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Total Economic Impact
Cost Savings And Business Benefits Enabled By Senzing
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY Senzing, October 2025
Total Economic Impact
A FORRESTER TOTAL ECONOMIC IMPACT STUDY COMMISSIONED BY Senzing, October 2025
Entity resolution is the process of connecting records, both within a single data source and across multiple disparate data sources, when those records represent the same entity or related entities despite variations or inconsistencies across those records. An entity may be an individual or an organization. Accurate entity resolution that reduces identity mismatches improves the quality of an organization’s data, AI, analytics, and decision-making for varied use cases that range from fraud detection and risk management to marketing, sales, and customer experience.
By deploying the Senzing entity resolution SDK, organizations can augment existing entity resolution systems or get into production with a new system. This product can help organizations combine data in real time and with explainable results for not only resolving entities but also detecting relationships among those resolved entities for a 360-degree view.
Senzing commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Senzing.1 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Senzing on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed five decision-makers from a total of four organizations with experience using Senzing. For the purposes of this study, Forrester aggregated the experiences of the interviewees and combined the results into a single composite organization that is a multibillion-dollar diversified global conglomerate with 1.5 billion records.
Interviewees said that prior to using Senzing, their organizations either used multiple software tools and information services to handle entity resolution, had attempted but failed to build their own entity resolution engine, or did not perform entity resolution beyond simplistic data matching for some situations.
Employees at the interviewees’ organizations had struggled to reduce the number of identity mismatches (especially those causing unintended disclosures) during interactions with their customers, reliably deliver accurate products when those products incorporated entity resolution, and develop new entity resolution-based products to generate incremental revenue and enhance existing products to retain current revenue. They sought to increase revenue by optimizing their marketing, sales, and customer service efforts with more accurate entity resolution and reduce the staff time and third-party expenses associated with their legacy approach to entity resolution.
Interviewees indicated that after the investment in Senzing, their organizations reduced the number of incidents caused by identity mismatches. While most of the interviewees said incremental revenue was not the initial motivation for acquiring Senzing, they found Senzing enabled their organizations to generate incremental income from new or improved existing products that rely upon entity resolution. The interviewees’ organizations also reduced or eliminated some costs of their prior approach to entity resolution.
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Reduction in incidents caused by identity mismatches valued at $18.5 million. This benefit spans two Senzing use cases with similar causes and financial impact: Identity mismatches that affect the composite organization’s own operations when unintended disclosures occur during interactions with customers and other identity mismatches that create issues for the composite organization’s customers (e.g., when those customers make faulty business decisions based on inaccurate entity information the organization provided to them as part of a product/service). Because the composite organization reduces its number of identity mismatches after deploying Senzing, it reduces the number of customer complaints resulting from those mismatches and their associated costs. Associated costs include staff time, legal fees, and, depending on the industry and nature of the disclosure, possibly fines from regulatory violations. Over three years, a 25% reduction in incidents is worth $18.5 million to the composite organization.
Incremental income valued at $5.7 million from the revenue associated with new or improved products. Senzing enables the composite organization to create new products and enhance its existing products that rely upon entity resolution. New products generate incremental revenue and income that increases as the portfolio of new products grows. Enhancing its existing products helps the composite organization retain revenue and income that otherwise might go to its competitors. The combined incremental income over three years is valued at $5.7 million.
Cost savings from retiring a legacy solution valued at $4.2 million. After deploying Senzing, the composite saves time for several groups of employees and can stop using a third-party identity verification service. Data stewards save 20% of their time, data scientists dedicated to product development and enhancements save 50% of their time, and data engineers save 95% of the time they previously needed to add a data source. The combined impact of these staff time and external service savings over three years is valued at $4.2 million.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
More accurate data for entity-related internal uses of AI. As the composite organization starts to use AI to glean insights from its customer and prospect records, the improved accuracy of its entity data that Senzing enables will result in higher-quality AI outputs.
Incremental revenue and income from existing products due to more effective customer engagement enabled by improved entity resolution. Because Senzing improves the composite organization’s existing entity resolution investments, it has more comprehensive and accurate information on each customer. That information enables the composite organization to understand which of its products each customer already has and generate incremental revenue and income by leveraging that information to engage with customers more effectively through its marketing, sales, and customer service operations.
Improved customer experience. By using Senzing, the composite organization reduces its volume of identity mismatches and consequently decreases the number of customers whose experience is degraded by an identity mismatch.
Staff time freed up to address other needs. Because Senzing enables the composite organization to reduce identity mismatches and certain tasks resulting from those and save time for its data stewards and data scientists, those employees have additional capacity to address other priorities.
