What is the primary problem Pecan AI solves?

Pecan AI addresses the bottleneck in enterprise AI adoption: the high cost and scarcity of data scientists with domain expertise. According to Slide 2, business teams want AI, but the traditional path is too expensive and difficult. Pecan solves this by providing a low-code platform that allows existing BI analysts to perform data science tasks.

How does Pecan AI differentiate its technology?

The deck identifies three 'key tech moats' on Slide 7. First is 'Drag & Drop-to-AI' technology using SQL. Second is automated data preparation and feature engineering, including cleansing and embedding. Third is automated deployment and monitoring with continuous database resampling and retraining, all performed without manual engineering.

What kind of ROI can customers expect based on this deck?

Pecan provides two detailed case studies. Slide 9 shows a mobile app company achieving a 37% uplift in revenue per user and a 91% increase in purchases. Slide 10 highlights a retailer that saw a 35% increase in sales conversion and a 20% improvement in sales representative productivity.

Who is the target user for the Pecan platform?

The platform is designed for two primary personas within a company, as shown on Slide 3. The 'Action Owner' is the business stakeholder who owns the KPI and takes action on insights. The 'Builder' is a BI or data analyst who understands the data and uses Pecan to build the models.

What is missing from this pitch deck?

This is a product and results-focused deck that omits several standard fundraising elements. There is no market sizing (TAM/SAM/SOM), no competitive matrix, no financial roadmap or projections, and no slide detailing the specific 'Ask' or use of funds for the current round.

Pecan AI Pitch Deck (2016): 11-Slide Series C Deck

See all 11 slides of the Pecan AI pitch deck — a 2016 Series C deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Pecan AI’s pitch deck is a masterclass in functional simplicity, focusing heavily on the 'how' and 'who' of their low-code predictive analytics platform. By positioning their product as an 'AI bridge' between business owners and analysts, they address the talent scarcity in data science. The deck, dated February 2021, successfully raised significant capital by demonstrating clear use cases—such as a 37% revenue uplift for a mobile app company—and a robust technical moat including automated feature engineering. While the deck lacks a formal 'Ask' slide or detailed market sizing, its technical…

Key takeaways

The core value proposition is 'Predictive analytics without data scientists,' directly addressing the scarcity of domain-expert data scientists (Slide 1, 2).
Pecan positions itself as a low-code 'AI bridge' that allows BI analysts to build and deploy models (Slide 3).
The team is anchored by two PhDs in AI and former members of the 8200 unit, signaling deep technical credibility (Slide 4).
The platform automates the entire 'AI value chain,' including data restructuring, encoding, and feature engineering (Slide 6).
Technical moats are defined by three pillars: Drag & Drop-to-AI, automated data prep, and continuous model retraining (Slide 7).
The business model relies on a subscription per use case and per entity, with expansion through increased usage (Slide 8).
Case studies demonstrate specific ROI, such as a 91% increase in purchases for a mobile app company (Slide 9).
Retailer impact is quantified as a 35% boost in sales conversion and a 20% increase in rep productivity (Slide 10).

Pecan AI: The Low-Code Predictive Analytics Playbook

Pecan AI’s pitch deck, dated February 2021, represents a specific moment in the evolution of enterprise AI. At only 11 slides, it is remarkably lean for a company that has raised a total of $116,000,000 according to catalogue data. The deck focuses almost exclusively on the product's ability to bridge the gap between business needs and technical execution. By targeting the 'BI Analyst' rather than the 'Data Scientist,' Pecan carves out a unique space in the crowded analytics market.

The Problem and the 'AI Bridge'

Slide 1: Title The deck opens with a clear, punchy subtitle: "Predictive analytics without data scientists." This immediately identifies the target audience and the primary pain point.

Slide 2: The Problem The problem is framed simply. Business teams want AI, but data science is "Hard and expensive" and requires "scarce" experts with domain knowledge. This slide sets the stage for a tool that democratizes access to complex modeling.

Slide 3: The Solution Pecan introduces the concept of an "AI bridge." It visualizes two personas: the Action Owner (business stakeholder) and the Builder (BI analyst). The slide maps out a six-step workflow from defining the use case to monitoring impact, all happening within the Pecan environment. This is a crucial slide because it explains the operational reality of using the software.

