Pricing is a complex and dynamic process. It’s an activity that is inherently contextual - with decisions being made in competitive and rapidly changing environments. Staying ahead of the competition requires constant analysis and adaptation.
Pricing 💕 Decision Intelligence
This makes it an ideal use case for taking a decision intelligence approach. It has a clear action (determine price) it’s dependent on both internal and external factors (like costs or market demand) and has tangible outcomes (like sales volumes and profit margins).
Because these factors can change quickly and dramatically, coming up with effective pricing strategies requires the exploration of multiple scenarios;
🔮 What if fuel prices go up?
🔮 What if the weather's bad?
🔮 What if our competitors undercut us?
All of these factors can - and should - be modelled within a decision support application. These models could be rendered as complex tables of options, but for speed and intuitiveness, you can't beat a good ol' fashioned slider.
Simple scenarios with Sliders
Sliders are interactive components that allow users to adjust a parameter within a specified range by dragging a handle along a bar. They are commonly used to adjust date ranges or the granularity of data and are a fantastic short cut to 'What-If' scenarios - particularly when paired with a visual representation of likely outcomes. This pairing of input assumptions and output simulation removes the complexity from models and allows people to get an instant feel for the 'economic physics' at play.
Example - Revenue Management
In this application - a revenue management tool for pricing analysts in the transport industry - sliders are used to set thresholds for the number of tickets to be released at various price points. Based on the underlying model, the results of these micro-decisions are aggregated up to give a view of expected ticket sales leading up to departure.
Example - Real estate analytics
Real-estate is somewhat late to the party when it comes to the use of data to achieve digital transformation. There is no shortage of available data, but it can often be fragmented and few are able to find signal in the noise.
Instead of presenting endless tables of data, we provided users with simple visualisations to understand the factors driving pricing recommendations. Intuitive, slider based components for quickly exploring pricing scenarios and their expected impact, helped users become familiar with pricing models in a way that they could feel.
Example - Automotive after sales
In this product - a tool to allow analysts to optimise the prices of thousands of auto-parts - the humble slider takes centre stage. But before it's used to make decisions, the user is provided with backward and forward looking contextual data on prices, margins and sales volumes.
The problem with dashboards
Inspired by the adage of “if you can’t measure it, you can’t manage it” a billion things were duly measured and their data arranged into neat dashboard widgets that promised to tame complexity and deliver insight.
But the world just isn’t laid out that way. It’s full of dependencies, feedback loops and intangible factors with outsized significance. Life is not like a box of chocolates.
One of the biggest problems with most dashboards is that they don’t capture the hidden relationships – the links of cause and effect at play behind the scenes.
Product leaders seeking to achieve real-world goals should, instead, look to systems that integrate data, models and visualisations with a structured logic of cause and effect. As an accessible front-end to all of this, the humble slider achieves hero status.//