[CONTENT]
$
USD
Total Economic Impact
Cost Savings And Business Benefits Enabled By The Digital Analytics Platform
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY Mixpanel, december 2025
Total Economic Impact
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY Mixpanel, december 2025
Product and marketing teams today face mounting pressure to make faster, data-driven decisions, yet many struggle with fragmented analytics environments that slow insights and erode trust in data. A unified, self-service analytics approach can empower teams to act on real-time behavioral trends, streamline workflows, and unlock measurable business impact. Adopting a modern analytics platform like Mixpanel can improve agility, reduce operational bottlenecks, improve agility, and foster a culture of evidence-based decision-making.
Mixpanel combines real-time user behavior tracking with data governance, enabling product and marketing teams to identify trends, optimize digital customer experiences, and take insights-driven actions with confidence. Its design and built-in templates support teams to validate hypotheses and prioritize initiatives, reducing their reliance on analyst or business intelligence (BI) team timelines. These capabilities can help organizations standardize reporting practices, reduce operational bottlenecks, and shorten analysis-to-action time as analytics workflows scale across functions.
Mixpanel commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying the Mixpanel digital analytics platform. The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Mixpanel on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed five decision-makers with experience using the Mixpanel digital analytics platform. For the purposes of this study, Forrester aggregated the experiences of the interviewees and combined the results into a single composite organization, which is a global software company with $750 million in annual revenue and 1,000 employees.
Interviewees stated that prior to adopting Mixpanel, their organizations relied on diverse, fragmented, and manual digital analytics setups (e.g., in-house pipelines, BI tools, and other solutions) that were slow, sampling-based, and lacked real-time visibility, leading to data accuracy issues and low trust in insights. Interviewees reported that their product and marketing teams were heavily dependent on analysts, BI teams, and data warehouse engineers for even basic reporting, creating bottlenecks. As a result, business users struggled to validate hypotheses or act quickly on emerging trends.
After investing in the Mixpanel digital analytics platform, interviewees reported that its self-service solution became the primary tool for product, marketing, and engineering teams to analyze user behavior and validate funnel optimization hypotheses, reducing reliance on multiple fragmented data systems and data teams. Mixpanel provided real-time visibility and clarity for product decisions and marketing attribution, enabling product and marketing teams to act on insights without waiting for data operations support. Several interviewees noted that they implemented event standardization and governance processes using Mixpanel’s features, which improved consistency and confidence in data.
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Profitability improvements from UX/UI optimization. Mixpanel accelerates funnel conversion (e.g., registration, sign-up, purchase) by enabling teams to validate hypotheses about multistep onboarding flows — such as whether reordering steps in an app sign-up process improves conversion or making certain questions optional reduces drop-off — and drives profit improvement through granular tracking and real-time behavioral insights.
With the Mixpanel platform, the composite organization identifies drop-off points, optimizes user experience by validating product design changes, and quickly confirms which changes improve conversion and engagement. These capabilities support strategic UX/UI innovation and unlock measurable revenue growth through faster progression across complex funnels. These efficiencies result in $3.2 million over three years.
Incremental profit growth through micro cohort targeting and engagement. The Mixpanel platform enables real-time personalized campaign optimization by empowering teams to create cohorts of 100,000 to 300,000 users in minutes, which marketing teams then export to in-house systems or campaign platforms for hyper personalized offers, driving incremental revenue growth.
The composite organization’s teams leverage Mixpanel to build micro cohorts quickly, experiment with moment marketing, and monitor campaign performance without relying on multiple teams or lengthy processes. These initiatives generate $479,000 over three years.
Reduced reliance on data teams. Mixpanel’s self-service analytics enable product, marketing, and growth teams to conduct their own analyses, lessening their dependency on analysts, BI developers, and data engineers by eliminating ad hoc requests, routine reporting, and dashboard creation.
Tasks such as cohort creation, which previously required multiple teams and days of effort, now take minutes, and dashboards that once consumed hours can be built in under 1 hour using Mixpanel templates. The composite’s data teams shift their attention to more value-added, innovative, and business insight-driven activities. It incurs significant productivity efficiencies: Analysts save 5 hours per week and BI/data operations teams save 3 hours per week, resulting in $698,000 in productivity benefits over three years.
Enhanced productivity for product managers (PMs), marketing teams, and app developers. The Mixpanel platform drives cross-functional efficiency and innovation for the composite by democratizing access to real-time analytics for PMs, marketers, and app developers. Its interface and built-in templates enable teams to validate hypotheses, refine product features, and optimize campaigns without delays. The composite’s PMs save 4 hours per week and marketing professionals save 3 hours per week by reducing their reliance on analysts and BI teams for data.
In addition, app developers gain instant visibility into event flows, enabling faster and more precise issue identification. By simulating user actions and tracing event sequences in Mixpanel, they quickly pinpoint root causes and intervene in application codes, reducing investigation time from weeks to minutes and saving an estimated 2 hours per month. These productivity gains result in faster speed to market and improve defect resolution stability — allowing engineering to focus on future velocity rather than maintenance — and amount to $1.8 million over three years.
Reduced reliance on legacy analytics solutions. The composite organization adopts Mixpanel as its primary self-service analytics platform, reducing dependency on BI dashboards and lowering query volume in data warehouses. These cost savings amount to $183,000 over three years.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Faster hypotheses validation and guided experimentation. Product and marketing teams use Mixpanel to test assumptions for digital, web, and app offerings. Its self-serve analytics and features (e.g., Signal, end-to-end experiments, feature flags, session replay, and metric trees) guide teams toward meaningful questions by surfacing patterns and enabling structured thinking (e.g., drop-off indicators, conversion triggers, hypothesis testing, experimentation, and debugging flows).
