Boris Robles-Slyusar
Boris Robles-SlyusarArizona, United States · LinkedIn

Welcome to my page, the living journal of my research work, and a bit about me.

By day I run marketing at a software company in Scottsdale. When I close the laptop I am a regular shopper like everyone else. That shopper is who my doctoral research at Arizona State University is for.

The question I am working on: can you trust a product ranking? I study the marketing tactics behind ratings, and I may build a prototype that re-ranks products with those tactics accounted for. The research comes first. A tool may come out of it.

Read the journal Partner on research

I lead marketing in corporate. In academia I play against my own team.

Heads up: this page is a living thing. The direction is set. As I go deeper, the problem statement gets refined, and the page moves with it. If you come back and something reads differently, that is the work, not a typo.

Why optimizehub.net? OptimizeHub is the marketing consulting practice I founded in 2024. I no longer have time to run it as a business, beyond potential occasional projects. So its domain, site and tooling now power my doctoral research and contributions. No commercial goal here.

Marketing, data and AI.

My research sits where all 3 meet. How products get found. What the numbers actually say. What AI answers are doing to both. The copper dot is me.

Why a marketer is doing a doctorate in information technology

When I was working on my bachelor's in international business, I thought the international part was somehow separate from business. It is not. Pick up a laptop, a pen, a notebook or a shirt, and odds are it was made abroad or moved through an international supply chain. I would argue all business is international.

Information technology works the same way. It is not only software, data analysis or computer science. Information comes first and technology follows it. Almost everything we touch now is information technology. Seriously, that is just the nature of the world we live in.

Marketing, my craft, is no exception. In my view, marketing is just a wrapper word for understanding human behavior, run on information and on technology. So I decided to study it through the technical lens. I consider myself a tech-savvy marketer, and the doctorate is where that gets tested.

Data partners

Organizations that share de-identified data for my studies. They see the findings first. 2 partners so far, and room for more.

Seventh Sense

Email optimization and analytics software for HubSpot and Marketo

Arobis AI

GEO, AEO and AI SEO agency for B2B SaaS

Open slot

Your organization · sector · data type

Become a data partner

Does your team sit on marketing, sales or product data, with a question nobody has time to answer properly? That is the kind of partnership this research runs on. Free to participate. Again, no commercial incentive. Just science.

Reach out on LinkedIn or email boris@optimizehub.net.

  • Your data stays yours. A written data use agreement, de-identified before analysis, never resold, never used in consulting.
  • Questions we design together. 1 or 2 questions your team actually wants answered, not a survey dropped in your inbox.
  • Findings first. Results and benchmarks before anything is published, and credit on this page if you want it.
  • Backed by research, on your own site. Once the findings are out, you can say the question your team asked was answered by a study, and link to it.

Research

My research grows out of my day job: how products get found, how ratings get made, and what a shopper can trust. I am early in the program, so this section will change as the proposal takes shape.

A transparency layer for product rankings

Public ratings are shaped by marketing: fabricated reviews, undisclosed affiliate content, review gating, satellite sites built for backlinks. The tool I have in mind takes the public ratings for a set of products. It re-ranks them after accounting for those tactics. The shopper sees a ranking they can trust, and the reasons behind it. Transparency is the point, not catching anyone.

QuestionDoes a ranking that accounts for marketing tactics feel more credible to shoppers than the platform's own?
MethodSignal detection per tactic, a re-ranking model, and surveys with consumers and marketers. Any survey goes through IRB review first.
Scope1 product category to start, then widen. Fakespot is the closest existing case study.
StatusIdea stage, refined weekly with my mentor. No proposal yet.

What my tool would potentially look for

  • 01Fabricated reviews

    Review classifiers, reviewer behavior graphs, timing bursts, near-duplicate text across products

  • 02Satellite sites for backlinks

    Shared hosting, analytics IDs and templates, heavy interlinking, thin content

  • 03Undisclosed affiliate content

    Affiliate URL parameters, missing disclosures on "neutral" reviews

  • 04Fake scarcity and urgency

    Timers that reset on reload, identical counters across products, evergreen "sales"

  • 05Inflated "was" prices

    Price history and page history

  • 06Hidden text aimed at AI crawlers

    Hidden CSS or white text, content served only to bots

  • 07Astroturfing in forums

    Account age, coordinated posting patterns, same phrasing across accounts

  • 08Review gating

    Rating distributions that do not match across platforms, near-zero negatives

  • 09Pay-to-play badges and awards

    Check that the awarding body exists and is independent, and that the seal links to a verifiable page

  • 10Bought followers, likes and views

    Engagement rate against follower count, bot-like account clusters

Surveys

Surveys with shoppers and marketers are part of the planned study design, and they will run from this page. Nothing opens before the protocol clears ASU's review board. Every survey will state who it is for, how long it takes and how answers are stored before you begin.

Opens after IRB approval

How much do you trust product ratings?

For anyone who shops online · planned as anonymous, no account needed

The first survey will ask shoppers which signals make a rating believable and which ones make them walk away. It will also ask whether seeing the reasons behind a ranking changes their choice.

Take the survey

Hear about the next survey

Send me a 1-line email and I will add you to the list. You get 1 message when a survey opens, and 1 when the results are out. Nothing else.

Email me to sign up

No list software behind this. Your email sits in my inbox, nowhere else.

  • Consent first, on the survey's own first page.
  • Anonymous by default. No names, no emails inside the survey.
  • Results come back here as a short summary.

Working on something where marketing, data and AI meet? Let's talk.

Research partnerships, speaking, or a marketing systems question. Email is the fastest way to reach me.

  • LinkedInlinkedin.com/in/borisslyusar
  • LocationGreater Phoenix Area, Arizona, United States
  • AffiliationThe Polytechnic School, Arizona State University
  • CompanyOptimizeHub LLC, Arizona · my own practice, now the infrastructure for this research

Partner on research

Have a question your data could answer?

If your team sits on marketing, sales or product data, with a question nobody has time to answer properly, that is the kind of partnership this research runs on. Free to participate. No commercial incentive. Just science. Reach me either way.

Message me on LinkedIn boris@optimizehub.net

Or read what partners get first.

Survey list

Send me a 1-line email

The subject "Notify me about the survey" is all it takes. You get 1 message when a survey opens, and 1 when the results are out. Nothing else.

Or open it in your mail app.