From Sales Tracking to Sales Intelligence

How AI Helped One Brand See the Full Picture

TL;DR

At Evidnt, we helped a brand go beyond tracking sales to understanding what drove those sales. By bringing together disparate data from media spend and social engagement to Amazon reviews and Reddit threads, we used AI to uncover patterns that informed better messaging and evaluation. What started as a standard sales report evolved into a comprehensive intelligence layer powered by AI, enabling the brand to act faster and smarter.

The Challenge: Finding Real Signals in a Sea of Data

Tracking sales is easy. Understanding why those sales are happening is the hard part. For most brands, data lives in silos—media reports here, social chatter there, maybe some product reviews sprinkled in. Even when the data exists, stitching it together to surface meaningful correlations is manual, time-consuming, and prone to false positives. The challenge isn’t a lack of data, but too much data, too many metrics, and a lack of clarity.

Using AI to Connect the Dots

The good news? AI excels at finding patterns in noisy, unstructured environments. We began by feeding initial data into a model that could detect anomalies and correlations, identifying which creative messages led to a spike in social mentions, or which review phrases on Amazon appeared just before a surge in sales. Once we identified those connections, we used Python-based workflows to automate the recurring insights and alert the team to new patterns as they emerged.

How We Brought It All Together

We pulled in social data (likes, comments, shares), Reddit threads discussing the brand or category, Amazon reviews, and media spend logs. Using natural language processing, we parsed thousands of lines of text and analyzed engagement patterns, looking for spikes and shifts in sentiment. For instance, one Reddit post praising the brand’s new flavor led to a noticeable uptick in positive sentiment and sales in the following days, data we might’ve missed if we were only looking at CTRs or GRPs

Turning Volume Into Value

The volume of data was overwhelming at first because of the numerous measurement values that seemed disconnected. But AI helped us filter out the noise and focus on high-signal inputs and identify correlations. For example, we learned that while media impressions drove awareness, it was the combination of positive sentiment on Reddit and above-average reviews on Amazon that consistently preceded sales jumps for that specific product. That insight became a trigger point for messaging tweaks and media pacing, especially during promo periods.

From Insight to Automation

Once we validated the key drivers, we built a custom AI reporting layer that mapped those drivers directly to sales over time. The brand’s team could now see, daily, which creative, platform, or conversation type was influencing sales. We even flagged unusual spikes and gave content or product teams suggestions based on real consumer language. For example, if a spike in comments mentioned “mango flavor,” the next campaign headline wasn’t far behind. This reporting model is now being extended to other use cases, like campaign testing, competitive benchmarking, and even new product feedback loops.

Conclusion

Sometimes the best work doesn’t start with a massive transformation; it starts with a simple sales report and a deeper question: what’s actually driving this? With AI, the answer doesn’t just sit in a dashboard. It becomes part of how teams think, act, and grow.

Using AI to Spot eCommerce Trends Before They Go Mainstream

TL;DR

Retailers and marketers can now leverage sales data, AI, and lightweight automation to identify emerging trends before they become established. By identifying anomalies in eCommerce data, pricing, and consumer behavior, it’s possible to surface actionable insights that inform product recommendations, merchandising, and content. In this post, we show how we built a simple system using publicly available Amazon data and GPT to automate trend analysis and generate marketing-ready outputs.

The Challenge: Finding Trends Fast, Before Everyone Else

For many retailers, understanding consumer demand involves examining lagging indicators, such as monthly sales reports, seasonal trends, or anecdotal customer feedback. By the time a trend is clear, competitors have already jumped in. The reality is, most traditional reporting systems show what happened, not what’s about to happen. In a market defined by speed and saturation, brands need tools that can surface early signals and give them a head start.

The core challenge is not just having the data, but having the time and tools to spot what’s different, unusual, or on the rise. You’re not just trying to see what’s selling. You’re trying to understand why, and whether it’s about to snowball.

Opportunity: Tapping Into the Signals Across eComm, Search, and Social

There’s no shortage of signals. Platforms like Amazon, Google, TikTok, and even Reddit provide valuable insights into what people are buying, searching for, and discussing. However, while these sources are individually useful, their real power lies in their combination. The problem? They’re siloed, messy, and often overwhelming. To take advantage of these signals, businesses need a system that can regularly ingest this data, analyze it for meaning, not just volume, and surface trends that are not yet obvious. That’s where AI becomes a force multiplier.