The quality and speed of customer support from Senzing. The composite organization receives prompt expert support from Senzing.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
Senzing subscription costs of $2.8 million. The composite organization pays Senzing subscription costs based on 1.5 billion records in its Senzing database.
Cloud consumption fees of $3.1 million. The composite organization deploys the Senzing SDK in its cloud infrastructure. Its incremental cloud consumption fees specific to using Senzing are for data ingestion and operating the Senzing core matching engine.
Implementation and management costs of $2.8 million. The composite organization implements Senzing and manages it on an ongoing basis using an internal team composed of IT and business staff. Implementation includes prioritizing use cases, transitioning from prior solutions, setting up needed infrastructure, deploying the Senzing SDK, loading data and mapping it to Senzing, and testing and optimizing the solution before going live in production mode.
The financial analysis that is based on the interviews found that a composite organization experience benefits of $28.4 million over three years versus costs of $8.7 million, adding up to a net present value (NPV) of $19.7 million and an ROI of 226%.
Impact of reduction in incidents caused by identity mismatches
Return on investment (ROI)
Benefits PV
Net present value (NPV)
Payback
| Role | Industry | Region | Records (DSRs) |
|---|---|---|---|
| Chief data officer | Financial services | Based in North America; global operations | 2 billion |
|
Director of product management Product and analytics lead |
Healthcare technology | Based in North America; global operations | 1.5 billion |
| Senior manager, data engineering | Information technology | Based in North America; global operations | 1 billion |
| Head of enterprise data management | Information services | Based in North America; global operations | 2 billion |
Prior to investing in Senzing, the interviewees’ organizations either used multiple software tools and information services to handle entity resolution, had attempted but failed to build their own entity resolution engine, or did not perform entity resolution beyond simplistic data matching for some situations.
The interviewees noted how their organizations had struggled with common challenges, including:
Reducing the number of unintended disclosures caused by false positives during interactions with their customers. Legacy approaches to entity resolution (or lack thereof) left the interviewees’ organizations vulnerable to inappropriately sharing entity information (i.e., unintended disclosures) when their customers logged in or interacted with their organizations’ customer-facing staff and a false positive prompted a system or employee to disclose information related to a different entity. Some unintended disclosures turned into incidents/disputes that required staff time, regulatory penalties, and legal fees to resolve and diminished customer satisfaction. As the director of product management at a healthcare technology company said, “Any mistake in getting the identity correct becomes some sort of an investigation and becomes expensive because remediation is always expensive.”
This challenge of correctly matching identities was exacerbated for those interviewees’ organizations that had a significant number of customers served by multiple business units and/or in multiple countries because of the increased number of records for a given customer entity and how fields in a record could vary from country to country.
Reliably delivering accurate products that incorporate entity resolution by minimizing identity mismatches in those products. Interviewees whose organizations had product portfolios that included products/services that incorporated entity resolution noted that false positives or false negatives generated in the production of those products caused the delivery of inaccurate entity information to their customers. When their customers then made faulty business decisions based on that inaccurate information (e.g., accepting or rejecting a potential customer’s application), those customers often pursued compensation from the interviewees’ organizations. Those incidents/disputes generated expenses for the staff time and legal fees required to resolve the dispute and, in some cases, prompted regulatory penalties. The incidents also reduced customer satisfaction. The head of enterprise data management at an information services company said: “When we give our customers the wrong data because of identity mismatches, the downstream impact is that we must explain what we did, and our customer must explain what they did. That turns into costs for us.” Conversely, onboarding a new customer or business that would otherwise be declined exposed the interviewees’ organizations to potential risk and future losses.
Developing new entity resolution-based products to generate incremental revenue and enhance existing products to retain current revenue. Interviewees at organizations with products that relied on entity resolution said that as part of their ongoing product portfolio management, they continually sought to develop, launch, and deliver new products as well as improve and enhance their existing products to minimize losing customers (and revenue) to competitors. Interviewees deemed the accuracy of those products’ entity resolution and their underlying capabilities (e.g., real-time matching) that could be leveraged to develop new products or improve existing ones as critical.
Increasing revenue by optimizing marketing, sales, and customer service with a more accurate view of customers. Across customer-facing functions, suboptimal entity resolution impaired revenue at the interviewees’ organizations. As the senior manager of data engineering at an information technology company explained: “We couldn’t effectively market and sell to prospects and then service the resulting customers because we did not have complete and accurate insight on an entire entity worldwide. How does everything connect? Entity resolution is key to that. We needed to figure out why our customer data was wrong because that was costing us opportunities and revenue. There are a lot of reasons but the way in which we matched was arguably the most important. That was the genesis of our heading down a path toward Senzing.”
