What are the best tools and methods for pricing strategy data analysis?

Apart from mentioned tools there is a necessary survey for mainly subscriptional pricing - the con-joint analysis. Basically it is the least bad approximation of consumer customer behaviour in relation to price and will give you the boundaries for your pricing, and tell what the wrong price is (on both ends). In order for this to be useful however you first need a hypothesis for your pricing, which in turn requires a lot more data and data analysis. But at the end you want to test your pricing ranges versus customers and the market, and then the con-joint comes into play.

For the industry I operate in, fashion and beauty, the best place to start is a competitive benchmarking tool. One that gathers competitive data, enriches it as well as visualise it in ways you can easily draw conclusions. When looking out for a tool, the key areas to consider is the user friendliness of the platform and the accuracy of the data.

Collecting relevant and reliable data is foundational in pricing strategy analysis. Using a mix of qualitative and quantitative methods ensures a comprehensive understanding of customer behavior and preferences. Surveys and interviews provide deep insights into customer willingness to pay and satisfaction levels. Tools like SurveyMonkey, Typeform, and Qualtrics facilitate this process effectively. Additionally, web analytics tools such as Google Analytics, Mixpanel, and Hotjar help track visitor behavior, while CRM systems like Salesforce, HubSpot, and Zoho CRM capture customer interactions and sales data. This multi-source data collection is crucial for formulating an informed pricing strategy.

I have conducted a few conjoint studies myself using the SPSS conjoint module. However, nowadays, it is simpler to use a fully automated system. For example, Conjointly is a leading platform for conjoint analysis. Built with simplicity in mind, it helps you uncover: - Importance of product features - Willingness to pay and optimal pricing - Best product and advertising messaging - Evaluation and comparison of concepts - Source of business and cannibalization Another company with vast experience is Sawtooth.

The first step in pricing strategy data analysis is to collect relevant and reliable data from various sources. Depending on your objectives, you can use tools like SurveyMonkey, Typeform, or Qualtrics to gather qualitative data through surveys or interviews. Web analytics tools like Google Analytics or Hotjar help track website visitor behavior, while sales data tools like Salesforce or HubSpot record customer transactions and interactions. This data collection helps you understand customer preferences, behavior, and pricing performance, providing a solid foundation for your pricing strategy.

Most of the B2B pricing world still uses Excel as their primary method of data analysis. Pricing software isn't focused enough on the majority of industries because the budget for tools isn't high enough. Tableau is probably the 2nd most common in the space but isn't focused enough on pricing. Pricing professionals as time goes on are going to need to be more strategic and figure out how to automate more of the data analysis for the function to continue to thrive.

Excel is a great common denominator in collecting, analysing and communicating pricing information. If you go down a lesser-known route (eg. Power BI), you may find that your audience doesn't speak that language and some of what you're trying to communicate is lost in computer translation. Whilst Excel is imperfect, it is widely "spoken".

Big companies like Netflix harness these analytical methods to refine their pricing strategies continuously. Netflix uses descriptive analysis through tools like Tableau to monitor subscription trends and user engagement. With Python and R, they perform inferential analysis to understand customer behavior and validate pricing models. For prescriptive analysis, Netflix employs advanced machine learning frameworks such as TensorFlow to predict optimal pricing strategies that maximize user retention and revenue. By adopting similar analytical approaches, businesses can enhance their pricing strategies, making informed, data-driven decisions that drive profitability and customer satisfaction.

We use many methods for analyzing data for pricing. The price index is one of them. Such helpful when comparing competitor prices to see if you're selling cheaper or more expensive than the rest. Dynamic pricing rules are also great for creating specific/ more brandish prices. You can specifically want to be %2 cheaper than your closest competitor or exactly stay at the same price(smartwatch).

The second step in pricing strategy data analysis is to apply the right methods to interpret data. Depending on your goals, you can use descriptive, inferential, or prescriptive analysis. The descriptive analysis summarizes data and identifies trends using tools like Excel, or Power BI. Inferential analysis tests hypotheses and draws conclusions with tools like R or Python. Prescriptive analysis recommends actions based on data, utilizing tools like Solver or TensorFlow to optimize pricing decisions.