Teams can quickly validate product assumptions, simulate user flows, and uncover behavioral patterns, accelerating experimentation cycles and enhancing analytical and decision-making capabilities.
Improved decision-making and roadmap alignment. With the Mixpanel platform, the composite’s marketing and product teams gain real-time visibility into user behavior and cohort performance, aligning them to prioritize high-impact initiatives and secure stakeholder buy-in through data-driven analysis.
High trust in data through governance and validation. With Mixpanel, the composite invests in governance frameworks and orchestration pipelines to ensure data accuracy, consistency, and compliance. Features such as Mixpanel’s Lexicon further strengthen this trust by standardizing event naming and enabling teams to manage digital interaction data effectively, reducing duplication, eliminating ambiguity, and ensuring clarity across the organization.
These governance controls combined with the Mixpanel platform’s streamlined design encourage nontechnical users to engage directly with analytics, creating a cultural shift and embedding data-driven thinking across teams.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
Software license and data egress costs of $705,000. The composite organization incurs Mixpanel platform licensing fees of $210,000 in Year 1, increasing by 2% in Year 2 and 3% in Year 3 as adoption and usage expand. In addition, data egress costs represent approximately 20% of licensing fees.
Initial implementation costs of $112,000. The composite organization adopts Mixpanel gradually through a phased deployment, prioritizing data governance and normalization before migrating events. The organization allocates eight internal champions across analytics, BI, engineering, marketing, product, and leadership teams, who each dedicate 15% of their time for onboarding, testing, and governance activities.
Ongoing implementation costs of $465,000. The composite organization allocates one analyst and one engineer at 50% capacity to support gradual deployment across business units, scale event ingestion as usage grows, and maintain governance and data accuracy.
Training costs of $107,000. During initial implementation, the composite organization has a small team of analysts, data warehouse engineers, and BI professionals spend approximately 7 hours to learn advanced Mixpanel platform capabilities and prepare dashboards and templates for onboarding. In Years 1 and 2, as it gradually deploys Mixpanel to additional groups, each new user completes about 5 hours of training.
The financial analysis that is based on the interviews found that a composite organization experiences benefits of $6.3 million over three years versus costs of $1.4 million, adding up to a net present value (NPV) of $4.9 million and an ROI of 354%.
Return on investment (ROI)
Benefits PV
Net present value (NPV)
Payback
| Role | Industry | Headquarters | Revenue |
|---|---|---|---|
| Principal product manager | Food service | India | $3B |
| Data warehouse engineer | Fintech | USA | $1.5B |
| Data executive | Healthcare | USA | NA |
| Senior R&D project manager | Telecommunications | Europe | NA |
| Chief product officer | Online gaming | Turkey | $100M |
Interviewees reported that prior to adopting Mixpanel, their organizations operated in disjointed analytics environments where analyses were based solely on sampled data or data was incomplete, delayed, or unavailable, hindering marketing and product teams’ ability to identify and respond to user behaviors and emerging trends.
Interviewees noted how their organizations struggled with common challenges, including:
Legacy tools lacked depth, speed, and accessibility. Interviewees reported that prior to Mixpanel, fragmented analytics environments required manual patching and cross-system stitching to piece together user journeys, draining time and resources.
The chief product officer at an online gaming company noted that they relied on sampled data that was delayed 8 to 10 hours, leaving gaps in real-time funnel analysis and attribution across marketing channels. Slow, incomplete data access hindered campaign agility and responsiveness, forcing teams to make decisions based on assumptions rather than facts.
Fragmented and chaotic data environments. Interviewees reported that teams worked in silos with overlapping event tracking and inconsistent naming conventions, creating confusion and eroding trust in data.
In the absence of a unified analytics platform with shared standards, the same interviewee noted that product and marketing managers were often unaware of whether a particular dataset existed and improvised by posting messages on internal channels to locate the data. As the organization scaled, this environment became untenable, rendering standardized processes and cross-team data access critical necessities for growth.
Heavy reliance on analysts and data engineers. Interviewees reported that
even basic questions such as conversion rates and funnel drop-offs required formal requests, prioritizations, and SQL queries. The data warehouse engineer at a fintech firm explained, “If a PM couldn’t find what they needed, their immediate response was to ping an analyst, and then the analyst would ping the data team.” The interviewees explained that this high dependency on data operations, analysts, and BI teams created queues and backlogs, delaying insights for product and marketing teams by days or weeks, especially for iterative research projects.
Inconsistent data governance. Interviewees’ organizations lacked standardized instrumentation and validation processes, which undermined data integrity and made cross-team collaboration nearly impossible. As the data warehouse engineer at a fintech firm explained: “Before, there was no normalization. You couldn’t be sure if the event you were looking at was the right one.” The absence of shared standards meant that even basic funnel analysis or attribution tracking required heavy analyst involvement and custom queries.
Low data literacy and tool usability. Many users lacked the technical skills to query data independently, reinforcing their dependence on specialized teams. Interviewees noted that even when tools were available, they were not intuitive for nontechnical users. This limited self-service capabilities and stalled agile decision-making.
The interviewees searched for a solution that could:
Democratize data access with self-serve analytics by autonomizing employee and leadership access to analyses, reducing the dependency on data teams, and empowering product and marketing teams.
Improve data accuracy and trust by normalizing data across merged companies.
