The Total Economic Impact™ Of Mastercard SessionM
Cost Savings And Business Benefits Enabled By The SessionM Loyalty Platform
A Forrester Total Economic Impact™ Study Commissioned By Mastercard, December 2024
Organizations invest in SessionM to improve customer engagement and loyalty. SessionM’s loyalty platform can drive meaningful customer interactions and business growth. Key results include increased transaction frequency, higher customer retention rates, and enhanced personalization capabilities. SessionM provides value by offering comprehensive data insights, seamless integration, and effective loyalty program management, making it a strategic asset for enhancing customer loyalty and operational efficiency across industries.
SessionM is a customer engagement and loyalty platform designed to manage and enhance loyalty programs. It offers comprehensive data insights, seamless integration with existing systems, and tools for personalized customer interactions. SessionM can track customer behavior, segment audiences, and automate marketing campaigns. These capabilities can help organizations streamline their loyalty initiatives and improve customer engagement.
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 SessionM.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of SessionM on their organizations.
Return on investment (ROI)
59%
Net present value (NPV)
$14.61M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed five representatives of four restaurant and retail organizations in the US and Canada with experience using SessionM to power their loyalty programs. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a multiproduct national specialty retailer with 650 stores across North America and revenue of $2.4 billion per year.
Interviewees said that prior to using SessionM, their organizations faced limitations with their existing loyalty programs. One organization used a cumbersome solution developed in-house that made it difficult to set up offers. Another organization had a punch-card rewards system that was limited in scope, lacked digital integration, and did not provide comprehensive data on customer behavior, which made it difficult to personalize offers and understand customer preferences. A third organization operated a discount club that was ambiguous, limited to in-store use, and focused on only one customer persona. And a fourth organization used an antiquated, in-store-only system that relied on surprise and delight features instead of a structured rewards ecosystem. These limitations showcased a common business need for better ways to easily identify more customers and a more comprehensive, flexible, and user-friendly loyalty solution with functionality to support deeper customer engagement capabilities both in-store and online.
Interviewees reported that after the investment in SessionM, their organizations improved loyalty platform functionality, ease of use, and loyalty program performance. Key results from the investment include increased revenues from larger transaction sizes and higher transaction frequency, higher customer retention rates, more effective referral programs, better data insights for personalized marketing, and reduced marketing execution costs.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Incremental and retained revenue. The composite organization leverages SessionM to enhance its loyalty program, resulting in a three-year revenue lift of $25.8 million. By using SessionM to improve targeted offers and rewards, streamline referral programs, and personalize customer experiences, the composite organization increases its average basket size, visit frequency, and program enrollments for members.
- Reduced marketing execution costs. Implementing a robust loyalty program with SessionM leads to $11.6 million in cost savings for the composite organization across various areas over three years. By leveraging SessionM’s data and advanced analytics capabilities, the composite organization drives efficiencies such as reducing third-party data acquisition, optimizing paid media spending, and decreasing its reliance on data scientists.
- Legacy program replacement savings. By retiring its in-house-developed legacy loyalty platform, the composite saves on IT costs related to hosting and maintaining the solution and promoting the old club membership program. The savings come from reduced integration costs and fewer bug fixes, updates, and security patches related to the legacy platform, plastic membership cards, and marketing efforts like digital ads, email marketing, in-store promotions, and mobile marketing. This provides the composite with savings of nearly $1.8 million over three years.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Data efficiency and integration. SessionM acts as a central repository for customer data, unifying and activating data from various systems through seamless integration. These integrations enhance business efficiency, support informed decision-making, and improve targeted marketing, driving better customer engagement and loyalty. By unifying data, SessionM provides a holistic view of customers, enabling the composite organization to create personalized and optimized experiences. The integration streamlines data management, making customer information easily accessible and actionable.
- Data utilization and independence. SessionM enables the composite organization to effectively use customer data without relying on third-party sources. By integrating with various platforms, SessionM allows the organization to structure and customize its data, which helps the marketing team segment its customer base and conduct A/B tests to find the most effective campaigns. These capabilities allow the composite to use data to create personalized offers and experiences in-house, reducing dependence on external data providers.
- Enhanced customer experience and engagement. Because SessionM leverages comprehensive data, it provides the composite organization with deep insights into customer behaviors, allowing it to tailor marketing strategies effectively. This results in more personalized offers and customers who engage more meaningfully with its brands. Enhanced brand awareness and positive engagement are further bolstered by the platform’s usability, which facilitates quick and accurate resolution of inquiries and make SessionM central to driving customer loyalty and satisfaction.
- Having a valued partner during and after implementation. SessionM collaborates with the composite organization’s multidisciplinary project team to tailor a multichannel loyalty program to its organizational goals. The composite values SessionM’s solutions, expertise, and commitment to service. The composite considers Mastercard to be a reliable partner due to its prompt problem-solving and strategic guidance, and leaders see SessionM as an extension of their own teams. This highlights supportive relationships and positive impacts on loyalty initiatives.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Fees to Mastercard. The composite organization pays annual subscription fees for access to the SessionM platform, an annual fee for ongoing technical support, and fees for professional and strategic services to support implementation and optimization of the platform. These costs total $1.6 million over three years for the composite organization. The annual subscription fees cover data management, loyalty program management, and campaign execution tools. The technical support fee includes updates and issue mitigation. Professional services encompass implementation, customization, and integration with CRM, customer data platform (CDP), and POS systems. Ongoing consulting services provide campaign strategy, data analysis, and optimization to maximize the loyalty program’s value.
- Internal program costs. The composite organization incurs additional internal costs related to loyalty program management totaling $22.9 million over three years. This includes the initial implementation of the SessionM platform, staff training, customer service, ongoing marketing efforts and optimization of the loyalty program, and the cost of rewards and incentives.
The representative interviews and financial analysis found that a composite organization experiences benefits of $39.19 million over three years versus costs of $24.58 million, adding up to a net present value (NPV) of $14.61 million and an ROI of 59%.
Key Statistics
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Return on investment (ROI)
59%
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Benefits PV
$39.19M
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Net present value (NPV)
$14.61M
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Payback
<6 months
Benefits (Three-Year)
Incremental and retained revenue Reduced marketing execution costs Legacy program replacement savings
TEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in SessionM.