This is one of the biggest areas of improvement that is needed in the pricing community. We need better data visualization tools for the pricing community that allow us to more quickly and efficiently communicate with stakeholders. There isn't a great tool right now.

Companies like Amazon leverage sophisticated data visualization tools to communicate their pricing strategies effectively. Amazon uses Tableau and Power BI to create interactive dashboards that provide real-time insights into pricing trends, customer behavior, and market dynamics. These dashboards help management make quick, informed decisions. Additionally, they use infographics and presentations created with tools like Canva and PowerPoint to share strategic insights across different departments and with external stakeholders. By adopting similar visualization techniques, businesses can enhance their ability to convey critical data insights, ensuring alignment and clarity in their pricing strategies.

I agree with Mr Herbert's conclusion that we need better tools, quicker and more agile ones that allows us to swiftly tinker with the data and disclose it in different ways. For me excel still rules, whatever that says about excel or the alternatives. Put in the data, show it in different diagrams, on different axes, with different scale on the axes, with/without other data, combine parameters that you do not know what the combination means, and keep doing it - for a long time. Do not hesitate to combine internal data with external, it will create new combinations and insights you otherwise will not catch. Be unorthodox and allow yourself to spend time on it. It is a grind, not an Eureka moment.

Both Power BI and Tableau can solve the majority of Pricing needs regarding data visualisation, from presenting effective dashboards to reporting key metrics, identifying patterns, analyzing the distribution of the data, etc. Basic charts suffice for most pricing problems, (line, bar, pie and waterfall charts) but both tools also support more complex visualizations like box charts, distribution charts, etc. In my opinion, combining Excel with Power BI is a great solution for handling small and big data sets and presenting the data effectively for the majority of companies. The advantage is that both tools are widely known, easy to use and get updates every month with relevant features.

The third step in pricing strategy analysis is to communicate insights to stakeholders using data visualization tools. Charts, graphs, dashboards, and infographics help present patterns and key messages clearly and compellingly. Tools like Excel, Tableau, Power BI, and Canva can assist in creating these visualizations.

Factors to take into account when choosing tools for respective business solutions:- 1. The ability of the tool to handle the scale of business data 2. Flexibility of customization to suit the users' need 3. Data security as well as compliance 4. Ease of use and tech support 5. Compatibility if there is an existing process/tools 6. ROI vs total cost of ownership 7. Real time analysis would be an advantage for businesses that require timely insights

I created a comprehensive diagnostic tool to assess an organization's *pricing capabilities*. It consists of 60 items that load on 11 factors. When working with clients, it helps to start with this assessment, as it provides a cross-industry benchmark and reveals some low-hanging fruits. Overtime, it can be used to track progress on pricing excellence. #pricing #value-based-pricing #culture

Leverage statistical software (SPSS, R, Python) for in-depth analysis, BI tools (Tableau, Power BI) for visualization, and pricing optimization software (Pricefx, Zilliant) for advanced modelling. Combine with competitive intelligence and CRM data to understand price elasticity, customer segmentation, and profitability. Employ A/B testing and churn analysis for continuous improvement.

Competitive Analysis: Examining competitor pricing strategies and market dynamics can provide insights into pricing trends, positioning, and potential opportunities. Time Series Analysis: If you have historical pricing data, time series analysis can help identify seasonal patterns, trends, and forecast future prices. Price Elasticity Analysis: Analyzing price elasticity helps determine the impact of price changes on demand and optimize pricing strategies accordingly. Remember that the choice of tools and methods depends on the specific requirements of your business, the available data, and your analytical capabilities. Combining multiple tools and methods can often provide a more comprehensive understanding of your pricing strategy.

Industry experience in direct correlation of any market sector plays a vital role in costing models where the requestor knows the product they are working with. For example try and ask for someone to obtain at the best cost 5,000 feet of wire that has only known semi conductors.