Enable real-time decision-making by optimizing web and app onboarding conversation and faster hypothesis validation.
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 global software technology company with $750 million in annual revenue and 1,000 employees.
The centralized data operations team consists of five data warehouse engineers and five BI specialists who oversee enterprise wide data management, reporting, and governance capabilities. The organization also employs 25 analysts, located across product verticals, who handle day-to-day analytical requests and work hand in hand with the data operations team.
In addition, the organization employs 60 professionals in product management, 50 in marketing, 40 in-app development, and 90 across functions such as design, customer experience, strategy, and business operations.
Deployment characteristics. The composite organization deploys the Mixpanel platform gradually, beginning with onboarding champions from analytics, BI, product, and marketing teams to drive adoption while data teams focus on aligning event structures, ensuring data accuracy, and integrating Mixpanel through software development kits (SDKs) or data pipelines.
In Year 1, PMs, marketing teams, and app developers increasingly use the Mixpanel platform for self-service analyses and dashboard creation by leveraging its built-in capabilities, while data teams continue to refine data practices and incorporate additional sources.
By Year 2, Mixpanel is broadly adopted across business and product units, supporting more consistent analytics practices and enabling fact-based decision-making.
$750 million revenue
1,000 employees
275 Mixpanel users
25 analysts
10 employees in data operations
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Profit improvement from enhanced insights | $765,000 | $1,297,313 | $1,868,471 | $3,930,784 | $3,171,424 |
| Btr | Profitability growth from campaign optimization | $127,500 | $195,075 | $268,537 | $591,112 | $478,884 |
| Ctr | Productivity improvement from faster insights | $280,688 | $280,688 | $280,688 | $842,064 | $698,030 |
| Dtr | Cross-functional efficiencies from self-serve analytics | $712,800 | $712,800 | $712,800 | $2,138,400 | $1,772,628 |
| Etr | Cost savings from tool consolidation | $73,584 | $73,584 | $73,584 | $220,752 | $182,993 |
| Total benefits (risk-adjusted) | $1,959,572 | $2,559,460 | $3,204,080 | $7,723,112 | $6,303,959 |
Evidence and data. Interviewees noted that the Mixpanel platform provided granular, unsampled visibility into multistep onboarding flows and real-time behavioral insights, which enabled them to dissect customer journeys, identify drop-off points, and take strategic action to improve user experience and accelerate funnel progression. Specific areas they were able to address included:
Complex onboarding funnels. The Data executive at a healthcare firm described their onboarding funnel as “a complex, 20+-intake process and emphasized that it was critical to “understand how people are moving through every single piece of that funnel.” He added: “If you can make that business 1% more efficient through a tool like Mixpanel, you’re operating on the order of millions of dollars in unlock.”
Product design and feature innovation. The interviewees reported that Mixpanel’s data democratization and granular user behavior patterns enabled their PMs to quickly discern any user behavior patterns that revealed friction, leading to strategic and innovative product design features.
The chief product officer at an online gaming company related that with the Mixpanel platform, she was able to observe irregularities in customer behavior such as customer confusion or frustration with key app features. These observations led to the creation of a new product with a revenue stream projected to drive a 10% to 30% increase in live gameplay volume. She emphasized, “Without Mixpanel, it would be impossible for me to identify which cohorts, which customers, how they behave, what I need to touch, and which point I need to improve.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
It generates $200 million annually, influenced by UX/UI innovation and strategic funnel optimization.
With Mixpanel, the composite realizes revenue lift from UX/UI improvements. In Years 2 and 3, this benefit includes incremental UX/UI revenue plus 100% of the prior year’s campaign efficiencies revenue lift, reflecting how improved user experience compounds with optimized campaign performance.
The composite adopts the Mixpanel platform in phases, reaching full implementation in Year 2. Revenue lift is estimated at 1.5% in Year 1, 2.5% in Year 2, and 3.5% in Year 3.
The pretax operating margin is 30% for a software tech company.1
Risks. The realization of these benefits will vary with:
Mixpanel adoption and implementation.
Revenue attributed to UX/UI innovation and funnel optimization.
The extent of self-serve analytics and cross-functional data democratization.
Data governance and accuracy.
An organization’s event volume and complexity.
Profit margin variability.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.2 million.
Estimated lift from enhanced digital experiences and funnel design in Year 3
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Revenue influenced by UX/UI innovation and strategic funnel optimization | Composite | $200,000,000 | $203,500,000 | $209,352,500 | |
| A2 | Estimated lift from enhanced digital experiences, funnel design, and data visibility with Mixpanel | Interviews | 1.50% | 2.50% | 3.50% | |
| A3 | Increased customer engagement due to better digital and funnel experiences | A1*A2 | $3,000,000 | $5,087,500 | $7,327,338 | |
| A4 | Pretax operating margin | Composite | 30% | 30% | 30% | |
| At | Profit improvement from enhanced insights | A3*A4 | $900,000 | $1,526,250 | $2,198,201 | |
| Risk adjustment | ↓15% | |||||
| Atr | Profit improvement from enhanced insights (risk-adjusted) | $765,000 | $1,297,313 | $1,868,471 | ||
| Three-year total: $3,930,784 | Three-year present value: $3,171,424 | |||||
Evidence and data. Interviewees explained that with the Mixpanel platform, they could identify and create micro cohorts defined by specific actions on their apps (e.g., purchase behavior, engagement with certain features, frequency of use). The principal product manager at a food service organization described that PMs exported these cohorts into their in-house systems for in-app personalization and targeted them with hyper personalized offers, resulting in incremental revenue growth. Specific features interviewees leveraged included:
Cohort analysis to measure impact. Prior to Mixpanel, creating segmentation/cohorts for targeted marketing required multiple teams and took several days, delaying time-sensitive campaigns. With Mixpanel, interviewees’ product and marketing teams can independently build and analyze cohorts in minutes, enabling rapid experimentation and moment marketing.