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 SessionM can have on an organization.
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Due Diligence
Interviewed Mastercard stakeholders and Forrester analysts to gather data relative to SessionM.
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Interviews
Interviewed five representatives at four organizations using SessionM 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 SessionM.
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.
TEI consultant:
Anna Orban-Imreh
Drivers leading to the SessionM investment
Interviews
| Role | Industry | Region | Annual Revenue |
|---|---|---|---|
| Director of brand marketing | Fast-casual dining | USA | $500 million |
| Director of martech | Fast-casual dining | USA | $500 million |
| Head of marketing | Specialty products retail | USA | $600 million |
| Loyalty program manager | Energy retail | Canada | $25 billion |
| Senior marketing manager | Fast-food dining | USA | $10 billion |
Key Challenges
Before choosing and implementing SessionM, the interviewees’ organizations faced significant challenges with their existing loyalty programs. They faced challenges with outdated and inefficient proprietary systems that lacked functionality, which caused operational issues. These systems were not points-based, lacked digital integration, and required comprehensive updates to align with modern loyalty standards. Their resulting loyalty programs, including punch-card rewards and in-store discount clubs, were limited, unclear, and failed to engage customers effectively.
The interviewees noted how their organizations struggled with common challenges, including:
- Complex and inefficient systems. The previous loyalty systems were challenging to use, lacked comprehensive functionality, and resulted in inefficiencies and execution problems, which failed to deliver the intended outcomes.
- Narrow reward options. The existing reward programs were confined to specific business categories, making it necessary to completely revamp the systems to align with contemporary standards and customer expectations.
- Unclear and unengaging programs. The discount clubs were vague, failed to engage broad customer bases, and lacked clear benefits. This led to low customer participation and confusion.
- Absence of digital integration. The previous systems were not based on a points structure and were mainly designed for in-store transactions. They lacked digital integration and cohesive loyalty strategies, limiting the organizations’ ability to engage customers through modern digital channels.
- Dependence on third-party customer data and paid media. Interviewees’ organizations struggled with reliance on third-party data and paid media for user acquisition and engagement. This underscored the need for integrated, first-party data solutions to better understand and target customers.
Investment Objectives
The interviewees’ organizations searched for a solution that could:
- Enhance loyalty program functionality. The organizations wanted to improve the overall functionality of their loyalty programs to better serve customers and streamline operations.
- Improve campaign execution. The organizations looked to develop and execute innovative campaigns to increase customer engagement and drive loyalty.
- Increase customer engagement. The organizations wanted to focus on metrics such as total active users and loyalty penetration to boost customer interaction and retention.
- Allow them to transition to a digital, points-based system. The organizations sought to move from limited or outdated loyalty systems to comprehensive and digital points-based programs to enhance customer engagement and retention.
- Track customer behavior. The organizations wanted to implement systems to monitor and analyze customer behavior to increase transaction frequency and improve retention rates.
- Expand digital capabilities. The organizations looked to invest in digital solutions to support loyalty programs and enhance omnichannel customer experience.
- Foster customer relationships. The organizations sought to offer compelling rewards and personalized experiences to keep active users and strengthen customer relationships.
- Reduce reliance on third-party data and paid media. The organizations wanted to shift towards integrated, first-party data solutions to better understand and target customers and reduce dependency on external data sources and paid media.
Following thorough evaluation processes including requests for proposals (RFPs) and proof-of-concept (POC) trials with multiple vendors, the interviewees’ organizations selected SessionM and began deployment. These organizations employed tailored strategies to address their unique challenges and enhance their loyalty programs.
- The loyalty program manager at an energy retailer said their organization undertook a six-month integration effort involving significant technical complexity and strategic partnerships for data management. The company enhanced the functionality of its loyalty program, improved campaign execution, and increased customer engagement by focusing on metrics like total actives and loyalty penetration.
- The senior marketing manager at a fast-food restaurant chain said their organization transitioned from a limited punch card system to a comprehensive and digital points-based loyalty program. The 18-month journey included extensive system integration and a pilot test phase, resulting in increased customer engagement and retention.
- The head of marketing at a specialty products retailer said their company replaced an outdated discount card with a comprehensive loyalty program to track customer behavior, increase transactions, and improve retention. The organization spent six to eight months on implementation, focusing on program design and integration with homegrown systems. SessionM provided support to navigate technical challenges.
- Interviewees from a fast-causal restaurant chain said their organization shifted from an ad hoc rewards system to a structured and digital points-based loyalty program to enhance retention and engagement. The company focused on growing its program and maintaining active users, relying on its agency partner and SessionM professional services for a six-month implementation and ongoing support.
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 five interviewees, 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 national specialty retailer based in the US that employs a multibrand strategy. It offers both national brands and its own private-label brands to cater to customers with varying price sensitivities and brand preferences. With an annual revenue of $2.4 billion and more than 650 stores across North America, the company offers a seamless omnichannel experience through its physical stores, websites, and mobile apps. This makes it convenient for diverse customer segments to shop through their preferred channels. The composite has a legacy loyalty solution developed in-house that manages 500,000 loyalty members who submit an average of 4.8 orders per year with an average basket size of $115. This generates $276 million in annual revenue.