Team and Technical Foundation

Slide 4: The Team Pecan highlights a "product-first, results-oriented team." The founders, Zohar Bronfman (CEO) and Noam Brezis (CTO), both hold PhDs in AI and are alumni of the elite 8200 unit. The slide also notes that the company was founded in April 2018 and had raised $16.5M to date from investors including S-Capital, Dell Capital, and Mindset Ventures at the time this deck was produced.

Slide 5: Platform Overview This slide serves as a high-level architecture diagram. It shows data flowing from Warehouses, CRM, ERP, and External sources into the Pecan Platform for "Data stitching" and "Model building." The output is categorized into four buckets: Conversion/Churn/LTV, Next-best offer, Upsell/Cross-sell, and Demand/Sales forecasting.

Slide 6: The AI Value Chain This is the most technical slide in the deck. It breaks down the "AI value chain" into three phases: Preparation, Modeling, and Action. It lists specific automated tasks like Data Restructuring, Imputation, Feature Engineering, and Impact Evaluation. The claim is that this entire chain is automated "in a matter of hours."

Moats and Monetization

Slide 7: 3 Key Tech Moats Pecan explicitly labels its competitive advantages. These include Drag & Drop-to-AI (allowing SQL-only builds), automated feature engineering, and continuous model retraining. A "Patent Pending" seal is prominently displayed at the bottom, reinforcing the idea of proprietary intellectual property.

Slide 8: Business Model The pricing strategy is straightforward: a subscription based on the number of use cases and entities. Growth is driven by "Upsell from customer growth" and the addition of new use cases. This suggests a land-and-expand strategy common in enterprise SaaS.

Proof of Concept: Case Studies

Slide 9: Case Study - Mobile App Pecan uses a "Challenge, Solution, Impact" framework. For a mobile app company, they moved from "BI-based segmentation" to "Scalable AI capabilities." The results are quantified: a 37% uplift in revenue per user and a 91% increase in purchases. The timeline for this transformation is noted as "10 Days."

Slide 10: Case Study - Retailer The second case study focuses on a retailer moving from "Manually prepared spreadsheets" to "Fully integrated recommendations." The impact listed is a 35% increase in conversion and a 20% increase in sales rep productivity. These specific, high-digit percentages are designed to prove the platform's efficacy in diverse sectors.

Slide 11: Contact A simple thank you slide with the company website and a general info email address.

What Works in the Pecan AI Deck

The deck is exceptionally disciplined. It does not waste time on the history of AI or broad market trends that investors already understand. Instead, it focuses on the specific friction point of AI adoption: the human talent bottleneck. By explicitly naming the "BI Analyst" as the user, Pecan makes its market entry strategy very clear.

The use of specific ROI metrics in the case studies (Slides 9 and 10) is a major strength. Claiming a "37% uplift" or a "91% increase" provides concrete numbers for an investor's model. Furthermore, the technical breakdown on Slide 6 provides enough detail to satisfy a technical due diligence lead without overwhelming a generalist partner.

What is Missing from the Pecan AI Deck

Despite its success in raising capital, this deck leaves several standard questions unanswered. There is no Market Size (TAM) slide. While the problem is universal, the deck doesn't quantify how many companies have the specific data infrastructure required to plug into Pecan.

There is also a total absence of a Competitive Landscape . In 2021, the AutoML and predictive analytics space was already crowded with players like DataRobot, H2O.ai, and cloud-native tools from AWS and Google. Pecan relies on its "low-code" positioning to differentiate, but a direct comparison would have strengthened the case.

Finally, there is no Ask . We know from catalogue data that the company eventually raised $116M, but this specific deck does not state how much they were seeking in February 2021 or how they intended to allocate that capital (e.g., sales expansion vs. R&D).

What a Founder Should Copy

Founders should emulate the Persona-Based Solution on Slide 3. Many technical decks fail because they don't explain who actually sits in the chair and uses the software. Pecan’s "Action Owner" vs. "Builder" distinction is a perfect way to explain how a product fits into an existing corporate hierarchy.

The "From/To" visualization in the case studies is another excellent tactic. By showing the transition from "Rule-based offers" to "High resolution user-level personalization," Pecan makes the value of their technology tangible. It moves the conversation from "We have an algorithm" to "We change how you work."

