You're three demos into evaluating mobile measurement partners, and every vendor has shown you the same polished dashboard tour. They've all promised "seamless integration", "real-time attribution", and "industry-leading accuracy". The pricing conversations have been vague. The technical questions get redirected to "our solutions team will follow up". And you're no closer to understanding which platform will actually work when your performance team needs to reallocate ₹5 lakh in ad spend on a Tuesday morning based on yesterday's ROAS data.

This is the MMP evaluation trap that wastes weeks of procurement time across mobile-first companies. Teams sit through feature demonstrations that look identical, receive proposal decks with carefully worded capabilities statements, and end up choosing based on brand recognition or whoever responds fastest to Slack messages. Three months later, they discover the "unlimited data exports" have API rate limits, the "fast integration" requires four weeks of engineering work, and the pricing model penalises growth with unexpected overages.

The problem isn't that vendors are dishonest. It's that most evaluation processes ask the wrong questions. Checklist-style RFPs focus on feature parity (does it support Meta postbacks? yes/no) rather than operational reality (how long does Meta postbacks setup actually take your customers, and what breaks most often?). Demo scripts show best-case scenarios with clean data, not the messy attribution conflicts that appear when you're running Meta, Google, TikTok, influencer campaigns, and QR codes simultaneously.

Here are 15 questions that cut through polished presentations and expose what you'll actually experience as a paying customer. These aren't gotcha questions designed to embarrass sales teams. They're diagnostic questions that reveal pricing transparency, technical limitations, data access policies, and support quality before you sign a contract. Ask them in every demo. Compare answers across vendors. The responses will tell you more about product quality than any feature matrix.

Why Most MMP Evaluations Fail: Feature Lists vs Operational Reality

Most MMP evaluation processes follow a predictable pattern. Marketing sends out an RFP with 50 feature requirements. Vendors return proposals claiming they meet all 50. Procurement schedules demos where sales engineers show polished dashboards with sample data. IT reviews SDK documentation and confirms compatibility. Finance negotiates a discount. Everyone signs, assuming the tool will work as advertised.

Six weeks after implementation, the performance team discovers problems. The dashboard that looked intuitive in the demo requires 7 clicks to see yesterday's campaign-level ROAS. The "automated postbacks" need manual configuration for each new campaign. The data export that was supposed to feed your data warehouse hits rate limits after 1,000 API calls. Support tickets take 48 hours to get initial responses. And the pricing model you negotiated has hidden fees for features you assumed were included.

This happens because evaluation criteria focus on what the platform can theoretically do, not what it's actually like to use daily. A feature checklist tells you AppsFlyer, Branch, Adjust, and Linkrunner all support deep linking. It doesn't tell you that one platform requires separate tools for dynamic links and attribution while another unifies them, or that setup time ranges from 2 hours to 2 weeks depending on architectural decisions made years ago.

The questions below shift evaluation from feature existence to operational experience. They're designed to surface the friction points you'll encounter after contracts are signed: pricing surprises, implementation roadblocks, data access limitations, and support responsiveness. Pay attention not just to what vendors answer, but how they answer. Immediate, specific responses with real examples signal operational maturity. Vague promises to "work with your team" or "customise a solution" often mean the capability doesn't exist in the core product yet.

Category 1: Pricing Transparency (5 Questions That Expose Hidden Costs)

1. "What is your exact all-in price for 100,000 attributed installs per month, including all features, data exports, and API access?"

This question eliminates pricing ambiguity immediately. Many MMPs advertise starting prices (₹2 per install, $0.50 per install) but bury additional costs in contracts. You need the total monthly cost for your realistic volume, with zero exclusions.

Watch for responses that segment pricing by feature tier ("basic attribution is X, but deep linking is extra"), by data source ("that price covers Meta and Google, but TikTok integration costs more"), or by team size ("that's for 5 seats, additional users are ₹15,000 per seat monthly"). These aren't necessarily dealbreakers, but you need transparent accounting before comparing vendors.

