The Total Economic Impact™ Of Mastercard SpendingPulse
Cost Savings And Business Benefits Enabled By SpendingPulse
A Forrester Total Economic Impact™ Study Commissioned By Mastercard, February 2025
Organizations strive to gain insights into consumer spending patterns to make data-driven decisions that enhance strategic planning and operational efficiency. By benchmarking their performance against industry standards and identifying emerging trends, organizations can adjust their strategies accordingly. Understanding macroeconomic impacts, regional variations, and sector-specific dynamics enables more informed decision-making, improved forecasting, and better alignment with market conditions.
SpendingPulse uses aggregated and anonymized Mastercard insights as a macroeconomic indicator of national retail sales, representing all payment types in select markets around the world. The SpendingPulse platform releases frequent, accurate, and near-real-time spend insights ahead of government published data with a strong correlation. It also allows businesses to monitor industry trends, benchmark performance, and make data-driven decisions by offering detailed reports and visualizations of spending behavior.
Mastercard commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying SpendingPulse.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of SpendingPulse on their organizations.
Return on investment (ROI)
105%
Net present value (NPV)
$1.03M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed five decision-makers with experience using SpendingPulse. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a food delivery and retail organization with revenue of $10 billion per year.
Interviewees said that prior to using SpendingPulse, their organizations relied on incomplete data sources and internal tools that often provided outdated or insufficient insights, leaving them with fragmented data and a lack of up-to-date visibility into market trends. These limitations led to challenges in accurately forecasting, benchmarking performance, and making informed strategic decisions.
After the investment in SpendingPulse, the interviewees reported notable improvements in their ability to access granular, near-real-time data on consumer spending patterns. Sales and marketing teams at the interviewees’ organizations leveraged the insights for targeted marketing campaigns and sales strategies, while finance departments used the data for financial analysis and decision-making. Procurement teams utilized the data for sourcing, purchasing, and inventory management. Key results from the investment include enhanced benchmarking capabilities and better alignment with market conditions, ultimately leading to making more data-driven decisions across the business.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Increased consumer engagement and increased revenue during critical promotional periods by up to 1.5%. SpendingPulse data enables the composite organization to fine-tune marketing strategies by analyzing consumer spending patterns, resulting in notable revenue lifts through targeted marketing and cross-selling promotions. Additionally, understanding regional spending patterns allows the composite to optimize inventory levels, reduce stockouts, and increase inventory turnover. Over three years and a cumulative total of 20 promotional cycles, the revenue lift attributable to the Mastercard solution is worth more than $1.0 million to the composite organization.
- Reduced food spoilage by 2.5% by optimizing regional inventory levels. The composite organization uses SpendingPulse data to proactively adjust procurement and inventory strategies based on shifts in consumer spending patterns and preferences and reduce spoilage. By complementing internal data with SpendingPulse insights, the composite makes data-driven decisions regarding product sourcing, inventory levels, and supply negotiations. This impact of improved operational efficiencies, optimized inventory management, and minimized overstocking and spoilage is valued at $366,000 over three years for the composite organization.
- Reduced time spent on researching data and creating reports, allowing data scientist resource reallocation. Access to SpendingPulse allows the composite organization to understand broader industry trends and macroeconomic events. Previously, data scientists relied on time-consuming internet searches and free public resources, which made it difficult to obtain comprehensive insights. With the Mastercard solution, the composite could complete tasks that once required several full-time employees in a couple of hours, allowing it to allocate resources more efficiently and focus on strategic decision-making. These savings are worth close to $630,000 to the composite over three years.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Enhanced decision-making confidence. SpendingPulse helps the composite organization make more informed decisions by providing comprehensive, near-real-time data on consumer spending patterns. This data helps the composite benchmark its performance against industry standards and identify emerging trends. As a result, the composite’s decision-makers feel more confident in their strategic choices, which are backed by robust and current data. This confidence translates into more decisive and proactive management, ultimately leading to better business outcomes for the composite organization.
- Improved cross-departmental collaboration. The detailed insights from SpendingPulse facilitate better communication and collaboration across different departments within the composite organization. By providing a common data source, the platform ensures that all teams, from finance to marketing to supply chain, are aligned in their understanding of market conditions and consumer behavior. This alignment fosters a more cohesive strategy and enables departments to work together more effectively, leveraging shared insights to drive overall business performance.
- Enhanced responsiveness to market changes. With SpendingPulse, the composite organization can quickly identify and respond to shifts in consumer behavior and market conditions. The near-real-time nature of the data allows its decision-makers to detect trends and anomalies as they happen, enabling them to adjust their strategies promptly. This agility helps the composite stay competitive and resilient in a rapidly changing economic landscape, ensuring it can capitalize on opportunities and mitigate risks more effectively.