Frequently asked questions

What is the primary problem Pecan AI solves?: Pecan AI addresses the bottleneck in enterprise AI adoption: the high cost and scarcity of data scientists with domain expertise. According to Slide 2, business teams want AI, but the traditional path is too expensive and difficult. Pecan solves this by providing a low-code platform that allows existing BI analysts to perform data science tasks.
How does Pecan AI differentiate its technology?: The deck identifies three 'key tech moats' on Slide 7. First is 'Drag & Drop-to-AI' technology using SQL. Second is automated data preparation and feature engineering, including cleansing and embedding. Third is automated deployment and monitoring with continuous database resampling and retraining, all performed without manual engineering.
What kind of ROI can customers expect based on this deck?: Pecan provides two detailed case studies. Slide 9 shows a mobile app company achieving a 37% uplift in revenue per user and a 91% increase in purchases. Slide 10 highlights a retailer that saw a 35% increase in sales conversion and a 20% improvement in sales representative productivity.
Who is the target user for the Pecan platform?: The platform is designed for two primary personas within a company, as shown on Slide 3. The 'Action Owner' is the business stakeholder who owns the KPI and takes action on insights. The 'Builder' is a BI or data analyst who understands the data and uses Pecan to build the models.
What is missing from this pitch deck?: This is a product and results-focused deck that omits several standard fundraising elements. There is no market sizing (TAM/SAM/SOM), no competitive matrix, no financial roadmap or projections, and no slide detailing the specific 'Ask' or use of funds for the current round.

Cover slide of the Pecan AI pitch deck — Series C 2016 — Pecan AI pitch deck, slide 1 (2016)

Pecan AI pitch deck: the facts

Company: Pecan AI
Year: 2016
Stage: Series C
Slides: 11
Sector: AI

Pecan AI pitch deck PDF

The full Pecan AI deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Pecan AI pitch deck was used for

This deck is from Pecan AI, a company providing a low-code predictive analytics platform that lets business teams build and deploy AI models without in‑house data scientists, as reflected in the OCR text describing drag‑and‑drop model building and automated feature engineering. The deck is catalogued as an 11‑slide, Series C‑stage AI deck, associated with a cumulative funding figure of $116M, which aligns with external data that Pecan had raised around this amount by the time of its $66M Series C in February 2022. It appears to be a fundraising deck around that Series C period, focused on scaling its end‑to‑end AI value chain platform and highlighting customer case studies in mobile gaming and retail. The slides emphasize Pecan’s mission to democratize predictive analytics for business teams by removing the need for scarce data science talent while driving measurable revenue impact, consistent with the company’s press releases and media descriptions.

Business model: Low-code predictive analytics and data science platform that enables BI analysts and business stakeholders to build and deploy AI-based predictive models without needing data science expertise.

Round: Series C
Year: 2022
Lead investor: Insight Partners
Investors: Insight Partners, GV (formerly Google Ventures), S-Capital, GGV Capital, Dell Technologies Capital, Mindset Ventures, Vintage Investment Partners
Founded: 2018
Founders: Zohar Bronfman, Noam Brezis
Headquarters: Dual headquarters in New York, USA and Tel Aviv / Ramat Gan, Israel.
Industry: Artificial intelligence; predictive analytics / data science platform for business users.

Raised: $66,000,000 Series C in February 2022, contributing to a total funding amount around $116M–$117M at that time.

Total funding: Approximately $116M–$120M total funding raised across multiple rounds (Seed/Series A, $35M Series B in 2021, $66M Series C in 2022).

Use of funds as presented: Scale Pecan’s global footprint, expand operations in Israel and the U.S., and accelerate research and development of its low-code predictive modeling and data science platform for business users.

What happened after the Pecan AI deck

Following earlier rounds, Pecan AI closed a $35M Series B in 2021 and a $66M Series C in 2022, bringing total funding to around $116M–$120M; the company has continued operating as a Series C-stage, AI-based predictive analytics platform with a growing customer base and expanded operations in Israel and the U.S.