Step One: Use AI to Find What’s Changing

We started with a relatively simple dataset: Amazon bestseller data. This included changes in sales rank over time, price fluctuations, review counts, availability, and product category data. While each metric alone offers limited value, when tracked together over time, they form patterns, some steady, some spiky.

AI helped uncover the initial factors driving demand and behavior. Then, using a basic Python script, we calculated a “trend momentum score” for each product, which is a weighted combination of rank changes over 1, 7, and 30 days. These scores helped us identify products moving faster than normal, flagging items with early momentum.

The next step was to go beyond detection. Once anomalies were identified, we utilized GPT to delve deeper, analyzing product titles, descriptions, and even brand names to understand why certain items might be trending. For example, one cluster of rising products featured terms like “keto,” “bulk pack,” and “low sugar”, clues that health-conscious snacking was gaining traction in specific subcategories.

How We Did It: Analyzing Amazon Data with AI

Using Amazon data, we ran a pipeline that first cleaned and ranked the data using simple anomaly detection. Then, we used GPT to extract keywords and interpret the results. A product with a 75% drop in sales rank over 7 days may seem insignificant on its own, but when paired with language like “high protein,” “gluten free,” and “family size,” it begins to tell a story about a shifting preference.

These aren’t just numbers, they’re starting points for action. AI helped us reduce the noise and spotlight the products and features worth watching. For instance, we found that lesser-known snack brands, such as Weighless and Vincinni, were showing rapid acceleration in sales rank, likely due to favorable pricing and emerging consumer preferences. By clustering these findings by subcategory, we were able to see where growth was concentrated and where opportunities were emerging.

From Insights to Automation: Making the Output Useful

Once trends were flagged, we automated the final steps. With GPT, we generated updated product descriptions using trending language, created headline ideas for blog posts or email campaigns, and even grouped products for bundle recommendations.

What would normally take a merchandising or marketing team hours of manual review became an automated insight engine. Imagine a Slack alert that tells you: “There’s a spike in high-protein cookies among new brands. Consider featuring a Keto Snack Bundle this week.” Or a live dashboard that ranks products not just by sales, but by change velocity—giving your team a playbook for where to act next.

And this approach is expandable. You can connect search trends, TikTok mentions, or inventory signals to the same system. The more inputs you feed, the smarter your outputs become.

Conclusion

This project focused not on creating a recommendation engine, but on developing a decision engine. By leveraging AI to identify anomalies and trends, and automating analysis and content creation, we built a streamlined system that allows brands to act proactively instead of reactively. Whether managing five SKUs or five thousand, the core objective is the same: understand your audience, recognize shifts, and respond faster than the market anticipates.

If you’re curious about building a system like this for your business or want help getting started with AI-powered trend analysis, let’s talk.

When AI Eats Its Tail

Using AI to Fix Google’s AI-Powered Ad Waste

TL;DR

Performance Max (PMax) campaigns are marketed as an easy, set-it-and-forget-it solution for advertisers, especially small businesses. However, after running a PMax campaign for a local construction business, we found that the majority of our budget was being spent on irrelevant mobile games and low-quality websites, rather than targeting high-intent local users. By building a clean reporting system using Google Looker and auditing our campaign placements with Gemini (Google’s own AI), we found that over 60% of impressions came from mobile apps, and only 30% landed on Google-owned platforms. We used this data to shift our strategy from PMax to a more effective manual SEM approach, and in doing so, uncovered lessons and a framework that could benefit advertisers of any size.

PMax: A Promise That Doesn’t Hold Without Oversight

Google’s Performance Max campaigns offer a compelling pitch: minimal setup, broad reach, and machine learning that optimizes your ads across channels. For small businesses with limited time and expertise, it’s easy to see the appeal.

We ran a PMax campaign for my dad’s construction and property management business, hoping to generate quality leads within our local service area. On paper, the results looked fine, a decent number of leads, solid clickthrough rates, and steady spend.

