"I have had the pleasure to work with Prorize staff in multiple large-scale revenue management projects as a colleague and customer. They pioneered the pricing algorithms for many industries, including the multi-family industry. They have superior ability and experience to analyze, formulate, and design an end-to-end pricing system which produces measurable benefits. Prorize staff is particularly hands-on and is extremely easy to work with. I strongly recommend Prorize for anyone considering any pricing and revenue management initiative."
Case Studies
Prorize gives the competitive-edge to the world’s leading companies...
The following are two brief case studies for self-storage and high-tech manufacturing industries.
For more information on these and other businesses, please contact us.
Pricing for Self-Storage Firms
Pricing and revenue management in a self-storage firm can be viewed as a special case of matching supply to demand. In this view, available units determine the supply which has relatively complex structure. A self-storage product can be best described by unit size, unit location, and unit attribute. For example, a 10X10 drive-up unit without climate control defines a particular product which is more popular than a similar unit located inside the building at the same store. Likewise, all things being equal, an air-conditioned unit may be more popular in Florida than California. On the other hand, customer requests represent the demand which is highly uncertain based on many factors, including market, site, unit size, unit location, unit attribute, customer type (e.g., commercial, residential, student, military), population density, day of week, week of month, season, lead time, number of competitor sites, proximity of competitor sites, and quality of competitor sites. In addition, the industry extensively practices move-in incentives and discounts, and rates could be modified for existing leases. All these reasons make it extremely difficult to match demand and supply in way that maximizes long-term profit.
A large self-storage firm partnered with Prorize to uncover the most effective scientific and disciplined pricing strategies and to quantify the associated benefits. We started with a workshop and on-site interviews with key personnel and users. We then reviewed and cleansed the data to choose a suitable pricing data structure for analysis and modeling. Next, we used advanced clustering and decision tree techniques to identify the most effective segmentation dimensions. After that, we evaluated multiple forecasting and market response models and selected the models with the most reliable rate sensitivity measures and the minimum forecast errors. These predictive models were then incorporated into the proprietary hierarchical optimization framework to identify the optimum rent guidance for each site and unit. More specifically, the optimization model considered move-in forecasts, move-out forecasts, unit availability, minimum rate differences across unit types, rate sensitivities, existing customer rates, seasonality and rate bounds and optimized expected revenue. Associated revenue lifts were estimated to be 3 to 5 per cent.
Pricing for High-Tech Products
The optimal pricing of high tech products (e.g., PC components) is very difficult as there are rapid changes in technology, competition, and customer preferences. Prices quickly erode as products become obsolete due to high rates of technological innovations and shifting consumer expectations. In addition, manufacturers push sales through multiple channels, including OEMs and distributors.
In the OEM channel, a manufacturer sells its products to an intermediary firm which incorporates them into finished products for final consumers. In this channel, the manufacturer negotiates prices and other contract terms with the intermediary firm. Prices are usually set during the negotiation and are in effect for the contract duration. The manufacturer gets high volume and revenue commitments from an OEM customer, but has relatively low pricing leverage. On the other hand, distributors sell manufacturer’s products in the spot market where prices often change based on market conditions. The manufacturer has smaller and more frequent sales to distributors, but has higher pricing leverage.
Prorize partnered with a global high-tech manufacturer to identify the validity and benefit of using data-driven, scientific price optimization approaches for both OEM and distributors channels. We employed entirely distinct pricing approaches for each channel as they have significantly different business environments, customers, and data. We developed separate segmentation strategies, data pooling processes and predictive sensitivity models to optimize expected profits and revenues for both channels. We also incorporated product-line positions and lifecycles, performance-adjusted competitive prices, and complex pricing rules into our optimization modules. We then recommended optimal list prices, regional prices and distributor-specific prices for the distributors channel and optimal negotiation bands (optimal floor, target, and ceiling prices) for the OEM channel.
In addition, we rigorously quantified expected revenue benefits using a holdout sampling method. In this method, the data from the most recent period was intentionally excluded from the model at the time of optimization. Revenue benefits were then estimated based on what would have happened in that period given optimum prices. We also simulated sales mechanisms based on statistically likely ranges of price sensitivities. We then projected an annual revenue increase between 3.7 and 8.3 per cent for the distributors channel and an annual revenue increase between 2.8 to 6.8 per cent for the OEM channel.