Total Economic Impact
Business Benefits And Cost Savings Enabled By Experian Ascend Platform
A FORRESTER TOTAL ECONOMIC IMPACT™ STUDY COMMISSIONED BY EXPERIAN, MARCH 2025
Total Economic Impact
A FORRESTER TOTAL ECONOMIC IMPACT™ STUDY COMMISSIONED BY EXPERIAN, MARCH 2025
For banks and lenders, the need for more accurate and efficient credit operations has never been greater. The sheer volume of applications and rising fraud threats require a balance between thorough review and efficient decision-making. Experian Ascend Platform provides advanced analytics and automates the review process, which can ultimately lead to more precise credit decisions, improved operational efficiency, better financial performance, and reduced risk.
Experian Ascend Platform™ is a cloud-based analytics, decisioning, and fraud platform that leverages robust data and artificial intelligence to improve credit and fraud decisions.1 The Experian Ascend Platform provides advanced analytics and automates the review process, which ultimately enables more precise credit decisions, higher operational efficiency, improved financial performance, and reduced risk.
Experian commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Experian Ascend Platform.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Experian Ascend Platform on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed six decision-makers from organizations located in the United States, the United Kingdom, Brazil, and South Africa with experience using Experian Ascend Platform. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that is a lending institution with regional operations. It processes 250,000 applications annually with a total loan origination amount of $312.5 million per year.
Interviewees said that prior to using Experian Ascend Platform, their organizations were using a mix of on-premises credit decisioning solutions that required manual-heavy interventions at different stages of the review and approval processes. These previous environments relied on static scorecards, which often left organizations behind the curve without any intuitive or predictive solutions. As a result, the credit-decisioning process was lengthy, required staff to look at different data sources, and ultimately impacted response times. Additionally, the lack of automated decisioning limited the organizations’ ability to scale applications reviews and led to inconsistencies across decisions. The absence of a consistent decisioning system also introduced significant risks, including increased exposure to fraud and default.
After the investment in Experian Ascend Platform, the interviewees’ organizations had a unified platform that provided up-to-date consumer and commercial data in one view along with robust analytics, automated decisioning, and enhanced fraud prevention. Key results from the investment include accelerated business growth, improved conversion rates, enhanced operational efficiencies for credit-decisioning and marketing teams, reduced default costs, faster response times, and improved data quality and verification processes.
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Approval rate optimization of 12% over three years. With Experian Ascend Platform, the composite organization experiences year-over-year improvements in approval rates due to a better credit-decisioning engine and enhanced risk assessment. These improvements reach 12% by Year 3. Over three years, the composite’s profit from these approval rate improvements is $5.6 million.
Business growth acceleration of 5% year-over-year. Experian Ascend Platform provides the composite organization with enhanced consumer data and analytics, leading to a 5% year-over-year increase in the volume of applications for its services. Additionally, Experian Ascend Platform equips the composite organization with a scalable platform that enables it to review a higher volume of applications without the need to hire additional full-time employees (FTEs). This results in new revenue from additional processed applications worth $8.2 million.
Decisioning efficiency improvement of 67%. Experian Ascend Platform fully automates the credit and fraud decisioning process entirely for a portion of the composite organization’s portfolio, eliminating the need for manual review. The platform also partially automates the decisioning for the remainder of the portfolio, yielding 67% efficiency improvements in such cases. This results in decisioning efficiency improvements of $2.7 million for the composite organization.
Marketing campaign productivity improvements of 67%. Experian Ascend Platform enables the composite to deepen customer understanding, equipping it to run smaller, more frequent, and highly targeted campaigns. This represents a 67% productivity improvement in its marketing team and a 90% reduction in direct mailing costs worth $610,000.
Default cost reduction of 20%. Experian Ascend Platform enhances the composite organization’s credit risk modeling and eliminates manual errors in fraud checks. This enables the composite to reject fraudulent applications more accurately and reduce credit risks, leading to lower default rates. This benefit is worth $3.2 million to the composite.
Legacy solution cost reductions worth $275,000. With the move to the cloud-based Ascend Platform, the composite reduces its operational overhead and costs associated with maintaining on-premises solutions, yielding $275,000 in benefits.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Improved customer and broker experience through faster decisioning. By automating the decisioning process and rendering a single view of data, Experian Ascend Platform significantly reduces the composite organization’s credit-decisioning time. This streamlined process minimizes friction and accelerates decision times for customers, ultimately enhancing the overall customer experience.
Reduced risk at origination. Experian Ascend Platform helps the composite mitigate risk by providing robust consumer data analysis and automated decision-making processes, which ensures decisions are based on current and relevant information. By reducing manual intervention and human error, the platform enhances the composite’s accuracy of risk assessments, leading to standardized and reliable decisions.
Improved compliance and audit process. Experian Ascend Platform enhances the composite’s transparency and traceability of credit-decisioning processes. Because the platform keeps a record of all decisions and data used to render decisions, it’s easier for the composite organization to conduct audits and maintain regulatory compliance.
Reduced risk of regulatory fines. By reducing the amount of fraudulent transactions, Experian Ascend Platform helps the composite avoid any potential reputation damage and fines from regulators and — in the case of merchants — card networks.
Sustainability and ecological footprint benefits. Experian Ascend Platform reduces the composite’s need for physical documentation, which lowers the organization’s carbon footprint.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
Implementation costs of $1.8 million. The composite pays a total of $1.8 million to implement Experian Ascend Platform, and this includes a deployment fee and internal efforts to deploy the platform across the organization’s portfolio over time.