Reducing staff time and third-party expenses associated with legacy entity resolution approaches. Interviewees noted their organizations’ prior approaches to entity resolution were complex, required excessive manual effort and significant costs, and lacked functionality. Their organizations’ data stewards spent too much time making sense of entity-related data, and data scientists spent excessive time writing entity resolution rules for their organizations’ products. To add data sources, data engineers had to accommodate the unique ways in which each data vendor matched and built an entity. In addition, the legacy approaches to entity resolution at the interviewees’ organizations typically involved using one or multiple third-party services because the interviewees’ organizations were unable to effectively leverage existing data from their internal systems.
The interviewees searched for a solution that could:
Save time and money and reduce risk compared to building a homegrown entity resolution solution.
Decrease time spent analyzing entity-related data, coding entity resolution rules, and adding new data sources.
Improve entity resolution within a broader initiative to connect data silos across business units and/or combine internal data with externally acquired data.
Produce more accurate entity data their organizations could leverage to add business value (via increased revenue or decreased expenses).
After evaluating multiple vendors, the interviewees’ organizations chose Senzing and began deployment.
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 multibillion-dollar diversified global conglomerate with multiple business units and entity resolution systems. It is headquartered in North America and has a worldwide customer base. Because of the nature of some of its services offerings, highly effective entity resolution is critical to the composite’s ability to develop, sell, and deliver offerings that delight customers and increase its revenue. For its own use in customer-facing activities, the composite needs reliable entity resolution across its extensive set of customers to improve how it serves those customers, supports its sales and marketing efforts, and minimizes the resolution costs, regulatory penalties, or customer dissatisfaction that can result from identity mismatches generated during entity resolution efforts. The composite has a significant number of customers who are served by more than one of the composite’s business units and/or in multiple countries, therefore exacerbating this challenge. The composite has 1.5 billion records and, prior to using Senzing, used multiple information services and software tools to handle entity resolution.
Deployment characteristics. To augment one of its existing entity resolution systems and replace another, the composite deploys the Senzing SDK in its cloud infrastructure using internal IT and business staff with guidance from Senzing support staff. After the composite implements Senzing, its IT and business staff invest time on an ongoing basis to continually optimize usage and explore new use cases.
Multibillion-dollar diversified global conglomerate
Based in North America
1.5 billion records
Replaces several services or software tools with Senzing
Deploys Senzing SDK in organization’s cloud infrastructure
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Reduction in incidents caused by identity mismatches | $7,437,500 | $7,437,500 | $7,437,500 | $22,312,500 | $18,495,962 |
| Btr | Incremental income from new or improved products | $1,713,409 | $2,321,393 | $2,929,376 | $6,964,178 | $5,677,034 |
| Ctr | Cost savings from retiring a legacy solution | $1,685,788 | $1,685,788 | $1,685,788 | $5,057,364 | $4,192,305 |
| Total benefits (risk-adjusted) | $10,836,697 | $11,444,681 | $12,052,664 | $34,334,042 | $28,365,301 |
Evidence and data. Interviewees noted that their organizations used Senzing to reduce the volume of identity mismatches that could occur when their records included multiple records that shared the same (e.g., date of birth), similar (e.g., name or address) core identifiers, or conflicting values. They leveraged Senzing to determine when the entities represented in those records were the same, possibly the same, or possibly related, and then used that insight to determine and address identity risks. This benefit can be broken into two use cases:
For the interviewees’ organizations, the first use case focused on false positives that affected their own operations when unintended disclosures occurred during interactions with customers. Examples of those interactions included a customer logging into their online account or a call center agent bringing up a customer’s records during a conversation with that customer. Interviewees stated a false positive during entity resolution provided the customer or call center agent with access to a different customer’s data and resulted in a customer complaint that needed to be resolved. The senior manager of data engineering at an information technology company noted, “If someone logs in from Company X and sees data for Company Y because of a false positive, it’s a problem — a big problem.”
The second use case focused on identity mismatches that created issues for the interviewees’ organizations’ customers (e.g., when customers made faulty business decisions based on inaccurate entity information the organizations provided as part of a product/service). As the head of enterprise data management at an information services company explained: “Our customers aren’t happy when they make a suboptimal business decision based on incorrect data we gave them. That results in disputes between us. By reducing our identity mismatches, which Senzing helps us do, we reduce those disputes.”
These two use cases shared a similar cause — faulty entity resolution that results in identity mismatches — and created similar impact for the interviewees’ organizations: Some of the resulting situations turned into complaints/disputes that the interviewees’ organizations had to resolve. Associated costs included at minimum staff time (e.g., legal, privacy), often external legal fees, and, depending on the industry and nature of the disclosure, possibly fines from regulatory agencies.