- Strengthened partner relationships. SpendingPulse data helps the composite organization demonstrate its value to partners by providing clear evidence of market trends and performance benchmarks. This transparency builds trust and strengthens relationships with partners, as they can see the direct impact of collaborative efforts on business outcomes. By leveraging insights from the Mastercard solution, the composite can better support its partners’ goals and foster long-term, mutually beneficial partnerships.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Annual Mastercard SpendingPulse subscription fee. The composite organization pays an annual subscription fee for access to the SpendingPulse platform, which includes initial delivery support such as onboarding, integration support, and user training across various departments like procurement, sales, marketing, and finance. The fee also covers annual support with regular touchpoints to discuss recent trends, forecasts, and ad hoc analyses. This cost totals $326,000 over three years for the composite organization.
- Initial and ongoing internal costs. The composite organization incurs additional internal costs for the initial adoption of the SpendingPulse platform, including staff training, integrating data flows with internal systems, and ongoing use and optimization of the data platform and reports. Initially, data scientists manually export raw data into spreadsheets for analysis while the composite invests six months in integrating SpendingPulse data with its enterprise resource planning (ERP) system, inventory management, and data analytics platforms. These internal costs add up to $654,000 over three years for the composite organization.
The representative interviews and financial analysis found that a composite organization experiences benefits of $2.01 million over three years versus costs of $980,000, adding up to a net present value (NPV) of $1.03 million and an ROI of 105%.
Key Statistics
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Return on investment (ROI)
105%
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Benefits PV
$2.01M
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Net present value (NPV)
$1.03M
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Payback
<6 months
Benefits (Three-Year)
Revenue lift from driving targeted marketing strageties Spoilage reduction Data analyst resource reallocationTEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in SpendingPulse.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that SpendingPulse can have on an organization.
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Due Diligence
Interviewed Mastercard stakeholders and Forrester analysts to gather data relative to SpendingPulse.
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Interviews
Interviewed five people at organizations using SpendingPulse to obtain data about costs, benefits, and risks.
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Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
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Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
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Case Study
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Disclosures
Readers should be aware of the following:
This study is commissioned by Mastercard and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in SpendingPulse.
Mastercard reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Mastercard provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Anna Orban-Imreh
Drivers leading to the SpendingPulse investment
Interviews
| Role | Industry | Region | Annual Revenue |
|---|---|---|---|
| Business analysis manager | Quick service restaurants | Global | $35 billion |
| Global business performance director | Hospitality | Global | $600 million |
| Procurement manager | Food distribution and retail | US | $35 billion |
| Partnership data analyst | Financial services | Global | $25 billion |
| Credit data analyst | Financial services | Global | $25 billion |
Key Challenges
Before implementing SpendingPulse, the interviewees noted their organizations typically relied on less comprehensive data sources and internal tools that often provided outdated or insufficient insights. These prior solutions included proprietary tools, manual data aggregation, and public resources like internet searches. These methods were time-consuming, lacked up-to-date visibility, and often resulted in fragmented data. As a result, the interviewees’ organizations faced challenges in accurately forecasting, benchmarking performance, and making informed strategic decisions. The absence of data sources and resources to effectively gather, analyze, and interpret data made it difficult for the interviewees to understand broader industry trends and support their organizations in responding effectively to market changes.
The interviewees noted how their organizations struggled with common challenges, including:
- Fragmented and outdated data. Interviewees mentioned that their organizations struggled with fragmented and outdated data, which hindered their ability to make timely and informed decisions. Prior to using SpendingPulse, their teams relied on internal tools and public resources that provided limited insights. The business analysis manager at the quick service restaurant said, “We never had dedicated support and as we tried to tell the revenue narrative and really speak to broader industry trends, it was actually pretty difficult.” This manager explained how the lack of comprehensive data sources made it challenging to understand market trends and respond effectively to changes.
- Inefficient data aggregation processes. For the interviewees’ organizations, the process of aggregating and analyzing data was often inefficient and time-consuming. The procurement manager at the food distribution and retail company described how their proprietary tool required significant effort and resources to gather and interpret data: “It took a long time with the proprietary tool and the data became stale very quickly. We weren’t able to be more dynamic or nimble in a lot of our purchasing decisions.” This inefficiency limited their organization’s ability to react swiftly to market demands and optimize its operations.
- Limited benchmarking capabilities. Several interviewees mentioned that their organizations faced difficulties in benchmarking their performance against industry standards due to the lack of granular and timely data. The credit data analyst at the financial services firm highlighted this issue, explaining that their holiday spend benchmarking was not as robust before implementing the Mastercard solution: “SpendingPulse is one of our primary benchmarks, especially around holiday spend. It provides the granularity we need to track consumer behavior and compare our performance to the industry.” Without such detailed benchmarks, the interviewees’ organizations struggled to gauge their market position accurately.
- Inadequate support for strategic decision-making. The absence of comprehensive data and dedicated support impeded strategic decision-making at several of the interviewees’ organizations. This uncertainty made it difficult for organizations to make confident and proactive decisions. The credit data analyst at the financial services firm emphasized the importance of having reliable data to inform their organization’s strategies: “Before SpendingPulse, our holiday benchmarking wasn’t as robust. The granularity of the Mastercard data is incredibly useful, providing essential insights for decision-making we didn’t have before.”