The principal product manager at a food service organization further explained that they receive about 6 million daily active users, and with Mixpanel, they can create cohorts of 100,000 to 300,000 individuals in less than 5 minutes.
Campaign performance by group. Interviewees also used the Mixpanel platform to distinguish user entry points by campaign to create targeting lists and analyze and monitor customer behaviors following campaign interactions. The senior R&D project manager at a telecommunications firm explained, “You can see which type of users who signed up for the free trial become a paying user.” She noted that this capability helped them understand which product plans to continue and optimize campaigns based on user engagement patterns.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization’s revenue influenced by Mixpanel’s campaign efficiencies is $100 million.
With Mixpanel, the composite realizes revenue lift in Years 2 and 3 from campaign efficiencies. This benefit is further amplified by UX/UI improvements, with 50% of the prior year’s UX/UI revenue lift added to campaign-driven revenue. The 50% allocation reflects that UX/UI improvements enhance the overall customer experience across multiple channels, not solely campaigns.
It phases its Mixpanel platform adoption, reaching full implementation in Year 2.
The revenue growth attributed to Mixpanel from campaigns is estimated at 0.5% in Year 1, 0.75% in Year 2, and 1% in Year 3.
The pretax operating margin is 30% for a software tech company.2
Risks. The realization of this benefit will vary with:
Mixpanel platform adoption and implementation.
Revenue attributed to better targeting and engagement.
The extent of self-serve analytics and cross-functional data democratization.
Data governance and accuracy.
An organization’s event volume and complexity.
Profit margin variability.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $479,000.
Revenue lift for better targeting and engagement
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Revenue influenced by campaign efficiencies | Composite | $100,000,000 | $102,000,000 | $105,308,750 | |
| B2 | Estimated lift for better targeting and engagement with Mixpanel | Interviews | 0.50% | 0.75% | 1% | |
| B3 | Revenue growth with increased customer engagement with Mixpanel | B1*B2 | $500,000 | $765,000 | $1,053,088 | |
| B4 | Pretax operating margin | Composite | 30% | 30% | 30% | |
| Bt | Profitability growth campaign optimization | B3*B4 | $150,000 | $229,500 | $315,926 | |
| Risk adjustment | ↓15% | |||||
| Btr | Profitability growth from campaign optimization (risk-adjusted) | $127,500 | $195,075 | $268,537 | ||
| Three-year total: $591,112 | Three-year present value: $478,884 | |||||
Evidence and data. Interviewees reported that the Mixpanel platform provided them with self-serve analytics and data democratization and helped generate significant productivity increases for their analysts, BI teams, and data engineers. Marketing, product, and other nonanalytic teams gained direct access to data and could create dashboards, conduct analyses, and take strategic action in real time without relying on data teams.
Prior to Mixpanel, multiple data teams were involved in projects such as cohort creation, dashboard development, and data validation:
Segment and cohort creation. Interviewees noted that in their prior legacy environments, multiple teams were involved in segment creation, consuming significant time and bandwidth. With the Mixpanel platform’s self-service capabilities, marketing teams can now create cohorts and analyze behavior.
The principal product manager at a food service company said: “From the point where a request was raised till a cohort was created, it took 5 to 8 hours for three people over a minimum of two to three days. It’s very inefficient for a quick commerce business. Now those cohorts are created in less than 5 minutes.” He added: “Imagine the analyst in those teams used to work two days straight on just creating 10 or 20 different cohorts, which now they won’t do at all. It’s all automated in Mixpanel.”
BI dashboard development. Interviewees reported that prior to Mixpanel, analysts and BI teams were heavily engaged in creating dashboards and addressing ad hoc requests, leading to bottlenecks and delays. The chief product officer at an online gaming company described, “Setting up those dashboards took a long time before Mixpanel” and explained that their research process would take anywhere from a couple of days to a week due to data team backlog.
The data warehouse engineer at a fintech company noted that it would take 2 to 3 hours to build a dashboard. With Mixpanel’s templates, PMs can now create them in 30 minutes to 1 hour, empowering analysts to focus on more strategic projects.
The senior R&D project manager at a telecommunications firm explained that their organization conducted a three-month evaluation of Mixpanel’s impact on analyst and BI team resources. She reported that, “The majority of the day-to-day questions were shifted to Mixpanel,” reducing reliance on analysts and saving one FTE. She added that BI teams also realized efficiency gains, with more than 20% of their quarterly tasks eliminated by moving routine dashboards to the Mixpanel platform.
Data operations team backlog reduction. Interviewees described that their business and marketing teams’ access to data was very inefficient, lacked visibility, and delayed decision-making before Mixpanel. Research requests often escalated to data operations teams.
A data warehouse engineer at a fintech firm stated, “On average we were getting about five to six requests a week of things that might take 30 minutes to fix or they might be much larger asks that became a project.” She added: “[After adopting the Mixpanel platform], questions have pretty much disappeared. … Now we only get requests for new data.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
In the composite organization’s prior state, data was unavailable or unclear, and teams reached out to analysts with routine questions. Requests often escalated and multiple teams were involved, creating significant bottlenecks.