Ongoing costs of $5.4 million. The composite’s ongoing costs for Experian Ascend Platform include annual platform fees, the cost of decisions processed through the platform, the cost of consumer names for marketing campaigns, and ongoing internal efforts needed to maintain the solution.
The representative interviews and financial analysis found that a composite organization experiences benefits of $20.6 million over three years versus costs of $7.3 million, adding up to a net present value (NPV) of $13.3 million and an ROI of 183%. Forrester assumes a yearly discount rate of 10% for this analysis.
Return on investment (ROI)
Benefits PV
Net present value (NPV)
Payback
| Role | Industry | Market | Loan volume |
|---|---|---|---|
| Chief credit officer | Fintech | US | $800 million |
| CEO | Credit union | US | $100 million |
| Senior credit risk manager | Banking | UK | $25 billion |
| Credit manager | Car leasing | UK | $600 million |
| Credit and collections manager | Consortium | Brazil | $200 million |
| Fraud manager | Airline | South Africa | $100 million (transaction volume) |
Interviewees noted that before adopting Experian Ascend Platform, their organizations were using a mix of on-premises solutions that required a high level of manual intervention. The lack of automation drove inconsistencies in decision-making and hindered the organizations from compiling up-to-date consumer information to develop predictive analytical models.
The interviewees noted how their organizations struggled with common challenges, including:
Long manual processes for credit decisioning. The organizations’ credit lifecycles were rife with manual interventions, from gathering information from multiple sources and performing checks across multiple systems to rendering decisions. These time-consuming tasks delayed response times to consumers. The credit manager at a car leasing firm described the previous process at their firm: “Before, underwriters would start from scratch on every single case. They would need to get reports, do due diligence checks manually, and interrogate the antifraud system manually.”
Lack of up-to-date data. The interviewees reported that their organizations’ previous on-premises environments prevented them from obtaining accurate and up-to-date data. The use of static scorecards and manual checks was always after the fact, giving the impression of being behind the curve.
Inconsistent decisioning. In the organizations’ prior environments, individuals manually assessed applications, which led to inconsistencies in risk assessments. The credit manager at a car leasing firm described the issues their firm faced: “The fact that we didn’t have an automated solution was holding us back. A lot of the decisions were made in an inconsistent way. Some underwriters would approve a particular risk, and others would decline the same risk dependent on their risk view.”
The interviewees’ organizations searched for a solution that could:
Improve the credit-decisioning process.
Automate the credit-decisioning process.
Reduce time to decision of applications.
Reduce fraud.
Simplify architecture and overall processes.
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 is a national lending institution that provides loans and credit services. It processes 250,000 loan applications annually with an approval rate of 25%, resulting in a total loan origination amount of $312.5 million. The average value of its loans is $5,000. The composite chooses Experian Ascend Platform to address credit decisioning, marketing, and fraud detection use cases.
Deployment characteristics. The composite deploys Experian Ascend Platform, and the deployment of credit decisioning is executed gradually over time. Following a six-month initial deployment period, the composite rolls out Experian Ascend Platform to 40% of its portfolio in Year 1, and it scales the adoption to 90% by Year 3. Similarly, the share of decisioning that is fully automated with the platform increases from 25% in Year 1 to 39% in Year 3.
$312.5M in loan origination
250,000 loan applications
25% approval rate
$5,000 average loan amount
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Profit from approval rate improvements | $510,000 | $2,040,000 | $4,590,000 | $7,140,000 | $5,598,122 |
| Btr | Accelerated business growth | $884,000 | $2,754,000 | $6,854,400 | $10,492,400 | $8,229,482 |
| Ctr | Improved decisioning efficiency with automation | $586,105 | $995,444 | $1,731,574 | $3,313,122 | $2,656,460 |
| Dtr | Improved marketing campaign productivity | $279,922 | $233,930 | $215,534 | $729,386 | $609,739 |
| Etr | Reduced default costs | $540,409 | $1,146,439 | $2,314,620 | $4,001,468 | $3,177,759 |
| Ftr | Legacy consolidation cost savings | $110,700 | $110,700 | $110,700 | $332,100 | $275,295 |
| Total benefits (risk-adjusted) | $2,911,135 | $7,280,513 | $15,816,828 | $26,008,476 | $20,546,857 | |
Evidence and data. Interviewees mentioned that with Experian Ascend Platform, their organizations experienced improvements in approval rates due to enhanced data access and credit-risk modeling. Additionally, they explained that marketing capabilities allowed them to refine their campaigns, contributing to higher conversion rates. Interviewees said Experian Ascend Platform contributed to improvements in approval rates in the following ways:
Enhancing credit risk modeling. The chief credit officer at a fintech said Experian Ascend Platform’s model-building tools provided insights into how applicants who might have been previously declined were performing elsewhere, further refining their organization’s decision-making process. They said: “There is a benefit to modeling off others’ data. … Maybe you want to check if you’re doing the right thing when dropping some people for credit reason and you want to see how they perform elsewhere. With Experian Ascend Platform, you can look at outside performance.”
Increasing predictability and model accuracy. Interviewees said Experian Ascend Platform contributed to improvements in predictability and accuracy of credit decisions. The chief credit officer at a fintech said: “[With Ascend,] everything is standardized. Even though we pull the data ourselves at time of application, we don’t have to worry about the accuracy of the data. We are sure that they’ve gone through the checks to make sure the data is correct.”