Solution Requirements/Investment Objectives
The interviewees’ organizations sought a solution to enable their teams to make data-driven decisions, optimize operations, and enhance overall performance, with the following specific investment goals in mind:
- Benchmarking against market performance. The interviewees noted their organizations looked to invest in digital solutions to enhance their ability to benchmark performance against the wider market and identify areas for improvement, while also leveraging comprehensive industry insights and benchmarks to make informed decisions on product sourcing, pricing strategies, and marketing positions.
- Near-real-time data and granularity. The interviewees’ organizations sought access to timely data with granular insights to understand consumer behavior and market trends.
- Enhanced forecasting capabilities. The interviewees’ organizations wanted to implement approaches to improve forecasting capabilities to anticipate market trends and adjust strategies accordingly.
- Efficient data aggregation and analysis. The interviewees’ organizations looked to streamline data aggregation and analysis processes to save time and resources.
- Risk mitigation. The interviewees’ organizations wanted to mitigate risks by understanding shifts in consumer spending patterns and preferences.
- Improved marketing and sales strategies. The interviewees’ organizations sought additional ways to gain insights that allowed their teams to develop targeted marketing campaigns and improve sales strategies.
After evaluating multiple vendors, the interviewees’ organizations chose the Mastercard solution and began their SpendingPulse deployments.
- The global business performance director said their hospitality firm evaluated five or six different providers and decided to select the Mastercard solution due to its comprehensive coverage of all US markets. Their organization immediately began using it to understand consumer behavior and market trends and for monthly performance benchmarking and forecasting. SpendingPulse data helped inform strategic decisions and supported various departments, including finance, marketing, and supply chain.
- The two interviewees from the foods service industry conducted internal evaluations and found SpendingPulse to be the most holistic tool, covering all necessary market segments.
- The business analysis manager noted that initially, their quick service restaurant manually exported raw data from the Mastercard solution into spreadsheets, built custom models and reports, and then compared this data with their internal sales data for comprehensive analysis. Over time, the interviewee’s organization integrated SpendingPulse data into their existing systems, using it for monthly and quarterly reporting.
- According to the procurement manager, in the first year of using the Mastercard solution, their food distribution and retail company integrated SpendingPulse with their ERP system, inventory management, and data analytics platforms for more automated monthly and quarterly reporting. The integration process spanned approximately six months and involved data engineers, who developed the database infrastructure and ensured data integrity; developers and programmers, who created the APIs, connectors, and middleware; and a consulting agency that assisted with the implementation, providing expertise and additional resources.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the interviewees’ organizations, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite organization is a leading food distribution and retail company worth $10 billion annually with a strong presence in the US and expanding global operations. The composite leverages advanced technology and strategic partnerships to deliver high-quality food products and services to a diverse range of clients, ensuring timely delivery of a broad range of food products to over 300,000 locations. This includes partnerships with local and international suppliers to maintain a diverse and high-quality product portfolio. In addition to distribution, the composite has a significant retail presence, offering a variety of food products through its own branded stores and online platforms.
Deployment characteristics. The composite organization utilizes advanced e-commerce solutions and business technologies to streamline its operations. This includes leveraging SpendingPulse data to optimize inventory management, forecast demand, and tailor marketing strategies. Initially, the composite organization manually extracts SpendingPulse data and feeds it into its spreadsheet model to analyze consumer spending patterns and make data-driven decisions regarding product sourcing, inventory levels, and marketing strategies. After a six-month integration effort, the composite organization successfully automates the data extraction and integration process, linking SpendingPulse data directly with its ERP system, inventory management, and data analytics platforms, such as Power BI. The initial rollout covers data scientists who use the integrated data to perform in-depth analyses and generate actionable insights. Over time, the deployment scales to additional departments, including procurement, sales, marketing, and finance.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Revenue lift from driving targeted marketing strategies | $248,625 | $497,250 | $497,250 | $1,243,125 | $1,010,564 |
| Btr | Spoilage reduction | $90,000 | $180,000 | $180,000 | $450,000 | $365,815 |
| Ctr | Data analyst resource reallocation | $218,500 | $273,125 | $273,125 | $764,750 | $629,562 |
| Total benefits (risk-adjusted) | $557,125 | $950,375 | $950,375 | $2,457,875 | $2,005,941 | |
Revenue Lift From Driving Targeted Marketing Strategies
Evidence and data. SpendingPulse data enabled the interviewees’ organizations to fine-tune their marketing strategies, leading to increased consumer engagement and higher revenue during critical promotional periods. Interviewees noted:
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Revenue lift from targeted marketing and cross-selling promotions during seasonal promotions. Interviewees’ organizations leveraged SpendingPulse data to generate notable revenue lift through targeted marketing and cross-selling during seasonal promotions. By analyzing consumer spending patterns and trends, they could tailor their marketing strategies to align with consumer preferences during peak spending periods.