By investing in Senzing, the interviewees’ organizations improved their entity resolution and thus reduced their volume of false positives (without materially affecting false negatives) and their associated incidents and expenses. For entities where identity matching remained less definitive, Senzing helped interviewees’ organizations pinpoint those entities where further investigation (which could include manual review) was most needed. The cumulative impact over time of that further investigation also contributed to the reduction in identity mismatches.
The product and analytics lead at a healthcare technology company said, “Senzing helps us identify the risky entities, which gives our data steward teams the ability to focus on those entities over others.” The director of product management at that same organization noted, “After Senzing flags identities that could have some negative experience, we proactively address those identity challenges to reduce the risk of their leading to customer complaints.” The senior manager of data engineering at an information technology company said, “Senzing provides key support for our data stewards’ efforts.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Prior to using Senzing, the composite organization has 3,500 unintended disclosures or other false positive-based incidents each year.
The composite attributes a 25% reduction in those incidents to its use of Senzing.
The composite pays an average of $10,000 to resolve each incident.
Risks. Results may not be representative of all experiences because the extent and value of a reduction in incidents caused by identity mismatches can vary based on:
The organization’s level of unintended disclosures or other identity mismatch-based incidents (whether as an absolute number or as a percentage of the relevant activities) before deploying Senzing.
The extent to which the organization leverages the capabilities of Senzing.
How aggressively an organization pursues fixes to erroneous data identified via its Senzing use.
The volume of records Senzing addresses.
The organization’s average cost to resolve an incident.
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 $18.5 million.
Incident reduction attributable to Senzing
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Unintended disclosure or other identity mismatch-based incidents prior to Senzing | Composite | 3,500 | 3,500 | 3,500 | |
| A2 | Incident reduction attributable to Senzing | Interviews | 25% | 25% | 25% | |
| A3 | Average cost to resolve an incident | Interviews | $10,000 | $10,000 | $10,000 | |
| At | Reduction in incidents caused by identity mismatches | A1*A2*A3 | $8,750,000 | $8,750,000 | $8,750,000 | |
| Risk adjustment | ↓15% | |||||
| Atr | Reduction in incidents caused by identity mismatches (risk-adjusted) | $7,437,500 | $7,437,500 | $7,437,500 | ||
| Three-year total: $22,312,500 | Three-year present value: $18,495,962 | |||||
Evidence and data. While most of the interviewees said incremental revenue was not the initial motivation for acquiring Senzing, incremental income emerged as an additional benefit. Interviewees said that for products that relied upon entity resolution, Senzing enabled their organizations to create new products and enhance their existing products. Examples included new insurance risk and claims products and improved payment processing and transaction risk screening products.
The interviewees’ organizations’ new products generated incremental revenue and income that increased over time as their portfolio of new products grew. Enhancing existing products by improving the entity resolution on which they relied helped interviewees’ organizations retain revenue and income that otherwise might be lost to competitors.
As the head of enterprise data management for an information services company explained: “Using Senzing for entity resolution unlocks potential new products for us. We can do a lot of new analytics. We can cross-connect datasets that we couldn’t accurately connect before, and it had been way too complex to do it. Now it’s simplified. We can do it in real time. We also can improve existing products in ways that are compelling to our customers. These are all advantages Senzing delivers.” The chief data officer at a financial services company noted their organization had released several new products in less than a year that would not have been possible without entity resolution and was building more monetization use cases around it. The interviewee noted: “Our customers have other options. Providing additional value is critical to protecting our customer base and the revenue we get from them. By improving what we’re already doing for our customers, we can retain them better.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Before deploying Senzing, the composite organization’s baseline revenue from existing products/services that incorporate and require entity resolution is $1.5 billion.
The percentage increase in baseline revenue attributable to the use of Senzing in new products/services is 0.55% in Year 1,1.10% in Year 2, and 1.65% in Year 3.
The percentage of baseline revenue retained (versus lost to competitors) attributable to using Senzing to enhance existing products/services is 1% annually.
Forrester applies a net margin of 8.67% to top-line revenue growth to focus the study on bottom-line financial improvement.2
Risks. Results may not be representative of all experiences because incremental income from new or improved products can vary based on:
The organization’s baseline revenue from existing products/services that incorporate and require entity resolution.
The extent to which the organization leverages the capabilities of Senzing.
The scope and strength of the organization’s efforts to identify and address its opportunities for new products and the vulnerabilities of its existing products.
The organization’s operating margin, which is influenced by geography, industry, business model, the competitive landscape, and its priorities and operational efficiency.