With Mixpanel, most routine inquiries are now resolved directly by product management, marketing, or business teams through self-service capabilities. This shift significantly reduces reliance on data operations and analyst teams for ad hoc reporting, routine questions, and manual dashboard creation, while eliminating time consuming and repetitive back-and-forth communication.
Analysts save 5 hours per week.
Data operations and BI teams save 3 hours per week.
Forrester applies a 50% productivity capture rate to reflect that not all time saved is reinvested into strategic initiatives.
Risks. The realization of these benefits may vary based on:
The number of analysts, BI specialists, and data engineers.
The extent of Mixpanel implementation and adoption.
Actual productivity recapture rate.
Salary levels by role and geographic location.
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 $698,000.
Time saved by data operations teams and analysts per week
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Analysts | Composite | 25 | 25 | 25 | |
| C2 | Analyst time saved per week (hours) | Interviews | 5 | 5 | 5 | |
| C3 | Fully burdened hourly rate for an analyst | Research data | $77 | $77 | $77 | |
| C4 | Productivity recapture rate | Composite | 50% | 50% | 50% | |
| C5 | Productivity efficiencies of analysts and BI team members | C1*C2*C3*C4*50 | $240,625 | $240,625 | $240,625 | |
| C6 | Data warehouse engineers and BI team members | Composite | 10 | 10 | 10 | |
| C7 | Data warehouse engineer and BI team member time saved per week (hours) | Interviews | 3 | 3 | 3 | |
| C8 | Fully burdened hourly rate for an engineer and BI team member | Composite | $95 | $95 | $95 | |
| C9 | Productivity recapture rate | Composite | 50% | 50% | 50% | |
| C10 | Productivity efficiencies of data warehouse engineers | C6*C7*C8*C9*50 | $71,250 | $71,250 | $71,250 | |
| Ct | Productivity improvement from faster insights | C5+C10 | $311,875 | $311,875 | $311,875 | |
| Risk adjustment | ↓10% | |||||
| Ctr | Productivity improvement from faster insights (risk-adjusted) | $280,688 | $280,688 | $280,688 | ||
| Three-year total: $842,064 | Three-year present value: $698,030 | |||||
Evidence and data. Interviewees reported that Mixpanel provided PMs, marketing managers, and app developers with real-time visibility into analytics and improved responsiveness, empowering teams to make fact-based decisions and take strategic action while eliminating delays or waiting on reports. Specific benefits for these teams included:
PM and marketing team productivity efficiencies. Interviewees noted that in their legacy environment, PMs depended on analysts for feature adoption and engagement metrics, often waiting several days for reports. Requests were submitted through ticketing systems and escalated when they were too complex for analysts to resolve. The principal product manager at a food service company reported that PMs would spend 8 hours per week trying to get the data, which using the Mixpanel platform reduced to 2 hours per week.
The chief product officer at an online gaming company described that for research and reporting: “If I didn’t have Mixpanel, I would most likely spend 80 hours or so on reporting in total in collaboration with the data team. … Now we spend 2 to 3 hours with Mixpanel.” Additionally, she emphasized that marketing teams faced similar challenges, with long turnaround times for campaign performance data — sometimes a week or more — requiring multiple back-and-forth clarifications that consumed significant resources and bandwidth.
Interviewees also reported that PMs could now access dashboards and run queries independently, eliminating the need for escalation and reducing delays in obtaining data, allowing them to validate hypotheses and refine features faster, shortening the cycle from testing to improvement. They also emphasized that Mixpanel enabled marketers to segment users, track conversions, and measure campaign impact in real time, improving responsiveness and decision-making.
App developers enhanced productivity. The senior R&D project manager at a telecommunications company described that developers couldn’t easily locate the source of a bug prior to Mixpanel. If something broke in the app, developers can now instantly check event flows and user journeys (e.g., sequence of events). They see what happened before and after the errors, so they can confirm and fix the bugs in minutes. She added, “[The developer] planned to monitor [an event] for two months and then see if [the bug] reproduces or not. On [Mixpanel], I showed him within 5 to 10 minutes where the issue was. … Mixpanel saves each app developer at least 1 hour per month.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
By leveraging Mixpanel’s data democratization and self-serve analytics, the composite’s PM team saves 4 hours per week, the marketing team saves 3 hours per week, and app developers save 2 hours per month.
Forrester assumes a productivity capture rate of 50%.
Risks. The optimization of this benefit will vary with:
The number of employees on PM, marketing, and app development teams.
The extent of adoption and implementation of Mixpanel.
Productivity capture rate.
Salary levels by skill set and geographic location.
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.
Time saved by PM and marketing teams per month
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Product management team | Composite | 60 | 60 | 60 | |
| D2 | Product management team member time saved with Mixpanel per week (hours) | Interviews | 4 | 4 | 4 | |
| D3 | Marketing team | Composite | 50 | 50 | 50 | |
| D4 | Marketing team member time saved with Mixpanel per week (hours) | Interviews | 3 | 3 | 3 | |
| D5 | Subtotal: Time saved by product management and marketing teams per month (hours) | ((D1*D2)+(D3*D4))*4 | 1,560 | 1,560 | 1,560 | |
| D6 | Average fully burdened hourly rate per person | Composite | $80 | $80 | $80 | |
| D7 | Productivity recapture rate | Composite | 50% | 50% | 50% | |
| D8 | Productivity efficiencies of product management and marketing teams | D5*D6*D7*12 | $748,800 | $748,800 | $748,800 | |
| D9 | Application development team | Composite | 40 | 40 | 40 | |
| D10 | Application development team member time saved with Mixpanel per month (hours) | Interviews | 2 | 2 | 2 | |
| D11 | Average fully burdened hourly rate per person | Composite | $90 | $90 | $90 | |
| D12 | Productivity recapture rate | Composite | 50% | 50% | 50% | |
| D13 | Productivity efficiencies of application development team | D9*D10*D11*D12*12 | $43,200 | $43,200 | $43,200 | |
| Dt | Cross-functional efficiencies from self-serve analytics | D8+D13 | $792,000 | $792,000 | $792,000 | |
| Risk adjustment | ↓10% | |||||
| Dtr | Cross-functional efficiencies from self-serve analytics (risk-adjusted) | $712,800 | $712,800 | $712,800 | ||
| Three-year total: $2,138,400 | Three-year present value: $1,772,628 | |||||
Evidence and data. Interviewees indicated that Mixpanel adoption reduced reliance on legacy analytics and BI tools.