- The procurement manager at the food distribution and retail company highlighted how they combined SpendingPulse data with internal data sources to identify rising demand for organic and gluten-free products in specific seasons. This insight allowed their organization to target offers and promotions effectively, translating to additional cross-selling revenue. The manager noted: “A good success story is how we managed our BBQ category, which is retail focused. We analyzed when people were likely to purchase certain grilling products and understood what accessories cross-sold and what meats they would be most interested in. The biggest insight was the demand for plant-based items, which was new to us. We aligned with the brands that were part of the insights we uncovered and purchased slightly less meat than anticipated. This also resulted in a 12% increase in sales, which amounted to an additional $10 to $12 million for that category.”
- The global business performance director stated their hospitality firm utilized SpendingPulse data to understand market trends and consumer behavior during key periods like Thanksgiving and New Year’s Eve. By benchmarking their performance against industry trends, they were able to optimize their marketing efforts and drive higher guest spending. The interviewee explained: “We know that during certain times of the year, we’re going to be busier, and this is reflected in the SpendingPulse data. We compare our performance with other industries to see if they are experiencing the same trends. While we use various data sources, one of the main ones is SpendingPulse.”
- The credit data analyst at the financial services firm stated they also used SpendingPulse data to enhance their existing customer marketing campaigns, particularly during certain peak seasons. By focusing on total retail and apparel spend, they were able to benchmark their performance and drive targeted promotions. The interviewee explained: “In addition to internal data, we use SpendingPulse data in our executive presentations. These insights from SpendingPulse help in sizing the market and showing market trends.”
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Revenue lift from aligning inventory with regional demand. By understanding regional spending patterns, implementing targeted offers and promotions that resonate with local consumer preferences, and adjusting inventory accordingly, the interviewees’ organizations could optimize their stock levels, reduce stockouts, and increase inventory turnover.
- The global business performance director noted their hospitality firm used SpendingPulse data to understand regional variations in spending patterns, which helped them make informed decisions about inventory and promotions. By focusing on high-growth areas and adjusting their strategies accordingly, they were able to drive higher revenue. The interviewee said: “The data presented to us in SpendingPulse allows us to identify opportunities based on the venue prospects. By analyzing SpendingPulse data, we can identify high-growth areas and make informed decisions about which regions to expand in or increase our marketing efforts.”
- The procurement manager at the food distribution and retail company highlighted how data scientists were able to identify regional demand for specific products, which allowed their organization to adjust its inventory and marketing strategies, resulting in higher inventory turnover and an increase in sales. The interviewee noted: “Combining SpendingPulse data with our internal data sources allowed us to be more targeted. We could see rises in demand in specific areas, so we devised targeted offers and promotions. This approach allowed us to scale our business significantly. We started purchasing more for those areas and we were doing a lot of targeted marketing. As a result, we saw a 15% increase in sales over four months, which amounted to around $2 million to $3 million. This also gave us a 20% boost in our inventory turnover and minimized all of our stockouts.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- SpendingPulse data informs strategy and planning for two of the composite’s seasonal promotions in Year 1 and all four of its seasonal promotions in Years 2 and 3.
- Seasonal promotions make up 10% of the composite’s annual revenue, with each seasonal promotion yielding on average $250 million.
- The percentage of revenue lift from targeted marketing and cross-selling promotions due to SpendingPulse is 1.2%, which is 10% of the experienced 12% revenue lift.
- SpendingPulse data informs strategy and planning for two of the composite’s regional promotions in Year 1 and all four of its regional promotions in Years 2 and 3.
- Regional promotions make up 5% of the composite’s annual revenue, with each regional promotion yielding on average $125 million.
- The percentage of revenue lift from aligning inventory with regional demand due to SpendingPulse is 1.5%, which is 10% of the experienced 15% revenue lift.
- The composite organization operates with a net profit margin of 3%.
Risks. The expected financial impact is subject to risks and variation based on various factors, including:
- The level of commitment from leadership to integrate SpendingPulse data into decision-making processes.
- The ability to integrate SpendingPulse data with existing internal systems and databases.
- The level of effective collaboration between marketing, sales, procurement, and finance departments to leverage SpendingPulse insights.
- The ability to customize SpendingPulse reports and dashboards to meet specific organizational needs.
- The implementation of regular feedback loops to assess the effectiveness of promotional strategies and make data-driven adjustments.
- The commitment to continuous market research to complement SpendingPulse insights and refine promotional strategies.
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.0 million.