The credit manager at a car leasing company also described the improvements in accuracy resulting from Experian Ascend Platform: “We now get a lot of data back, which helps us understand why and how customers are passing and failing automated assessments. It gives us a lot of confidence that the affordability assessments are accurate and working to a high degree of accuracy.”
Delivering consistent improvements. The credit manager at a car leasing company highlighted how Experian Ascend Platform contributed to improvements in approval rates for their organization: “Before, our approval rate was 60% overall. Since we’ve switched over to Experian Ascend Platform, we’re seeing a consistent return of around 66% approval rate, which is brilliant.”
Model Development Speed
Interviewees highlighted that the combination of Experian Ascend Platform’s data, analytics capabilities, and sandbox environment enabled quick iteration and testing of models, which reduced the time needed to develop and deploy credit- and fraud-decisioning models and allowing them to respond quickly to market changes and emerging trends.
The chief credit officer at a fintech said: “We have the ability to build models from other existing models, which we couldn’t do otherwise because we would be limited to the extract methodology of our own data. So, we wouldn’t be able to model off anyone else’s data, just our own. With the sandbox methodology, we can model off other people’s data. … Having the sandbox helps reduce the time it takes to build models. It can take banks several months to build a model. Now, we can do it in hours. … From a timing perspective, it’s like a day-and-night difference.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Prior to adopting Experian Ascend Platform, the composite previously processed 250,000 loan applications per year.
The composite deploys Experian Ascend Platform for 40% of its portfolio in Year 1, 60% in Year 2, and 90% in Year 3.
The composite’s approval rate prior to adopting Experian Ascend Platform was 25%.
With Experian Ascend Platform, the composite’s approval rate increases by 3% in Year 1, by 8% Year 2, and by 12% in Year 3.
The composite’s improved approval rates with Experian Ascend Platform are 26% in Year 1; 27% in Year 2; 28% in Year 3.
The composite’s average loan amount is $5,000.
The composite’s operating margin is 16%.
Risks. Risks that could impact the realization of this benefit include:
The organization’s previous approval rate.
The organization’s average loan amount.
The organization’s operating margin.
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 $5.6 million.
Approval rate improvement attributed to Experian Ascend Platform in Year 3
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Loan applications prior to Experian Ascend Platform | Composite | 250,000 | 250,000 | 250,000 | |
| A2 | Share of portfolio with Experian Ascend Platform deployed | Composite | 40% | 60% | 90% | |
| A3 | Approval rate prior to Experian Ascend Platform | Composite | 25% | 25% | 25% | |
| A4 | Approval rate improvement attributed to Experian Ascend Platform | Interviews | 3% | 8% | 12% | |
| A5 | Improved approval rate attributed to Experian Ascend Platform | A3*(1+A4) | 26% | 27% | 28% | |
| A6 | Additional loan approvals attributed to Experian Ascend Platform | A1*A2*A3*A4 | 750 | 3,000 | 6,750 | |
| A7 | Average loan amount | Composite | $5,000 | $5,000 | $5,000 | |
| A8 | Revenue increase due to approval rate improvement | A6*A7 | $3,750,000 | $15,000,000 | $33,750,000 | |
| A9 | Operating profit margin | TEI methodology | 16% | 16% | 16% | |
| At | Profit from approval rate improvements | A8*A9 | $600,000 | $2,400,000 | $5,400,000 | |
| Risk adjustment | ↓15% | |||||
| Atr | Profit from approval rate improvements (risk-adjusted) | $510,000 | $2,040,000 | $4,590,000 | ||
| Three-year total: $7,140,000 | Three-year present value: $5,598,122 | |||||
Evidence and data. Interviewees said that with Experian Ascend Platform, their organizations processed more applications due to enhanced customer insights and more targeted marketing campaigns. Additionally, they explained that Experian Ascend Platform’s automated decisioning capabilities allowed their organizations to overcome previous challenges in scaling application reviews without the need for added overhead. As a result, the organizations saw an increase in the number of loans originated, which contributed to business growth.
Facilitating expansions into new market segments. The CEO at a credit union described how Experian Ascend Platform’s data and insights allowed their organization to access new market segments, hence contributing to additional loan applications: “Experian Ascend Platform has allowed us to take a deeper dive into markets we wouldn’t necessarily have looked at initially. It’s allowed us to now look at areas that are potential targets.”
Refining audience targeting. The same interviewee explained how improvements in data quality gained from Experian Ascend Platform helped their organization deepen its understanding of existing customers and new customer prospects, which ultimately drove more targeted marketing campaigns and contributed to increasing the volume of loans. They said: “We no longer have to do a scattered approach with a few hundred thousand pieces per campaign. The cost of deploying a campaign that size versus the results [is] typically unbalanced. … Now, we can do this for a tenth of that cost and produce campaigns of 10,000 names that will get a 30% to 40% conversion rate compared to before when we would get a 2% conversion rate.”
Improving ease of building and adjusting decisioning strategies. The senior credit risk manager at a bank said: “Implementing Experian Ascend Platform has allowed us to originate more loans because it’s allowed more flexibility in our strategies, which has meant we can do a higher volume of loan originations. … There’s been a 5% uplift on all loan originations numbers across all our portfolios.”
