70% of OSS RFPs miss the Best Vendor. How to improve Your odds

Most carriers still rely on traditional RFP processes to select OSS/BSS vendors. These methods are seen as rigorous and comprehensive.

However, they are slow, inflexible and often fail to surface the best-fit vendor. Despite consuming vast time and effort, statistically, they are only ~30% likely to find the best-fit vendor for your needs.

We’ve continually refined our OSS procurement model over the years. It uses probabilistic filtering and adaptive techniques to significantly improve those odds with far less resource consumption. Today’s article walks you through our approach, which we refer to as The Inverted Pyramid.

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A Personal Buying Anecdote

But first, I’d like to share an anecdote. Not about buying an OSS, but buying a car.

Not long ago, I was in the market for a used car. Like many buyers, I had a rough idea of what I wanted: a particular body-shape, low-mileage vehicle, ideally white or silver, within a specific price band and a loose affinity for certain makes/models.

First, I scanned through online listings – filtering out anything too expensive, too many kilometres, the right colour or previously written off. The online search surfaced many cars and I spent weeks scrolling and reaching out to sellers. Then, I started to look at a few in person. Even with a filter on cars within a 25km radius of home, I found myself driving all over Melbourne.

It was quickly dawning on me that it didn’t make any sense to spend forever looking. But choosing poorly by deciding too early wouldn’t be clever either. Without realising it initially, I ended up applying a strategy similar to The Secretary Problem.

What is the Secretary Problem, I hear you ask?

The Secretary Problem is a classic mathematical model that explores how to select the best option from a pool when each option must be evaluated one at a time and decisions are irreversible. It proposes that the best strategy is to pass over the first few candidates (the “observation phase”) and then select the next one that is better than all previously observed. This results in a roughly 37% chance of selecting the best overall candidate.

In my case, I reviewed the first four cars that matched my criteria, purely for reference. Then, I committed to purchasing the next car that clearly exceeded the initial 4 benchmarks. I was really close to buying a black vehicle, but ended up finding a white one with ~40,000 less kms on the clock.

I’m sure this car isn’t the absolute best deal possible – but it was very close to optimal. Importantly, it meant I could spend far less time searching, worrying, or negotiating. It avoided analysis paralysis and we still drove away with a good car.

This same logic applies to OSS vendor selection. The goal is to find the best-fit or very near best-fit solution and make a decision to proceed with it without wasting months on procrastination, stakeholder delays and endless analysis.

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The Buyer/Seller Chasm in OSS Procurement

This car-buying experience mirrors a broader truth in OSS procurement: the longer and more rigid the process, the worse it ends up being for both buyer and seller.

The overall procurement intent is there:

The buyers (carriers) desperately need better tools to transform their operations.

The sellers (vendors) already have better tools to offer, which they desperately want to sell.

Despite this intent, unfortunately traditional telco procurement approaches introduce a vast chasm between buyers and sellers, as outlined in our Buyer/Seller Chasm Series of Articles. There are plenty of reasons for the chasm, but most come down to a gap in trust, risk, skills and confidence in their ability to deliver outstanding transformation results.

This is a major reason why traditional OSS procurement processes routinely stretch to 12–18 months, draining momentum from transformation efforts and pushing vendors (and operators) into long, expensive sales cycles. All parties lose: buyers face delayed transformation and compromised outcomes, while vendors suffer from opportunity costs and fatigue.

However, The Secretary Problem, elegant though it is, does not suit OSS vendor selection. It’s a one-shot decision model where rejected vendors cannot be re-evaluated.

But the real world demands flexibility, discovery and iteration. That’s why our PAOSS Inverted Pyramid Approach (see diagram below) is designed to not only surface the best vendor efficiently, but to also maintain an open, informed and fair process for all qualified participants.

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Or, to show our Inverted Pyramid process as a car-sales analogy:

2. The Two Approaches in Brief

When applied to OSS vendor selection, the Secretary Problem helps frame the challenge of making a high-stakes decision with imperfect information and limited time.