Revenue Lift From Driving Targeted Marketing Strategies
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Seasonal promotions supported with SpendingPulse data | Composite | 2 | 4 | 4 | |
| A2 | Average revenue per seasonal promotion before SpendingPulse | Composite | $250,000,000 | $250,000,000 | $250,000,000 | |
| A3 | Revenue lift from targeted marketing and cross-selling promotions | Interviews | 1.2% | 1.2% | 1.2% | |
| A4 | Regional promotions supported with SpendingPulse data | Composite | 2 | 4 | 4 | |
| A5 | Average revenue per regional promotion before SpendingPulse | Composite | $125,000,000 | $125,000,000 | $125,000,000 | |
| A6 | Revenue lift from aligning inventory with regional demand | Interviews | 1.5% | 1.5% | 1.5% | |
| A7 | Operating profit margin | Composite | 3.0% | 3.0% | 3.0% | |
| At | Revenue lift from driving targeted marketing strategies | (A1*A2*A3)+(A4* A5*A6)*A7 | $292,500 | $585,000 | $585,000 | |
| Risk adjustment | ↓15% | |||||
| Atr | Revenue lift from driving targeted marketing strategies (risk-adjusted) | $248,625 | $497,250 | $497,250 | ||
| Three-year total: $1,243,125 | Three-year present value: $1,010,564 | |||||
Spoilage Reduction
Evidence and data. The interviewees’ organizations leveraged SpendingPulse to complement their own data in identifying shifts in consumer spending patterns and preferences, with the goal of proactively adjusting procurement and inventory strategies to avoid overstocking. This enabled executives in the food services industry to make data-driven decisions regarding product sourcing, inventory levels, and supply negotiations. Interviewees reported that their organizations used SpendingPulse data to improve operational efficiencies, optimize inventory management, and reduce overstocking and spoilage.
- The procurement manager at the food distribution and retail company said: “We aligned with the brands that were part of the insights we uncovered and purchased slightly less meat than anticipated. This alignment allowed us to allocate certain transportation modes, such as trucking and backhauls more efficiently. We rerouted many of our shipping lanes, which helped us save costs and improve our margins.” The interviewee added: “We also understood destocking efforts based on the insights we gathered. We reduced our inventory by about 30% for declining categories and pared back our SKUs, focusing on our staples. This freed up a lot of capital in terms of inventory cost and also helped us avoid spoilage.”
- The global business performance director at the hospitality firm said: “We use SpendingPulse data to understand customer behavior and demand. For example, around Thanksgiving, there’s a drop-off in guest spend in our restaurants because people are spending more money at home during the Thanksgiving holiday. This helps us plan our inventory accordingly and reduce spoilage.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- SpendingPulse data informs strategy and planning for two of the composite’s regional promotions in Year 1 and all four of its regional promotions in Years 2 and 3.
- Regional promotions make up 5% of the composite’s annual revenue, with each regional promotion yielding on average $125 million.
- The composite operates with 80% cost of goods sold (COGS) and 2% cost of spoilage per promotion.
- The percentage of reduction of spoilage from optimizing regional inventory levels due to SpendingPulse is 2.5%, which is 10% of the experienced 25% reduction in food spoilage.
Risks. The expected financial impact is subject to risks and variation based on various factors, including:
- The effective integration of SpendingPulse data with internal data sources to identify shifts in consumer spending patterns and preferences.
- The availability of data analytics capabilities to interpret SpendingPulse insights and make informed decisions.
- The availability of robust inventory management systems that can quickly adapt to changes in demand and supply.
- The flexibility in procurement processes to adjust orders based on timely data.
- The level of collaboration between procurement, sales, and marketing teams to align inventory with promotional activities and consumer demand.
- The efficient allocation of transportation modes and rerouting of shipping lanes based on SpendingPulse insights.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $366,000.
Spoilage Reduction
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Regional promotions supported with SpendingPulse data | A4 | 2 | 4 | 4 | |
| B2 | Average revenue per regional promotion before SpendingPulse | A5 | $125,000,000 | $125,000,000 | $125,000,000 | |
| B3 | Average cost of goods sold | B2*0.8 | $100,000,000 | $100,000,000 | $100,000,000 | |
| B4 | Average cost of spoilage per promotion | B3*0.02 | $2,000,000 | $2,000,000 | $2,000,000 | |
| B5 | Reduction of spoilage from optimizing regional inventory levels | Interviews | 2.5% | 2.5% | 2.5% | |
| Bt | Spoilage reduction | B1*B4*B5 | $100,000 | $200,000 | $200,000 | |
| Risk adjustment | ↓10% | |||||
| Btr | Spoilage reduction (risk-adjusted) | $90,000 | $180,000 | $180,000 | ||
| Three-year total: $450,000 | Three-year present value: $365,815 | |||||
Data Analyst Resource Reallocation
Evidence and data. Interviewees reported experiencing better resource allocation due to spending less time on researching data and creating reports with SpendingPulse. They noted that having access to SpendingPulse data streamlined this process and made it easier to understand broader industry trends and macroeconomic events.
- The business analysis manager at the quick service restaurant highlighted the challenges and effort required to gather meaningful data before using SpendingPulse. The interviewee mentioned that data scientists had to rely on free public resources and internet searches, which was time-consuming and made it difficult to obtain comprehensive insights: “Before SpendingPulse, we really didn’t have any external data sources. I spent time searching online, looking up what other companies were saying or using free public resources, but we never had dedicated support. There were times when something happened, and we couldn’t always answer whether it happened to other companies as well. As we tried to tell the revenue narrative and speak to broader industry trends, it was actually pretty difficult to compile meaningful market insights.”
