. This led many other insurers to copy the pricing model, including automotive giant Tesla.
One in particular, recently took it a step ahead with the use of smartphone apps. Not foregoing core requirements such as credit scores, past driving background checks, fraud and security, and safety tests, the insurer removed the occupation and educational background criteria to avoid discriminating against customers.
It set a lower pricing strategy based on real-time driving behaviour from its smartphone app. Through this, it was able to improve its customer relationship by appealing to a wider target audience using its price transparency. It ultimately established itself as a strong brand by letting its clients know about the efforts that it does to lower insurance costs.
Cons of a Price Prediction Algorithm – Real-time smart pricing strategy tool and analysis
1. With Uber’s 93 million riders, its pricing algorithm in several instances caused a price hike of as much as 200-500% as soon as demand surged. What’s more, they still occur quite distastefully during major alarming and unpleasant events such as terrorist attacks or state-wide protests.
Many market analysts and Uber users criticise the insensitive price increase even though it once had manually prevented the automated algorithm from taking place during the London Bridge chaos.
2. Coca-cola also was a victim of poor application of a pricing algorithm. 30 years ago, it rolled out a vending machine that adjusts its pricing based on the day’s weather or temperature. On hot summer days, the machine increased its prices. Of course, customers didn’t take this too well and Coca-Cola faced a heated backlash. And as a result, it had to pull out the vending machine idea quickly.
The downside is, even though it’s meant to be seamless and accurate, price algorithms can affect brand image and reputation. It can lead customers to unfavourable responses. In one case, a price prediction algorithm put a furniture price of up to $14k. And in another case, a book sold on an eCommerce platform outraged customers with a price of more than $20 million.
What are the Psychological Effects of real-time smart pricing strategy tool and analysis?
A pricing algorithm that is done without considering the implications of how it can largely affect buyer decisions and relationships in a B2B and B2C setting will damage a brand’s credibility.
To address these risks, we suggest some ways for you to do your price algorithms right:
Set a Creative Narrative
IKEA, for instance in one of its locations worldwide, is the first to convert customers’ time into a currency. How? In its creative algorithmic pricing, IKEA lets its shoppers pay using the distance or time that it took for them to arrive at one of its stores. In exchange for this, visitors receive discounts from regular prices using Google maps to show proof at the checkout counter.
But is it foolproof and infallible? It is a creative marketing tactic and will lead to more customers visiting IKEA more often. But there were instances that shoppers got their items completely for free.
In that context, it may not be the best algorithmic model as it can chip away margins in the long run. But it certainly keeps the hype snowballing to entice more visitors which could possibly make up for the losses. And whether time as a currency will be one of its permanent models, or if other industries will copy the trend – that remains to be a mystery.
Be Attentive and Set Boundaries
Without consistent monitoring, algorithmic pricing can go out of control. This is quite common in the airline industry when it comes to segmenting their customers using awards vs. rewards travel.
Amusement parks also find creative solutions through price prediction algorithms. We’ve all had unpleasant customer service experiences in this scenario. One where we struggled to find parking spaces – and then we had to face endless lines for hours in amusement rides, food stalls, and even comfort rooms. This leads us to ask whether all the trouble was worth the money spent on expensive tickets.
But a giant like Disney dealt with this differently. It gave visitors the freedom to choose their booking while increasing the ticket prices for multiple-day trips. They also lowered their off-peak hours or seasonal prices, allowing people to plan out their trips more conveniently. Ultimately, Disney ended up with happy and satisfied visitors.
Setting up a pricing capability helps a company transform and shape up its future and sustainability. Some organisations succeed in using a fixed-pricing or dynamic pricing that segments a specific customer base.
More importantly, pricing professionals who use the right tools in the correct way will also aid in overall revenue management. There should be key metrics set up to guide the right pricing decisions for a company and its customers.
At the same time, a pricing structure should align with your value proposition, brand reputation, product quality, marketing, and customer service or relationship management. Otherwise, you won’t be able to maximise the opportunities that a cohesive pricing system brings.
When should you Override a Price Prediction Algorithm?
Some B2B and B2C firms make the mistake of letting machines lead their actions, instead of using those tools to make more informed decisions. Complete reliance on AI-driven pricing can be misused and eliminate the purpose of price floors and price ceilings.
Algorithmic pricing can also lack long-term output and only addresses short-term benefits. Why? It’s because it mostly functions based on real-time supply and demand factors without considering other more complex denominators that pricing professionals are capable of responding to.
There needs to be consistent management and monitoring by pricing teams to avoid exploiting such a pricing system. Moreover, pricing professionals can make real-time decisions on unforeseen events better than machines can.
Just like Uber’s case, it had to refund the expenses of victims and witnesses of the terrorist attacks, the following day. It was the right move to make up for the stress and anxiety that high prices added into the already tense situation they experienced.
Bottomline: Real-time smart pricing strategy tool and analysis
It’s quite a task to find out about people’s willingness to pay, let alone to raise that quoted range. That’s why a poor pricing algorithm that is inflexible can create tension with clients. It can send out the message that you are insensitive and are mainly after the profits.
You need to ask what your boundaries and framework are when planning a price prediction algorithm. How can you create and take advantage of price-value opportunities? You also need to be strategic when deciding how far or high will the algorithms raise prices during supply and demand fluctuations. After all, artificial intelligence relies on human instructions.
For a comprehensive view on integrating a high-performing pricing team in your company,
Are you a business in need of help to align your pricing strategy, people, and operations to deliver an immediate impact on profit?
If so, please call (+61) 2 9000 1115.
You can also email us at firstname.lastname@example.org if you have any further questions.
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