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Sell-in (wholesale) Pricing
Most companies use simple approaches for sell-in pricing, which includes delegation of most of decision making to related sales people within certain boundaries. We believe that sell-in pricing excellence is very linked to central decision making and salesforce automation by creating a pricing engine including all complexities of business. There are many dimensions that, in theory, need to make an impact on pricing of a product or service and they all start with the right calculation of costs. We start with ensuring all related costs such as capital costs are included in COGS or COSS. For mark-up – or mark-down in some cases – we include all relevant dimension according to client specifics. These dimensions include buyer segment or relation level, size of transaction, demand trend for the good or service, current stock, promotions, competition, etc. We then develop the logic for basket pricing for multiple product or service purchases and combine all these in a pricing engine. Our engagements usually include the design of the processes and tools required to run the engine. We utilise our machine learning engineers for developing predictive models and service designers for designing the user experience of tools whenever necessary.
Strategy Consulting
Experience & Service Design
Machine Learning & AI Teams
New Digital Technologies
Sell-out (retail) Pricing
Retail pricing needs to be approached separately for retailers with low number of SKUs and high number of SKUs. For retailers with low number of SKUs, we determine price elasticity of demand based on primary research and historical data on category and define the optimal price levels. Defined price levels will include both current nominal values and price indexes with respect to competition for future iterations. If applicable based on product portfolio, we also define main, filler and killer products for bundling and their respective price levels using various research techniques such as MaxDiff. For retailers with high number of SKUs, we believe dynamic pricing is needed to be competitive and to generate maximum value in existing market conditions. Dynamic pricing requires detailed analysis of shopper data in order to identify segments of categories (or SKUs) that drive volume but are highly price sensitive and categories (or SKUs) that drive profits but are not sensitive to price levels. The first segment will likely drive traffic to stores, while the latter will generate value at basket level. We design sophisticated pricing engines that can continuously segment and determine price levels for categories (or SKUs) using both shopper data, stock levels, geographic location, channel and category managers' inputs.
Strategy Consulting
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