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How Pricing Analytics Can Drive Better Returns

How Pricing Analytics Can Drive Better Returns

In a previous blog, I had talked about how pricing based on advanced analytics can lead to “profit-margin lift of between 3 and 8 percent from setting prices at much more granular levels” (McKinsey)

With the advent of big data, the Price element in Michael Porter’s famous ‘4Ps of Marketing’ is even more important today, especially in margin-sensitive industries like retail, manufacturing, BFSI and healthcare. Considering that a 1% increase in pricing can lead to an 8.7% increase, on average, in operating profits for companies (McKinsey), pricing is now a strategic imperative that cannot be taken lightly.

As product offerings increase, retailers, e-tailers and Consumer Packaged Goods (CPG) brands need to get on top of their pricing strategy and drive it as a key advantage in today’s competitive consumer environment. Across-the-board discounts might help gain market share, but margins are driven by smart granular pricing, often at SKU levels, based on sales and other data.

Combine this with effective precision marketing enabled by digital channels, pricing can turn into a potent weapon in any marketer’s arsenal. Here’s a look at some of the best analytics-led methodologies that will help improve your pricing strategy:

Optimal Price Model

Optimal Pricing Models help in narrowing upon a launch price for new products as well as understanding the pricing for existing products based on its lifecycle.

These models are based on algorithms that use mathematical equations predicting how demand varies at various price points. Along with inventory costs and levels, prices can be simulated to correspond to various margin levels.

Predicting Threshold from Price Elasticity

Product prices can be given upper and lower cutoffs within which there would be a minimal impact on sales. This helps marketers provide merchandizers pricing flexibility for tactical purposes.

Analytics can be used to understand this price elasticity by predicting the impact of changes to retail sales of both one’s own brands as well as that of competitors.

Lift Models for Promotion

Historical data, such as lift charts, can be analyzed to understand the impact of various discounts. Lift Models for promotion consider time series parameters (Trend, Seasonality and Randomness) that affect sales, and provide a valuable input to pricing.

Strengthening Pricing Models

Pricing Models can be made more effective through the application of additional data such as domain expertise and relevance scoring of data sources.

Domain expertise about the industry, where SWOT-based predictions, or actual data, about how related products and services are faring under a given set of market factors, can strengthen the prediction of one’s own pricing significantly.

When considering a particular source of data, its relevance (and therefore, its impact on the pricing algorithms) can be weighted based on expert assessment. This can play a significant role in the outcome of the algorithms.
Using the latest big data techniques, sometimes new languages themselves, can improve algorithms’ performance as well, both in terms of time and output quality.

Finally, providing a “plug-and-play simulator” to various stakeholders, such as marketers and merchandizers, can help them make better data-based decisions even during tactical activities.

In conclusion, the pricing methodologies and models I spoke about above can help an organization avoid impulsive reactions to price changes by competitors, while maximizing margins and costs within one’s system.

Pricing strategies can provide a strong direction to marketing plans too, helping define relevant metrics and improving ROI-specific marketing activities.

All of this requires that your organization has implemented the right technology and has the right talent support to leverage your sales data, domain expertise and market inputs. Therein lies the key to pricing your way to market leadership.

Raghavender Sridhar

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