The chief product officer at an online gaming company explained that their team no longer maintains licenses for a previous BI platform because Mixpanel now supports most product reporting.
The principal product manager at a food service organization noted that although their organization continues to use a data warehouse and BI tools for certain workflows, query volume on those platforms decreased by 7% to 8% after the Mixpanel platform enabled self-service analysis.
Similarly, a data warehouse engineer at a fintech organization described Mixpanel as the primary self-service tool for product and marketing teams, reducing the need for analysts to build dashboards in traditional BI systems.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization reduces its reliance on BI and analytics tools after adopting Mixpanel but is continuing to deploy them across the organization at a lower frequency.
Query volume on data warehouse platforms decreases by 8%, resulting in annual savings of $81,760.
Risks. The realization of these benefits will vary with:
Other analytics and BI tools, their adoption, and contractual obligations.
Data query solutions’ contract characteristics.
The extent of Mixpanel adoption and implementation.
An organization’s data infrastructure and requirements.
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 $183,000.
Cost savings from consolidating legacy analytics tools
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| E1 | Reduction in data warehouse query costs | Interviews | $81,760 | $81,760 | $81,760 | |
| Et | Cost savings from tool consolidation | E1 | $81,760 | $81,760 | $81,760 | |
| Risk adjustment | ↓10% | |||||
| Etr | Cost savings from tool consolidation (risk-adjusted) | $73,584 | $73,584 | $73,584 | ||
| Three-year total: $220,752 | Three-year present value: $182,993 | |||||
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Accelerated hypothesis validation and guided experimentation. Interviewees noted that Mixpanel’s self-serve analytics enabled faster validation of product hypotheses by providing real-time behavioral insights and iterative experimentation.
Product and marketing teams validated event instrumentation by simulating user flows on their own devices and checked which events fired in Mixpanel, which allowed them to make quick adjustments to assumptions before launching changes and ensured accurate data collection for subsequent analyses and experimentation. They built funnels, retention charts, and cohorts without relying on analysts, and used advanced features like Signal, end-to-end experiments, feature flags, session replay, and metric trees to suggest questions they might not have considered, uncover patterns, reduce bottlenecks for experimentation, debug flows, and enable structured thinking.
For example, interviewees emphasized that Signal helped less experienced team members ask the right questions and identify patterns like user drop-offs, reducing reliance on assumptions and improving the quality of decision-making. The chief product officer at an online gaming organization explained: “Signal helps me find patterns like where trends are breaking. … It basically guides you to which questions you should be asking.”
Improved decision-making and roadmap alignment. Interviewees reported that the Mixpanel platform provided clarity and confidence in product planning by enabling teams to validate assumptions with data, such as which cohorts to prioritize, how customers behave, and which changes are most likely to drive impact.
Product and marketing teams used A/B testing and cohort analysis to measure the real impact of changes against control groups, ensuring that they based roadmap decisions on observed outcomes rather than guesswork.
Mixpanel’s impact analysis also helped align stakeholders across interviewees’ organizations by showing expected versus actual results.The chief product officer at an online gaming firm used Mixpanel insights on conversion improvements from redesigned basket flows to justify roadmap priorities to senior leadership, turning subjective debates into evidence-based decisions. She explained, “This visibility reduces internal debate and accelerates prioritization of high-value initiatives.”
High trust in data due to initial investment in governance and validation. Some interviewees’ organizations invested in restructuring pipelines and implemented governance frameworks to enforce data standards and privacy compliance prior to integrating event data with the Mixpanel platform.
The data executive at a healthcare company emphasized the cultural dimension, saying, “If you erode trust, it’s very difficult to win it back.” His team built an internal orchestration pipeline to validate all event data before the integration with Mixpanel, ensuring schema consistency and filtering out incorrect data types. The data warehouse engineer at a fintech company noted, “We convinced PMs that if we fix the data, they’ll be able to use it.”
Interviewees reported that robust governance practices ensured data accuracy, consistency, and compliance; boosted employee confidence in data; and fostered a cultural shift toward fact-based decision-making. This enhanced trust combined with Mixpanel’s straightforward design and self-service analytics empowered nontechnical users to perform their own analyses.
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement the Mixpanel platform and later realize additional uses and business opportunities, including:
Improved organizational agility. Interviewees highlighted that self-service analytics and governance features minimized their reliance on analysts for routine reporting, freeing data teams to concentrate on value-added, strategic, and innovative insights and advanced modeling. This shift not only avoided incremental hiring but also expanded bandwidth for innovation, allowing product and marketing teams to experiment, validate hypotheses, and accelerate decision-making without waiting for specialized support.