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 $5.7 million.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Baseline annual revenue from existing products/services that incorporate and require entity resolution | Composite | $1,500,000,000 | $1,500,000,000 | $1,500,000,000 | |
| B2 | Percentage increase in baseline revenue attributable to using Senzing in pertinent new products | Interviews | 0.55% | 1.10% | 1.65% | |
| B3 | Subtotal: Incremental annual revenue from new products that is attributable to Senzing | B1*B2 | $8,250,000 | $16,500,000 | $24,750,000 | |
| B4 | Percentage of baseline revenue retained (vs. lost to competition) by using Senzing to enhance existing products | Interviews | 1% | 1% | 1% | |
| B5 | Subtotal: Revenue retained due to Senzing-enabled improvements to existing products | B1*B4 | $15,000,000 | $15,000,000 | $15,000,000 | |
| B6 | Net margin | Research data | 8.67% | 8.67% | 8.67% | |
| Bt | Incremental income from new or improved products | (B3+B5)*B6 | $2,015,775 | $2,731,050 | $3,446,325 | |
| Risk adjustment | ↓15% | |||||
| Btr | Incremental income from new or improved products (risk-adjusted) | $1,713,409 | $2,321,393 | $2,929,376 | ||
| Three-year total: $5,637,668 | Three-year present value: $4,612,305 | |||||
Evidence and data. Interviewees reported that deploying Senzing helped save time for several groups of employees and allowed their organizations to stop using a third-party identity verification service.
At interviewees’ organizations, data stewards handled entity resolution quality assurance for customer records by validating entity resolution outcomes (including manual review of possible matches), investigating entity resolution decisions that did not meet standard matching criteria, and making sense of their organizations’ entity-related data. Interviewees said Senzing reduced manual effort so data stewards could do their work more efficiently. The director of product management at a healthcare technology company said: “Senzing frees up time for our data stewards. Instead of manually trying to dig up answers, they can instead take the guidance Senzing provides and say, ‘Here’s what we think it is; here’s why we think that.’”
Interviewees also noted that data scientists dedicated to product development and enhancements at interviewees’ organizations worked on entity resolution specific to each product or service that incorporated and required entity resolution. Interviewees indicated that with Senzing, those data scientists saved time they previously had to spend developing rules for various products, the datasets underlying those products, and use cases. They also saved time investigating instances of identity mismatches related to those products. The head of enterprise data management at an information services company said: “For every product that does entity resolution, we have a data science team focused on the entity resolution piece. Now they don’t have to write a set of business rules to ensure that we have the best match. Senzing will do it for us. So that team’s involvement can be significantly lower than before, and some of their time can be used for other things.”
Data engineers at interviewees’ organizations added new data sources to entity resolution efforts as needed, which could range from zero to many sources each year. Senzing saved substantial time for those data engineers by streamlining that process. The head of enterprise data management at an information services company indicated: “When our data engineers had to write all their own rules, get the fields lined up, and tinker with a lot of stuff, it could take someone two months to add a source. With Senzing, it takes them just a few days, mostly for testing.”
After deploying Senzing, interviewees’ organizations reduced or eliminated their payments for external identity verification services because applying Senzing to their internal data met that need.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization has eight data steward FTEs supporting customer entity resolution.
The composite attributes Senzing with saving data stewards 20% of their time in Years 1, 2, and 3.
The composite has 10 data scientist FTEs dedicated to product development and enhancements.
The composite attributes Senzing with saving data scientists 50% of their time in Years 1, 2, and 3.
The average fully burdened annual salary for a data steward and data scientist FTE is $140,000.
Each year the composite adds two data sources.
Prior to Senzing, data engineers worked 280 hours to add one data source.
With Senzing, 95% of those hours are saved in Years 1, 2, and 3.
The fully burdened hourly rate for data engineers is $80.
Data stewards, data scientists, and data engineers reinvest 50% of the time they save into tasks that provide the organization with greater value (productivity realization factor).
The composite saves $1.5 million on identity verification services in each of Years 1, 2, and 3.
Risks. Results may not be representative of all experiences because cost savings from retiring legacy solutions can vary based on:
The number and nature of the organization’s legacy entity resolution solutions.
The infrastructure needed for the legacy solution.
Prevailing local compensation rates.
The extent to which the organization leverages the capabilities of Senzing.
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 $4.2 million.