However, the likelihood of “success” is limited so we’ll analyse two other models that are a better fit for OSS procurement:

• The Traditional Carrier-Led RFI/RFP Process
• The PAOSS Inverted Pyramid – A Probabilistic Filtering Approach

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a. The Traditional Carrier-Led RFI/RFP Process

In a typical carrier-led RFP process, the awareness of potential vendors is often limited to a narrow pool of 30 to 40 companies. These are usually the vendors the organisation has worked with before, heard about through industry channels, seen in previous procurement cycles or perhaps even in a magic quadrant analysis.

This traditional process suffers from what we refer to as “The Three Forevers” problem:

• Forever to write the RFP
• Forever for vendors to respond to the RFP
• Forever to review vendor responses

And only then do you start on the really time-consuming deep-dive with your preferred bidder or bidders (eg POC, contract negotiations, etc).

The traditional approach involves evaluating too many vendors, with too many stakeholders, over too long a timeline. This effort often diverts key operational resources away from business-as-usual activities to help with vendor decision-making.

The result is slow, bureaucratic and frequently paralysing.

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b. The Inverted Pyramid – A Probabilistic Filtering Approach

In contrast, the PAOSS Inverted Pyramid Approach begins with a far broader awareness of the vendor landscape.

Over 600 vendors are tracked and curated in our Blue Book OSS/BSS Vendor Directory. It contains detailed metadata on each listing, including product capabilities, supported regions, functional domains, deployment models and a variety of other factors.

It ensures that established and emerging vendors are represented, current and comprehensive.

In our case, the decision problem is simplified through staged filters:

• Filter 1 – Each vendor profile enables a rapid first-pass reduction of the field, eliminating clearly misaligned options without requiring any vendor outreach at all. This generally leaves a long-list of around 30–50 vendors.
• For Filter 2, a carrier-specific, lightweight RFI is then sent to the long-listed vendors, focusing on high-value differentiators. Responses are scored using multi-criteria decision-making (MCDM) and probabilistic ranking techniques, allowing evaluators to quickly identify strong candidates without exhaustive reviews. This typically leaves a short-list of around 3-6 vendors with the carrier barely investing any time at all (see section 3 later for the actual effort comparison)
• Product demos (Filter 3) and PoCs (Filter 4) are only pursued with a handful of high-scoring vendors, concentrating analysis effort where it matters most

The PAOSS Inverted Pyramid Approach retains the principle of narrowing to a strong shortlist quickly, then choosing confidently from those, with a high probability of success (more on that below).

Crucially, this model remains open to discovery. In the unlikely event that a new vendor emerges from outside the initial 600+ during the process, it can be added to the pipeline without disrupting progress.

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TL;DR:

If you’re short on time, here’s a summary of how the procurement models compare (further details in the sections that follow):

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Conclusion: The PAOSS Inverted Pyramid Approach delivers better outcomes than the Traditional Model – in less time, with fewer resources and without overwhelming BAU resources or vendors. The Secretary Problem, while elegant mathematically, is impractical for OSS procurement and offers lower reliability.

3. Quantifying the Differences

a. Probability of Success (Finding the Right Vendor)

i) The PAOSS Inverted Pyramid Approach

Across the four filters, the PAOSS Inverted Pyramid Approach yields a ~71% chance of selecting the best-fit vendor.

This is based on the following estimates:

• Filter 1: Functional Block = 98% chance of keeping the best-fit (thanks to the comprehensive vendor directory)

• Filter 2: Capabilities RFI = 95% (based on well-targeted questions and up-to-date vendor responses)

• Filter 3: Product Demos = 90% (some misalignment risk, but mitigated via scenario planning)

• Filter 4: PoC and Financials = 85% (due to implementation or commercial barriers)

The cumulative chance of the best vendor making it through all filters = ~71% (ie 0.98 x 0.95 x 0.90 x 0.85).

However, one of the most underappreciated insights in vendor selection is that even if you don’t choose the statistically best-fit vendor, the performance delta may be negligible within a well-qualified shortlist.