But in practice, something felt off. Many leads (90%) were outside our region, and most of them weren’t responding. We weren’t just getting bad traffic, we were getting irrelevant traffic and maybe even fraud.

Step One: Build Clarity Before Making Decisions

To understand what was happening, we needed visibility — not the cluttered, siloed dashboards inside Google Ads or GA4, but something clean and decision-ready and practical.

We built a custom reporting dashboard in Google Looker, focused on core questions: where is our budget going, what’s converting, and is it helping the business? The result was an automated report that stripped away vanity metrics and zeroed in on ad placements and outcomes.

Step Two: Let AI Audit AI

Armed with our data, we ran it through Gemini, Google’s own AI assistant, and asked it to categorize the placement data and highlight problem areas.

Here’s what we found:

• 60.4% of impressions came from mobile apps like Solitaire – Card Game, My Talking Angela 2, Which Dress? Left or Right, and Beat Maker Pro. These were apps where user intent was non-existent or accidental, classic examples of “fat-finger” clicks and incentivized ad engagement.

• 14.8% of impressions came from questionable websites like dealday.today and monkey.app, which showed all the hallmarks of low-quality “Made for AdSense” content or non-relevant platforms.

• Only 30.2% of impressions came from Google-owned properties — Search, YouTube, Gmail — where we would expect to see higher user intent and better lead quality.

Despite this mix, PMax still displayed the message: “Your campaign is limited by budget.” In reality, the budget was just being routed into low-value placements that generated noise, not business.

Step Three: Shift the Strategy — and Keep the AI

We used the insights to pivot. Instead of pouring more money into PMax, we moved our budget into manual SEM. We tightened geographic controls, focused on high-intent keywords, and used Looker to monitor ongoing performance.

The change wasn’t just in impressions, it was in lead quality. Calls came from within our market. Form fills were real. And most importantly, we had full visibility into what was working and why. AI didn’t just help us uncover the problem, it helped us build a repeatable system for better decisions.

This Isn’t Just an SMB Problem

Our campaign spend was small, but the problem is systemic. Larger advertisers with hundreds of campaigns and broader budgets face the same lack of transparency, just at scale.

There’s a clear opportunity here for others:

• Build a lightweight AI tool that automatically audits PMax and other campaigns for performance

• Custom alerts when spend shifts into mobile games or low-quality web domains

• Smart dashboards that connect ad spend to actual business impact

If we can build this for a local construction company, there’s no reason larger brands and agencies can’t implement similar AI-powered controls to protect spend and improve outcomes.

Conclusion: Build With AI, Don’t Blindly Trust It

Performance Max can be powerful, but it’s not foolproof. In our case, automation was optimized for conversions, not real leads. Engagement instead of intent. Spending money, not necessarily achieving results. By using AI internally to audit, analyze, and automate reporting, we transformed a black-box system into one we could understand and act on. That’s the future: AI not as your autopilot but as your co-pilot. Whether you’re running ads for a family business or a global brand, visibility is crucial. AI can assist, but only if you ask the right questions.

Scaling Myself with AI

How We Built Internal Prompt Assistants at Evidnt

TL;DR: We used AI to scale sales and client communication at Evidnt by turning our internal knowledge into prompt-driven assistants tailored to each team.

The result? Consistency, speed, and confidence across every client interaction—without months of training. Here’s how we did it, what we learned, and how others can try it too.

The Problem We Wanted to Solve

As the founder and primary salesperson at Evidnt, I found myself repeating the same narratives: why we built the platform, how our measurement works, what makes us different, and how to upsell our services. I had spent years crafting those answers.

But as we grew, I needed my team to articulate it just as clearly, without relying on months of shadowing or my presence in every conversation. The challenge: How do you scale your voice, your logic, and your insights so that even the most junior hire can sound like a seasoned expert?

Training alone wasn’t enough. We needed an internal system that could bring our knowledge to life, on demand, in-context, and on-brand.