Percentage of data scientist time saved on product development
Percentage of data engineer time saved on adding a data source
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Data steward FTEs supporting customer record ER | Composite | 8 | 8 | 8 | |
| C2 | Percentage of work time saved | Interviews | 20% | 20% | 20% | |
| C3 | Data scientist FTEs dedicated to product development and enhancements | Composite | 10 | 10 | 10 | |
| C4 | Percentage of work time saved | Interviews | 50% | 50% | 50% | |
| C5 | Productivity realization factor | TEI methodology | 50% | 50% | 50% | |
| C6 | Fully burdened annual salary for a data steward and data scientist FTE | Composite | $140,000 | $140,000 | $140,000 | |
| C7 | Subtotal: Time savings for data stewards and data scientists | ((C1*C2)+(C3*C4))*C5*C6 | $462,000 | $462,000 | $462,000 | |
| C8 | Data sources added | Interviews | 2 | 2 | 2 | |
| C9 | Hours worked to add one source prior to Senzing | Interviews | 280 | 280 | 280 | |
| C10 | Percentage of hours saved | Interviews | 95% | 95% | 95% | |
| C11 | Productivity realization factor | TEI methodology | 50% | 50% | 50% | |
| C12 | Fully burdened hourly rate for a data engineer | Composite | $80 | $80 | $80 | |
| C13 | Subtotal: Time savings for adding data sources | C8*C9*C10*C11*C12 | $21,280 | $21,280 | $21,280 | |
| C14 | Third-party identity verification services | Interviews | $1,500,000 | $1,500,000 | $1,500,000 | |
| Ct | Cost savings from retiring a legacy solution | C7+C13+C14 | $1,983,280 | $1,983,280 | $1,983,280 | |
| Risk adjustment | ↓15% | |||||
| Ctr | Cost savings from retiring a legacy solution (risk-adjusted) | $1,685,788 | $1,685,788 | $1,685,788 | ||
| Three-year total: $5,057,364 | Three-year present value: $4,192,305 | |||||
Interviewees mentioned the following additional benefits that their organizations experienced but could not quantify:
More accurate data for entity-related internal uses of AI. The senior manager of data engineering at an information technology company noted the critical need for accurate entity data as their organization starts to use AI to glean insights from their customer and prospect records: “AI is only as good as the data that supports it. Having accurate entity resolution feeding that is going to be critical.”
Incremental revenue and income from existing products due to more effective customer engagement enabled by improved entity resolution. The senior manager of data engineering at an information technology company described several challenges their organization faced before Senzing because it lacked comprehensive and accurate information on each customer and thus could not effectively engage with them. For lead generation and other marketing efforts and when its sales reps and service teams worked directly with current customers, the interviewee’s organization struggled to understand what the customer already had and how to use that information to determine what else to market or sell to them. The interviewee said: “To renew or expand an existing customer, we must understand what they currently have. We can’t suggest they replace something that’s out of warranty or out of date if we don’t even know they have it.”
In explaining the impact of Senzing, the same interviewee said: “Senzing provides us with a better picture of each entity and that helps us push for a renewal, suggest a replacement of some product that’s out of warranty, spot other selling opportunities, and appropriately deliver services. What asset is owned by what company? What service contracts do they have? All this is centered around our ability to know who we’re talking to. We could not do this without effective entity resolution — it is absolutely key.”
Improved customer experience. By using Senzing, interviewees’ organizations reduced their identity mismatches and the resulting negative impact on the customer experience. The director of product management at a healthcare technology company said, “Identity mismatch results in a negative customer experience and that’s always expensive in terms of customer satisfaction.”
Staff time freed up to address other needs. Interviewees noted Senzing reduced mismatches and the amount of work resulting from them, saving time for their data stewards and data scientists and allowing those employees to focus on other priorities. The head of enterprise data management at an information services company said, “The fact that we have reduced identity mismatches also means we can focus on improving our data more, because we can spend that energy on something else.”
The quality and speed of customer support from Senzing. Interviewees proactively commented on the value of customer support from Senzing. The head of enterprise data management at an information services company observed: “I get access to people who are not only experts on Senzing but also experts on entity resolution. All of that is another differentiator for Senzing.” The chief data officer at a financial services company said: “Senzing’s support is great. They’re nimble and respond quickly.”
The value of flexibility is unique to each customer. There are multiple scenarios in which a company might implement Senzing and later realize additional uses and business opportunities, including:
Applying Senzing to new use cases. Interviewees expected to expand their use of Senzing to other use cases over time. For example, the senior manager of data engineering at an information technology company mentioned plans to leverage Senzing to ensure correct identification of individuals when personalizing marketing and the customer’s experience (e.g., via customized views) and said: “Everybody wants customized views. They want to know what they care about and very little of what they don’t.” That interviewee also mentioned how Senzing would enable their organization to develop a “household view” of consumers to understand and potentially leverage the relationships among its individual customers who are members of the same household, analogous to the organization’s current efforts within their commercial accounts.
The director of product management at a healthcare technology company described how their organization leverages a combination of Senzing insights and its own custom logic to reduce fraudulent takeovers of customer accounts. This interviewee said their organization’s cybersecurity analysts and business analysts conducted security reviews on suspect activities at its various customer-facing portals. These reviews included scanning system logs and Senzing entity resolution output from internal data to determine login patterns, examine an individual’s entity relationship network, spot frequent changes to contact information, and identify other indications of fraud. The director of product management went on to say that approximately 10% of their organization’s 7,000+ reviews each year detect an account takeover that it then addresses.
