Marketing Analytics: Pricing Strategies and Price Analytics

Learn how to set a pricing policy by analyzing objectives, demand, costs, and competitors to select methods and finalize the price, with emphasis on analytical steps using Excel.

Celebrate reaching this milestone in marketing analytics: pricing strategies and price analytics, and stay motivated to join the top 50% of learners.

Explore six pricing objectives: survival, maximum current profit, maximum market share, maximum market skimming, product quality leadership, and other objectives—and the conditions that govern their use.

Estimate demand by mapping the demand curve and measuring price elasticity with surveys, price experiments, and statistical techniques to link price changes to quantity demanded and revenue.

Examine linear and power demand curves using q for quantity and p for price; learn to estimate q = a − b p and q = a p^b in Excel.

estimate the linear demand curve using two points from survey or existing price, plot a scatterplot, fit a linear trend line, and derive the equation to compute demand and revenue.

Estimate a power demand curve with elasticity using q = a × p^b, solve for a from a point, and use Excel with absolute references to predict demand across prices.

Estimate a power demand curve in Excel by collecting multiple price–demand data points, plotting a scatterplot, applying a power trendline, and using the resulting equation to predict demand.

Model the demand as a quadratic function of price using three data points. Use Excel trendline to fit the equation and predict quantity demanded at other prices.

Learn to estimate a subjective demand curve from three price–quantity data points by plotting a scatterplot in Excel, adding a second-order polynomial trendline, and displaying the equation to predict demand.

Use Excel Solver to maximize revenue by choosing the price that best fits the predicted demand curve, applying constraints and nonlinear optimization methods.

Estimate production costs to set prices that cover fixed and variable costs, and note demand caps price while short-run cost falls with output before rising, and experience lowers costs.

use excel solver to maximize profit by pricing and producing at optimal levels; derive the cost function from production data and compare profit outcomes under demand y=-20x+1500 and cost y=-0.01 to 5x+26.25.

Understand complementary goods and how including them in profit maximization lowers the optimum price as purchases of one product trigger demand for its complement, boosting overall revenue.

Explore how complementary goods between printers and cartridges affect the optimum price for profit maximization, showing cartridges shift price from 42.5 to 37.5 and increase total profit.

Determine price ranges from demand and cost analytics, differentiate your product to command a premium, and anticipate competitors' reactions by adjusting prices based on their objectives and perceived feature value.

Analyze non-linear pricing through price bundling and two-part tariffs, showing how bundles lower per-unit costs and boost revenue while enhancing consumer surplus.

Explore when bundling increases revenue by examining negative versus positive valuation correlations, and compare pure bundling, mixed bundling, and no bundling with practical examples.

Examine bundling problems through consumer surplus and solve for the best bundle using Excel's evolutionary solver. Apply to a burger joint offering single, two-item, and three-item bundles.

Learn how to solve the bundling problem in Excel by pricing seven product bundles, using the evolutionary solver to maximize revenue, and computing consumer surplus with lookups and if statements.

Use Excel's Solver to maximize total revenue by adjusting bundle prices within bounds, applying the evolutionary solver with constraints, and observe a price reversal where only bundles sell.

Solve bundling problems in pricing analytics by identifying price reversals, applying penalties, and using Excel's evolutionary solver to maximize total revenue while optimizing bundle prices.

Explore non-linear pricing strategies, including quantity discounts and two-part tariffs, with standard and non-standard quantity discounts and practical pricing examples in Excel.

Analyze how individual demand curves reveal a customer's willingness to pay across quantities and solve quantity discount problems by estimating price as a function of quantity in Excel.

Solve a notebook bundles pricing problem using standard quantity discounts and non standard quantity discounts with Excel solver to maximize profit, given the demand curve Q = 20 − 2P.

This lecture shows how to use Excel's evolutionary solver to maximize profit from standard quantity discounts, using midpoint demand, cutoffs, and HP/LP pricing.

Explore non-standard quantity discounts and non-linear pricing, where a cutoff prices all units at the lower price, and Excel-based analysis maximizes profit by extracting consumer surplus.

Explore implementing a two-part tariff with fixed and variable prices, model profit via Excel formulas, and optimize with a solver to maximize profit under constraints.

Price skimming starts with a high initial price to extract consumer surplus from high-valuation buyers in the early product life cycle, then lowers as competition enters.

Maximize revenue with price skimming by setting four decreasing quarterly prices based on customers' perceived value, using countif and solver to optimize; it outperforms pricing but has downsides to consider.

Explore revenue management techniques that maximize revenue for time-sensitive and perishable goods. Learn to use markdown pricing and overbooking strategies to balance value, demand, and customer experience.

Master handling uncertainty in pricing by optimizing overbooking to maximize revenue, using no-shows, capacity 100, $200 tickets, $100 overbooking cost, and a norm inverse based Q in Excel.

Learn to apply lookup functions such as vlookup, hlookup, index, and match to retrieve student scores from tables, perform exact and approximate matches, and optimize data retrieval with index-match.