The best answers provide a single number: "For 100,000 attributed installs monthly, your total cost is ₹80,000, which includes unlimited deep links, all postback configurations, unrestricted API access, and data exports. No seat limits, no feature paywalls, no overage fees until you hit 150,000 installs." This clarity lets you model costs as you scale and compare vendors accurately.

Refer to Linkrunner’s super simple pricing breakdown.

2. "Are there any features, integrations, or data access capabilities that cost extra beyond the base price you just quoted?"

Even after getting an all-in price, probe for exceptions. Some platforms charge separately for:

• Fraud prevention modules

• Advanced analytics dashboards

• Premium support SLAs

• Custom attribution models

• Data warehouse connectors

• Historical data retention beyond 90 days

• White-label or custom domain options

The goal isn't to eliminate all add-on costs (some legitimate services warrant separate fees), but to know the complete pricing structure upfront. A vendor who says "everything is included" and later invoices you for fraud detection has damaged trust before you've run a single campaign.

3. "What happens to our pricing if we grow from 100,000 to 300,000 attributed installs? Is there a volume discount, or does per-install pricing stay flat?"

This question reveals whether the vendor's incentives align with your growth. The ideal answer includes volume-based pricing tiers that reduce per-unit costs as you scale. For example: "Your current rate is ₹0.80 per install. At 200,000 monthly installs, you'd drop to ₹0.70 per install. At 500,000, you'd pay ₹0.60 per install."

Watch for platforms that penalise growth with flat or increasing per-unit costs. If you're paying ₹2 per install regardless of volume, a successful quarter where you triple user acquisition also triples your MMP bill with no efficiency gain. That model works against performance marketing teams who need to scale winners aggressively.

Also ask about contract lock-in at scaled pricing. Some vendors offer attractive rates at high volume but require annual commitments. If your installs drop seasonally or a key channel underperforms, you're stuck paying for capacity you're not using.

4. "Do you charge separately for SDK bandwidth, postback volume, or API calls? If so, what are the limits and overage costs?"

This is where hidden costs accumulate invisibly. Your MMP's SDK runs in every user's app, sending events to their servers. Some platforms meter this traffic and charge overages when you exceed thresholds. Similarly, postback volume (the number of conversion events sent to ad networks) and API calls (how often you pull data into your own systems) may have caps.

A transparent answer specifies limits and costs: "You get 10 million SDK events monthly included. Beyond that, it's ₹0.001 per event. API calls are rate-limited to 100 requests per minute with no additional fees. Postbacks to ad networks are unlimited and included."

Red flag responses include: "We'll customise limits based on your needs" (which means they'll estimate low and charge overages later), or "SDK bandwidth is unrestricted" without defining what triggers an overage (often it's tied to install volume in ways that aren't obvious).

If you send detailed event taxonomies (50+ event types per user session) or run heavy experimentation (frequent API pulls for real-time optimisation), these costs compound quickly. Make sure the pricing model supports your actual usage patterns, not just baseline attribution.

5. "If we need to pause or downgrade our account, what is the notice period and are there early termination fees?"

This question tests vendor confidence in retention through product value rather than contractual lock-in. The best platforms allow monthly commitments or have reasonable notice periods (30-60 days) with no penalties for pausing during seasonal lulls or switching to a smaller plan.

Concerning responses include 12-month minimum commitments with no mid-contract downgrades, or early termination fees equivalent to 50-100% of remaining contract value. These terms trap you in expensive platforms even when they're not delivering value.

Also ask what happens to your data if you pause or cancel. Do you retain API access to historical data? Can you export your full dataset before termination? Some vendors restrict data access immediately upon cancellation, which creates leverage in renewal negotiations but leaves you unable to analyse past campaigns or migrate cleanly to alternatives.

Understanding exit costs and data retention policies before signing gives you negotiating power and prevents lock-in regret.

Category 2: Technical Implementation (4 Questions About SDK, Postbacks, and QA)

6. "What is the realistic timeline from SDK integration to receiving accurate attributed data in our dashboard, including QA and validation?"

Vendor documentation might claim "integrate in 30 minutes", but that's misleading. A realistic implementation includes SDK installation, deep link configuration, event mapping, postback setup for each ad network, QA across iOS and Android, and validation that attribution matches expected patterns.