Scaling up operations with automation. The credit manager at a car leasing firm described the challenges their organization faced with scalability: “Before, we were unable to scale up. We had reached a limit of what [Experian] PowerCurve could do on-premises. We were constrained by staffing resources. The only way we could scale up was to recruit more people or train more people in that role, which takes a long time to get up to speed on.” They added: “[With Experian Ascend Platform,] we’ve halved our processing time. We can now double the number of deals that we can underwrite in a day manually. … [Overall,] we estimate that we’re seeing 10% more business today than where we were last year, and we think that’s purely because we have an automated solution. A lot of brokers like to work with funders that have automated solutions and therefore will get quicker decisions back.”
Modeling and assumptions. Based on the interviews, Forrester assumes the volume of loan applications the composite organization processes increases by 5% in Year 1, by 10% in Year 2, and by 16% in Year 3.
Risks. Risks that could impact the realization of this benefit:
The organization’s volume of loan applications processed prior to using Experian Ascend Platform.
The organization’s approval rates.
The share of the organization’s portfolio to which Experian Ascend Platform is deployed.
The organization’s operating margin.
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 $8.2 million.
Increase in loan applications processed with Experian Ascend Platform in Year 3
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Loan applications prior to Experian Ascend Platform | A1 | 250,000 | 250,000 | 250,000 | |
| B2 | Share of portfolio with Experian Ascend Platform deployed | Composite | 40% | 60% | 90% | |
| B3 | Increase in loan applications reviewed attributed to Experian Ascend Platform | Interviews | 5% | 10% | 16% | |
| B4 | Loan approval rate with Experian Ascend Platform | A5 | 26% | 27% | 28% | |
| B5 | Additional loans originated with Experian Ascend Platform | B1*B2*B3*B4 | 1,300 | 4,050 | 10,080 | |
| B6 | New revenue from additional loans | B5*A7 | $6,500,000 | $20,250,000 | $50,400,000 | |
| B7 | Operating profit margin | TEI methodology | 16% | 16% | 16% | |
| Bt | Accelerated business growth | B6*B7 | $1,040,000 | $3,240,000 | $8,064,000 | |
| Risk adjustment | ↓15% | |||||
| Btr | Accelerated business growth (risk-adjusted) | $884,000 | $2,754,000 | $6,854,400 | ||
| Three-year total: $10,492,400 | Three-year present value: $8,229,482 | |||||
Evidence and data. Interviewees said that prior to using Experian Ascend Platform, credit decisioning required their organizations to perform extensive manual efforts, including searching and checking data from multiple sources. But they explained that Experian Ascend Platform’s automated decisioning streamlined these processes by consolidating data into a single report for review and that, in some cases, it eliminated manual intervention with auto-approve or auto-decline. As a result, the interviewees’ organizations experienced improvements in decisioning efficiency and significantly reduced the time needed to render decisions.
Compiling credit reports. The senior credit risk manager at a bank said application reviews previously took between 30 minutes and 1.5 hours depending on the loan complexity, and they described how Experian Ascend Platform’s credit report compilation functionality streamlined decisioning: “Before, the credit decisioning team would have to link up different sources of information to do manual bureau checks and manual affordability calculations and assess income on an isolated, manual basis. Doing all of this in isolation on a check-by-check basis is timely and open to human error. … Experian Ascend Platform has allowed us to onboard everything on one place, consolidate all that application detail together on the front-end system, and feed all that information into Experian Ascend Platform to run credit checks around it and present that story back.”
Improving loan underwriting. The credit manager at a car leasing firm described the how Experian Ascend Platform contributed to efficiency improvement across the loan underwriting process: “Before, it would take up to 20 to 30 minutes to underwrite a single case, depending on its complexity. … Now, all that data is in one place and it’s all on an easily consumable and digestible format, [so] the actual processing time manually has gone down massively to 10 to 15 minutes.”
The CEO at a credit union outlined how little savings added up with scale: “We process 100,000 loans per year. On average, a credit report review would save 5 minutes at the minimum with Experian Ascend Platform. So, we save about 500,000 minutes a year.”
Automating decisions. Interviewees said Experian Ascend Platform rendered instant approvals or declines for customers. The senior credit risk manager at a bank said: “Customer onboarding is now instant. The broker puts all the information into our digital application form, which then calls out to Experian Ascend Platform. It’s [done in] real time, and the decision comes back instantly as to whether that application is good to go or not.”
The CEO at a credit union elaborated on how automated declines helped their organization save time from manual tasks: “In a decline situation, nobody has to review it, which is even more time saved. … Experian Ascend Platform immediately does that on our side and issues the appropriate compliance documentation through the Equal Credit Opportunity Act, alerts, and feedback to the consumer.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Prior to deploying Experian Ascend Platform, the average time the composite spent per decisioning was 30 minutes.
The fully burdened hourly salary for a loan underwriter FTE is $33.
With Experian Ascend Platform, the composite fully automates 25% of decisioning in Year 1, 31% in Year 2, and 39% in Year 3.
The composite’s decisioning effort reduction for applications partially automated is 33%.
The composite has an 80% productivity capture rate, which means 80% of the time saved through efficiency improvements is used for productive work.
Risks. Risks that could impact the realization of this benefit include:
The complexity of the organization’s credit products.
The average time the organization spends per application decisioning.
The organization’s previous rate of automation.
The salaries of loan underwriter employees.
The share of applications the organization fully automates with Experian Ascend Platform.
