This document discusses using simplified analytics and a step-by-step approach to improve pricing strategies through better data and understanding of how demand varies at different price levels. It recommends identifying current pricing tools and data, integrating new data sources, and conducting simple analytics on price variability, sales velocity, and historical trends to gain actionable insights. Traditional and promotional price optimization techniques are described that can help set objectives and measure impacts on revenues, profits, and market share.

1. Fessal Rahman
International Pricing

2. Executive Summary
Research shows pricing is one of the most powerful levers for improving profitability.
However many companies are unable to identify potential opportunities due to poor data
quality, data silos, or simply poor infrastructure, knowledge and resources.
In this day and age utilising traditional methods and gut instincts only is a huge
competitive advantage for your competitors who are applying simplified analytics to
provide actionable insight even on incomplete data sets. In the following slides we will
look at some simple yet effective solutions you can take to begin the journey.
In summary fully understand how demand varies at differing price levels, analytics
ensures companies mitigate against missed opportunities, unnecessary risk and falling
margins along with market share. However data from operational transactions is not
enough in the social media inspired world we live in.
“The price of light is less than the cost of darkness.”

3. Simplified Approach
• A step by step approach ensuring mechanisms are created to be repeatable, include high
level objectives, data governance, technology enablers, execution ownership.
• Identify current strategy, tools and data sets available, then create a simplified analytics
eco system. This need not be an exhaustive exercise, start small then add to in later
development phases.
• Once the data sources have been identified, integrate into the newly created analytics eco
system. Not all data is insightful, remember the 3 Vs. when it comes to data, Volume,
Velocity & Variety.
• Once the data has been ingested, begin simplified analytics, price variability across
product lines, sales velocity, sales across verticals & geographies. Remember to add
historical data as this is essential for understanding trends.
“Life is really simple, but we insist on making it complicated.”

4. Types of Price Analytics
Traditional Price optimization
• Set the objective, e.g. which vertical or geography is most profitable, improve understanding
of seasonal trends, elasticity etc. Impact of price changes on revenues, profits, market share,
demand forecasting, controlling inventory levels. The potential list of objectives is limited
only by your imagination.
• The how, e.g. through seasonal and trend modeling, price and cross elasticity modeling,
measure changes in demand vs. changes in price. Measure competitor prices, economic
conditions, add to the data sets, ingest new data, revise modeling and re-run analysis.
• Inputs & Outputs e.g. historical data, prices & promos, competitor prices, sales transactions,
add external data inputs particularly from social media. Output will be clear and actionable
insight around seasonal models, elasticity coefficients and optimum price points across
verticals and geographies.
“The moment you make a mistake in pricing, you're eating into your reputation or your profits.”

5. Types of Price Analytics
Promotional Price optimization
• Set the objective, e.g. price options across product bundling promotions, deploying loss
leaders, simulate how buyers will react to targeted promos.
• The how, e.g. similar modeling techniques to traditional optimization, coupled with causal
modeling to understand all the variables and the relationship between these variables.
• Inputs & Outputs e.g. traditional price data as well as promotional prices across the product
sets involved, historical sales transaction data, add shopper behavior and competitor activity
data if available. Outputs will provide actionable insights around seasonal models, causal
coefficients, ability to measure net impact of a chosen promotion, quantifying other aspects
such as affinity and cannibalization is possible with the right data sets.
“In the fashion industry, everything goes retro except the prices.”

6. Other Analytic Levers
• Markdown Price Optimization, driving minimal inventory for products approaching
end of life. Through a mixture of price elasticity, demand forecasting you can achieve
optimal markdown strategies.
• Predictive Pricing Analytics, leverage data sets to increase understanding of what if
scenarios when different pricing strategies are adopted. A great source of immediate
revenue opportunities, these quick wins typically accelerate adoption and support of
further analytical development.
• Real time Pricing Analytics, fast and insightful data streams about your customers and
businesses, make changes in strategy to improve results, no more post mortems.
• Price Testing, a simple and highly effective lever to fully appreciate the impact of
pricing strategies across a time period which captures enough transactional data
whilst limiting the impact on long term customers.
“With power like this comes great opportunity”