Deployment characteristics. The composite’s SessionM implementation process spans six months and involves extensive collaboration between the organization’s internal teams and SessionM’s professional services consultants. This includes integrating SessionM with the organization’s POS system, CDP, digital marketing platform, web- and application-hosting services, e-commerce systems, and its enterprise data warehouse to facilitate seamless data flow and deep customer insights.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Incremental and retained revenue | $4,890,404 | $10,512,498 | $16,883,329 | $32,286,231 | $25,818,531 |
| Btr | Reduced marketing execution costs | $4,680,450 | $4,671,836 | $4,662,368 | $14,014,655 | $11,618,883 |
| Ctr | Legacy program replacement savings | $701,250 | $704,438 | $707,785 | $2,113,472 | $1,751,449 |
| Total benefits (risk-adjusted) | $10,272,104 | $15,888,771 | $22,253,482 | $48,414,357 | $39,188,863 | |
Incremental And Retained Revenue
Evidence and data. Interviewees said their organizations leveraged SessionM to enhance their loyalty programs, which resulted in improved revenue. They reported measurable improvements in four key areas that each directly impacted revenue generation:
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Growth in membership enrollments. By leveraging advanced analytics, personalized offers, and seamless
integration across channels, interviewees’ organizations increased their
loyalty membership enrollment. By understanding customer behavior and
preferences, they created more engaging and relevant loyalty programs that
attracted and retained existing members. For instance:
- The loyalty program manager at the energy retailer said their company integrated SessionM with its coalition loyalty program, which enabled it to manage customer data more effectively and create targeted offers to provide more relevant experiences that attracted more loyalty members. The interviewee explained how SessionM enabled the marketing team to run effective campaigns and engagement with customers across channels: “We target people based on behaviors and segment them accordingly to determine the appropriate offers. For example, in our first promotional period this year, we ran a comprehensive campaign that involved not only core promotions, but also media, ad tech channels, and social media.”
- The senior marketing manager at the fast-casual restaurant chain said their company focused on attracting new loyalty members by offering an easy, accessible program. They said: “We grow our program and maintain active users by offering something compelling to keep them motivated to stay active with us. Providing an easy way for people to sign up and access the program was crucial and top of mind when choosing a platform with a stable foundation.”
- The senior marketing manager at a fast-food restaurant chain said their organization transitioned from a punch-card rewards program to a points-based system with SessionM. Though users were required to accept new terms and conditions, by fully integrating the new loyalty program into its digital ecosystem, the interviewee said the organization made it easy for customers to sign up and participate, which led to an increase in active loyalty memberships: “We did not simply migrate people over; we provided an incentive for them to take up the new program by converting their existing punches into loyalty points.”
- The head of marketing at the specialty products retailer said their company saw a substantial increase in loyalty memberships by transitioning from a discount club to a comprehensive loyalty program. This allowed for better data collection and personalized marketing efforts, resulting in 50% of online customers becoming new loyalty members.
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Increase in average basket size. SessionM helped interviewees’ organizations increase average
basket sizes through targeted rewards and offers that encourage customers to
purchase more during each transaction. For example:
- The senior marketing manager at the fast-food restaurant indicated that loyalty program members who come to the store to redeem rewards tend to spend more overall: “What we’re seeing is when people come in and spend $10 of reward points, they’re spending $100 overall. We get an extra trip out of it and a bigger purchase.”
- The director of brand marketing at the fast-casual restaurant chain explained that by feeding SessionM data into the business analytics solution Mastercard Test & Learn, the company can identify which offers are most likely to resonate with specific customer segments. The interviewee said the company’s loyalty members tend to have a higher average check compared to non-loyalty members because they are more likely to spend more money per transaction by purchasing add-ons or additional entrees: “SessionM’s ability to handle complex loyalty program rules and integrations with other systems ensures a seamless experience for loyalty members. Overall, our loyalty members are twice as valuable to us as our non-loyalty members. They come twice as often and are more likely to spend more money with us. Our targeted offers and personalized rewards have proven to increase basket sizes.”
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Improvement in order frequency. Interviewees reported that SessionM allowed their organizations to
offer more personalized and engaging customer experiences and that this led
to increased member visits and higher purchasing frequency. For
example:
- The loyalty program manager at the energy retailer said SessionM allowed their company’s marketing team to manage customer data more effectively and create attractive offers, which contributed to increasing the frequency of member visits. The interviewee attributed this improvement to the new platform and the expertise of the SessionM consulting team: “The functionality is much broader and greater than we had in our old platform. And the campaign managers that we have through SessionM are just fantastic. They’re able to help us on the innovation side, and we can come up with more creative campaigns than ever before.”
- The senior marketing manager at the fast-food restaurant chain said their organization saw an increase in the frequency of member visits by transitioning to SessionM, which supported a points-based program that focused on active users rather than just sign-ups. The interviewee said: “We typically see that loyalty customers visit our brand on a more frequent basis compared to non-loyalty customers. Loyalty customers visit more often, and the number of visits has a direct relationship to how much money they’re spending.”
- The head of marketing at the specialty products retailer said that after launching its comprehensive loyalty program, the company experienced an increase in trip frequency. The executive said, “We saw an average trip frequency increase by 45% from before the program to after the program launched in the first year.”
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Roll-out of referral programs. Interviewees said SessionM facilitates the implementation of
referral programs with capabilities for tracking referrals, managing
rewards, and integrating with marketing and CRM systems. They acknowledged
that SessionM offers user-friendly interfaces that ensure a smooth setup and
ongoing management of referral programs with minimal manual intervention.
For example:
- The head of marketing at the specialty product retailer said SessionM is set up to automatically issue rewards and point accelerators and that this ensures customers receive their rewards promptly, enhancing their satisfaction and encouraging repeat purchases and referrals. The interviewee said: “If a member refers a friend who signs up for our loyalty program, both individuals receive bonus points. Additionally, members earn points when they share their registry and when someone makes a purchase from their registry. All these activities are always on and managed through SessionM.”
- The director of brand marketing at the fast-casual restaurant chain said their company relies on SessionM’s ability to support personalized integrations for email and push notifications and to unify data from various systems and activate it across channels. This is functionality that is essential for tracking referrals and ensuring rewards attribution. The platform’s focus on points earning and burning, tier migration, and personalized offers ensure that both the referrer and the referred friend are rewarded. The interviewee said their organization now has the ability to segment customers based on behaviors and preferences that would allow it to tailor referral offers to the most engaged customers.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization generates $2.4 billion in annual revenue.
- Prior to implementing SessionM, the composite offered loyalty club membership to 500,000 customers. Forrester assumes a 5% growth in membership numbers year-over-year.
- Legacy program members spent an average of $115 per order. Forrester assumes the average order value (AOV) would have grown by 2% per year.
- Legacy program members made an average of 4.8 purchases per year. Forrester assumes order frequency would have grown by 3% per year.
- After implementing SessionM, the number of loyalty program members grows by 20% (from 500,000 to 600,000) in Year 1. Membership grows by an additional 20% in Year 2 and by 15% in Year 3.