- The procurement manager noted their food delivery and retail company had three or four individuals dedicated to gathering data, pulling insights, and developing strategies, which was time-consuming, and the data eventually became stale. Their organization transitioned to using SpendingPulse, which allowed them to perform these tasks in a couple of hours, significantly reducing effort and enabling more efficient resource allocation. The procurement manager said: “We had like three or four individuals that would be doing this on a full-time basis of gathering all the data, pulling together the insights, and then thinking of strategy and giving those recommendations. That’s honestly something that took a long time to put together and the reports became very stale eventually. SpendingPulse allows us to do the same in a couple of hours, reducing the effort and enabling us to allocate resources more efficiently.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite employes five full-time data scientists in support of its annual promotions.
- The average fully burdened annual salary for a data scientist is $230,000.
- In Year 1 of the deployment, 20% of data scientists’ time is reallocated to other projects due to access to SpendingPulse data. This efficiency further increases to 25% in Years 2 and 3.
Risks. The expected financial impact is subject to risks and variation based on various factors, including:
- The existing level of automated processes for data collection, analysis, and reporting.
- The ability to customize SpendingPulse reports and dashboards to meet specific organizational needs.
- The ability to seamlessly integrate SpendingPulse data with internal systems to ensure easy access and utilization.
- The commitment to develop expertise in using SpendingPulse and other analytical tools to maximize their potential.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $630,000.
Data Analyst Resource Reallocation
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| C1 | Data scientist FTEs before SpendingPulse | Composite | 5 | 5 | 5 |
| C2 | Fully burdened annual salary for a data scientist FTE | TEI standard | $230,000 | $230,000 | $230,000 |
| C3 | Reduced data analyst resource requirement | Interviews | 20% | 25% | 25% |
| Ct | Data analyst resource reallocation | C1*C2*C3 | $230,000 | $287,500 | $287,500 |
| Risk adjustment | ↓5% | ||||
| Ctr | Data analyst resource reallocation (risk-adjusted) | $218,500 | $273,125 | $273,125 | |
| Three-year total: $764,750 | Three-year present value: $629,562 | ||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
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Enhanced decision-making confidence. SpendingPulse enabled the interviewees’ organizations to make more informed decisions by providing comprehensive, near-real-time data on consumer spending patterns. This data helped them benchmark performance against industry standards and identify emerging trends. Consequently, decision-makers felt more confident in their strategic choices, leading to more decisive and proactive management and better business outcomes.
- The business analysis manager at the quick service restaurant said: “We always look at our own data comparing it versus forecast. But when things don’t go as expected, whether good or bad, sometimes we’re looking for answers to what happened. Is this unique to us? Did this happen across the industry? So, we leverage SpendingPulse to better understand these drivers on a macroeconomic national level.”
- The global business performance director at the hospitality firm shared: “We use SpendingPulse to gauge and benchmark performance against how we’re performing. It helps us understand if the market is growing or declining and why, which helps us focus a little bit more on where we need to improve.”
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Improved cross-departmental collaboration. SpendingPulse insights facilitated better communication and collaboration across departments in the interviewees’ organizations. By providing a common data source, it ensured all teams were aligned in understanding market conditions and consumer behavior, fostering a cohesive strategy and effective teamwork to drive business performance.
- The business analysis manager at the quick service restaurant noted: “My team’s job is to help tell the broader story or narrative around what happened with our revenue. We consolidate inputs from various teams to help tell that story. SpendingPulse fits into that by providing data that helps us understand broader industry trends.”
- The procurement manager at the food distribution and retail company shared: “We uncovered more use cases because we understood its value. Our strategic category managers, finance, and sales teams can key into things and employ strategies within their departments. This alignment helps us make more actionable plans or campaigns.”
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Enhanced responsiveness to market changes. With SpendingPulse, the interviewees’ organizations quickly identified and responded to shifts in consumer behavior and market conditions. Timely data allowed decision-makers to detect trends and anomalies promptly, enabling swift strategy adjustments. This agility helped them stay competitive and resilient, capitalizing on opportunities and mitigating risks effectively.
- The business analysis manager at the quick service restaurant explained: “In the post-pandemic environment, there’s a lot more volatility. SpendingPulse helps us understand what’s happening and if it’s unique to us or happening to everyone. It allows us to see how the economy responds with a live dataset, which is really helpful.”
- The global business performance director at the hospitality firm said: “We use SpendingPulse data to understand the behavior and what’s going on. For example, around Thanksgiving, there’s a drop-off in guest spend in our restaurants because people are spending more on Thanksgiving at home. This helps us plan accordingly.”
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Strengthened partner relationships. SpendingPulse data helped the interviewees’ organizations demonstrate their value to partners by providing clear evidence of market trends and performance benchmarks. This transparency built trust and strengthened relationships, as partners could see the direct impact of collaborative efforts on business outcomes. By leveraging the platform’s insights, the interviewees said their organizations better supported their partners’ goals and fostered long-term, mutually beneficial partnerships.
- The credit data analyst at the financial services firm shared: “Our goal is to keep the partner happy. If their sales go up and they’re happy with the service we’re providing, that’s beneficial to us. SpendingPulse data helps us demonstrate the value that [we have] generated for the partner, which is crucial for contract negotiations and maintaining strong relationships.”