For example, the chief product officer at an online gaming firm explained that her team used session replay to evaluate how customers were interacting with app features during live sessions, which led them to design a new product and unlock a new revenue stream.
With the Mixpanel platform, interviewees’ organizations experienced greater flexibility in responding to business needs, developing new features, and innovating products and processes.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Ftr | Software license fees | $277,200 | $282,744 | $291,226 | $851,170 | $704,475 | |
| Gtr | Initial and ongoing implementation costs | $112,200 | $112,200 | $112,200 | |||
| Htr | Implementation costs | $187,000 | $187,000 | $187,000 | $561,000 | $465,041 | |
| Itr | Training costs | $15,785 | $66,000 | $37,125 | $0 | $118,910 | $106,467 |
| Total costs (risk-adjusted) | $127,985 | $530,200 | $506,869 | $478,226 | $1,643,280 | $1,388,183 |
Evidence and data. Interviewees noted incurring software licensing and data fees for the Mixpanel digital analytics platform. Pricing may vary. Contact Mixpanel for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Mixpanel licensing fees are $210,000 in Year 1 and increase 2% in Year 2 and 3% in Year 3, consistent with Mixpanel adoption and usage.
Data egress costs represent 20% of licensing fees.
Risks. Software costs will vary by:
The amount of data that an organization integrates into Mixpanel.
Data accuracy and governance measures.
The volume of events an organization tracks.
The number of users.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $704,000.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | License fees | $210,000 | $214,200 | $220,626 | ||
| F2 | Data egress and pipeline costs | $42,000 | $42,840 | $44,125 | ||
| Ft | Software license fees | F1+F2 | $252,000 | $257,040 | $264,751 | |
| Risk adjustment | ↑10% | |||||
| Ftr | Software license fees (risk-adjusted) | $277,200 | $282,744 | $291,226 | ||
| Three-year total: $851,170 | Three-year present value: $704,475 | |||||
Evidence and data. Interviewees consistently reported that their initial adoption of the Mixpanel platform required significant planning and phased implementation. They emphasized the importance of internal champions and structured onboarding sessions to accelerate familiarity and reduce resistance.
Data validation was a critical early activity. Interviewees highlighted challenges in normalizing data from multiple sources, especially during mergers or when legacy tracking systems were in place. To address this, they implemented data governance frameworks and used lexicon tools to standardize event definitions and eliminate duplication.
Interviewees described a range of implementation approaches, including: 1) using Mixpanel’s SDK and server libraries/pipelines to stream event data for real-time product analytics and self-service reporting; 2) hiring third-party professional services to connect Mixpanel with their existing infrastructure; or 3) connecting Mixpanel with enterprise data warehouses and enabling centralized analytics and advanced queries, which allowed teams to join Mixpanel’s event data with broader business datasets for deeper insights.
Interviewees discussed the importance of structured onboarding and training. The data warehouse engineer at a fintech company noted that their champions led “white glove” sessions and created documentation to drive adoption and reduce reliance on analysts.
Interviewees began with limited data ingestion or piloted use cases before expanding to full-scale deployment. This gradual approach allowed data operations teams to validate data accuracy and build confidence in the platform’s outputs.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
During initial implementation, the composite organization adopts a phased deployment approach, starting with one business unit and limited data ingestion before scaling to full integration.
Eight champions across analytics, BI, data engineering, marketing, product, and executive leadership dedicate 15% of their time over six months to onboarding, testing, and resolving data governance issues, including event normalization and lexicon setup.
Champions lead structured onboarding sessions and act as internal advocates to accelerate adoption and reduce resistance.
Data and engineering teams spend additional time on technical setup, including SDK implementation, data pipeline configuration, and governance frameworks to ensure accuracy and compliance.
Although full enterprise rollout takes about six months, once data governance is established and users are trained, marketing and product teams begin seeing value quickly. Individual white glove onboarding sessions can enable users to be up and running and start building reports in 15 minutes.
Risks. The realization of these benefits will vary with:
An organization’s data environment complexity.
Complexity of technical setup and data governance.
Capacity and bandwidth of IT and data teams to set up Mixpanel.
The need for professional services or third-party support.
Availability of business champions.
Event volume and ongoing governance discipline.
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 $112,000.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| G1 | Time period dedicated for initial onboarding, setup, and deployment | Composite | 0.5 | |||
| G2 | Data operations team, BI developer, client app dev (SDK), product analyst, project manager, and training champion | Interviews | 8 | |||
| G3 | Average fully burdened annual salary for an internal champion | TEI standard | $170,000 | |||
| G4 | Time dedicated to implementation and deployment tasks | Interviews | 15% | |||
| Gt | Initial implementation costs | G1*G2*G3*G4 | $102,000 | |||
| Risk adjustment | ↑10% | |||||
| Gtr | Initial implementation costs (risk-adjusted) | $112,200 | ||||
| Three-year total: $112,200 | Three-year present value: $112,200 | |||||
Evidence and data. Interviewees reported that ongoing implementation extended beyond the initial six-month setup and continued into the first year as their organizations scaled Mixpanel platform usage across teams. The process typically involved small, dedicated teams — often two to three core resources, such as analysts and data warehouse engineers — along with application development teams for SDK integration, and in some cases Mixpanel’s customer success or third-party professional services.
Interviewees described continued, gradual integration across teams. After initial pilots and limited ingestion, their organizations expanded data flows and dashboards to other business units.
The data warehouse engineer at a fintech firm discussed advanced governance and parity checks and noted that they continued well into Year 1 at their organization to maintain data accuracy and trust across merged systems.