Expanding Senzing use to additional functions, business units, or geographies. Interviewees from decentralized organizations expected usage to expand as awareness of Senzing’s capabilities grows across the organization. Those whose global organizations initially deployed Senzing in select regions anticipated increased usage in other geographies.
Leveraging the capabilities of Senzing more completely. Interviewees noted their organizations would continue to explore additional ways to capitalize on the functionality provided by Senzing and gain even greater value from it.
Capitalizing on new features and functionality as Senzing introduces them. The interviewees said they anticipate using enhanced and new features as Senzing continues to evolve.
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 |
|---|---|---|---|---|---|---|---|
| Dtr | Senzing subscription costs | $472,500 | $945,000 | $945,000 | $945,000 | $3,307,500 | $2,822,575 |
| Etr | Cloud consumption fees | $0 | $1,150,000 | $1,265,000 | $1,380,000 | $3,795,000 | $3,127,724 |
| Ftr | Implementation and management | $479,336 | $925,496 | $911,768 | $911,768 | $3,228,368 | $2,759,248 |
| Total costs (risk-adjusted) | $951,836 | $3,020,496 | $3,121,768 | $3,236,768 | $10,330,868 | $8,709,547 |
Evidence and data. Interviewees noted that Senzing subscription costs were based on their organizations’ current count of data source records (DSRs) processed in their Senzing database. A DSR is a single record that is mapped and loaded into Senzing (searches are not counted). Senzing subscription costs included customer service and support. Contact Senzing for likely costs specific to your organization.
Modeling and assumptions. Based on the interviews, Forrester assumes there are 1.5 billion DSRs in the composite’s Senzing database.
Risks. These costs may not be representative of all experiences because Senzing subscription costs will vary based on:
The nature of an organization’s business.
The resulting number of DSRs in its Senzing database.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.8 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| D1 | Senzing subscription costs | Senzing | $450,000 | $900,000 | $900,000 | $900,000 |
| Dt | Senzing subscription costs | D1 | $450,000 | $900,000 | $900,000 | $900,000 |
| Risk adjustment | ↑5% | |||||
| Dtr | Senzing subscription costs (risk-adjusted) | $472,500 | $945,000 | $945,000 | $945,000 | |
| Three-year total: $3,307,500 | Three-year present value: $2,822,575 | |||||
Evidence and data. Interviewees estimated their organizations’ incremental cloud consumption fees specific to using Senzing. Those incremental fees covered the cloud services needed to load, resolve, and store data and did not include the total cloud expense for operating all apps that incorporate Senzing or the overall operational system(s) that Senzing and those apps were part of, since those expenses were not specific to Senzing.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite deploys the Senzing SDK in their cloud infrastructure.
Cloud consumption fees reflect the composite’s requirements for redundancy and disaster recovery capabilities.
Fees increase over Years 1 to 3 to reflect the composite’s expanded use of Senzing as part of new products or enhancements to existing products.
Risks. These costs may not be representative of all experiences because cloud consumption fees can vary based on:
The scope, complexity, and details of what the organization is doing with Senzing (e.g., volume of transactions/records processed, number of data sources and destinations, complexity of the data, size of the Senzing database).
Whether the organization requires redundancy and disaster recovery capabilities.
The extent to which the organization leverages the capabilities of Senzing.
Prevailing cloud consumption fees and the organization’s ability to negotiate those fees.
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 $3.1 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | Cloud infrastructure used to operate Senzing | Interviews | $1,000,000 | $1,100,000 | $1,200,000 | |
| Et | Cloud consumption fees | E1 | $0 | $1,000,000 | $1,100,000 | $1,200,000 |
| Risk adjustment | ↑15% | |||||
| Etr | Cloud consumption fees (risk-adjusted) | $0 | $1,150,000 | $1,265,000 | $1,380,000 | |
| Three-year total: $3,795,000 | Three-year present value: $3,127,724 | |||||
Evidence and data. Interviewees described how their organizations’ IT and business staff collaborated to plan for and deploy Senzing with guidance from the Senzing support team and subsequently invested time on an ongoing basis to manage Senzing.
The interviewees said that their organizations’ joint implementation team prioritized initial use cases for Senzing and established implementation priorities and timelines. Business staff, such as data analysts and data scientists, provided business requirements, ensured Senzing would operate in ways that satisfied those requirements, and validated the accuracy of Senzing outputs.
The interviewees’ organizations typically spent about six months implementing Senzing. Some interviewees’ organizations conducted small internal pilots during this initial period.