Ask for a median timeline based on actual customer implementations, not best-case scenarios. A honest answer sounds like: "Most teams with existing apps see attributed data within 24-48 hours of SDK installation. Complete setup including all postbacks, deep links, and QA typically takes 1-2 weeks with 8-12 hours of engineering time total."

Red flag responses cite best-case examples ("one customer went live in 3 hours") without acknowledging that they had unusual circumstances, or provide vague estimates ("it depends on your team's velocity") that shift accountability to you.

Also ask what commonly breaks during implementation. Every platform has edge cases. Vendors who honestly discuss common QA issues (Android App Links verification failing, postback delays on TikTok, SKAN conversion value mapping confusion) demonstrate they've supported real implementations and have debugging protocols.

7. "How do we validate that attribution is working correctly before we trust it for budget decisions? What QA tools or processes do you provide?"

This separates mature platforms from ones that assume everything works after SDK installation. Attribution accuracy isn't binary (working/broken); it exists on a spectrum where 60% match rate might be acceptable for some use cases but catastrophic for others.

Strong answers include built-in validation tools: "Our dashboard has an attribution QA section showing click-to-install match rates by source, conversion delays by network, and discrepancy reports comparing our numbers to what Meta and Google report. We also provide test device registration so you can trigger attribution events manually and verify they appear correctly."

The best platforms also offer validation playbooks: specific steps to verify deep links route correctly, check that SKAN postbacks decode properly, confirm fraud filters aren't blocking legitimate traffic, and reconcile install counts across your MMP and ad platforms.

Weak responses deflect to general statements like "our attribution is industry-leading" without giving you tools to verify that claim independently. If a vendor can't explain how you validate their data accuracy, you're trusting their engineering completely with no ability to diagnose problems when numbers don't match expectations.

8. "What SDK maintenance is required as iOS and Android release new versions? How quickly do you support new OS features like SKAN updates?"

SDKs aren't set-and-forget infrastructure. iOS and Android release major updates annually, often with attribution implications (new privacy frameworks, deprecated APIs, changed behaviours). Your MMP needs to update their SDK to maintain compatibility and add support for new measurement capabilities.

Ask about their release cadence and backwards compatibility guarantees. A good answer: "We release SDK updates within 2-4 weeks of major iOS/Android releases. Our SDKs maintain backwards compatibility for 3 versions, so you're not forced to update immediately. When Apple announced SKAN 4.0, we had a compatible SDK in beta 2 weeks before iOS 16.1 launch."

Also clarify how updates are communicated and whether they require app resubmission. Some SDK updates are server-side (no action needed from you), while others require code changes and App Store review cycles. Knowing this helps you plan maintenance windows and avoid being caught off-guard by breaking changes.

Finally, ask about SDK size and performance impact. Lightweight SDKs (under 1MB, minimal battery/data usage) integrate easily and don't slow your app. Bloated SDKs (5MB+, constant background activity) face resistance from engineering teams concerned about app performance and user experience.

9. "How do you handle attribution conflicts when multiple touchpoints claim credit for the same install?"

This is a core technical question that reveals attribution logic quality. In real campaigns, users often click multiple ads before installing. A user might click a Meta ad on Monday, search Google on Tuesday, click a TikTok ad Wednesday, and install Thursday. Which source gets credit?

Different MMPs use different logic. Some use simple last-click attribution. Others use configurable windows (last click within 7 days wins). Advanced platforms offer probabilistic models that assign fractional credit.

The answer you want includes specificity about tiebreaker logic: "We use last-click attribution within a configurable window (default 7 days for clicks, 1 day for views). If multiple clicks occur within the window, the most recent click wins. You can adjust windows by source (Meta gets 7 days, organic search gets 1 day) to match expected user behaviour in your category."

Also ask how they handle edge cases. What happens if a user clicks an ad, uninstalls, then reinstalls 3 weeks later? Do they attribute the reinstall to the original ad, or treat it as organic? What about users who clear cookies or switch devices? These scenarios happen constantly at scale. Platforms with mature logic have defined handling; newer platforms often haven't thought through edge cases.