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 $2.7 million.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Applications reviewed with Experian Ascend Platform | B1*B2*(1+B3) | 105,000 | 165,000 | 261,000 | |
| C2 | Average time spent per application decisioning prior to Experian Ascend Platform (minutes) | Interviews | 30 | 30 | 30 | |
| C3 | Average fully burdened hourly rate for a loan underwriter employee | Research data | $33 | $33 | $33 | |
| C4 | Share of applications decisioning fully automated with Experian Ascend Platform | Interviews | 25% | 31% | 39% | |
| C5 | Subtotal: Labor savings for fully automating application decisioning with Experian Ascend Platform | C1*C2/60*C3*C4 | $433,125 | $843,975 | $1,679,535 | |
| C6 | Share of applications decisioning partially automated with Experian Ascend Platform | 1-C4 | 75% | 69% | 61% | |
| C7 | Effort reduction with partial automation | Interviews | 33% | 33% | 33% | |
| C8 | Subtotal: Labor savings for partially automating application decisioning with Experian Ascend Platform | C1*C2/60*C3*C6* C7 | $428,794 | $619,913 | $866,898 | |
| C9 | Productivity capture for decisioning FTEs | TEI methodology | 80% | 80% | 80% | |
| Ct | Improved decisioning efficiency with automation | (C5+C8)*C9 | $689,535 | $1,171,110 | $2,037,146 | |
| Risk adjustment | ↓15% | |||||
| Ctr | Improved decisioning efficiency with automation (risk-adjusted) | $586,105 | $995,444 | $1,731,574 | ||
| Three-year total: $3,313,122 | Three-year present value: $2,656,460 | |||||
Evidence and data. Interviewees said their organizations used Experian Ascend Platform to deepen customer understanding, which enabled them to craft highly targeted audiences based on customer profile and behavioral attributes. They pivoted from running broad, seasonal campaigns to smaller but higher-frequency ones, improving marketing effectiveness and reducing campaign costs. Interviewees described how Experian Ascend Platform contributed to their marketing teams’ productivity improvements:
Deepening customer insights with analytical tools. The CEO at a credit union said: “Experian Ascend Platform took our existing portfolio and did historical trend analysis on our performance. We’re able to identify trend patterns and consumers that we underserved who we could have potentially offered a higher credit amount to.”
Similarly, the chief credit officer at a fintech shared: “Using the data and attributes in the [Ascend Analytical Sandbox] gave us real value. When [my organization] started, we didn’t have our own data. [For example,] even if you’re in a large company, there are benefits to modeling off other people’s data when you want to expand to the subprime segment.”
Improving speed of campaign deployment. The CEO of a credit union said: “The biggest benefit for us has always been how fast we can deploy campaigns and the ability to get very precise with our target audience. We know who our customers are, and we know how to apply those filters through Experian Ascend Platform to narrow down to those consumers. I’ve been able to target a lot better with a lot less.” The interviewee shared that campaign deployment lead time shrank from three months to two days.
Refining targeting. The same interviewee shared: “We run a lot more campaigns now, in the range of 10 to 20 per year. But they’re a lot smaller and a lot more targeted. We no longer have to produce a list [of half a million names] anymore. We can produce a 10,000-name list and get a much better conversion rate compared to our old method.” They said refining campaign targeting also raised conversions.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Prior to adopting Experian Ascend Platform, the composite ran one large-scale campaign each quarter.
Each of these campaigns required a marketing team of four to each spend 12 days on planning and execution. This totaled 384 person-hours per year.
The composite uses Ascend to deepen customer insights and run highly targeted campaigns. It experiments with three small-scale campaigns in Year 1, and it expands to eight and 10 campaigns in years 2 and 3, respectively.
Each of these targeted campaigns require 67% less effort to plan and execute.
The composite has a 50% productivity capture rate, which means half of the time saved is put back into productive work.
The composite’s campaign channel includes direct mail. Using Experian Ascend Platform, it refines its mailing lists by 90%, which saves $1.25 per piece on printing and mailing costs.
Risks. The value of this benefit may vary, depending on factors including the frequency and scale of existing marketing campaigns, marketing channels, and an organization’s marketing effectiveness.
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 $610,000.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Marketing campaigns prior to Experian Ascend Platform | Interviews | 4 | 4 | 4 | |
| D2 | Marketing campaigns with Experian Ascend Platform | Interviews | 3 | 8 | 10 | |
| D3 | Marketing team time spent per campaign prior to Experian Ascend Platform (hours) | Interviews | 384 | 384 | 384 | |
| D4 | Marketing team time reduction per campaign with Experian Ascend Platform (hours) | Interviews | 67% | 67% | 67% | |
| D5 | Average fully burdened salary for a marketing team member | Research data | $38 | $38 | $38 | |
| D6 | Productivity capture for marketing FTEs | TEI methodology | 50% | 50% | 50% | |
| D7 | Subtotal: Marketing productivity improvements | (D1-D2*(1-D4))* D3*D5*D6 | $21,961 | $9,923 | $5,107 | |
| D8 | Direct mail sent per campaign prior to Experian Ascend Platform | Composite | 62,500 | 62,500 | 62,500 | |
| D9 | Percent reduction in direct mail | Interviews | 90% | 90% | 90% | |
| D10 | Average cost of printing and mailing per piece | Interviews | $1.25 | $1.25 | $1.25 | |
| D11 | Subtotal: Cost savings from marketing campaigns printing and mailing | (D1*D8-D2*D8*(1 -D9)*D10 | $289,063 | $250,000 | $234,375 | |
| Dt | Improved marketing campaign productivity | D7+D11 | $311,024 | $259,923 | $239,482 | |
| Risk adjustment | ↓10% | |||||
| Dtr | Improved marketing campaign productivity (risk-adjusted) | $279,922 | $233,930 | $215,534 | ||
| Three-year total: $729,386 | Three-year present value: $609,739 | |||||
Evidence and data. Interviewees said that with Experian Ascend Platform, their organizations rejected fraudulent applications and improved credit risk modeling, which reduced default rates. Interviewees described how Ascend contributed to improved loan quality:
Enhancing credit risk insights. The CEO of a credit union said: “Experian Ascend Platform gave us insight into the consumers who we were overextending credit to that we shouldn’t have. This resulted in a 20% drop in default rates.”