Raised $35M Series B financing in May 2021 led by GGV Capital, bringing total financing to more than $50M and supporting
Raised a $66M Series C round in February 2022 led by Insight Partners, with participation from GV and existing investors
By mid‑2022, reported more than $115M raised, over 100 employees in Israel and the US, and prominent customers including
Subsequent profiles and coverage into 2024–2025 describe Pecan AI as an ongoing company (Stage: Series C, alive) with ap

What the Pecan AI deck got right

Strong problem-solution framing around democratizing AI. The deck presents a **clear and compelling problem statement**: business teams need and want AI, but data science is hard, expensive, and requires scarce domain‑expert data scientists; Pecan positions itself directly as the solution to this pain, aligning tightly with its public narrative about democratizing predictive analytics.
Coherent end-to-end technical story for non-technical users. Slides describe an **end-to-end platform** that automates the entire "AI value chain" in hours, including in‑platform ETL for business analysts, drag‑and‑drop model building, automated data harmonization, encoding and embedding, feature engineering, external enrichment, and continuous model monitoring and retraining—creating a coherent story of technical depth and usability for non‑experts.
Clearly articulated technology moats. The deck highlights **three explicit technology moats**—Drag & Drop‑to‑AI data preparation and deployment, automated feature engineering and monitoring, and end‑to‑end automation—framing the platform as differentiated and defensible, and giving investors specific levers to probe in technical diligence.
Impactful customer case studies with quantified uplift. Case-study slides provide **concrete, quantified business impact**: a mobile app company achieving 37% uplift in revenue per user and increased purchases via AI‑based personalized offers, and a retailer boosting sales conversion by 35% while improving rep productivity by 20%, illustrating the platform’s ability to drive measurable revenue outcomes.
Credible founding and executive team highlighted in the deck. Slide 4 showcases a **strong, experienced leadership team**, including co‑founders Zohar Bronfman and Noam Brezis and senior executives with backgrounds at major technology companies, reinforcing external coverage that emphasizes their PhD‑level expertise in computational psychology and neuroscience and deep experience in data and analytics.
Strong alignment with market demand for low-code AI. The deck’s focus on **low-code, drag‑and‑drop workflows** for BI analysts and business stakeholders, combined with automated feature engineering and monitoring, aligns with a clear market trend toward empowering non‑data‑scientist users, which external sources validate as Pecan’s core positioning.

What could have been stronger

Limited visibility into overall business traction and scale. The OCR text and description of the deck do not show any **explicit quantitative traction metrics** such as revenue, ARR, customer count, retention or detailed growth KPIs; case studies describe percentage uplift (e.g., 37% uplift in revenue/user, 35% conversion uplift), but there is no aggregate view of the business’s scale, which makes it harder for investors to assess commercial traction from the deck alone.
Lack of explicit competitive positioning in the slides. While the deck claims strong tech moats (drag‑and‑drop AI, automated feature engineering, end‑to‑end AI value chain), it does not visibly present **competitive landscape or differentiation vs. other low‑code/AutoML platforms**, nor does it benchmark performance, deployment speed or cost savings against competitors, leaving investors to infer positioning from the claims.
Go-to-market and economics not clearly articulated. The slides as captured via OCR emphasize technology and case studies but do not show **clear unit economics, pricing model, or go‑to‑market motion** (e.g., sales model, target segments, ACV, payback periods), limiting investors’ ability to evaluate scalability and capital efficiency based solely on this deck.
Case-study impact not rigorously substantiated in the deck. The case studies focus on uplift percentages and qualitative impact, but there is no **methodological detail on model robustness, statistical significance of uplift, or long‑term performance**, which could prompt investor questions on the repeatability and reliability of these results.

How an investor would read this deck

What draws attention

Verify how the **Series C deck’s narrative about low-code predictive analytics and no‑data‑scientist workflows** translated into actual product capabilities and adoption by business analysts, using the described features like drag‑and‑drop modeling, in‑platform ETL and automated feature engineering.
Confirm the **timeline alignment** between this deck (catalogued as Series C, 11 slides, $116M raised) and the **February 2022 $66M Series C round**, and how much of the cumulative $116M total funding was already committed or closed at the time the deck was used.
Assess the **commercial traction** behind the case studies (mobile gaming uplift, retail sales conversion gains) by looking for named customers, contract sizes, and whether similar uplifts were achieved at scale across the customer base, beyond the spotlighted examples.
Evaluate the **defensibility of the three tech moats** (drag‑and‑drop AI, feature engineering & monitoring, end‑to‑end AI value chain automation) in the context of broader AutoML and low‑code ML platforms, and whether Pecan’s approach materially reduces time‑to‑value for non‑technical users.
Clarify the **use of funds** for the Series C: how much was dedicated to R&D versus sales, marketing and international expansion, relative to the objectives stated in press releases to scale its global footprint and accelerate platform development.