- The partnership data analyst at the financial services firm noted: “SpendingPulse helps us identify who’s outperforming the benchmark or our internal benchmark and then focus on what they’re doing better. This helps us develop best practices and strengthen our partnerships.”
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement SpendingPulse and later realize additional uses and business opportunities, including:
- Leveraging SpendingPulse to enhance reach in international markets. Several interviewees recognized the potential benefits of leveraging international data for their global organizations. While the current focus for all interviewees was on US data, some shared considerations for expanding the use of SpendingPulse data to international markets in the future. The global business performance director at the hospitality firm noted: “Currently, we use the platform just for the US. We don’t have it live in any other market. The way SpendingPulse is broken down or segmented means that we can assess the fine-dining channel, and that’s where the majority of our restaurants sit within. We are interested in leveraging similar insights for the UK market.” The credit data analyst formulated the use case for the financial services firm as follows: “The vast majority of our card business is [in the] US, so we use US data almost exclusively. However, having access to more granular international data could be beneficial for benchmarking and understanding global trends.”
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Enhancing business strategies by integrating the Test & Learn solution. Several interviewees mentioned they see potential in further leveraging Mastercard’s Test & Learn business experimentation platform in conjunction with SpendingPulse for enhancing data-driven decision-making and refining business strategies. The procurement manager noted their foods distribution company experimented with Test & Learn to improve pricing strategies and promotions and had plans to expand into scenario analysis to optimize sales and target the right audience. This interviewee said: “We’ve just started with Test & Learn, focusing on pricing strategies and promotions. This is something we’re still working through and trying to understand.”
Meanwhile, the partnership data analyst reported their financial services firm was looking into Test & Learn to further refine marketing strategies through controlled experiments, identifying the most effective approaches to optimize campaigns. This interviewee shared an example of its application at the financial services firm: “We saw some separation with our targeting campaigns. The email component was much more powerful with accounts less than three months on book performing significantly better than the rest. This spurred us to focus more on our email journey, reviewing and enhancing our marketing materials to ensure their effectiveness.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Dtr | Fees to Mastercard | $0 | $131,250 | $131,250 | $131,250 | $393,750 | $326,399 |
| Etr | Internal costs | $26,224 | $284,746 | $209,616 | $259,952 | $780,538 | $653,626 |
| Total costs (risk-adjusted) | $26,224 | $415,996 | $340,866 | $391,202 | $1,174,288 | $980,025 | |
Fees To Mastercard
Evidence and data. Interviewees’ organizations paid an annual subscription fee for access to the SpendingPulse platform. This fee included initial delivery support, such as onboarding, integration support, and training of users in various departments. Additionally, it covered annual support with regular touchpoints to discuss recent trends and forecasts, as well as ad hoc analyses.
- The business analysis manager at the quick service restaurant elaborated on the value of regular meetings with the Mastercard team: “ We connect with the Mastercard team every month and they provide an overview of trends they are seeing specifically for our company, the competitive space, and the broader economy.”
- At the food distribution and retail company, SpendingPulse was rolled out to several departments. The procurement manager shared the following about the core user groups: “We have users in procurement, sales, marketing, and finance departments. There are also discussions about expanding usage to other areas, such as product development and supply chain management, as we feel the platform’s insights can support broader operational and strategic decisions.”
Modeling and assumptions. Based on the interviews, Forrester assumes the composite pays $125,000 per year for the SpendingPulse subscription which entitles users from various departments to access the platform.
Risks. Organizational differences that may impact the costs associated with SpendingPulse subscription fees include the type of subscription and the number of active platform users. Pricing may vary. Contact Mastercard for additional details.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $326,000.
Fees To Mastercard
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| D1 | Annual SpendingPulse subscription for up to 10 users | Composite | $0 | $125,000 | $125,000 | $125,000 | |
| Dt | Fees to Mastercard | D1 | $0 | $125,000 | $125,000 | $125,000 | |
| Risk adjustment | ↑5% | ||||||
| Dtr | Fees to Mastercard (risk-adjusted) | $0 | $131,250 | $131,250 | $131,250 | ||
| Three-year total: $393,750 | Three-year present value: $326,399 | ||||||
Internal Costs
Evidence and data. Interviewees’ organizations incurred additional internal costs associated with the initial adoption of the platform, including staff training, integrating SpendingPulse data flows with internal systems, and the ongoing use and optimization of the data platform and the reports it enabled.
- The business analysis manager at the quick service restaurant explained how data scientists saved custom views to automate data pulls, allowing these pulls to show specific sectors or timeframes and automating and customizing the data flow, saying, “The data was then pasted into prebuilt models in [a spreadsheet], which ran calculations and generated insights.”
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The procurement manager noted that, while their food distribution and retail company used a similar approach initially, their organization invested in integrating SpendingPulse data with its internal technology stack. This interviewee shared the following insights about their organizations’ adoption and integration efforts:
- Data extraction and transformation. The procurement manager said, “Initially, we exported raw data from SpendingPulse into [a spreadsheet] and other tools for further analysis, and we built custom models and reports in [a spreadsheet] to compile and display the data as needed.”
