How To Use Data To Understand Pricing

Data Lineage Dispatcher

 

Is pricing a mystery for your business? You are not alone. It has always been a delicate operation for businesses to increase/decrease prices. It is also tricky to determine when and how to communicate pricing changes to customers; even the most tiny adjustments can be sensitive for customers who might balk at the change and turn to competitors. It can be extremely hard to gauge the value perceptions that exist in your customer base when you don’t have the proper tools.

The wealth of data available today, however, can shed great light on pricing, and help you determine the optimal price for your product or service. Using data intelligently, you can maximize profit without losing business or turning potential customers off.

Conjoint study.
A conjoint study that focuses on how your customers value different attributes of products — price among them -can be the key. Conjoint analysis is a great marketing research technique that can identify why your customers make the purchase decisions they do. It can help you determine whether your customers will react positively or negatively to any new pricing strategies you have in mind, no matter if you are changing prices by a few pennies or thousands of dollars.

Keys to a smart study.
How can you most intelligently use a conjoint analysis to test pricing strategies? There are a few things to keep in mind as you undergo this process. First, make sure any prices you are testing are realistic, and can be easily changed. Be sure to test just a few prices at a time. This will help you avoid a skewed outcome that favors respondents who focus only on price.

You’ll also want to consider pricing as it relates to other product features. A price increase can be undertaken as part of a package that includes another feature such as quantity or efficacy, for example. Explore different options fully before determining the specifics of your study.

As you get your price testing options in line, you’ll also want to make sure the respondents you are testing are the right ones. Gleaning data from the proper sample of respondents who represent the right population of your customers is extremely important if you want to get robust study results. Before you start your test, evaluate respondent sampling very carefully.

Just as you want to make sure the right respondents are included, you also want to make sure the right mix of competitors is included in your study. Brand is a very important part of any purchase decision. To make sure your study is as realistic as possible, include competing brands as well as your own brand.

Once you have the findings from your study in hand, look for external data that can supplement those findings. Often available, this data can shed an entirely different light on your findings, and can enhance your decision-making about price significantly.

Gleaning intelligent data from a conjoint study can truly help you de-mystify the realm of pricing. Keep a few key things in mind as you formulate, undergo and evaluate your study — and its resulting data — and you’ll be well on your way to truly informed pricing strategies that maximize the profitability of your product or service.

 

anil kaulAbout Absolutdata Technologies, Inc.
 
Absolutdata is a leader in applying decision engineering to help the world’s largest companies make better decisions by bridging data, insights and action. Absolutdata has built strong expertise and traction with Fortune 1000 companies across 40 countries. The company specializes in big data, high end business analytics, predictive modeling, reporting and data management services. These services provide significant value to clients by helping them optimize marketing spends, undertake targeted marketing, and using customer relationship analytics to achieve superior retention and cross sell. The company is investing in emerging areas such as big data, digital and social media analytics. 
 

www.absolutdata.com

 

 

 

 

Image source: “Data Lineage Dispatcher” by User:Nichtich – Own work. Licensed under Public Domain via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Data_Lineage_Dispatcher.png#/media/File:Data_Lineage_Dispatcher.png

 

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