In today’s marketplace of instant gratification, are you falling victim to analytics, machine learning and AI providing meaningless price recommendations — and ultimately pricing actions that are not in sync with your long-term interests?
It is quite common for companies to use customer price sensitivity (or elasticity) in their pricing algorithms and models. In these models, in a situation of low customer price sensitivity (elasticity measured at greater than -1), prescriptive pricing models typically recommend price increases that drive revenue higher to the detriment of sales volume. In a situation of high customer price sensitivity (elasticity measured at lower than -1), prescriptive pricing models typically recommend price decreases. Leading companies leverage data, advanced forecasting techniques, and sophisticated pricing analytics to predict — with a high degree of accuracy — consumer behavior in the marketplace. For instance, in cases where either the origin or destination city have more than one nearby airport, it’s not uncommon to see flights on the same airline from the same origin city to the same destination city and same dates with drastically different fares, which likely does not make sense to the end consumer. This could be partly attributed to traditional revenue management concepts relying purely on forecast, elasticity and capacity, without considering the effect of such fare discrepancy on the brand perception or loyalty.
However, with the rise of instant gratification culture, many of today’s purchase decisions are being made with a very short term objective, in which the only thing that seems to matter is the here-and-now, and analytical models can tend to recommend cashing in on this trend by pricing more aggressively. Few companies seem to be considering long-term customer value and potential brand erosion if consumers begin pushing back on higher prices.
Since most companies’ price models are “optimally” pricing based on historical transactions that do not factor in longer-term perspectives, these pricing models are behaving in a counter-intuitive way. The result: a disconcerting trend emerging in which prices are non-sensical in the longer-term view, leading to your customers’ aversion to purchase from you and serious brand erosion. Striking the balance between these competing objectives is often in the minds of those closest to your customer: your experienced agents, customer relationship managers, and sales professionals.
Pricing models in our KaizenValueAccelerators™ (KVA) suite of pre-built analytical building blocks help relieve this tension and strike the balance between near-term revenue/share/volume/profit objectives and long-term objectives like maximizing Customer Lifetime Value (CLV) and retention of high value customers. Our KVAs reflect the logic and thought processes of your associates who know your customers the best, incorporating it into pricing decision-making processes through narrow artificial intelligence and supervised/unsupervised/reinforced machine learning techniques.
To ensure you’re not making highly transactional pricing decisions — or if you’d just like more perspective on our how our KVAs can help you strike the balance between achieving your near-term revenue/margin/volume goals and ensuring long-term customer value — contact us today!
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