Just like in my car analogy and Secretary Problem earlier, I was eventually considering the same make, model and year of car, so the delta should’ve been relatively negligible. That’s not the same in the case of OSS though, where there is only approximate equivalence between vendors.

This is where statistical concepts from utility theory, regret minimisation and Pareto efficiency can come into play.

Say your Filter 2–3 shortlist has these final scores:

• Vendors A and B are statistically indistinguishable in expected performance

• C is still acceptable, but more context-sensitive

• D starts to drift outside the near-optimal zone

But more importantly, the PAOSS approach yields a ~95% probability of selecting a near-optimal vendor (ie one whose business fit, integration complexity and long-term support are close to ideal for the telco buyer).

That 95% estimate is based on the idea that the remaining 2-3 finalists are so closely matched on functional fit and delivery quality that choosing between them becomes less about capability and more about commercial alignment, cultural fit, or partner preference. In many telco evaluations, these factors become the ultimate tiebreaker, meaning that any of the final few options would likely succeed operationally.

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ii) The Traditional Telco RFI/RFP Approach

Traditional RFPs are less adaptive. With a smaller starting pool (typically only 30 to 40 vendors) the likelihood that the absolute best vendor is even included at the outset is low. If we assume the top vendor is evenly distributed across a population of 600, then the chance they are in a random subset of 40 is roughly 40 / 600 = 6.7%. In reality, it should be better than that!

Still, many good vendors drop out or are excluded due to:

• Lack of brand recognition or prior relationships

• Resource limits (e.g., unwillingness to invest in a 9-month sales cycle)

• Misalignment with arbitrary RFP criteria

• Most carriers unconsciously use implicit filtering at the awareness and pre-RFP stage (eg vendors already known to them or seen at trade shows)

This can reduce the real chance of the best-fit vendor making it to the final decision stage

If most carrier-led RFPs consider ~30–40 vendors, let’s assume:

• 5–10 of these vendors could be classified as highly capable or “best-fit” based on the buyer’s actual requirements

• That’s ~15–25% of the pool

• We’ll be generous and estimate the chance of the best-fit vendor surviving the traditional process is ~30%

When it comes to near-optimal choices, the difference becomes even more stark. Whilst the probabilistic model achieves a near-optimal selection rate of ~95%, as explained earlier. Traditional RFPs may only reach ~60% due to exclusion of emerging or less visible players and inconsistent evaluation focus.

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iii) In summary:

This means the PAOSS model is 2.4 times more likely to find the best-fit vendor (0.71 / 0.30) and 1.6 times more likely to find a vendor whose performance is nearly indistinguishable from the best (0.95 / 0.60).

Also:

• More evaluation doesn’t mean better results – beyond a certain point, it wastes effort and distorts decisions

• If multiple vendors are statistically equivalent in capability, over-analysing introduces more regret than confidence

• The probabilistic model helps you spend effort where it has the highest marginal value, and stop when the outcome is good enough

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b. Operator (and Vendor) Effort

The structured PAOSS model front-loads effort where it provides the most insight:

• Filter 1: PAOSS conducts the initial vendor screening based on metadata from over 600 vendors in the directory. The carrier only needs to participate in a 1-hour introductory session to outline the context of their transformation project
• Filter 2: PAOSS provides a pre-structured RFI with suggested filtering questions. The carrier may review and customise the questions, involving up to 5 stakeholders for 2 hours each (10 hours total). RFI responses should take less than 1 hour per vendor
• Filter 3: PAOSS proposes the demonstration scenarios aligned with the transformation objectives. The carrier may spend around 5 hours reviewing and tailoring these workflows. During the demo phase, assume 3 to 6 vendors, with 5 participants attending each 3-hour demo. This results in approximately 45 to 90 person-hours. It’s likely that aggregated vendor effort is longer because it requires preparation and demonstration, though they’re unlikely to have as many participants involved
• Filter 4: The PoC and commercial assessment phase typically spans 20 working days of effort, including preparation of test scenarios, environment setup, PoC execution, financial review, and proposal evaluations. This represents a more intensive but focused investment, occurring only after high-confidence candidates are identified.