2. Why Using AI Internally Is a No-Brainer (But Not Plug-and-Play)

AI can instantly access, retrieve, and synthesize complex knowledge. For internal enablement, sales, customer success, and onboarding, it’s a natural fit. But implementing it thoughtfully requires more than just “add GPT.” Here’s what we learned building ours:

a) Prompt Rigor

Prompts aren’t one-and-done. Early on, we saw hallucinations—incorrect data, made-up numbers, overly confident claims. Over time, we learned to treat prompts like code: test them, version them, stress-test them under different inputs. This dramatically reduced errors and made responses more reliable.

b) Security Considerations

As a smaller company, we didn’t encounter the same compliance challenges as larger enterprises, but we were cautious not to upload any confidential information to public LLMs. Instead, we carefully vetted all content and used tools that ensured secure data handling. This remains a major concern for bigger organizations, it's important to review terms of service and data flow policies thoroughly.

c) Prompt Sensitivity

LLMs are surprisingly finicky. Too much instruction? The output becomes rigid and robotic. Too little? You get generic, unhelpful fluff. We had to strike a balance—clear goals with room for the model to reason. We continually tracked output quality across various use cases.

d) Dealing with Jargon and Style

I’m not a fan of AI’s default tone, overuse of emojis, unnecessary padding, or vague corporate jargon. To fix this, we gave the model example answers and a tone-of-voice guide. This helped us maintain clarity, confidence, and conciseness in our responses.

e) Feedback Loops

A common trap: assuming it “just works.” We made it a practice to review assistant outputs weekly, track accuracy, and collect team feedback. These feedback loops helped the assistants become more aligned with real-world use and enabled us to adapt as our messaging evolved.

3. How We Did It: A Practical Blueprint

We began by compiling our internal sales and account management materials, including FAQs, onboarding documents, pitch decks, and process explanations. These became the foundation of our assistants.

Here’s how we operationalized it:

Step 1: Create a Knowledge Base

We embedded our materials into a searchable store using tools like OpenAI’s Assistants API. If you’re working with just 10-20 docs, no need for complex infrastructure, you can use OpenAI’s file upload directly. For larger use cases, third-party libraries such as LangChain or LlamaIndex are good solutions.

Step 2: Define Roles and Use Cases

We mapped assistants to specific roles:

• Sales: outbound messaging, follow-ups, brand positioning

• Account Management: onboarding templates, campaign reporting, upsell framing

• Strategy and Planning: explaining methodologies, articulating performance logic

Step 3: Build the Interface

We developed a simple internal React app. Team members choose a use case (for example, "follow-up email after first pitch”), enter a few details such as brand, product, and campaign goals, and the assistant generates a message that aligns with our tone, value proposition, and playbook. Users can also upload campaign data, email follow-ups, and ask specific questions, receiving clear responses in our consistent tone.

Step 4: Iterate and Improve

We reviewed weekly logs, marked outputs as strong or weak, and refined prompts accordingly. Eventually, assistants could even recommend upsells or handle nuanced client objections—pulling from actual case studies and results.

4. What You Can Try

If you want to explore this for your team, start simple:

• Gather your key materials: Sales decks, FAQs, customer docs, onboarding guides

• Use OpenAI’s Assistants API: You can upload files and prompt the model to reference them

• Create specific use cases: Don’t try to solve everything—pick three common communication moments (e.g., intro email, explaining product value, handling objections)

• Define your tone and structure: Give the model a few example responses with the style you want

• Build a basic front end: Even Streamlit or Google Sheets + API calls can be enough to start

• Review outputs regularly: Ask your team for feedback, and refine prompts based on their input

5. Final Thoughts

This isn’t about replacing people. It’s about scaling the best parts of your team—your voice, your logic, your know-how—so that everyone can perform at their highest level faster.

It took me months to train new hires to speak the way I wanted. With AI, we got them 80% of the way there in a week.

This approach worked for us at Evidnt. If you’re thinking about how to scale your own sales, CX, or strategy teams—I’m happy to share more or help you think through how this could work in your org.

Alex Andreyev of Evidnt On How Artificial Intelligence Can Solve Business Problems

https://medium.com/authority-magazine/alex-andreyev-of-evidnt-on-how-artificial-intelligence-can-solve-business-problems-1ae55d29d538

An Interview With Chad Silverstein - May 29, 2025

Less Spreadsheet Time, More Strategy Time: The objective isn’t simply to create better dashboards — it’s to have fewer dashboards overall. AI provides answers more rapidly, enabling us to dedicate more time to asking better questions.