- Integration with internal systems. The interviewee added: “Over the course of six months, we integrated SpendingPulse data with our ERP system, inventory management, and data analytics platforms. This involved setting up data connectors and ensuring compatibility with existing data structures and workflows.”
- Automation and customization. Regarding automation and customization, the procurement manager stated: “We saved custom views on the SpendingPulse tool to automate data pulls, showing specific sectors or timeframes. The data was then pasted into prebuilt models in [a spreadsheet], which ran calculations and generated insights.”
- Initial setup and configuration. The interviewee continued, expanding on setup and configuration: “Configuring SpendingPulse to align with our existing systems required setting up data connectors and ensuring data consistency. This included mapping data fields and transforming data to match our internal formats.”
- Testing and validation. The interviewee said, “To ensure accurate data transfer and integration across systems, we conducted thorough testing user acceptance testing [UAT] was performed to gather feedback and make necessary adjustments.”
- Training and support. When it came to training and support, the interviewee stated: “We provided training sessions to users to familiarize them with the integrated systems. Ongoing support mechanisms were established to assist users with any issues or questions.”
- Continuous improvement. The interviewee concluded: “Now, we continuously monitor and optimize the integrated systems to ensure optimal performance. Adjustments are made based on user feedback and system performance metrics.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- A blended team of two technology and data engineers initially configure the SpendingPulse platform for the composite organization’s needs and provide ongoing oversight of customizations and maintenance of the data integration they implement. In Year 1, a third technology engineer supports the initial integrations.
- Combined, these engineers spend 80 hours each initially on platform setup and data extraction, a total of 350 hours each in Year 1 on data extraction, customization, and integration work, and 40 hours each in Years 2 and 3 on the ongoing maintenance of the technology integrations.
- The fully burdened hourly rate for an engineer FTE is $94.
- Initially, two data scientists are involved in platform setup and the creation of spreadsheet models. In Year 1 of the deployment, seven users in various departments have access to SpendingPulse. This number grows to eight in Year 2 and 10 in Year 3.
- Each data scientist invests 40 hours initially, while SpendingPulse users spend on average four hours per week, or 208 hours per year, customizing and reviewing SpendingPulse data. This number includes formal and informal training for platform users.
- The fully burdened hourly rate for a data scientist is $110.
Risks. Organizational differences that may impact the costs associated with internal platform setup, training, customization, and integration costs include:
- The number of active platform users.
- The scale of technology and data integrations.
- The requirement for ongoing support of the integrated analytics environment.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $654,000.
Internal Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| E1 | Blended team of technology and data engineers involved in integration work | Composite | 2 | 3 | 2 | 2 | |
| E2 | Hours spent integrating and customizing SpendingPulse data flow | Composite | 80 | 350 | 40 | 40 | |
| E3 | Fully burdened hourly rate for an engineer FTE | TEI standard | $94 | $94 | $94 | $94 | |
| E4 | Subtotal: Cost of data integration | E1*E2*E3 | 15,040 | 98,700 | 7,520 | 7,520 | |
| E5 | SpendingPulse users | Composite | 2 | 7 | 8 | 10 | |
| E6 | Hours reviewing and leveraging SpendingPulse data | Composite | 40 | 208 | 208 | 208 | |
| E7 | Fully burdened hourly rate for a data scientist FTE | TEI standard | $110 | $110 | $110 | $110 | |
| E8 | Subtotal: Cost of leveraging SpendingPulse data | E5*E6*E7 | $8,800 | $160,160 | $183,040 | $228,800 | |
| Et | Internal costs | E4+E8 | $23,840 | $258,860 | $190,560 | $236,320 | |
| Risk adjustment | ↑10% | ||||||
| Etr | Internal costs (risk-adjusted) | $26,224 | $284,746 | $209,616 | $259,952 | ||
| Three-year total: $780,538 | Three-year present value: $653,626 | ||||||
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Total costs Total benefits Cumulative net benefits Initial Year 1 Year 2 Year 3-
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
Cash Flow Analysis (Risk-Adjusted Estimates)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($26,224) | ($415,996) | ($340,866) | ($391,202) | ($1,174,288) | ($980,025) |
| Total benefits | $0 | $557,125 | $950,375 | $950,375 | $2,457,875 | $2,005,941 |
| Net benefits | ($26,224) | $141,129 | $609,509 | $559,173 | $1,283,587 | $1,025,916 |
| ROI | 105% | |||||
| Payback | <6 months | |||||
Appendix A: Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Total Economic Impact Approach
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Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
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Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
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Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
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Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
Appendix B: Supplemental Material
Related Forrester Research
A Retailer’s Guide To The 2024 Holiday Season, Forrester Research, Inc., October 09, 2024.
Budget Planning Guide 2025: Digital Business And Strategy, Forrester Research, Inc., July 31, 2024.
2025 Budget Planning: Gear Up To Spend (Slightly) More, Forrester Research, Inc., July 31, 2024.
Appendix C: Endnotes
1 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
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