Prescient
"Optimize marketing spend using data-driven insights."
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Updated 3 Days Ago
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3 Advocates
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2 Verified Clients
Prescient AI is a marketing decision engine that leverages marketing mix modeling (MMM). It forecasts and optimizes top-of-funnel ad spending. The platform provides both short-term and long-term analysis. Daily-refreshing models help tailor campaigns to maximize every marketing dollar. It aligns with multi-touch attribution (MTA) for precise adjustments.
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Operations
at a Glance
Who are your typical customers?
Small businesses
Medium businesses
Large businesses
What verticals do your typical customers belong to?
Retail & E-commerce
Healthcare & Life Sciences
Financial Services
How do you collect and monitor consumers and their data?
Behavioral
API
Is your platform CCPA and GDPR compliant?
Yes
What is your standard 'time-til-live' for clients?
Days
What does a client need to install to start using your service?
Manual API Integration
How do you provide customer service?
Dedicated account manager
Live chat
Email support
How do brands measure the success of a campaign using your platform?
Performance
Revenue Growth
Brand Awareness
What integrations do you offer?
E-Commerce Platforms
CDPs
Digital Advertising
What is your pricing model?
Subscription-based
Prescient's Website Claims...
Our bot logged these claims from Prescient's website.
smartest marketing decision engine
Prescient AI claims that its marketing decision engine is the smartest in the world, allowing for unparalleled forecasting, testing, and optimization of marketing spend.
dynamic, daily-refreshing model
Prescient AI claims that its MMM offers a dynamic, daily-refreshing model that integrates with MTA for real-time feedback, ensuring quick optimization of marketing campaigns.
cookie-free value measurement
Prescient AI claims that its solution helps measure marketing value without cookies, preparing businesses for a future where many platforms won't rely on them.
BlurbSTAR Case Study
Eko Health & Prescient AI
Prescient doubled Eko's ROAS via optimized marketing mix models.
2x
ROAS increase
48%
revenue lift
1.
Situation
Eko Health's Advertising Challenge
→
Need for better revenue incrementality
→
Tracking tools were becoming unreliable
→
Desire for specific, rapid insights
Eko Health designs and develops digital stethoscope technology, utilizing AI-powered algorithms to help healthcare professionals detect various heart diseases. Eko wanted to leverage Marketing Mix Models (MMM) to generate incremental revenue. Despite their satisfactory performance data from Google Analytics and other channels, the team suspected there were additional efficiencies they could uncover through predictive modeling. As traditional tracking tools became less reliable, Joe Tertel, Senior Director of Consumer and Performance Marketing at Eko, sought a faster, more precise solution for their unique product line.
2.
Task
Adopt Effective MMM Solution
→
Optimize budget utilization
→
Seek in-depth, timely insights
→
Measure non-Amazon campaign impact
Eko Health aimed to find an MMM that provided comprehensive and granular insights into their budget and media spend's impact on revenue. The goal was to discover a tool that offered quick, hyper-specific recommendations tailored to Eko's unique requirements, allowing them to optimize their ad spend efficiently. They needed a solution that reduced dependency on external data models and provided credit to campaigns run on non-major media channels like Amazon.
3.
Action
Implementation of Prescient AI
→
Seamless integration with rapid insights
→
Granular top-of-funnel revenue analysis
→
Accurate, real-time budget recommendations
Eko Health integrated Prescient AI’s custom-built MMM solution that provided near-perfect spend forecasting. On the same day of data connection, actionable recommendations began to flow in. Eko implemented these recommendations within the first week, witnessing a positive impact on ROAS metrics. Prescient’s platform detailed insights across campaigns and channels, highlighting immediate and future revenue impacts. Granular top-of-funnel insights helped Eko optimize spend in real-time, supported by highly accurate budget allocation algorithms. Prescient's forecasting accuracy reached within 2% of real data up to 84 days out.
4.