- After the Session M implementation, the average basket size increases by 12% in the first two years and by an additional 8% in Year 3.
- With SessionM, the composite realizes a 10% growth in trip frequency by loyalty program members in Year 1 compared to with the legacy program. Year-over-year growth is consistent at 10% thereafter.
- In Year 1, 15% of loyalty program members generate two successful referral purchases at an average value of $105 per order. This referral rate increases to 17% in Year 2 and to 19% in Year 3.
- Forrester attributes 30% of the resulting revenue lift to the SessionM loyalty platform for its central role in driving customer loyalty and engagement.
- The composite organization has a net operating profit margin of 12%.
Risks. The expected financial impact is subject to risks and variation based on factors including:
- Key stakeholders’ experience levels with loyalty programs and the organization’s success with previous customer engagement initiatives.
- The organization’s overall customer-centric approach, executive buy-in, and cross-functional support for the loyalty platform.
- The robustness of data collection and insights and the effectiveness of integrating the platform with the organization’s martech stack.
- The support for continuous improvement and the comprehensiveness of employee training and empowerment.
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 $25.8 million.
Incremental And Retained Revenue
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Revenue | Composite | $2,400,000,000 | $2,400,000,000 | $2,400,000,000 | |
| A2 | Legacy program members | Composite | 500,000 | 525,000 | 551,250 | |
| A3 | Average order value per legacy program member | Composite | $115.00 | $118.45 | $122.00 | |
| A4 | Average purchases per legacy program member | Composite | 4.8 | 4.9 | 5.0 | |
| A5 | Revenue from legacy program members | A2*A3*A4 | $276,000,000 | $304,712,625 | $336,272,147 | |
| A6 | Active loyalty program members with SessionM | Interviews | 600,000 | 720,000 | 828,000 | |
| A7 | Average order value per loyalty program member with SessionM | Interviews | $128.80 | $144.26 | $155.80 | |
| A8 | Average purchases per loyalty member with SessionM | Interviews | 5.28 | 5.81 | 6.39 | |
| A9 | Revenue from loyalty program members | A6*A7*A8 | $408,038,400 | $603,468,432 | $824,325,336 | |
| A10 | Incremental revenue from the new loyalty program with SessionM | A9-A5 | $132,038,400 | $298,755,807 | $488,053,189 | |
| A11 | Attribution to the SessionM platform | Interviews | 30% | 30% | 30% | |
| A12 | Net operating profit margin | TEI standard | 12.0% | 12.0% | 12.0% | |
| A13 | Subtotal: Net revenue from loyalty members | A10*A11*A12 | $4,753,382 | $10,755,209 | $17,569,915 | |
| A14 | Advocate referral rate | Composite | 15% | 17% | 19% | |
| A15 | Average order value of a referral purchase | Composite | $105 | $105 | $105 | |
| A16 | Average referrals | Composite | 2 | 2 | 2 | |
| A17 | Attribution to the SessionM platform | Composite | 30% | 30% | 30% | |
| A18 | Net operating profit margin | Composite | 12.0% | 12.0% | 12.0% | |
| A19 | Subtotal: Net revenue from referrals | A6*A14*A15*A16*A17*A18 | $680,400 | $925,344 | $1,189,339 | |
| At | Incremental and retained revenue | A13+A19 | $5,433,782 | $11,680,553 | $18,759,254 | |
| Risk adjustment | ↓10% | |||||
| Atr | Incremental and retained revenue (risk-adjusted) | $4,890,404 | $10,512,498 | $16,883,329 | ||
| Three-year total: $32,286,231 | Three-year present value: $25,818,531 | |||||
Reduced Marketing Execution Costs
Evidence and data. Implementing a robust loyalty program led to cost savings across various areas for interviewees’ organizations. By leveraging internal data and advanced analytics, they could optimize their marketing efforts and reduce their reliance on third-party data purchases, data scientists, and technical resources. The organizations saw measurable improvements in three areas of cost savings:
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Reduction in third-party data acquisition
costs. Forrester research underscores how loyalty
leaders are reducing their reliance on third-party data and associated costs
by focusing on collecting and utilizing their own data.3
- The senior marketing manager at the fast-food restaurant chain reported that after implementing the new loyalty program and gaining the ability to acquire more first-party data through app usage, their organization reduced its reliance on third-party data. They explained that this shift allowed their company to create lookalike profiles and other insights in-house, which previously required purchasing third-party data. They said: “We have seen a reduction. By acquiring more first-party data through app usage, we are now less reliant on purchasing third-party data to create lookalike profiles and similar insights.”
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Reduction in paid media spending. Interviewees said that in addition to leveraging the first-party
data captured during interactions with loyalty program members, SessionM
aggregated customer data from various sources, enabling precise and
efficient segmentation. This allowed for more effective targeting of
marketing efforts and reduced unnecessary spending on broad, untargeted
campaigns. By supporting highly targeted marketing, SessionM helped the
organizations identify and focus on segments that required additional
incentives, which optimized overall marketing spend.
- Interviewees from the fast-casual restaurant chain said marketers gained the ability to engage customers more effectively through owned channels and that improved targeting and personalization through owned data reduced the need for extensive paid media campaigns. The director of brand marketing said: “We grow our program and maintain active users by fostering strong relationships with our guests and offering compelling incentives. Our bidirectional integration between SessionM and our CDP creates a golden record for customers, enabling effective data aggregation for transactions and loyalty use cases.”
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Reduction in reliance on data
scientists. Interviewees reported that SessionM’s
graphical interface is user-friendly and that it simplified the process of
creating and managing promotions. This allowed marketers to build complex
campaigns without needing data engineers. By integrating data from multiple
systems, SessionM provided a comprehensive view of the customer, which
enabled more effective marketing strategies and reduced the need for
multiple data queries across different platforms. The platform automated
many manual tasks associated with running promotions and analyzing data,
reducing the need for extensive manual data collection and
analysis.