Category 3: Data Access and Exports (3 Questions About Lock-In Risk)

10. "Can we export our complete raw attribution data (clicks, installs, events, revenue) without restrictions, rate limits, or additional fees?"

Data portability is crucial for three reasons. First, you may want to analyse attribution data in your own BI tools (Looker, Tableau, Power BI) alongside other business metrics. Second, you might build custom models or ML pipelines that need raw event streams. Third, if you ever migrate to a different MMP, you need historical data to compare before/after accuracy and maintain continuity.

The ideal answer is unrestricted access: "You can export unlimited raw data via CSV downloads, scheduled reports, or our API. No rate limits on exports. No additional fees. Your data belongs to you, and we facilitate access however you need it."

Concerning responses include rate limits that make large exports impractical ("100 API calls per hour, with each call returning max 1,000 rows"), fees for bulk exports ("CSV downloads are ₹5,000 per export for data beyond 90 days"), or restrictions on data types ("you can export aggregated reports, but raw event-level data requires an enterprise contract").

These limitations create vendor lock-in by making it expensive or time-consuming to move your data elsewhere. Platforms confident in retention through product value don't restrict data access. Those relying on switching costs often do.

11. "Do you provide real-time webhooks or data streams for ingesting attribution events into our data warehouse?"

Beyond periodic exports, real-time data access enables advanced use cases. Teams with modern data stacks (Snowflake, BigQuery, Redshift) want attribution events flowing continuously into their warehouse where they can join them with CRM data, product analytics, customer support tickets, and financial records.

The best platforms offer flexible ingestion options: "We provide webhooks that push attribution events to your endpoint in real-time, plus native connectors for Snowflake, BigQuery, and Redshift. You can also poll our API for updates every few minutes if you prefer pull-based ingestion."

If real-time streaming isn't available, ask about batch export frequency. Daily batches might be sufficient for weekly budget reallocation decisions but inadequate for real-time bidding optimisation or fraud detection alerts.

Also clarify webhook reliability. Do they guarantee delivery with retry logic, or could events be lost if your endpoint is temporarily down? Do they provide webhook logs so you can audit what was sent? These details matter when you're building critical infrastructure on top of their data feeds.

12. "If we migrate to a different MMP in the future, what data can we take with us and in what format?"

This question signals you're evaluating long-term flexibility, not just committing blindly. Vendors who bristle at migration questions often have retention problems. Confident platforms welcome the question because they know customers stay for product value, not data hostages.

A complete answer specifies exactly what's exportable: "You can export all historical attribution data (clicks, installs, in-app events, revenue) as CSV or via API, all campaign configurations and deep link mappings as JSON, and all dashboard report definitions. We'll provide a migration guide and support calls to help you transition smoothly if you decide to switch providers."

Some platforms make migration deliberately painful by restricting historical data exports to recent periods (last 90 days only), charging hefty fees for bulk data downloads, or formatting data in proprietary schemas that don't map cleanly to competitors' data models.

Also ask if they provide migration support or just data dumps. The best vendors offer transition assistance (helping you validate that your new MMP is tracking accurately before you fully switch over) because they're confident you'll either come back or recommend them despite leaving.

Understanding export capabilities and migration support before signing prevents situations where you're stuck with an underperforming platform because switching would lose 18 months of historical attribution data that finance needs for cohort analysis.

Category 4: Support and SLAs (3 Questions That Reveal True Responsiveness)

13. "What is your median first response time for support tickets, and do you have formal SLAs?"

Support quality varies dramatically across MMP vendors. Some treat support as a profit centre, reserving fast response times for enterprise customers paying extra for premium SLAs. Others provide responsive support as part of base pricing because they recognise that attribution issues directly impact marketing spend decisions.

Ask for specific response time commitments: "What's your median time to first response for a P1 issue (attribution completely broken, can't track any installs) versus a P3 issue (dashboard feature request)?"

Strong answers include guaranteed SLAs with consequences: "P1 issues get a response within 2 hours 24/7. P2 issues (partial breakage, specific network not tracking) get a response within 8 business hours. P3 issues (feature requests, how-to questions) get a response within 24 hours. If we miss these SLAs, you receive service credits."