Eliminating manual errors. The senior credit risk manager at a bank said: “We’re able to process our unsecured lending decisions and fraud items a bit quicker and slicker through our other hosted systems. … When you’ve got human error occurring, you have to go back and rework those cases when that human error becomes an issue later down the lifecycle of that mortgage. Implementing Experian Ascend Platform has removed some of that rework and allows it to flow through better.”
Reducing fraud. The credit manager at a car leasing firm said the time to value of Experian Ascend Platform’s fraud solution is fast: “We’ve seen a massive increase in avoided fraud. It has pretty much paid for itself 10 times over in the first six months of operation. In avoiding applications that we believe to be suspect or fraud, we have saved over £500,000 worth of business by not underwriting that business.”
Reducing chargeback and associated costs. The fraud manager at an airline described the impact of improved fraud detection in their organization’s credit card transactions. They said: “A lot of fraud slipped through because of the high volume of work. There is no more manual investigation now. This year, we’ve had four chargebacks among our 4.6 million transactions, down from 6,660 cases in 2016. And we saved a lot of person-hours just on managing chargebacks. … The biggest benefit apart from the cost saving on the actual chargebacks is the reputational damage we avoided by not having created card fraud.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Prior to using Experian Ascend Platform, the composite’s default rate was 3%.
With Experian Ascend Platform, the composite’s default rate falls by 20% in Year 1. This rate decreases by 5% year-over-year, yielding reductions of 26% and 32% in years 2 and 3, respectively.
In the event of a default, the composite loses an average of 80% of the loan amount.
With Experian Ascend Platform, the composite reduces the time spent on debt collection as its default rate decreases. On average, the collections team avoids 4 person-hours per case.
The fully burdened hourly rate for a member of the collections team is $31.
Risks. Risks that could impact the realization of this benefit include:
The organization’s default rate and factors leading to default (e.g., credit-risk appetite, economic environment, etc.).
The organization’s fraud-prevention strategy and processes.
The organization’s collections strategy.
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.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| E1 | Loans approved with Experian Ascend Platform | B1*B2*(1+B3)*B4 | 27,300 | 44,550 | 73,080 | |
| E2 | Default rate prior to Experian Ascend Platform | Composite | 3% | 3% | 3% | |
| E3 | Default rate reduction with Experian Ascend Platform | Interviews | 20% | 26% | 32% | |
| E4 | Avoided defaulted loans | E1*E2*E3 | 164 | 347 | 702 | |
| E5 | Average loan amount | A7 | $5,000 | $5,000 | $5,000 | |
| E6 | Average share of loan amount defaulted | Composite | 80% | 80% | 80% | |
| E7 | Subtotal: Avoided default loss | E4*E5*E6 | $655,200 | $1,389,960 | $2,806,280 | |
| E8 | Collections team time per default and fraud case (hours) | Interviews | 4 | 4 | 4 | |
| E9 | Average fully burdened salary for a collection FTE | Research data | $31 | $31 | $31 | |
| E10 | Subtotal: Reduced collections effort (rounded) | E4*E8*E9 | $20,311 | $43,089 | $86,995 | |
| Et | Reduced default costs | E7+E10 | $675,511 | $1,433,049 | $2,893,275 | |
| Risk adjustment | ↓20% | |||||
| Etr | Reduced default costs (risk-adjusted) | $540,409 | $1,146,439 | $2,314,620 | ||
| Three-year total: $4,001,468 | Three-year present value: $3,177,759 | |||||
Evidence and data. Interviewees said moving to the cloud-based Ascend Platform enabled their organizations to reduce operational costs associated with maintaining on-premises solutions.
The credit manager at the car leasing business said their organization said on IT labor by migrating to Experian Ascend Platform: “By removing the on-premises solution, we’ve reduced the need for expertise on-site — the cost equivalent of one headcount in the IT department — to manage that solution and manage upgrades, updates, and security patches.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
By consolidating its legacy systems, the composite reallocates one IT FTE.
The fully burdened annual salary for an IT FTE is $93,000.
Moving to the cloud saves the composite $30,000 annually on maintenance and update fees.
Risks. Risks that could impact the realization of this benefit include:
The complexity of the organization’s system landscape.
The organization’s IT maturity.
The organization’s change management strategy and processes.
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 $275,000.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| F1 | IT FTE reallocation | Interviews | 1 | 1 | 1 | |
| F2 | Average fully burdened salary for an IT FTE | Research data | $93,000 | $93,000 | $93,000 | |
| F3 | Maintenance and update cost savings | Interviews | $30,000 | $30,000 | $30,000 | |
| Ft | Legacy consolidation cost savings | (F1*F2)+F3 | $123,000 | $123,000 | $123,000 | |
| Risk adjustment | ↓10% | |||||
| Ftr | Legacy consolidation cost savings (risk-adjusted) | $110,700 | $110,700 | $110,700 | ||
| Three-year total: $332,100 | Three-year present value: $275,295 | |||||
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Improved customer and broker experience through faster decisioning. Automation reduced friction during application and accelerated decision times for brokers and customers alike. The credit manager at a car leasing firm said: “We’re certainly seeing more applications from certain brokers who weren’t using us this time last year, and there’s no real change in our pricing structure or strategy. So, we can only assume that they’ve started using us because either more consistent decisioning or they’re getting faster decisioning — both of which Experian Ascend Platform enables.”