Intoday’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Alex Andreyev of Evidnt.

Alex Andreyev is the CEO and Co-founder of Evidnt, a data and analytics platform that provides deep sales insights and analytics for CPG brands. Alex is a known innovator and leader, having developed the first multicultural data platform for Gravity, which was later acquired by Dentsu. He also contributed to the development of IPG’s maturity canvas and led data, analytics, and programmatic teams for major companies such as Coca-Cola, Johnson & Johnson, Sara Lee, Este Lauder, Darden, IBM, and AMEX. Alex is recognized as one of the foremost thought leaders in the retail and CPG data development and management space.

Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?

Throughout my career, I’ve been fortunate to land in entrepreneurial roles — even within large organizations. About 10 years ago, I found myself working at the forefront of automated media buying at a major agency. So when AI tools started to become more accessible to the public, I was instantly curious. We began experimenting with content creation using those early tools, just to explore what was possible. As AI evolved, so did our approach. We started applying it internally — automating some of our processes, generating content, and eventually using it to make complex retail data more useful and accessible. The common thread has always been: how do we use AI to simplify the complex, speed up decision-making, and unlock new value for our teams and clients?

Can you share the most interesting story that happened to you since you started working with artificial intelligence?

Honestly, the most interesting story isn’t even from work. I have a young daughter, and my wife doesn’t speak Ukrainian — my native language. There aren’t many Ukrainian children’s books available here in the States, so I started using AI to help me write bilingual storybooks for her. It’s been one of the most personal and meaningful uses of AI in my life. It’s not just about language learning — it’s about preserving culture, building connections, and creating something special for my family family.

You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

The first trait is curiosity. I’ve always been someone who wants to understand how things work — and then figure out how to improve them. Curiosity is what led me to test new tools and experiment while AI was still a question mark for most people. The second trait is conviction. If you truly believe in where the puck is going, you have to skate there — even if others don’t see it yet. That mindset has helped me build things before they were obvious, such as using AI in performance marketing long before it became mainstream. The third trait is adaptability. Things break. Tests fail. Assumptions get proven wrong. Being willing to say, “Okay, that didn’t work — what can we learn from it?” has been significant, especially in a field like AI where the only constant is change.

Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?

We sit on billions of dollars in transaction data across tens of thousands of stores. Traditionally, analyzing this kind of data would take weeks and even longer to translate into action. The problem is that trends in CPG move much faster than that. Social media, seasonality, competitive activity — everything shifts overnight. AI has changed the game for us and our brand clients. We now leverage it to detect anomalies and identify trends in near real-time, helping our clients make quick business and marketing decisions. It’s not just about speed; it’s about highlighting what truly matters and giving brands the confidence to act on it.

What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?

The biggest concern is the fear that AI will replace jobs. My view is the opposite — AI won’t replace people, but it will raise the bar. Think of it like this: the “bell curve” of creative or strategic output will shift. More work will be solid, and more ideas will be usable — but at the same time, everything might start to feel a little… similar. That’s where human creativity, strategy, and judgment become more critical than ever. The winners will be those who learn how to collaborate with AI, not compete against it.

In your opinion, what is the most significant way AI can make a positive impact on businesses today?

It gives us time back. We’ll be able to solve problems faster, automate repetitive tasks, and spend more time truly thinking, creating, and enjoying the work we do. I genuinely believe this will lead us to a four-day workweek in the near future without sacrificing output or effectiveness.

Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.

1. Speed-to-Insight: AI enables us to transition from raw data to recommendations in seconds. We’ve utilized it to analyze vast datasets, identify anomalies, and transform insights into action more quickly than any analyst team could accomplish independently.

2. Faster Prototyping & Testing: Whether it’s product mockups or campaign copy, AI empowers teams to test and iterate without the delays of full-scale production.

3. Streamlining Internal Operations: From automating reports to enhancing resource planning, AI minimizes busywork, allowing individuals to concentrate on high-impact tasks.

4. Idea Generation & Brainstorming: AI offers diverse perspectives, which helps ignite new ideas. It serves as a tool for expanding — not replacing — human creativity.