- The senior marketing manager at the fast-food restaurant chain highlighted the use of advanced analytics and integration with existing systems to reduce reliance on manual processes in gaining a comprehensive view of customer behavior and preferences. They explained: “Our investment in digital channels aims to better understand and anticipate customer needs. As a result of our company’s digital transformation, our data set has expanded beyond a singular category to encompass all parts of the business, allowing us to assess the overall impact more effectively.”
- The head of marketing at the specialty retailer said that transitioning to a new loyalty program allowed for better data management and personalized marketing efforts that were not possible with the company’s old program. They said: “With SessionM, we now have detailed information on our customers, including their points, purchases, spending, and redemptions. We can segment customers — such as identifying those with over 1,000 points — and analyze extensive demographic data. Pulling data from SessionM has enabled us to create dashboards, eliminating the need for one-off reports and new data requests.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization generates $2.4 billion in annual revenue, and the average basket size of non-loyalty club members is $105.
- The number of sales transactions by non-members is just under 20,000 per year. To generate this number of transactions, the composite purchases around 400 million consumer profiles, and Forrester assumes a 5% conversion rate from profiles to sales transactions.
- The average cost per customer profile is $0.005 or $5 per thousand profiles.
- The resulting cost of third-party customer profiles is $2,022,857 in Year 1. This decreases slightly with the assumed year-over-year growth in loyalty club membership rates prior to SessionM.
- With SessionM, the composite reduces its reliance on third-party data by 35%.
- The composite previously spent 1% of its revenue on paid media. This annual investment drops by 18% with SessionM.
- Prior to using SessionM, three full-time data analysts were dedicated to market data analytics and segmentation efforts. This effort is reduced by 25% with SessionM.
Risks. The expected financial impact is subject to risks and variation based on factors including:
- The ability to capture high-quality first party data from loyalty members.
- Previous investment levels in third-party data, paid media, and data analyst services.
- The robustness of data collection and insights.
- The effectiveness of integrating the platform with the organization’s martech stack.
- The support for continuous improvement and the comprehensiveness of employee training and empowerment.
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 $11.6 million.
Reduced Marketing Execution Costs
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Revenue | Composite | $2,400,000,000 | $2,400,000,000 | $2,400,000,000 | |
| B2 | Average order value for non-members prior to SessionM | Composite | $105.00 | $105.00 | $105.00 | |
| B3 | Sales transactions by non-members prior to SessionM | (B1-A5)/B2 | 20,228,571 | 19,955,118 | 19,654,551 | |
| B4 | Consumer profiles purchased prior to SessionM (at 5% conversion rate) | B3/0.05 | 404,571,429 | 399,102,357 | 393,091,020 | |
| B5 | Cost per customer profile purchased | Composite | $0.005 | $0.005 | $0.005 | |
| B6 | Cost of third-party data before using SessionM | B4*B5 | $2,022,857 | $1,995,512 | $1,965,455 | |
| B7 | Reduced reliance on third-party data with SessionM | Composite | 35% | 35% | 35% | |
| B8 | Subtotal: Savings on third-party data acquisition | B6*B7 | $708,000 | $698,429 | $687,909 | |
| B9 | Ad spend on paid media channels | B1*1% | $24,000,000 | $24,000,000 | $24,000,000 | |
| B10 | Reduced reliance on paid media ad spend with SessionM | Interviews | 18% | 18% | 18% | |
| B11 | Subtotal: Savings on paid media ad spend | B9*B10 | $4,320,000 | $4,320,000 | $4,320,000 | |
| B12 | Data scientist FTEs dedicated to market data analytics and segmentation prior to SessionM | Interviews | 3 | 3 | 3 | |
| B13 | Average loaded cost for a data scientist FTE | TEI standard | $230,000 | $230,000 | $230,000 | |
| B14 | Reduced reliance on data scientists with SessionM | Interviews | 25% | 25% | 25% | |
| B15 | Subtotal: Savings on data scientist FTEs | B12*B13*B14 | $172,500 | $172,500 | $172,500 | |
| Bt | Reduced marketing execution costs | B8+B11+B15 | $5,200,500 | $5,190,929 | $5,180,409 | |
| Risk adjustment | ↓10% | |||||
| Btr | Reduced marketing execution costs (risk-adjusted) | $4,680,450 | $4,671,836 | $4,662,368 | ||
| Three-year total: $14,014,655 | Three-year present value: $11,618,883 | |||||
Legacy Program Replacement Savings
Evidence and data. Interviewees described different approaches their organizations took with their previous loyalty solutions or platforms involving various levels of investment in either subscription fees or in-house developed platforms. Often, these systems required high levels of ongoing investment and resources to maintain but were inadequate for the organizations’ evolving business needs.
- The loyalty program manager at the energy retailer said their company maintained a solution that was developed in-house, but it was cumbersome and required complex technical solutions and substantial resources to manage data and integrations. They explained: “The in-house platform was difficult to use. Setting up offers took a lot of time and was really finicky. Offers often didn’t execute as intended, and we were limited with the types of offer constructs we could use with that platform. Also, there were lots of files going back and forth between us and our coalition partner, requiring numerous technical solutions and daily extracts to be set up in the background.”
-
The head of marketing at the specialty products retailer said
their organization’s discount club was outdated and confined to in-store
transactions. This required physical card distribution and lacked detailed
customer data for personalized marketing. They said: “In the old program, we
printed plastic cards that could be scanned at the store, costing us around
$50,000 to $75,000 a year. We no longer have that expense since we stopped
using plastic cards for in-store membership sign-ups.”
The same interviewee mentioned that the marketing efforts around their company’s legacy program included promoting the discount card in stores, but there was no online component, which limited its reach and effectiveness. - The senior marketing manager at the fast-food restaurant chain said promotions were mainly limited to in-app notifications and coffee-cup advertisements. While still costly, the marketing efforts for previous loyalty programs were less integrated and relied heavily on in-store promotions and basic advertising.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The annual cost of $450,000 to maintain the in-house developed legacy platform included bug fixes, updates, integrations, security patches, and general upkeep.
- The composite issued plastic membership cards to all members of the legacy loyalty program. This cost about 15 cents per member per year.
- The composite allocates an annual marketing budget of $300,000 to promote its legacy loyalty program via digital ads, email marketing, in-store promotions, and mobile marketing.