The senior credit risk manager at a bank also described the impact of Ascend on their organization’s response time: “[With Experian Ascend Platform,] we were able to be more automated, reduce time to offer on our mortgages, and even offer same-day decision for our small and medium enterprise customers.”
The credit and collections manager at a consortium highlighted the impact of Experian Ascend Platform on their organization’s customer experience: “Today, we do a CES (customer effort score) survey, and we have reached much more significant results after Experian Ascend Platform was implemented.”
Reduced risk at origination. Each interviewee mentioned that Experian Ascend Platform helped their organization mitigate risk by providing high-quality data analysis and reducing manual intervention, thereby minimizing the potential for human error through automated decisioning. The senior credit risk manager at a bank said: “We’re now less open to manual error. Building our scorecard and affordability rules in one place allows us to show how these calculators are being used, monitored, and maintained. Previously, we would have spreadsheets doing some of that, which was prone to human error. As a result, we’ve tightened up the risk management framework around the systems.”
Improved compliance and audit process. Interviewees noted that Experian Ascend Platform enhanced the transparency and traceability of their organization’s credit decisioning process, making it easier to conduct audits. The credit manager at a car leasing firm said: “[Experian Ascend Platform] has made our response to audits a lot easier because we’ve got a lot more transparency in the data that’s coming back, and Experian did a huge amount of work to help us understand regulated lending and affordability assessments. … From a compliance perspective, that’s absolutely golden to be able to go into any kind of compliance audit and say, ‘I have a high degree of accuracy and here is all the evidence to prove it.’”
Reduced risk of regulatory fines. Interviewees said Experian Ascend Platform reduced the risk of fines from fraudulent transactions and potential loss of merchant status for their organizations. The fraud manager at an airline noted: “Our big concern before was fraud escalations. With credit card fraud, you have to deal with the issuers. And if you exceed a certain threshold, they put you under review. If you don’t bring your number of frauds down, you start incurring penalty fines. Eventually, if the fraud keeps escalating, then there is a risk of being discontinued and losing the merchant status. Then, if all your sales are online and you can no longer proceed credit card payments, you just have to close your doors.”
Sustainability and ecological footprint benefits. Interviewees noted that Experian Ascend Platform reduced the need for physical documentation, minimized paper usage, and lowered carbon footprints. The fraud manager at an airline said: “With each fraudulent chargeback, you have to print out three to four pages. We used to get 6,600 chargebacks per year, [and that’s] a lot of paper compared to [what we use for] the four frauds we now have [with Experian Ascend Platform]. And [we also save on the cost of] equipment, [including] the ink for the printers, the mailing, and the electricity use of printers. Currently, we are totally paperless.”
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Experian Ascend Platform and later realize additional uses and business opportunities, including:
Future-proofing lending strategies with enhanced market intelligence and competitive benchmark. Interviewees mentioned that Experian Ascend Platform provided their organizations with access to comprehensive market data and analytics, enabling them to benchmark performance against peers, identify market trends, and make informed strategic decisions. This allowed them to test new products and access new markets and segments with greater agility and ease, which further enhanced their offerings.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Gtr | Implementation cost | $519,200 | $955,900 | $519,200 | $0 | $1,994,300 | $1,817,291 |
| Htr | Ongoing costs | $0 | $1,408,407 | $2,131,382 | $3,191,892 | $6,731,681 | $5,439,958 |
| Total costs (risk-adjusted) | $519,200 | $2,364,307 | $2,650,582 | $3,191,892 | $8,712,603 | $7,257,249 | |
Evidence and data. The interviewees spoke of their organizations’ implementation costs for Experian Ascend Platform. They said Experian charges a one-time, fixed fee for implementation and that their organizations used internal IT, credit risk, and project management resources and efforts to deploy the platform across portfolios.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite’s initial deployment of Experian Ascend Platform lasts six months.
After the initial deployment period, the composite dedicates an additional 18 months to deployment in years 1 and 2 to reflect the gradual deployment of Experian Ascend Platform.
Two system architects, two credit risk analysts, and two project managers are fully dedicated to implementation of Experian Ascend Platform.
The fully burdened annual salary for a system architect is $168,000.
The fully burdened annual salary for a credit risk analyst is $100,000.
The fully burdened annual salary for a project manager is $129,000.
Risks. Risks that could impact this cost include:
The complexity of the organization’s previous environment and ecosystem.
The salaries of implementation team members.
The organization’s use cases for Experian Ascend Platform.
The scale and pace of the implementation.