Risks that stand out

The **competitive landscape in low‑code and AutoML predictive analytics** is crowded, with major cloud providers and specialized vendors offering similar capabilities; without clear comparative metrics or differentiation in the deck, there is risk of commoditization and pricing pressure.
By emphasizing **no‑data‑scientist workflows**, there is a risk that complex real‑world use cases may still require expert intervention, potentially limiting the platform’s effectiveness for the most valuable, high‑complexity problems.
The deck’s reliance on **select case studies with high uplift percentages** could mask variability in outcomes across customers; if those results are not consistently reproducible, investors face execution and adoption risk.
Rapid scaling plans funded by the Series C (global footprint expansion, accelerated R&D) increase **operational and burn-rate risk**, especially if sales cycles for predictive analytics platforms in large enterprises remain long and require significant customer education.

Questions this deck invites

What concrete **KPIs (ARR, customer count, active projects, retention, NRR)** were associated with Pecan AI at the time this Series C deck was used, and how do they compare to the uplifts claimed in the case-study slides?
Which **customer logos and verticals** (e.g., gaming, retail, insurance, healthcare, financial services) were already live on the platform at Series C, and how repeatable were the results shown in the mobile app and retailer case studies ac
How does Pecan’s **drag‑and‑drop AI and in‑platform ETL** integrate with common data stacks (Snowflake, BigQuery, Redshift, Salesforce, major ERPs), and what are average implementation times for non‑technical business teams compared to trad
What is Pecan’s **pricing and packaging model** (seat‑based, usage‑based, project‑based, or enterprise license), and how do unit economics (LTV/CAC, payback period) look for its target segments at Series C stage?
How does Pecan measure and ensure **model governance, monitoring, and retraining** for business‑built models, particularly given the deck’s claim of continuous DB resampling, automated monitoring and feature re‑training, and how does this c

What founders can take from the Pecan AI deck

Aligning a fundraising deck closely with the **company’s public narrative**—democratizing predictive analytics for business teams via a low-code platform—creates coherence between slides, press releases and media coverage, reinforcing credibility.
Highlighting **concrete business outcomes** (e.g., 37% uplift in revenue/user for a mobile app, 35% sales conversion uplift and 20% productivity boost for a retailer) helps translate technical capabilities into financial impact that investors can grasp quickly.
Emphasizing a **clear problem statement**—business teams want AI but data science is hard, expensive and scarce—paired with an end‑to‑end solution narrative (automated AI value chain, drag‑and‑drop modeling, automated feature engineering) is an effective way to show product‑market fit.
Showcasing **technology moats** (drag‑and‑drop AI, in‑platform ETL, automated feature engineering and monitoring) can signal defensibility, but their impact is stronger when tied to competitive benchmarks and performance comparisons.
Including detailed **founder and leadership bios** in the deck, as seen in the slide listing the CEO, CTO and VPs with prior experience at large tech companies, strengthens the case that the team can execute on ambitious AI and analytics goals.
Using **vertical-specific case studies** (gaming, retail) illustrates versatility of the platform across industries and can help investors imagine additional use cases and market expansion paths.

Pecan AI pitch deck: common questions

What does Pecan AI do, according to the pitch deck and external sources?

Pecan AI provides a **low-code predictive analytics and data science platform** that lets BI analysts and business stakeholders build and deploy AI-based predictive models (for churn, LTV, next-best offer, demand forecasting, etc.) directly from data in warehouses, CRMs, ERPs and external sources, without requiring data science expertise or coding. The deck’s slides describe drag‑and‑drop model building, in‑platform ETL, automated feature engineering and monitoring, and end‑to‑end automation of the "AI value chain" in a matter of hours without data scientists in the loop.

Who founded Pecan AI and what team does the deck highlight?

External sources state that Pecan AI was **founded in 2018** by **Dr. Zohar Bronfman (CEO)** and **Dr. Noam Brezis (CTO)** in Tel Aviv, Israel, with offices and operations in New York and Israel. Slide 4’s OCR shows these founders and senior team members (including CTO Tomer Meron and VPs for product, customer success, digital solutions and R&D), positioning the company as a product‑first, results‑oriented team focused on transforming how business teams drive impact with their data.