Result
Increased Revenue and Efficiency
→
30% increase in ad spend
→
48% revenue lift and 2x ROAS
→
Ongoing granular insights supporting growth
Eko Health increased its ad spend by 30%, capitalizing on the insights provided by Prescient. The implementation of Prescient’s MMM led to significant improvements: a 48% revenue lift, a doubling of ROAS, a 43% growth in website traffic, and more efficient ad spend. Prescient’s platform proved vital for continuous campaign optimization by illustrating how media spend impacted incremental revenue. The Eko team is now leveraging these insights for even more granular modeling, supporting their growth targets with enhanced precision.
Keywords
EKO HEALTH
PRESCIENT AI
MARKETING MIX MODELS
INCREMENTAL REVENUE
ROAS
AD SPEND OPTIMIZATION
AI-POWERED ALGORITHMS
PREDICTIVE MODELING
DIGITAL PERFORMANCE MARKETING
CAMPAIGN OPTIMIZATION
Ad Measurement & Attribution Q & A
Answers provided by Prescient
staff
and
vetted by Blurbs.
1.
How do you track and attribute conversions across different channels (DTC, Amazon, Walmart, Target, etc.)?
"MMM powered "halo-effects," which redistributes “credit” from bottom-of-funnel (ex: branded search) to top-of-funnel campaigns (ex: CTV, Podcast, Influencer). These "halo-effects" extend across DTC, Marketplaces, and Retail/Wholesale."
2.
What attribution models do you support (last-click, multi-touch, data-driven, incremental, media mix modeling)?
"Our core measurement is Media Mix Modeling (MMM), but we allow marketers to validate any form of measurement into our models, i.e., incrementality test, post-purchase surveys, MTA, etc."
3.
How do you handle first-party data, and do you integrate with our CRM, CDP, or customer databases?
"We have pipelines and are agnostic to ingest data from any source. We have pre-built integrations to access 1st and 3rd party data across 75 integrations."
4.
Can your platform track users without relying on third-party cookies, and how do you handle privacy compliance (GDPR, CCPA)?
"We do not track individual users that contain any PII. Our models look at aggregated (daily, weekly, monthly, and annually) revenue, orders, customers, and media spend."
5.
Do you provide Amazon Attribution or retail media tracking across different marketplaces?
"Yes, this is our most significant value proposition via our "halo-effects" for omnichannel brands.
Brands benefit from our ability to measure, forecast, and optimize spend at the campaign level across all sales channels."
Brands benefit from our ability to measure, forecast, and optimize spend at the campaign level across all sales channels."
6.
Can your solution measure the impact of ads on both online and offline sales?
"This is our most significant value proposition via our "halo-effects" for omnichannel brands.
Brands benefit from our ability to measure, forecast, and optimize spend at the campaign level across all sales channels."
Brands benefit from our ability to measure, forecast, and optimize spend at the campaign level across all sales channels."
7.
What integrations do you offer with ad platforms (Google, Meta, TikTok, Amazon Ads, DSPs, etc.)?
"There are too many to list here, but all the common ones, especially those associated with top-of-funnel channels and eCommerce platforms.
You can find the full list of integrations:
https://prescientai.com/platform/integrations"
You can find the full list of integrations:
https://prescientai.com/platform/integrations"
8.
How frequently is attribution data updated, and do you offer real-time reporting?
"Our models can refresh at the marketer's desired cadence. Uniquely, we can refresh daily at the campaign level."
9.
How do you measure incrementality, and do you support A/B testing or holdout groups?
"Marketers can share holdout results, and we’ll validate the impact in MMM via accuracy scores from backtesting. Our MMM is measurement-agnostic, letting them test which method drives the highest accuracy to inform our optimization product."
10.
What is your pricing model, and how does it scale with our business?
"It is a SaaS platform based on the historical GMV broken down by each sales channel: DTC, marketplace, and Offline (retail/wholesale)."