5. Less Spreadsheet Time, More Strategy Time: The objective isn’t simply to create better dashboards — it’s to have fewer dashboards overall. AI provides answers more rapidly, enabling us to dedicate more time to asking better questions.

How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?

Focus on where you’re wasting the most time on or don’t have enough time for but are important for growth. For many small businesses, this includes content, marketing, and reporting. Today’s AI tools can assist you in writing newsletters, generating social media posts, creating visuals, and tracking your performance — all without the need for a full-time team. You don’t require a data science team to begin. You just need to be open to experimenting.

What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?

Consider what happened with U.S. automakers — they had an opportunity to lead in EVs and hybrids, but they hesitated, and now they’re trying to catch up. AI is similar. The only thing to fear is falling behind. You don’t need to have all the answers — you just need the curiosity to begin exploring.

In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?

We’re already witnessing a shift. People are moving away from traditional search engines and using AI to obtain more tailored answers. This transition — from “search” to “ask” — will transform how businesses connect with consumers. Whether you’re a brand, publisher, or marketer, understanding how to be visible in AI-powered tools like GPT will be just as crucial as being present in search engines.

How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?

Ideally, it provides everyone more time for what truly matters: less time gathering data and more time generating ideas. Less time on status updates and more time devoted to strategic thinking. AI should enhance our work experience, making it feel more fulfilling — not more robotic. That’s the guiding principle.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. :-)

I’d love to encourage the next generation to use AI not as a crutch, but as a learning companion. Don’t just ask for answers; ask to understand. AI can be an incredible partner in curiosity, and I hope that’s how my kids — and their generation — grow up using it it.

How can our readers further follow you online?

Follow our insights and posts on LinkedIn (https://www.linkedin.com/company/evidnt), and feel free to connect with me on LinkedIn (https://linkedin.com/in/alexandreyev).

This was great. Thank you so much for the time you spent sharing with us.

Alex Andreyev Of Evidnt On Where to Assign Your Marketing Budget and Why

https://medium.com/authority-magazine/cmo-perspectives-alex-andreyev-of-evidnt-on-where-to-assign-your-marketing-budget-and-why-8823cc88e556

An Interview With Kieran Powell Kieran Powell, EVP of Channel V Media Feb 24, 2025

Keep testing with real-world sales as the benchmark — Clicks don’t equal sales, so ensure your metrics tie back to actual revenue.

Inan age where marketing landscapes are rapidly evolving and consumer behaviors are constantly shifting, Chief Marketing Officers (CMOs) play a pivotal role in steering their organizations’ marketing strategies towards success. With a plethora of channels, platforms, and techniques at their disposal, the decision on where to allocate the marketing budget is more critical than ever. We’re seeking to explore questions like: What factors influence their decisions? How do they balance between digital and traditional marketing channels? What role does data play in their decision-making process? And importantly, why they choose to invest in certain areas over others? As part of this series, we had the pleasure of interviewing Alex Andreyev.

Alex Andreyev is the CEO and Co-founder of Evidnt, a data and analytics platform that provides deep sales insights and analytics for CPG brands. Alex is a known innovator and leader, having developed the first multicultural data platform for Gravity, which was later acquired by Dentsu. He also contributed to the development of IPG’s maturity canvas and led data, analytics, and programmatic teams for major companies such as Coca-Cola, Johnson & Johnson, Sara Lee, Este Lauder, Darden, IBM, and AMEX. Alex is recognized as one of the foremost thought leaders in the retail and CPG data development and management space.

Thank you so much for your time! I know that you are a very busy person. Our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

I’ve spent most of my career at the intersection of marketing, data, and technology, helping brands optimize their media investments. My journey began in direct response and performance marketing, working with brands like AMEX, TD Ameritrade, MetLife, Caesars, and others. While leading performance marketing for Coca-Cola, I saw firsthand the gaps between media spending and real-world sales data. Brands were making decisions based on incomplete or delayed insights, leading to significant lost revenue. That realization led me to found Evidnt, where we help CPG brands bridge that gap — providing them with real-time data to measure, target, and optimize their campaigns.

It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?

