Tag: Pricing analytics

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Pricing Analytics for the Pharmaceutical Industry

In the pharmaceutical industry, clinicians, KOLs, payers, and patients play a crucial role in building effective pricing strategy for new products. The pharma companies need a strong understanding of their customers and current market conditions before setting an optimal launch price. The implementation of pricing analytics tool offers holistic insights on the product value, competitors, payer strategies, and customers’ willingness to pay.

One of the major challenges that pricing managers in the pharma industry deal with is getting all the internal stakeholders to agree on the pricing strategy. With pricing analytics, this is tackled as the assessment is a robust way of analyzing data. Quantzig’s pricing analytics assessment identifies the value drivers for products in the same segment and understands how it was perceived to recommend the optimal price. It also offers a solution to get a 360-degree view of the client’s sales to optimize price points.

The Business Challenge

A leading pharma retailer in Europe was facing challenges in setting prices for its products. The client lacked a robust pricing strategy, and mechanisms to track, monitor and manage the prices across different websites in different geographies. They wanted Quantzig’s pricing analytics assessment team to benchmark prices of existing retail products across various categories – personal care, beauty, household, grocery – against competitor products in the same categories in the market and identify which products would form the key price benchmarks.

Our Approach

By assessing sales data, customer feedback, product data, pricing data, and social media data, Quantzig’s analytics team analyzed the business problem. The team captured price adjustments from different websites and systems for consolidation and aggregation to perform price optimization on client’s various products.

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Pricing Analytics Assessment Benefits

  • Shared real-time visibility of product prices, market share, and discounts
  • Created analytical capability to monitor and benchmark prices across geographies
  • Benchmarked product prices against competitor offerings to gain insights into industry standard pricing
  • Analyzed online data for 1000+ price points across the client’s business product portfolio

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Pricing Analytics Predictive Insights

  • Tracked the effect of discounts on sales and the performance trends for top products online
  • Analyzed sales sensitivity of each product based on unit discount
  • Developed pricing strategy based on comparable products from different brands online
  • Analyzed social chatter about products prices and performance
  • Forecasted sales for the client’s products based on current prices

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Trade Promotions Optimization for a Leading CPG Manufacturer

Trade Promotion Optimization (TPO) is the process of utilizing integrated goals, factoring in promotion and supply constraints, and predictive analytics for precise promotion planning and increasing client loyalty. With declining sales and intense competitive pressure, the manufacturers in the consumer packaged goods (CPG) industry need a reliable trade promotion optimization (TPO) strategy. The existing attempts at TPO lack depth and are primarily based on qualitative information. TPO is one of the key activities for manufacturers in the CPG industry to increase short-term sales of the products.

Quantzig’s trade promotions optimization utilizes data from various promotions, price points, Captureand sales forecasts to offer actionable insights to the client. Our solutions help businesses generate better forecasts to analyze promotion efficiency of their products. Our ability to derive insights into the impact of trade promotions on consumer buying behavior offers a significant competitive advantage to manufacturers in the CPG industry.

The Business Challenge and Quantzig’s Approach

To improve sales and battle competitive pressure, the client, a leading CPG manufacturer in the US approached Quantzig to perform a trade promotions optimization. The client was struggling with promotion planning and execution due to the seasonal nature of their products. The client’s capability to plan promotions lacked depth and was based on qualitative or incomplete information. Volume planning was not linked to realistic promotional outcomes leading to inappropriate inventory levels and reduced profits. As a result, the client needed to develop a solution that could improve the ROI and overall cost savings.

The primary objective of this trade promotions optimization was to develop capabilities for precise promotion planning and increase client loyalty for their products. The team developedCapture a pricing simulations model that could model sales volumes based on different market scenarios that were needed to replace the instinct-based approach of the category management department.

