- Technology, Media & Telecom
- Pricing Analysis
- Data Science and Quantitative Modeling
- Europe and Central Asia
- Burkina Faso
- Congo, Dem. Rep.
- Ivory Coast
- South Africa
- Middle East
- Saudi Arabia
- West Bank and Gaza
- North and Latin America
- Asia- Pacific
- Hong Kong
- New Zealand
- South Korea
- Sri Lanka
Quantum-Web provides two solutions to operators, telecom regulators and international organisations:
- Tariffs Database
- Pricing Analysis
In terms of ongoing market tracking, we have developed a retail prices database to benchmark retail broadband prices for 100 countries with up to 6000 tariffs collect every quarter and include the following metrics for each tariff:
|MVNO||EDGE||Type of Contract|
|Cable Modem||Satellite||Duration of Contract|
|3G||Powerline||Monthly landline/ Cable TV Rental|
|Sub Access Technology||WiMAX||Bundled Type|
|ADS2||Client Type||Double Play|
|ADSL Naked||Business||Quad Play|
|FTTB||Device Platform||Mobile TV|
|FTTC||Wireless Modem||Free Wi-Fi|
|Overage cost or throttling speed|
Choosing a pricing strategy becomes more complicated with rapid technology development as new bundled tariffs are constantly introduced into the market and typically have a short life cycle.
Our consultants provide pricing analysis and taking complex national and international pricing structures and transform them into meaningful, understandable pricing strategies, cost models and competitive market analysis through:
- Nested Logit Model
- Price Optimisation
- Price Elasticity
- Conjoint Analysis
Nested Logit Model
The Nested Logit Model and its special case the Multinomial Logit Model are among the most popular models to study purchase behaviour of customers who face multiple substitutable products.
We are assisting operators to find very efficient solutions for the Nested Logit Models and to explore the implications for oligopolistic competition and dynamic pricing.
The selection of nests and products depend on:
- Product features
Based on our theoretical and practical knowledge of telecom tariffs, the price-sensitivity parameters are identical for all the products within the basket of products offered by operators and the markup, defined as price minus cost, is constant across all the products offered by operators and as the consequence price optimizations can be reduced to a single-dimensional problem.
The Nested Logit Model has been developed to relax the assumption of independence between all the alternatives, modeling the “similarity” between “nested” alternatives through correlation on utility components, thus allowing differential substitution patterns within and between nests. The Nested Logit Model has become very useful on contexts where certain options are more similar than others, although the model lacks computational and theoretical simplicity.
Price Optimization Models are mathematical programs that calculate how demand varies at different price levels, and then combine that data with information on costs and inventory levels to recommend prices that will improve profits. The modeling allows companies to use pricing as a powerful profit lever, which often is underdeveloped. Price Optimization Models can be used to tailor pricing for customer segments by simulating how targeted customers will respond to price changes with data-driven scenarios. Given the complexity of pricing thousands of items in highly dynamic market conditions, modeling results and insights help to forecast demand, develop pricing and promotion strategies, control inventory levels and improve customer satisfaction.
Why Optimise Prices
Optimization capabilities now exist and competitive pressure to use them is mounting
Price Optimization can dramatically improve a company’s financial and growth performance.
Pricing Actuaries should lead the effort but it will be most successful with a collaborative effort of multiple disciplines.
Price Optimization Inputs and Their Challenges
- Estimated costs
- Competitive position
- Competitive Information
- Price elasticity
- Company’s objectives
- Craft well
- Use all available information
- In Price Optimisation, errors will be ruthlessly exploited
- Price elasticity varies with the competitive position
- Easy to get lots of competitive information
- Hard to get complete & timely competitive information
- Competitive position more important for measuring elasticity of new business than renewing business
- The human decision making process
- Customer attributes and attitudes
- Company triggered changes
- Customer triggered changes
- External influences
- Key price elasticity variables
- Premium/premium change
- Competitive position
Price elasticity measures the sensitivity of the quantity demanded or the quantity supplied to the change in the price. In other words, how much will a change in price affect the quantity demanded or supplied?
Price elasticity is calculated by taking the percentage change in quantity divided by the percentage change in price. On a linear supply or demand curve (a straight line), you can use the following price elasticity formulas:
Ep = (% Change Q) / (% Change P)
Ep = ((Change in Q) / (Q1)) / ((Change in P) / (P1))
Ep = ((Q2 – Q1) / (Q1)) / ((P2 – P1) / (P1))
Price elasticity model is constructed from statistical analysis of historic customer purchasing behaviour
Price elasticity does NOT have a unit attached to it. That is, price elasticity is not measured in dollars or %, it is simply a ratio.
We should really look at price elasticity in two separate ways: the price elasticity of demand and the price elasticity of supply.
Price Elasticity of Demand
The first law of demand states that as price increases, less quantity is demanded. This is why the demand curve slopes down to the right. Because price and quantity move in opposite directions on the demand curve, the price elasticity of demand is always negative.
Price Elasticity of Supply
The typical supply curve is characterized by a line that slopes up to the right. Thus, as the price increases, more quantity is supplied. Because price and quantity move in the same directions on the supply curve, the price elasticity of supply is usually positive.
Conjoint Analysis is a statistical technique, mostly used in our pricing analysis, to determine what product (or service), features, or pricing would be attractive to most of the customers in order to affect their buying decision positively.
In conjoint studies, target responders are shown a product with different features and pricing levels. Their preferences, likes, and dislikes are recorded for the alternative product profiles. We then apply statistical techniques to determine the contribution of each of these product features to overall likability or a potential buying decision. Based on these studies, a marketing model can be made that can estimate the profitability, market share, and potential revenue that can be realised from different product designs, pricing, or their combinations.
It is an established fact that some mobile phones sell more because of their ease of use and other user-friendly features. While designing the user interface of a new phone, for example, a set of target users is shown a carefully controlled set of different phone models, each having some different and unique feature yet very close to each other in terms of the overall functionality. Each user interface may have a different set of background colours; the placement of commonly used functions may also be different for each phone. Some phones might also offer unique features such as dual SIM. The responders are then asked to rate the models and the controlled set of functionalities available in each variation. Based on a conjoint analysis of this data, it may be possible to decide which features will be received well in the marketplace. The analysis may also help determine the price points of the new model in various markets across the globe.
If you want to find out what we can do to improve any of your existing projects, feel free to contact Andrea Riddling on
+ 44 (0)20 3286 9570, (firstname.lastname@example.org),
or order a free sample