Risks. The expected financial impact is subject to risks and variation based on factors including:
- The ongoing costs of maintaining and supporting an in-house-developed legacy system or annual or monthly fees paid for software licenses.
- The costs associated with integrating the legacy platform with other business systems and expenses for customizing the legacy system to meet evolving business needs.
- The number of employees needed to manage and operate the legacy system.
- The annual budget allocated to advertising the legacy loyalty program.
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 $1.8 million.
Legacy Program Replacement Savings
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| C1 | Subtotal: Total cost of the legacy membership management system | Interviews | $450,000 | $450,000 | $450,000 |
| C2 | Cost of issuing and administering plastic membership cards per member | Interviews | $0.15 | $0.15 | $0.15 |
| C3 | Subtotal: Cost of issuing plastic membership cards prior to SessionM | A2*C2 | $75,000 | $78,750 | $82,688 |
| C4 | Subtotal: Cost of marketing the legacy program | Composite | $300,000 | $300,000 | $300,000 |
| Ct | Legacy program replacement savings | C1+C3+C4 | $825,000 | $828,750 | $832,688 |
| Risk adjustment | ↓15% | ||||
| Ctr | Legacy program replacement savings (risk-adjusted) | $701,250 | $704,438 | $707,785 | |
| Three-year total: $2,113,472 | Three-year present value: $1,751,449 | ||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
-
Data efficiency and integration. SessionM plays a role as a central repository for customer data,
unifying and activating data from disparate systems enabled by seamless
integration between SessionM and other platforms and systems. Interviewees
said they appreciate the ability to comprehensively track and manage
customer interactions and loyalty metrics in SessionM. By unifying and
activating data, SessionM enabled the interviewees’ organizations to gain a
holistic view of their customers, enhancing their ability to personalize and
optimize customer experiences. The seamless integration between SessionM and
other platforms streamlined data management processes, ensuring that all
relevant customer information was easily accessible and actionable.
- The head of marketing at the specialty products retailer said: “SessionM was able to integrate, providing a platform where retail members can check their points balance, access their information, and redeem points and other rewards.”
- The senior marketing manager at the fast-food restaurant chain noted: “We can link SessionM to our homegrown data platform, allowing us to gather all relevant information and create a comprehensive view of the customer profile.”
- The director of martech at the fast-casual restaurant explained: “Think of SessionM as a data conduit and repository that feeds various locations where decisions are made. For registrations, data flows into our marketing automation, customer engagement and customer support systems, culminating in SessionM, which then acts as a conduit to different platforms.”
-
Data utilization and independence.
SessionM empowered the interviewees’ organizations to
effectively utilize customer data without depending on third-party sources.
By seamlessly integrating with various platforms and systems, SessionM
allowed the businesses to structure and customize their customer
data.
- The senior marketing manager at the fast-food restaurant chain said this allowed their company to segment its customer base and conduct A/B tests to identify the most effective campaigns. Similarly, the loyalty program manager at the energy retailer said their organization’s loyalty program leverages this data to create personalized offers and experiences in-house, reducing reliance on external data providers.
- The senior marketing manager at the fast-food restaurant chain explained: “SessionM provides a wealth of knowledge that we complement with our own data science work to analyze customer lifetime value and behavior over time. By integrating customer and transactional data, we achieve a comprehensive 360-degree view. We frequently run A/B tests to segment our customer base and determine the most effective bonus point campaigns.”
- The director of brand marketing at the fast-casual restaurant chain said: “We perform extensive segmentation, especially with our CDP. We analyze active and lapsed members, creating a lapsing journey to reengage those at risk of leaving. SessionM’s integrations allow us to manage this data in an account preference center, which has been a significant advantage for us.”
-
Enhanced customer experience and
engagement. Interviewees said they highly value the
platform’s role in enhancing customer experience and engagement through
better understanding of customer behavior, creating targeted segments, and
generating personalized offers. By leveraging comprehensive data collection,
SessionM enabled executives to gain deep insights into customer behaviors
and preferences, and this allowed the interviewees’ organizations to tailor
their marketing strategies effectively. This resulted in more personalized
and relevant offers. Additionally, SessionM’s support for segmentation and
targeted content creation helped these companies engage their customers more
meaningfully. Improved customer service was another significant benefit, as
SessionM’s integrations facilitated quick and accurate resolution of
customer inquiries. Enhanced brand awareness and positive customer
engagement were further bolstered by the platform’s usability and
functionality, making SessionM a central tool for driving customer loyalty
and satisfaction.
- The senior marketing manager at the fast-food restaurant chain said: “Guest care can access customer accounts and provide resolutions for missing points or rewards. The capabilities of bonus point and accelerator campaigns are a positive aspect. SessionM’s integration across our organization allows us to leverage customer analytics and enables our customer care team to quickly resolve issues with the loyalty program. This includes addressing cases where customers did not receive bonus points they were entitled to. Overall, this has made customer care much easier.”
- The head of marketing at the specialty products retailer said: “It allows our customer service team to handle customer questions efficiently. For example, if a customer says they didn’t get their points, the team can quickly verify and resolve the issue. This has made customer service smoother because customers understand the program and know what to expect. They feel assured that if they call, they will be taken care of promptly [because] everything can be managed within the system.”
- The senior marketing manager at the fast-food restaurant chain said: “We store customer demographic information, such as birthdays [and] kids’ birthdays in SessionM, which makes our efforts more personalized. We know who is expiring, how close they are, and their points balance. Dynamic banners in our emails show members their balance and notify them when they are close to redeeming something.”
-
Having a valued partner during and after
implementation. Interviewees explained that SessionM
collaborates with organizations to tailor loyalty programs to customer
goals, and they said they value SessionM’s innovative solutions, expertise,
and commitment to service. They frequently highlighted the expertise and
dedication to service provided by SessionM’s team, including their prompt
problem-solving and strategic guidance, as key benefits. Many interviewees
also described SessionM as an extension of their own teams, emphasizing the
supportive relationships and the positive impact on their loyalty
initiatives.
- The senior marketing manager at the fast-food restaurant chain said: “[SessionM representatives] were able to find solutions. They brought ideas to the table. It was a great implementation.”