To meet the specific requirements of the CPG manufacturer, the analytics team analyzed various historical sales data, category and product data, pricing data, and shipment data to offer insights on trade promotion optimization strategy. The research team focused on developing a solution that would improve the client’s promotional executions by combining these optimizations with simulations. The team developed effective pricing and promotions strategy and simulated sales based on different scenarios.

An overview of the data used and models applied are given below

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Business Benefits and Insights

With the objective of improving overall sales and ROI, and to create precise promotion planning to increase client loyalty, the predictive analytics team analyzed promotion efficiencies, based on time of day/month, retailer segments, and region to improve promotion reach and effectiveness and created a promotion plan Trade promotion3options based on levers such as cost, timing, and depth of promotional activities. The trade promotions optimization improved the client’s trade promotion planning process based on accurate volume forecasts and promotional efficiency.

It derived insights about the promotional impact on consumer buying behavior. The client achieved cost savings on packaging destruction costs from seasonal products due to the ability to forecast demand with high accuracy. A better forecast to analyze promotional efficiency helped the client achieve time savings of more than three months in the promotion planning process. The analysts assessed campaign effectiveness based on available data to make dynamic adjustments. The solutions from the trade promotion optimization engagement helped the client generate price simulations and discount offers based on retailer segment and promotional tactics. The team provided the user with accurate baselines and lift coefficients, and the ability to deliver one-off promotion predictions based on numerous pricing scenarios.

The client was able to create an effective promotion planning with the help of trade promotion optimization which helped improve customer loyalty. There was an increase in ROI and improvement in cross-selling and up-selling based on targeted promotions and pricing. The historical sales were broken into base, incremental, and total sales and incremental sales were mapped to the set of individual promotions and campaigns. The solutions helped the client develop an optimized annual plan for strategic promotions based on profit, volume, and revenue.

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Retail Supply Chain and Marketing Analytics – Connecting the Dots, Driving Growth

The one industry that is highly unpredictable and prone to disruption is retail. Proponents of this sector strive to wade through intense competition, high customer expectations, manage multiple channels and wide array of product offerings among several other challenges to create a competitive advantage and maintain their market position. To stay abreast of competition and latest trends, retailers must devise strategies based on insights and analytics to understand the business and anticipate customer behavior.

Analytics collects data from various channels used for marketing and combines it to provide a common direction that helps to extract analytical results which deliver actionable insights to drive the firm’s marketing efforts to the fore. It comprises of technologies and processes that assist marketers in ascertaining the success of their marketing efforts, by measuring performance through ROI and effectiveness.

Making Inroads into Retail Supply Chain

Advanced marketing strategies and analytics such as customer analytics can help tone down the anxiety in a significant business area like retail supply chains. Insights derived from historical as well as real-time data can help retailers understand customer behavior and segment them accordingly.

The adage “Customer is the King” is gaining importance in today’s competitive market environment. Increasingly, retailers are tailoring messages and creating a customized experience for the customers, to which the latter are responding in a favorable manner. Personalized marketing encompasses more than just mentioning customer’s name in an email, for example, personalized follow ups and insights derived from previous purchases and point-of-sale terminals can help build customer loyalty. Additionally, firms can use customer data to device practice scalable pricing and understand customer expectation in order to maximize retail ROI. Pricing analytics go hand in hand with personalized marketing so as to create happy consumers and build loyalty.

The sole purpose of analytics and big data is risk management – to foresee and mitigate possible risks, thereby creating significant savings in retail supply chain. With the help of data- external and internal- firms can develop strategies based on insights that help in supplier management, assessing store performance and effectiveness of marketing campaigns and marketing channels.

The Quantzig Approach

We, at Quantzig, track latest developments and innovations in the industry through different sources and methodology, reach out to key stakeholders and marketing experts in order to understand the market scenario. We help clients understand and identify fluctuations in consumer interests and devise insights based marketing tactics and strategies to stay ahead of the competition.

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70% loss reduction achieved by leading FMCG company by implementation of multi-factor price forecasting methods

Business Challenge: Implementation of a robust price forecasting mechanism.

