Using predictive pricing analytics to calculate and design a future pricing strategy is becoming more popular, especially in big-ticket items, and necessities. Even luxury brands are getting in on the game to optimise their pricing strategies. How does it work and how can you accurately predict the price that people will willingly pay when predictions are far from definitive?
What is it? – A strategy used to make informed assumptions about how your competitors will price comparable products in the future. If you can make accurate predictions about the pricing strategy of your competitors, you can implement them first.
How to do it? – Predictive pricing utilises the information from price optimisation software and historical data analysis. By tracking the current and past data of your competitors, you can create a simulation of future pricing activities, based on the repetition of patterns and strategy cycles.
Why do it? – It is a method of competitor monitoring that works in advance. It is a planned and proactive approach to making competitive repricing decisions. Automation of data collection from your competitor monitoring software makes the collation of information quick, and easy to analyse. Maintaining comprehensive pricing data from your competitors gives you a better understanding of the patterns that influence repricing strategy and how to beat them to benefit your business.
It is a reasonably straightforward process when you have the data, but data is the vital component. With more sound data comes a more accurate predictive pricing model.
The English proverb above isn’t always accurate, but in this case, knowing your demographic buying habits and trends is to know your audience. It is highly improbable that a precise price prediction can be made without knowing something of the spending patterns of the buyers in question. Historical data is good at spotting trends and fluctuations over time.
At the same time, if you don’t know what your past and current metrics are, you can’t judge any success or failure in your repricing strategy. With predictive pricing, you are attempting to predict how much your customers are willing to pay, when they are likely to buy, and how to schedule your repricing strategy to exploit that information best.
How do you define your target audience? Do you aim at specified age ranges, incomes, occupations or education levels? If you don’t outline your target audience along the correct demographic lines, then it is likely you are missing out on profit, by not knowing your customers well enough to target them accurately.
It can be quite simple to segment your audience along appropriate demographic lines by analysing historical data. Creating an accurate predictive pricing strategy will require a deeper understanding of your target customers buying habits over time.
Accurate customer profiling and analysis of their buying habits, as well as correct audience segmentation, set a sound basis of information for planned repricing activity. However, poor preparation and a “last minute” approach will not keep your business ahead of the competition.
The very function of predictive pricing is to be just that little bit ahead of your competitors. By making beneficial discounts just before a rival, you can achieve increased revenue and ‘poaching’ of your rival’s customer-base. Using predictive pricing models to schedule your pricing alteration according to your data, will ensure that you are pricing optimally for your season, demographics and profit margin.
The predictive models for pricing aren’t only about monitoring the buying habits of your customers but also perceived value and periods of high demand. Increased demand and perceived value for a product type, usually supports an increased price tag that the customer is willing to pay.
Identifying good pricing opportunities doesn’t have to be difficult, but you do have to grasp them when they apply. While planning and preparation are crucial components of predictive pricing, it is also vitally important that you are prepared to seize a fleeting opportunity when it presents itself, to maximise revenue from opportune events.
While easy wins are, by definition, simple fixes to increase revenue, it can take skill to identify them from the data you have and implement them quickly enough to take advantage. While seizing the opportunity when it knocks is a spontaneous act, it requires planning and preparation to be in a position to enact it.
It is impossible to predict the future. However, making educated assumptions based on empirical data and historical market analysis is reasonably possible and can make a significant impact on the way that businesses target their audience segments.
It doesn’t take artificial intelligence and machine learning to make generic assumptions about festive season shopping and demand surrounding toys and “gift” items, but for intricate information about the projected demand for specific product types, or walking a price: value tightrope to appeal to both older and younger buyers, requires vast amounts of research data.
To make predictive pricing strategies work for your business you need superior data only available through web crawling and competitor monitoring software.