Weak answers are vague: "We respond as quickly as possible" or "it depends on the issue complexity". These responses mean support is best-effort with no accountability.

Also ask about support channels. Is it email-only, or can you reach them via Slack, phone, or video calls? For time-sensitive attribution issues when you've just launched a campaign spending ₹2 lakh daily, waiting 24 hours for an email response isn't acceptable. Real-time support channels (shared Slack channels, dedicated account managers) provide faster resolution.

14. "Can you share examples of the most common support issues your customers face and how long they typically take to resolve?"

This question reveals product maturity and vendor honesty. Every platform has recurring issues. Vendors who candidly discuss common problems and resolution times demonstrate they've invested in support infrastructure and aren't hiding product gaps.

A transparent answer sounds like: "The most common issues are SKAN postback delays (usually resolved within 1 business day by adjusting conversion value mapping), deep link routing failures on specific Android devices (resolved within 2-3 days with SDK configuration changes), and discrepancies between our install counts and ad platform reports (typically requires 3-5 days to investigate attribution logic and reconcile, often due to different deduplication windows)."

This response shows they've categorised and tracked support issues, built solutions for common problems, and can estimate resolution times based on historical data.

Red flag responses include claiming they have no common issues ("our customers rarely need support"), deflecting to user error ("most issues are integration mistakes"), or being unable to estimate resolution times ("it varies case by case"). These responses suggest either they haven't scaled enough to see patterns or they're avoiding discussing known product limitations.

15. "Do you provide onboarding support, ongoing training, and technical account management, or is support purely reactive ticket-based?"

This differentiates platforms that view support as cost centres versus strategic customer success functions. The best MMPs provide proactive support: onboarding calls to ensure correct implementation, ongoing training as new features launch, regular account reviews to optimise your attribution setup, and dedicated technical contacts who understand your business.

A strong answer outlines a support journey: "Every customer gets a 2-week onboarding program including 3 implementation calls, QA validation sessions, and configuration review. After launch, you have a dedicated account manager who schedules monthly check-ins to review your setup, suggest optimisations, and train your team on new features. You also get access to our Slack community where our product team answers questions in real-time."

Basic reactive support is: "You can submit tickets anytime and we'll respond based on SLAs. We have a documentation site and video tutorials for self-service help."

For growing teams spending ₹10-50 lakh monthly on user acquisition, proactive support accelerates time-to-value and prevents expensive misconfigurations. For example, an account manager might notice you're tracking 50 in-app events but only sending 3 to Meta for optimisation, and proactively suggest updating your postback configuration to improve algorithmic learning.

If you're evaluating affordable MMPs like Linkrunner versus enterprise platforms, understand that support models differ. Enterprise MMPs often charge ₹5-10 lakh annually for premium support packages that include dedicated account management. Platforms like Linkrunner typically include responsive technical support at base pricing tiers (with response SLAs) but may not assign dedicated account managers unless you're at higher volume. Decide which model fits your team's needs and budget.

Red Flag Responses That Should End the Conversation

Beyond the specific questions above, watch for response patterns that indicate deeper problems:

Inability to provide specific numbers. If a vendor can't tell you exact pricing, median support response times, or typical implementation timelines, they either lack operational data (which suggests they haven't scaled) or they're deliberately obscuring unfavourable metrics.

Deflection to "it depends on your needs." While some customisation is legitimate, overuse of this phrase often masks the absence of standard solutions. If everything requires custom configuration, you'll spend weeks in professional services conversations and end up with a bespoke setup that's expensive to maintain.

Competitive trash-talking without specifics. Vendors who spend demo time criticising competitors rather than demonstrating their own capabilities are often weak on product differentiation. It's fine to acknowledge competitive differences ("our SKAN implementation is more automated than X"), but prolonged competitor criticism signals insecurity.

Pressure tactics and artificial urgency. Responses like "this pricing is only available if you sign by Friday" or "we have limited onboarding slots this quarter" are sales manipulation, not genuine constraints. Reputable vendors give you time to evaluate thoroughly.