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 $1.8 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| G1 | Experian implementation fee | Interviews | $75,000 | $75,000 | $75,000 | ||
| G2 | Deployment duration (months) | Interviews | 6 | 12 | 6 | ||
| G3 | System architect FTEs | Interviews | 2.0 | 2.0 | 2.0 | ||
| G4 | Fully burdened salary for a system architect | Research data | $168,000 | $168,000 | $168,000 | ||
| G5 | Credit risk analysts FTEs | Interviews | 2.0 | 2.0 | 2.0 | ||
| G6 | Fully burdened salary for a credit risk analyst | Research data | $100,000 | $100,000 | $100,000 | ||
| G7 | Project manager FTEs | Interviews | 2.0 | 2.0 | 2.0 | ||
| G8 | Fully burdened salary for a project manager | Research data | $129,000 | $129,000 | $129,000 | ||
| G9 | Internal implementation efforts | G2/12*(G3*G4+ G5*G6+G7*G8) |
$397,000 | $794,000 | $397,000 | ||
| Gt | Implementation cost | G1+G9 | $472,000 | $869,000 | $472,000 | $0 | |
| Risk adjustment | ↑10% | ||||||
| Gtr | Implementation cost (risk-adjusted) | $519,200 | $955,900 | $519,200 | $0 | ||
| Three-year total: $1,994,300 | Three-year present value: $1,817,291 | ||||||
Evidence and data. Interviewees said the following about ongoing costs of Experian Ascend Platform:
Each interviewee said their organization pays an annual platform fee to Experian.
The cost of decisioning is based on the volume of decisions made through the platform with fees per decision determined by the number of API calls.
The costs of marketing campaigns are calculated on a per-name basis.
The organizations dedicated the equivalent of one week per quarter per FTE to making updates to and assessing the solution.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
An average decision for the composite organization requires seven API calls.
One system architect, one project manager, and one credit risk manager each dedicate one week per quarter to making updates and bank impact assessments, equivalent to 160 hours per year each.
Risks. Risks that could impact this cost include:
The organization’s volume of credit decisions.
The organization’s number of API calls per credit decision.
The organization’s volume of marketing campaigns.
The roles and seniority levels of internal staff dedicated to ongoing management and management of the solution.
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 $5.4 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| H1 | Platform fee | Interviews | $1,000,000 | $1,500,000 | $2,250,000 | ||
| H2 | Cost per API call | Interviews | $0.3 | $0.3 | $0.3 | ||
| H3 | Average API calls per application | Interviews | 7 | 7 | 7 | ||
| H4 | Cost per decision | H2*H3 | $2.10 | $2.10 | $2.10 | ||
| H5 | Total cost of applications processing | H4*B1*B2*(1+B3) | $220,500 | $346,500 | $548,100 | ||
| H6 | Marketing campaign cost per piece | Interviews | $1 | $1 | $1 | ||
| H7 | Total cost of marketing campaigns | D8*(1-D9)*D2*H6 | $18,750 | $50,000 | $62,500 | ||
| H8 | Quarterly project management system architect FTEs | Interviews | 1 | 1 | 1 | ||
| H9 | Fully burdened hourly salary for a system architect | Research data | $81 | $81 | $81 | ||
| H10 | Quarterly project management project manager FTEs | Interviews | 1 | 1 | 1 | ||
| H11 | Fully burdened hourly salary for a project manager | Research data | $62 | $62 | $62 | ||
| H12 | Quarterly data scientist FTEs | Interviews | 1 | 1 | 1 | ||
| H13 | Fully burdened hourly salary for a data scientist | TEI Standard | $66 | $66 | $66 | ||
| H14 | Quarterly credit risk manager FTEs | Interviews | 1 | 1 | 1 | ||
| H15 | Fully burdened hourly salary for a credit risk manager | Research data | $48 | $48 | $48 | ||
| H16 | Time spent per FTE (hours) | Interviews | 160 | 160 | 160 | ||
| H17 | Internal efforts | ((H8*H9)+(H10*H 11)+(H12*H13)+( H14*H15))*H16 | $41,120.00 | $41,120.00 | $41,120.00 | ||
| Ht | Ongoing costs | H1+H5+H7+H17 | $0 | $1,268,208 | $1,937,620 | $2,901,720 | |
| Risk adjustment | ↑10% | ||||||
| Htr | Ongoing costs (risk-adjusted) | $0 | $1,395,029 | $2,131,382 | $3,191,892 | ||
| Three-year total: $6,718,303 | Three-year present value: $5,427,796 | ||||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($519,200) | ($2,364,307) | ($2,650,582) | ($3,191,892) | ($8,725,981) | ($7,257,249) |
| Total benefits | $0 | $2,911,135 | $7,280,513 | $15,816,828 | $26,008,476 | $20,546,857 |
| Net benefits | ($519,200) | $546,828 | $4,629,931 | $12,624,936 | $17,282,495 | $13,289,608 |
| ROI | 183% | |||||
| Payback | 12.0 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 Experian Ascend Platform.
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 Experian Ascend Platform can have on an organization.
Interviewed Experian stakeholders and Forrester analysts to gather data relative to Experian Ascend Platform.
Interviewed six people at organizations using Experian Ascend Platform 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 PV 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 Note: The link to the product varies by location. Readers in the UK should refer to: https://www.experian.co.uk/business/platforms/ascend. Readers in Brazil should refer to: https://www.serasaexperian.com.br/.
2 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.
Readers should be aware of the following:
This study is commissioned by Experian 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 Ascend. For any interactive functionality, the intent is for the questions to solicit inputs specific to a prospect's business. Forrester believes that this analysis is representative of what companies may achieve with Experian Ascend Platform based on the inputs provided and any assumptions made. Forrester does not endorse Experian or its offerings. Although great care has been taken to ensure the accuracy and completeness of this model, Experian and Forrester Research are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein. The interactive tool is provided ‘AS IS,’ and Forrester and Experian make no warranties of any kind.
Experian 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.
Experian provided the customer names for the interviews but did not participate in the interviews.
Alexis Ouelhadj
Sanny Mok
March 2025
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