- The head of marketing at the specialty products retailer said, “We felt like SessionM is really an extension of the team and not just an agency that is supplying us with a software solution.”
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement SessionM and later realize additional uses and business opportunities, including:
-
Progressive profiling. Several interviewees mentioned they see potential in further
leveraging SessionM’s capabilities to integrate progressive profiling to
create an even more seamless and engaging customer experience. By using
progressive profiling, they would like to collect customer data
incrementally to ensure accuracy while not putting too much burden on
customers. The resulting high-quality data will contribute to enhanced
customer understanding, allowing these organizations to further tailor
interactions and offers based on individual preferences and behaviors.
Interviewees said they view this approach as one that further improves
customer satisfaction and builds trust since customers feel their data is
being handled respectfully.
- The director of martech at the fast-casual restaurant chain said their company is working on progressively profiling its guests to build more detailed customer profiles over time through various interactions. They said, “We’re not super advanced at it yet, but we are in the process of implementing progressive profiling for our guests.”
- The senior marketing manager at the fast-food restaurant chain said their organization is currently exploring more personalized reward strategies. They noted the company is “considering personalized rewards and exploring personalized surprise and delight offerings” as a way to move beyond mass loyalty offers to more segmented and targeted approaches.
-
Advanced personalization and decision engine
solutions. Interviewees said that while SessionM
provides a strong foundation for customer data management and
personalization, Dynamic Yield by Mastercard enhances these capabilities
with advanced real-time personalization, deeper insights, and robust
optimization tools. One interviewee mentioned exploring Dynamic Yield for
website personalization, aiming to tailor online experiences using machine
learning. They are interested in this capability as a way to deliver
personalized content and product recommendations at scale, optimizing
engagement and conversions. Additionally, the organization is evaluating the
Test & Learn platform as an addition to SessionM to enhance data-driven
decision-making, optimize business strategies, and provide precise
measurement and analysis. The organization would use the solution to
identify optimal store locations and improve customer data insights for more
targeted marketing efforts.
The head of marketing at the specialty products retailer said: “Our e-commerce team is exploring Dynamic Yield with Mastercard. They conducted a proof of concept, and we’re looking to have the system in place within six months. We’re also meeting with them to discuss Test & Learn initiatives and customer data enhancements. Our focus is on better understanding our customers and their segments. Who are these people and what additional information can we append to the data to create more targeted and personalized content for them?”
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 | $262,500 | $556,500 | $556,500 | $556,500 | $1,932,000 | $1,646,433 |
| Etr | Internal program costs | $660,000 | $6,362,400 | $7,908,040 | $13,252,005 | $28,182,444 | $22,935,997 |
| Total costs (risk-adjusted) | $922,500 | $6,918,900 | $8,464,540 | $13,808,505 | $30,114,444 | $24,582,430 | |
Fees To Mastercard
Evidence and data. Interviewees’ organizations paid monthly or annual subscription fees for access to the SessionM platform, which included access to features such as data management, loyalty program management, and campaign execution tools. Mastercard charged an annual fee for ongoing technical support to address any issues, perform updates, and ensure the platform runs smoothly. The initial professional services Mastercard provided included implementation, customization, and integration services to connect SessionM with CRM, CDP, or POS systems and managing data flows between these systems. Ongoing consulting services delivered by the SessionM team included loyalty program design and best practices, campaign strategy, data analysis, and optimization to help interviewees’ organizations maximize the value of their loyalty programs.
Interviewees said they value the company’s collaborative approach, innovative solutions, and deep expertise, and they view Mastercard as an essential partner in achieving their organization’s loyalty program goals. They also said they appreciate how Mastercard tailors its services to meet specific needs, bringing creative ideas and effective solutions to the table.
- The director of brand marketing at the fast-casual restaurant chain noted that SessionM provided ongoing support through technical account managers and appreciated the professional services team, which included integration engineers and solutions architects who helped with both strategy and technical issues. They said: “We have an active account team from SessionM that provides both strategic and technical assistance. They have been very supportive throughout our journey.” The interviewee added: “We wanted to focus more on digital, which is where SessionM has significant experience. We felt very comfortable choosing them because of their expertise in that area.”
- The head of marketing at the specialty products retailer remarked about the relationship with the SessionM team and the value of its ongoing guidance: “We were initially concerned about being a small company in Mastercard’s large portfolio and whether we’d get the attention we needed. However, they’ve far exceeded our expectations. The customer success team is fantastic [and] always ready to help with issues and brainstorm new ideas. This week, they visited us, bringing great ideas and showing genuine care for our program’s success. We love our partnership with SessionM and Mastercard.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite pays an annual subscription fee of $250,000 for the SessionM platform, allowing for managing up to 1 million active loyalty program members.
- The composite’s annual support contract with Mastercard amounts to $130,000 per year.
- In the six months leading up to the production go-live date with the new loyalty platform, Mastercard provided $250,000 worth of consulting services to the composite organization. In the following years, the composite purchases $150,000 worth of ongoing consulting services per year.
Risks. Organizational differences that may impact the costs associated with SessionM licensing and recurring costs include the number of active loyalty members, the scale of deployment, the type and extent of integration with the martech stack and other partners, and the requirement for ongoing expert services.
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 $1.6 million.
Fees To Mastercard
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| D1 | SessionM subscription fee for up to 1 million active loyalty program members | Composite | $0 | $250,000 | $250,000 | $250,000 | |
| D2 | Support contract | Composite | $0 | $130,000 | $130,000 | $130,000 | |
| D3 | Implementation and consulting services | Composite | $250,000 | $150,000 | $150,000 | $150,000 | |
| Dt | Fees to Mastercard | D1+D2+D3 | $250,000 | $530,000 | $530,000 | $530,000 | |
| Risk adjustment | ↑5% | ||||||
| Dtr | Fees to Mastercard (risk-adjusted) | $262,500 | $556,500 | $556,500 | $556,500 | ||
| Three-year total: $1,932,000 | Three-year present value: $1,646,433 | ||||||
Internal Program Costs
Evidence and data. Interviewees incurred additional internal costs associated with the initial implementation of the platform, training staff, providing customer service, ongoing optimization of the loyalty program, and the cost of rewards and incentives. They shared the following insights about their organizations’ incentive approaches and the efforts involved in SessionM program development, employee training, and program marketing.
