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Price Optimization in a Dynamic Market

Updated: May 7

Price optimization to improve pricing, profitability, and sales performance in a dynamic market for existing and new products🚀


Who is it for🙋

Imagine you are working in pricing, marketing, or other relevant functions in your company and want to optimize your pricing and/or improve your expected sales performance for an existing or new product.


The challenge you face

Optimizing Pricing and improving your sales performance are complex processes influenced by many pricing drivers:


  • Country conditions (inflation, exchange rates, spending capacity, etc)

  • Market conditions (supply constraints, materials & other costs, competition, etc)

  • Product characteristics (product variety and features, quality, bundling, etc)

  • Customer characteristics (customer diversity, customer personas, segmentation, etc)


It is challenging to identify the impact of these factors and set your pricing accordingly based on your experience and simple data analysis. Furthermore, the few specialized software tools you could use are too complex, with a long learning curve.

The solution💡

Unlike most price optimization models that don’t work well in volatile environments or expect too much from the users in terms of time and expertise, FutureUP’s Predictive AI models take into account the changing country, market, product, and target audience conditions and allow the user to generate results quickly, in real-time, and with measured accuracy:


➡️Optimum price suggestions

Identifying the best price across multiple dimensions (countries, markets, time, or other), for various optimization types (revenue, profit, profitability, and market penetration) and optimization conditions (profitability, penetration levels, and other KPIs).


➡️Key pricing drivers identification

Finding the most important pricing drivers with the largest performance impact.


➡️What-if scenarios

For changing macro, market, product, and customer conditions.


The model can leverage all available data:


  • Historical data (product sales, competitive intelligence, market indicators, macro & demographics indicators, etc.)

  • Prospective data (pricing research, market research, market trends, macro & demographics trends, etc.)


All these can be combined or used independently as presented in the following indicative example:

Product

Data to use

Existing product with historical data availability

Historical data

Existing with limited historical data availability

Historical & Prospective data

New product

Prospective data


Dream outcome😀

Boost profitability and sales performance with effortless, real-time price optimization.

Our Predictive AI models do the heavy lifting of data analysis, generating optimum price suggestions per country and market, enabling you to interact effortlessly with intuitive dashboards, including key predictive insights.


Success stories🏆

Check out how our Predictive AI model optimized prices for an existing product resulting to 3x conversion rates and 2x revenue increase. Also, how we used our Predictive AI model to optimize prices for a new product launch leveraging prospective data.



 

Interested in learning more about AI-Powered Price Optimization and Strategic Forecasting?



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