The client wanted to develop a robust price forecasting mechanism that could help them negotiate better rates with suppliers and optimize future raw material inventory levels.

Situation: Lack of visibility on future prices and negotiation support.

Client was experiencing a lack of visibility on future Pyridine prices resulting in budgeting issues and supply chain fluctuations. In addition, the client also did not have access to fact based insights that could enable them negotiate long-term contracts with suppliers at individual category or product levels.

Solution/Approach: Pricing analytics solution enabling dynamic pricing at SKU and product levels.

We developed a multi-factor price forecasting model capturing various macro and micro-economic factors including Pyridine specific demand and supply factors. This model provided accurate price forecasting information and insights for improving supply chain efficiency.

Impact: Reduced levels of losses and improved supply chain efficiency.

Our model helped client in reducing losses by 70% in the first year along with improved supply chain efficiencies and optimized Pyridine inventory levels.

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9-11% sales growth achieved by a leading supermarket through implementation of pricing analytics

Business Challenge: Implementation of a robust pricing analytics solution

A leading supermarket chain in the US wanted to conduct a pricing analysis exercise for its grocery line of products that had premium as well as standard variants.

Situation: Reliance on traditional price setting methods resulting in reduced sales than competitors

Client had seen frequent change in competitor pricing and wanted to understand the impact on own products and also decide on optimal pricing for both premium and standard variants.

Solution/Approach: Price analytics solution enabling dynamic pricing at SKU and product levels

We conducted an in-depth analysis on the current demand, actual sales, and SKU performance. We constructed models to analyze price elasticity and sensitivity across the SKUs and variants. We also created scenarios to simulate the price change impact in relation to competitor pricing levels as well.

Impact: Improved sales growth through implementation of retail pricing strategy

Client achieved visibility on current SKU performance, demand and price variations. Also, they were able to implement a robust process based on price elasticity model for better understanding of impact of changes in competitor pricing. The key benefits achieved include reduced wastage and inventory cost for low performing SKUs, resulting in overall sales growth of 9-11%.

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Oil company improves pricing process and efficiency by implementation of pricing analytics solution

Business Challenge: Implementation of robust pricing model.

A leading energy client wanted to develop price model to better manage the impact of price changes on its business.

Situation: Setting up analytical models for price modeling.

The client wanted to improve its visibility on the oil market prices and based on this, setup analytical models for data based insights.

Solution/Approach: Pricing analytics solution for accurate price forecasting.

We collected data on oil market prices and changes in global oil market. We determined the optimum price points for maximization of profits and assessed price sensitivity based on production and supply, and effect of competitive pricing. The solution enabled clients to accurately forecast prices based on factors affecting price changes in future, as well as assess the impact on the margins.

Impact: Improved pricing mechanism and better control over volatility of supply demand conditions.

Client was able to gain visibility on oil market based on the statistical model. They were able to precisely track and forecast price impact on margins. Our solution helped the client in improving the pricing mechanism and establish better control over changes due to supply and demand issues.

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Implementation of pricing analytics helps leading telecom operator improve revenue growth

Business Challenge: Implementation of a robust pricing analytics solution.

A leading telecom operator in APAC region wanted to implement a robust model for determining right pricing ranges using data analytics based solutions.

Situation: Reliance on traditional price setting methods resulting in reduced revenue.

Client was facing issues with implementation of robust pricing strategy in correlation with actual customer data.

Solution/Approach: Pricing analytics solution based on call data records.

We developed a subscriber oriented pricing strategy by conducting in-depth analysis on the customer call data records. We made efficient use of call flow and usage data to enable the client in deriving optimal pricing levels based on past customer data and also to simulate the new prices to assess the expected success rate.

Impact: Improved revenue growth through implementation pricing analytics.

Client achieved improved revenue through implementation of customer oriented pricing strategies. Our solution also helped the client in developing scenarios based on the adjusted price levels and simulated possible outcomes before actual implementation.