Markdown Optimization – Modeling Markdowns to Maximize Revenue

Markdowns are omnipresent in retailing and play a significant role for any retailer’s strategic decision making.The need for markdowns arises because of variations between predicted sales and actual demand in the selling season.

Despite the benefits it offers, many retailers find it difficult to implement the tool because of the complexities involved and adverse consequences that result because of sub-optimal markdowns. The challenges most retailers face today are threefold i.e. determining optimum markdown levels, determining timing of markdowns and identifying goods for markdown.

In this paper, Perceptive presents a methodology that enables a retailer to track performance of its merchandise and identify items that are under or over-performing and also discusses various best practices using which you can maximize value from markdown optimization.

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The post Markdown Optimization – Modeling Markdowns to Maximize Revenue first appeared on Perceptive Analytics.

Optimizing Marketing Spend with Marketing Mix Modeling

Marketers in CPG industry are constantly faced with the challenge of allocating their fixed marketing budget among various marketing channels like traditional communication mediums and digital channels.

To achieve effective budget allocation for marketing, companies have to rely on many of the marketing tools available today and create models that will show the impact each of this channel has on sales.

This case study will summarize the various marketing mix models available for marketers by analyzing the strengths and weaknesses of each of them. In particular, we focus on regression models, influence maximization models; agent based models and empirical methods being used by the marketers.

Check out the insightful case study!

The post Optimizing Marketing Spend with Marketing Mix Modeling first appeared on Perceptive Analytics.

Financial Modeling to Determine Customer Tariff

The client decided to spin off its gas division and focus independently on it. The newly formed entity provides two types of services viz., gas distribution and gas filtration.A major challenge in gas distribution business is fixing the tariff rate.

Faced with this challenge, the client approached Perceptive Analytics to build a model that could fix the tariff rate to be given to its end users.

We helped client assess the financial viability of the spin-off decision. The model presented a comprehensive control of key variables affecting the tariff rate. It calculated key metrics like ROI and IRR for a given tariff rate and also vice-versa enabling the client to set a competitive per gallon price.

Check out the model we developed!

The post Financial Modeling to Determine Customer Tariff first appeared on Perceptive Analytics.