Requiring contract signatures before technical deep-dives. If a vendor won't let your engineering team review SDK documentation, API specs, or architecture diagrams without an NDA and contract in place, they're hiding something or creating artificial barriers to evaluation.

Vague answers to data access questions. If you ask "can we export raw attribution data" and get responses like "we'll work with you on data sharing" instead of clear yes/no with specifics, assume the answer is no but they want to delay the conversation until you're committed.

Trust your instincts. If answers feel evasive, sales tactics feel manipulative, or you're getting different information from sales versus technical teams, those are signals to proceed cautiously or eliminate the vendor from consideration.

Putting This Into Practice: Your MMP Evaluation Checklist

Schedule at least three vendor demos. More than five becomes diminishing returns unless you have highly specific requirements.

Before each demo, send the vendor these 15 questions in advance. This gives them time to prepare specific answers rather than improvising during the call. It also signals you're a sophisticated buyer who values operational details over feature marketing.

During the demo, take notes on answers but also note response style. Are they specific and confident, or vague and evasive? Do they acknowledge tradeoffs honestly, or claim their platform is perfect for everything? Do technical and sales team members give consistent answers, or do you hear contradictions?

After each demo, score the vendor using the framework above. Document specific quotes and claims so you can reference them during contract negotiations.

For final evaluation, don't just compare scores. Consider your team's specific needs:

• If you're a small team with limited engineering resources, prioritise fast implementation and proactive support over raw feature depth.

• If you're scaling aggressively, prioritise volume-based pricing discounts and data access for your own analytics infrastructure.

• If you're running complex multi-channel campaigns, prioritise attribution conflict resolution logic and fraud prevention capabilities.

No single MMP is universally "best". The right choice depends on your budget, growth stage, technical sophistication, and campaign complexity.

Making the Final Decision

The questions in this guide help you move beyond surface-level feature comparisons to understand what daily MMP usage actually feels like. Pricing transparency prevents budget surprises. Technical deep-dives reveal implementation effort and QA requirements. Data access questions protect against lock-in. Support questions ensure you'll get help when attribution breaks at the worst possible time.

When you ask these questions across vendors, patterns emerge. Some platforms excel at transparent pricing but require significant technical implementation work. Others offer turnkey setup but restrict data access. A few manage to deliver across all categories by building customer-centric products rather than maximising revenue extraction.

Teams evaluating Linkrunner alongside legacy MMPs often note pricing transparency as a key differentiator. When you ask Linkrunner "what's the all-in cost for 100,000 attributed installs?", the answer is straightforward: ₹80,000 monthly with unlimited deep links, unrestricted API access, and no hidden fees. Request a demo from Linkrunner to see how this pricing clarity and simplified implementation translates into faster time-to-value compared to platforms that require weeks of contract negotiations before you even see real pricing.

The right MMP becomes invisible infrastructure that your performance team trusts completely. You stop worrying about whether attribution is accurate and start focusing on creative testing, audience expansion, and budget optimisation. You spend less time reconciling discrepant install counts across platforms and more time finding campaigns that actually drive revenue.

Bad attribution doesn't just waste the MMP subscription cost. It cascades into misallocated marketing spend, missed optimisation opportunities, and lost confidence in performance marketing as a growth channel. Choosing carefully now prevents expensive mistakes later.

Use these questions. Score honestly. Choose the platform that demonstrates operational maturity through transparent answers, not the one with the most polished sales deck.

Frequently Asked Questions

How many MMP demos should I schedule before making a decision?

Three to five is optimal. Fewer than three doesn't give you enough comparison points to understand market norms versus outlier practices. More than five creates analysis paralysis and wastes your team's time on incremental differences. Schedule demos with one legacy enterprise MMP (AppsFlyer, Adjust, or Branch), one or two affordable alternatives focused on specific markets or use cases (like Linkrunner for India-first mobile apps), and one specialist if you have unique needs (gaming-specific attribution, CTV focus, etc.).

Should I send these questions to vendors before the demo or ask them live?