Total estimated customer effort across all four filters is in the ballpark of 250 hours with much of the screening, analysis, and vendor communication carried out by PAOSS in the background.

By contrast, the traditional RFP approach demands a disproportionately higher effort from the carrier:

• Writing and refining hundreds or even thousands of requirements
• Gaining stakeholder consensus on scope and scoring
• Drafting RFI/RFP documentation
• Managing responses from 10 or more vendors
• Reviewing those extensive proposals and attending full-length demos
• Running PoCs and commercial negotiations with multiple vendors

This often exceeds 1,200 hours of internal effort and can balloon further due to inefficiencies and coordination overhead. To put this in perspective, 1,200 hours is equivalent to five people working full-time for 30 days.

Realistically, the actual effort for a typical carrier RFP process often reaches several thousand person-hours when legal, finance, architecture, procurement and operations teams are all engaged.

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c. Time-to-Decision (Duration)

The PAOSS Inverted Pyramid Approach aims to deliver fast validation loops:

• Filter 1: Typically completed in 2 weeks, including vendor screening and internal alignment
• Filter 2: RFI issuance, vendor response and scoring typically take another 3 weeks
• Filter 3: Demo preparation, execution and internal debrief span another 3 weeks (depending on availability of buyer and seller resources for demos)
• Filter 4: PoC setup, review, proposal clarification, and commercial alignment require about 4-6 weeks

Total: approximately 12-14 weeks from initiation to decision, with clear checkpoints to allow early exit where appropriate.

In contrast, traditional RFP processes often stretch out between 9 and 18 months. This extended duration stems from upfront documentation development, rigid internal governance, long vendor turnaround times and heavy coordination / governance overhead. Each stage typically runs in series, making it difficult to shorten without skipping steps or accepting higher risk. By the time a decision is reached, significant organisational momentum and sunk cost bias can obscure the original intent of selecting the best-fit vendor.

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d. Flexibility and Scalability

The PAOSS Inverted Pyramid Approach is designed with discovery and scale in mind. At the outset, over 600 vendors are considered, significantly reducing the risk that a relevant vendor is overlooked. This breadth means that by the time the shortlist is being evaluated, the chances of a surprise late-stage vendor (a dark horse) emerging are much lower. Even if a new vendor is identified mid-process, the structured filtering approach allows for efficient review without significantly disrupting the overall flow. Filters are built to handle scale efficiently – whether you’re screening 30 or 300 long-list vendors, the evaluation structure remains intact.

In contrast, traditional RFPs typically start with a narrow vendor set of just 30 to 40 providers. If a promising candidate is identified outside of this initial pool during the evaluation, it often triggers major delays. The process may need to be paused, reevaluated, or restarted to ensure fairness and completeness. This lack of adaptability can create bottlenecks, frustrate stakeholders, and erode confidence in the process.

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4. Summarising the Metrics

To further illustrate the differences, here are three tables to compare the approaches:

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Recommendations and Closing Thoughts

For carriers and integrators involved in OSS/BSS procurement, the findings are:

• Rethink vendor discovery – expand your funnel using tools like the PAOSS Vendor Directory
• Apply structured filters early – include ALL known vendors, but then reduce options quickly but rationally
• Use adaptive ranking – not every decision needs exhaustive precision and not every requirement needs to be listed initially

The PAOSS Inverted Pyramid Approach doesn’t eliminate rigour. It redistributes it, front-loading intelligence where it has the most impact. In doing so, it avoids the sunk-cost traps and vendor fatigue of traditional procurement cycles.

Are you currently undergoing an OSS/BSS transformation or looking to find a best-fit solution to integrate into your operations stack?

Reach out to explore how the PAOSS Inverted Pyramid Approach can simplify your decision process, reduce time-to-value and dramatically improve your chances of selecting a vendor that will deliver success over the long term.