A few interviewees reported that although their organizations completed basic onboarding in weeks, full adoption took roughly 1.5 years, requiring ongoing training and documentation updates as they added new features and users.
Modeling and assumptions. Based on the interviews, Forrester assumes that the composite organization has one analyst and one engineer each dedicate 50% of their time to Mixpanel platform implementation and governance as it continues gradual deployment across business units and usage increases.
Risks. The realization of these benefits will vary with:
An organization’s data environment complexity.
Complexity of technical setup and data governance.
Capacity and bandwidth of IT and data teams to set up Mixpanel.
The need for professional services or third-party support.
Availability of business champions.
Event volume and ongoing governance discipline.
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 $465,000.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| H1 | People involved in ongoing implementation | Interviews | 2 | 2 | 2 | |
| H2 | Average fully burdened annual salary for a person involved in ongoing implementation | Composite | $170,000 | $170,000 | $170,000 | |
| H3 | Average time dedicated to implementation and deployment tasks | Composite | 50% | 50% | 50% | |
| Ht | Ongoing implementation costs | H1*H2*H3 | $170,000 | $170,000 | $170,000 | |
| Risk adjustment | ↑10% | |||||
| Htr | Ongoing implementation costs (risk-adjusted) | $187,000 | $187,000 | $187,000 | ||
| Three-year total: $561,000 | Three-year present value: $465,041 | |||||
Evidence and data. Interviewees noted that training requirements for Mixpanel were relatively light but essential for adoption. Most reported that basic proficiency could be achieved quickly, often within a few hours of guided onboarding or short online courses, while advanced use required ongoing hands-on experience.
A few interviews emphasized that although initial training sessions and Mixpanel University resources helped, adoption extended well beyond the first weeks as teams scaled usage and new employees joined.
The senior R&D project manager at a telecommunications company summarized, “You can learn the basics very fast within a week or two, but becoming an expert takes longer because you need to apply it to real tasks.” She estimated that this would take 5 hours for basic features and 20 hours for advanced features.
Several interviewees’ organizations also implemented white glove onboarding or step-by-step migration, personalized training, and ongoing support for different teams to ensure adoption.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization is a software technology company, and its employees are highly proficient in adopting and learning new solutions.
During the initial implementation, a small team of analysts, data warehouse engineers, and BI professionals spend 7 hours in training to learn the advanced capabilities of Mixpanel. In this phase, analysts also set up dashboards, templates, and additional training materials to ensure smooth and efficient onboarding.
The fully burdened hourly rate for the initial implementation team --comprising analysts, data operations and BI teams, is $82.
In Year 1, the composite gradually deploys the Mixpanel platform to marketing, product management, and app development teams, and in Year 2 to design, customer experience, strategy, and business operations teams. Each member requires 5 hours of training to become proficient in the platform. The blended fully burdened hourly rate is $75.
Risks. The realization of these benefits will vary with:
The number of users signed on to Mixpanel.
The extent of adoption and implementation of Mixpanel.
The proficiency, desire, and openness of employees to embrace a new analytics solution.
The complexity of the organization’s data structure and governance.
The time required to achieve practical mastery.
Salary levels by skillset and geographic location.
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 $106,000.
Mixpanel training costs over three years
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| I1 | Mixpanel user deployment | Composite | 25 | 160 | 90 | |
| I2 | Fully burdened hourly rate per person | Composite | $82 | $75 | $75 | |
| I3 | Training time per person (hours) | Interviews | 7 | 5 | 5 | |
| It | Training costs | I1*I2*I3 | $14,350 | $60,000 | $33,750 | |
| Risk adjustment | ↑10% | |||||
| Itr | Training costs (risk-adjusted) | $15,785 | $66,000 | $37,125 | ||
| Three-year total: $118,910 | Three-year present value: $106,467 | |||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($127,985) | ($530,200) | ($506,869) | ($478,226) | ($1,643,280) | ($1,388,183) |
| Total benefits | $0 | $1,959,572 | $2,559,460 | $3,204,080 | $7,723,112 | $6,303,959 |
| Net benefits | ($127,985) | $1,429,372 | $2,052,590 | $2,725,853 | $6,079,830 | $4,915,773 |
| ROI | 354% | |||||
| Payback | <6 months |
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.
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.
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Mixpanel.
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 Mixpanel can have on an organization.
Interviewed Mixpanel stakeholders and Forrester analysts to gather data relative to Mixpanel.
Interviewed five decision-makers at organizations using Mixpanel to obtain data about costs, benefits, and risks.
Designed a composite organization based on characteristics of the interviewees’ organizations.
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.
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.
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.
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.
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.
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 present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PVs of costs and benefits feed into the total NPV of cash flows.
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
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.
1 Source: Pre-tax Unadjusted Operating Margin for a Software Company, Stern School of Business.
2 Ibid.
Readers should be aware of the following:
This study is commissioned by Mixpanel 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 Mixpanel.
Mixpanel 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.
Mixpanel provided the customer names for the interviews but did not participate in the interviews.
Lalé Varoglu
December 2025
ABOUT FORRESTER CONSULTING
Forrester provides independent and objective research-based consulting to help leaders deliver key transformation outcomes. Fueled by our customer-obsessed research, Forrester’s seasoned consultants partner with leaders to execute on their priorities using a unique engagement model that tailors to diverse needs and ensures lasting impact. For more information, visit forrester.com/consulting.
© Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester®, Technographics®, Forrester Wave, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies.
https://mainstayadvisor.com/go/mainstay/gdpr/policy.html
PDF