Send them in advance. This gives vendors time to prepare specific answers with real data rather than improvising generic responses. It also demonstrates you're a sophisticated buyer focused on operational details, which often prompts vendors to bring technical team members to the demo rather than just sales reps. You'll get better information and save time.

What if a vendor refuses to answer pricing questions until after a "discovery call"?

This is a red flag that suggests complex or unfavourable pricing that they want to justify through relationship-building first. Some enterprise MMPs do require understanding your scale before quoting (which is reasonable for highly variable usage), but they should still provide pricing ranges or example scenarios upfront. If they're completely opaque about costs until you've invested hours in calls, consider whether you want to work with a vendor who doesn't respect your evaluation time.

How do I validate the claims vendors make during demos?

Ask for customer references at similar scale and use cases, then actually call those references with specific questions about the claims. For example, if a vendor says "implementation takes 2-4 hours typically", ask references how long it actually took them, what broke during setup, and how much ongoing maintenance is required. References selected by vendors will be positive overall, but you can still extract honest operational details through specific questioning. You can also review third-party comparisons and migration guides that analyse actual customer experiences across platforms.

What's a reasonable budget for MMP costs as a percentage of overall marketing spend?

General industry guidance suggests MMPs should consume 2-5% of your total user acquisition budget. If you're spending ₹20 lakh monthly on ads, your MMP should cost ₹40,000-₹1,00,000 monthly. Below 2% might mean you're underinvesting in attribution infrastructure (risking data quality issues). Above 5% often indicates you're overpaying for capabilities you don't need, or you're too early-stage for a paid MMP and should consider free tiers or simpler solutions. For specific benchmarks on when to adopt an MMP based on your current scale, evaluate both your install volume and campaign complexity.

If I'm currently using Firebase Analytics and dynamic links are being deprecated, should I move to an MMP or build custom attribution?

For most teams, adopting a dedicated MMP is more cost-effective than building custom attribution infrastructure. Custom solutions require ongoing engineering maintenance (adapting to iOS/Android changes, building fraud prevention, maintaining SDK compatibility, supporting new ad networks), which compounds to more than MMP subscription costs unless you're at massive scale (1M+ monthly installs). Modern affordable MMPs provide complete attribution and deep linking for less than the cost of one engineer's time, letting your team focus on growth rather than maintaining measurement infrastructure.

How long should the MMP evaluation process take from first demo to signed contract?

Plan for 3-4 weeks minimum. Week 1: Send questions and schedule 3-5 vendor demos. Week 2: Conduct demos and follow-up technical calls. Week 3: Reference calls, SDK technical review by engineering, and internal scoring/comparison. Week 4: Contract negotiation and legal review. Rushing evaluation to save a week often leads to discovering critical limitations after signing. That said, don't let evaluation drag beyond 6 weeks unless you have genuinely complex requirements. Excessive evaluation time signals indecision or internal misalignment about attribution priorities.

What should I do if I'm locked into a legacy MMP contract but unhappy with service?

First, review your contract's termination clauses and data export rights. Some contracts allow downgrades mid-term or have out clauses if SLAs are consistently missed. Second, begin exporting your historical attribution data now so you have it available regardless of contract status. Third, run a parallel evaluation with alternative MMPs (you can use free tiers or trial periods to test) so you're ready to switch immediately when your contract expires. Finally, document specific service failures (support response times, attribution accuracy issues, pricing surprises) to use as negotiation leverage during renewal discussions or as justification to your finance team for early termination fees if switching mid-contract makes financial sense.

Are there differences in evaluation criteria for B2C consumer apps versus B2B or enterprise apps?

Yes. B2C apps typically need broader channel coverage (Meta, TikTok, influencer, QR, offline), higher fraud prevention sensitivity (more volume means more fraud attempts), and cost efficiency at scale (you're driving 100,000+ installs monthly). B2B and enterprise apps often prioritise longer attribution windows (B2B sales cycles extend weeks or months, not hours), integration with sales CRM systems (linking ad clicks to Salesforce opportunities), and account-based attribution (tracking company-level engagement, not just individual installs). Make sure the vendors you evaluate have strong capabilities in the areas your business model requires, rather than just generic mobile attribution features.