How much funding has Pecan AI raised and what are the key rounds relevant to this deck?

Public data indicates Pecan AI has raised about **$116M–$120M** in total funding. This includes a **$35M Series B** announced in May 2021, led by GGV Capital with participation from Vintage, Dell Technologies Capital, S-Capital and Mindset Ventures, bringing total financing to over $50M at that time. In February 2022, Pecan announced a **$66M Series C** led by Insight Partners, with participation from GV and existing investors S-Capital, GGV Capital, Dell Technologies Capital, Mindset Ventures and Vintage Investment Partners, with over $100M raised in the preceding 12 months and total capital around $116M–$117M.

Who invested in Pecan AI’s funding rounds around the time of this deck?

The **Series C round in February 2022** was led by **Insight Partners**, with participation from **GV (Google Ventures)** and existing investors **S-Capital**, **GGV Capital**, **Dell Technologies Capital**, **Mindset Ventures**, and **Vintage Investment Partners**. Earlier funding included the **$35M Series B** led by **GGV Capital** with participation from Vintage and existing investors Dell Technologies Capital, S-Capital and Mindset. Company profiles and databases list these firms among Pecan AI’s significant investors.

What was Pecan AI raising money for with this Series C-stage deck?

According to press releases and coverage, the Series B and C funds were intended to **scale Pecan’s global footprint**, expand operations in the U.S. and Israel, and **accelerate R&D** for its low-code predictive modeling platform. The deck itself frames the use of funds around strengthening its end‑to‑end AI value chain automation, scaling its drag‑and‑drop AI tooling for business analysts, and driving adoption in use cases like promotional pricing, upsell conversion and demand forecasting, as evidenced by the described case studies and technology moats.

Sources

Funding and outcome facts on this page were researched on 2026-08-21 from the pages below.

Pecan AI pitch deck slides

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What each slide of the Pecan AI pitch deck says

Slide 1

fa Pecan Predictive analytics without data scientists February 2021

Slide 2

Business teams need & want Al, however, data science is: e Hard and expensive e Requires data scientists with domain expertise, which are scarce

Slide 3

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Slide 4

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Slide 5

The Pecan platform is the fastest and simplest way to build and deploy advanced predictive analytics without data science experience Warehouse .,.o Conversion, Churn, LTV Pecan's computational engine automatically Ingests data 4 CRM, Sales and Ops - Next-best offer/action Salea, Prices & peormos £ [o» @ Pecan Platform 3 ERP Upsell, cross-sell predictions and insights . External Data "-\ Domand forocast, Enrichment per Lze cane Sales lorecast

Slide 6

Pecan's end-to-end solution The Pecan platform automates the entire Al value chain" in a matter of hours Without data scientists in the loop

Slide 7

3 key tech moats Drag & Drop-to-Al Data preparation & Deployment technology Feature engineering & monitoring In-platborm ETL, allowing business Fully automated harmonization, Continuous DB resampling, with analysts to build models using only cleansing, encoding & embedding, automated model monitenng and drag-n-drop and SOL external enrichment and feature refraining - without code or engineering selection & engineering

Slide 9

A mobile app company ncréased promotional revenues using Pecan's Al-Based offers Challenge The company's CRM and monetization team were lpoking to Impeove their promational efforts and Increase their customer's LTV Solution Pecan's ingested all raw historical game and user data, enriched the data by engineering efficient AFM [recency, frequency, monetary) metrics using Pecan's Al and deployed a pricing model for the game promotional packages. Evary user received personalized offer based on his game patterns. al Impact o 37% ek I Uglift In Revenua/Usar Increase in purchases From Bl-based segmentation Rule-based offers Q B3 Pecan Promotion Bricing Optimization Personalization v To Scalable Al…

Slide 10

A rtaller boosted sales conversion rate by 35%, while enhancing sale rep productivity by 209 Challenge From The company was struggling to meat revenue and * Manually prepared spreadsheets growth targets, Sales reps used simplistic and * Nenspecific campaign unfocused outreach mathods to identify and » Slow leaming curve larget s, Solution Pecan's platfarm ingested il raw historical sales and customer data, enriched the modeds by adding cutting edge external features and automatically built predictive madels for upsell conversion indication using Pecan's pro-buill use caso librarny. a1l Impact » Fully integrated recommendations - a 35% a 20% e Targeted and affective sales campaign G In conve…

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