- The director of brand marketing at the fast-casual restaurant chain shared the following about the makeup of the resources involved in program management and testing offers: “We have a dedicated loyalty manager who oversees the program and a specialist who handles a lot of the testing to ensure everything is functioning properly. Overall, there are about five or six people involved in testing offers spread between the marketing and marketing technology teams.”
-
The head of marketing at the specialty products retailer talked
about their organization’s ongoing efforts aimed at fine-tuning the loyalty
program: “Now that we have very high penetration of customers, we are
focusing on retention and nurturing to keep them coming back. We are
continuing to tweak the program’s messaging and offers to keep it fresh and
exciting; we’ve been doing things like sending a special gift for their
birthday. They get either a special coupon or a discount depending on what
they typically redeem.”
The interviewee also said SessionM allowed their organization to track the financial impact of promotions, including the cost of points and offers. This transparency helped in optimizing marketing budgets and reducing waste. - The senior marketing manager at the fast-food restaurant chain said their organization focused on increasing the number of active users rather than just sign-ups to ensure sustained engagement. The organization initially implemented an aggressive couponing strategy to drive user engagement and redemption rates. The interviewee also mentioned specific advertising and marketing campaigns to promote the new program, including dedicated loyalty media at the launch. They said, “This campaign was a larger initiative that included not only core promotions, but also media and other ad tech channels, social media, and various other types of advertising.”
-
Speaking about the effort involved in transitioning membership
data from their organization’s previous loyalty program, the loyalty program
manager at the energy retailer said, “We did transfer the active members
that we had at the time and two years’ worth of transactional data into the
platform.”
The interviewee also provided additional insight into the organization’s approach to rewards and discounts: “We have six different promotional periods throughout the year. They each run several weeks in length, and we have various campaigns based on the season — about four or five larger campaigns a year. Sometimes, we provide cents-per-purchase discounts. But, more often, the rewards are collectors’ points. The cost of a reward point is calculated as a dollar amount. We review the revenue from each promotion and whether it was a positive or a negative ROI for us. Some promotions may have a negative ROI, but they increase loyalty penetration.”
The same interviewee added: “We are seeing a sizable number of points being redeemed. I think that is a function of the program being well-received by customers. But I think also that number has trended up over the last year or so as a function of consumers tightening their belts and being more cost-conscious.” - On the topic of reward points and discounts, the director of brand marketing at the fast-casual restaurant chain noted: “Our redemption rates have increased significantly since we’re making sure that the audience that we’re going after is most likely to redeem, and that’s great for us. And, therefore, we’re okay with the cost of the discounts because these are qualified people. And we believe that by getting them back in, they’ll likely return faster.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite’s internal loyalty program development costs are $350,000 during the initial implementation phase, and these cover setup expenses for planning, program development, testing, and implementation support. Ongoing program optimization and support costs amount to $200,000 per year after the launch of the new loyalty platform.
- Leading up to the launch of the new loyalty program, the composite invests $50,000 in hands-on training to master the technical aspects of loyalty program management with SessionM. Training participants include members of the IT team, the loyalty program manager, a loyalty platform specialist, and members of the marketing, martech, and customer care teams. Ongoing upskilling and training efforts cost $10,000 per year.
- Leading up to the launch of the new loyalty program, the composite organization invests $200,000 in program marketing efforts, and it continues to allocate an annual marketing budget of $300,000 to promote its loyalty program.
- With SessionM, the organization changes the structure of rewards, accelerates membership growth, and improves reward redemption rates, resulting in higher reward costs compared to the legacy program. Forrester calculated the resulting cost overage or increase in recurring costs for points and promotions per year proportionate to membership growth. This calculation also considers that the new loyalty program doubled the value of each reward point while reducing the number of promotions per year.
Risks. Organizational differences that may impact the costs associated with internal program development, training and marketing costs include the number of active loyalty members, the scale of deployment, the type and extent of marketing efforts in support of the loyalty program, and the requirement for ongoing customer support.
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 $23 million.
Internal Program Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| E1 | Program development costs | Composite | $350,000 | $200,000 | $200,000 | $200,000 | |
| E2 | Employee training costs | Composite | $50,000 | $10,000 | $10,000 | $10,000 | |
| E3 | Program marketing costs | Composite | $200,000 | $300,000 | $300,000 | $300,000 | |
| E4 | Recurring cost of points overage | Composite | $0 | $3,522,422 | $6,175,127 | $9,539,277 | |
| E5 | Recurring cost of discounts and promotions overage | Composite | $0 | $1,751,578 | $504,000 | $1,998,000 | |
| Et | Internal program costs | E1+E2+E3+E4+E5 | $600,000 | $5,784,000 | $7,189,127 | $12,047,277 | |
| Risk adjustment | ↑10% | ||||||
| Etr | Internal program costs (risk-adjusted) | $660,000 | $6,362,400 | $7,908,040 | $13,252,005 | ||
| Three-year total: $28,182,444 | Three-year present value: $22,935,997 | ||||||
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 | ($922,500) | ($6,918,900) | ($8,464,540) | ($13,808,505) | ($30,114,444) | ($24,582,430) |
| Total benefits | $0 | $10,272,104 | $15,888,771 | $22,253,482 | $48,414,357 | $39,188,863 |
| Net benefits | ($922,500) | $3,353,204 | $7,424,232 | $8,444,977 | $18,299,913 | $14,606,433 |
| ROI | 59% | |||||
| 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 vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Total Economic Impact Approach
-
Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
-
Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
-
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
-
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
Discounts And Points Top The List Of Important Loyalty Program Elements For US Consumers, Forrester Research, Inc., March 5, 2024.
The State Of Retail Loyalty In 2024, Forrester Research, Inc., August 18, 2024.
Use Personalization To Activate Loyalty Program Value, Forrester Research, Inc., September 24, 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 vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
2 Source: How To Build An Agile Loyalty Program That Drives Growth, a commissioned study conducted by Forrester Consulting on behalf of Mastercard, May 2024.
3 Ibid.
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