The mix of IT roles involved with implementation varied among the interviewees’ organizations and included architects, developers, DevOps, software engineers, data engineers, and project managers, along with a senior IT leader who set the overall vision and ensured impact. IT staff determined how to transition from prior solutions; set up needed infrastructure (e.g., servers, network, databases, connectivity); deployed the Senzing SDK; mapped the data to Senzing; loaded the data into Senzing; and tested and optimized the data coming out of Senzing before going live in production mode.
IT and business staff typically got up to speed on Senzing through their involvement with technical deployment (for IT), by reviewing Senzing’s public documentation, and via direct support from Senzing as needed.
Interviewees reported that, on an ongoing basis, IT staff ensured 24/7/365 global operations of Senzing, tuned input/output operations per second (IOPS) performance, managed its cloud infrastructure, and supported end users. Business staff continued to provide business requirements and ensured Senzing satisfied those requirements. IT and business staff determined how to optimize their organization’s use of Senzing and maximize its impact by further leveraging its existing functionality, capitalizing on its new capabilities as they were announced, and identifying and pursuing new use cases.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite deploys the Senzing SDK in its cloud infrastructure.
IT staff in various roles spend a combined total of 5,200 hours on initial technical deployment, data mapping, data loading, testing, and optimization. IT staff spend a combined total of 11,440 hours on management and support in each subsequent year.
The fully burdened hourly rate for an IT team member is $65.
Business staff in various roles spend a combined total of 1,040 hours on initial planning for the use of Senzing and the same amount of time on ongoing optimization and exploration of new use cases in each subsequent year.
The fully burdened hourly rate for a business staff team members is $70.
A senior IT leader spends 208 hours during the initial period and Year 1 to strategize and craft the roadmap for the use of Senzing and ensure results. This time is reduced to 104 hours in Years 2 and 3.
The fully burdened hourly rate for a senior IT leader is $120.
Risks. These costs may not be representative of all experiences because implementation and management costs will vary based on:
The prior state and overall maturity of the organization’s operations related to entity resolution.
The scope and complexity of the organization’s Senzing deployment (e.g., number of use cases) and its broader data transformation efforts that may affect that deployment.
The experience and capabilities of the organization’s IT and business staff.
Whether the organization implements Senzing on-premises, in full cloud mode, or in hybrid mode.
The volume of records the organization loads and the number of data sources from which it loads them, along with its pace and timing for that loading.
The extent to which the organization invests IT and business staff time to leverage the capabilities of Senzing.
Prevailing local compensation rates.
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 $2.8 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Combined total IT time required for initial technical deployment and ongoing management, support, use case expansion, and optimization (hours) | Interviews | 5,200 | 11,440 | 11,440 | 11,440 |
| F2 | Blended fully burdened hourly rate for an IT team member | TEI methodology | $65 | $65 | $65 | $65 |
| F3 | Combined total business staff time required for initial planning and ongoing optimization (hours) | Interviews | 1,040 | 1,040 | 1,040 | 1,040 |
| F4 | Fully burdened hourly rate for a business staff team member | TEI methodology | $70 | $70 | $70 | $70 |
| F5 | Senior IT leadership time to strategize, craft roadmap, and ensure results | Interviews | 208 | 208 | 104 | 104 |
| F6 | Fully burdened hourly rate for a senior IT leader | TEI methodology | $120 | $120 | $120 | $120 |
| Ft | Implementation and management | (F1*F2)+(F3*F4)+(F5*F6) | $435,760 | $841,360 | $828,880 | $828,880 |
| Risk adjustment | ↑10% | |||||
| Ftr | Implementation and management (risk-adjusted) | $479,336 | $925,496 | $911,768 | $911,768 | |
| Three-year total: $3,228,368 | Three-year present value: $2,759,248 | |||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($951,836) | ($3,020,496) | ($3,121,768) | ($3,236,768) | ($10,330,868) | ($8,709,547) |
| Total benefits | $0 | $10,836,697 | $11,444,681 | $12,052,664 | $34,334,042 | $28,365,301 |
| Net benefits | ($951,836) | $7,816,201 | $8,322,913 | $8,815,896 | $24,003,174 | $19,655,754 |
| ROI | 226% | |||||
| 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 Senzing.
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 Senzing can have on an organization.
Interviewed Senzing stakeholders and Forrester analysts to gather data relative to Senzing.
Interviewed five decision-makers from a total of four organizations using Senzing 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 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.
2 Source: Margins by sector (US), NYU Stern School of Business, January 2025.
Readers should be aware of the following:
This study is commissioned by Senzing 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 Senzing. Forrester does not endorse Senzing or its offerings.
Senzing 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.
Senzing provided the customer names for the interviews but did not participate in the interviews.
Mary Anne North
October 2025
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