Early in my career, I failed to properly place tracking pixels on a campaign page, meaning we were spending money without knowing if it was driving sales. It was a painful but valuable lesson that taught me two key things: 1) Always verify your operational setup before launching any campaign, and 2) Media investment should always be directly connected to performance results. This experience solidified why I’m so passionate about helping CPG brands improve sales based on data rather than assumptions.

Are you working on any exciting new projects now? How do you think that will help people?

Yes! We’re currently developing a platform for brands, marketers, and investors that allow them to query near real-time data in a transparent way, with deep, actionable insights — similar to ChatGPT, but for marketing and sales data. This will help brands optimize their strategies and investments faster, with real sales data, instead of relying on delayed or incomplete insights.

Thank you for that. Let’s now shift to the central focus of our discussion. Can you share an experience where a unique or unconventional budget allocation led to unexpected success in your marketing campaign?

With faster and more accurate data, we no longer rely on user-level targeting. Instead, we use auto-optimization for markets where sales are increasing, applying hyper-local targeting. This strategy has led to nearly a 40% increase in sales for some brands, proving that focusing on market-level trends rather than individual users can be more effective.

How do you balance investing in emerging marketing trends versus traditional, proven strategies in your budget decisions? Can you give us an example?

Ongoing testing and targeting are key. Early in Evidnt’s journey (2021), we probabilistically connected sales to individual users and tested one-to-one targeting against our buyer cohort-based targeting. What we found was eye-opening — geo-cohort targeting not only expanded reach and lowered CPMs (since we weren’t competing for the same cookied users), but it also drove an 18% improvement in sales lift versus one-to-one targeting. This proves that you don’t always need direct identity matching to drive impact — sometimes, context and intent matter more.

In what ways has data-driven decision-making influenced your approach to allocating marketing budgets, and can you provide an example of this in action?

Data is everything. Getting real-time sales data from tens of thousands of retailers nationwide gives us a strong performance indicator to optimize clients’ targeting and media spend in real-time. Our Evidnt Nexus platform takes this further by allowing brands to integrate their own sales data — whether from distributors, Shopify, Amazon, Walmart, or Target — giving them a holistic view of performance. This doesn’t just impact advertising; it helps brands optimize investment across marketing, promotions, and discounting strategies.

How do you evaluate the ROI of different marketing channels and decide where to invest more or cut back?

One-to-one marketing doesn’t make sense in every channel or every step of the buying journey. With the limitations on cookies and IDs and the lack of clear feedback from platforms like Google and Meta, brands must take a more holistic approach to sales attribution. Outside of loyalty programs and email marketing, brands need to rethink their strategies and focus on what actually drives sales, not just audience identity.

Based on your experience and success, what are the “5 Things To Keep in Mind When Deciding Where to Assign Your Marketing Budget, and Why?”

1 . Get your data house in order — Ensure you’re tapping into all sales channels (DTC, retail, e-commerce, distributors, RMNs) to make data-driven marketing decisions.

2 . Tap into new and emerging data sources — Privacy changes mean brands must explore alternative datasets beyond cookies and user IDs.

3 . Reevaluate your targeting strategy — One-to-one marketing isn’t dead, but it should be one part of a larger approach rather than the sole focus it’s become over the past decade.

4 . Demand transparency and speed from data providers — Brands need faster, more accurate data to optimize in near real-time, not months later.

5 . Keep testing with real-world sales as the benchmark — Clicks don’t equal sales, so ensure your metrics tie back to actual revenue.

Could you discuss a challenging budget decision you faced, how you navigated it, and the impact it had on your overall marketing strategy?

TikTok is a massive driver of trends, so being in front of those users can help brands break through the noise of established competitors. However, with geopolitical pressures surrounding TikTok, we help brands test and measure its true impact on sales. For example, when TikTok was temporarily removed in the U.S., many brands saw a 30–40% drop in online traffic. However, when we looked at offline sales data, the change was minimal. It was a small “blackout,” but we’ll see if a longer delay significantly impacts offline sales. Regardless of what happens next with TikTok, we’ll continue helping brands make data-driven reallocation decisions.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)

I’d push for marketing transparency at scale. Too much ad spend disappears into the digital ether with no accountability. Imagine if every marketing dollar could be tracked to real impact — whether sales, awareness, or brand loyalty. It’s time to stop treating marketing as a cost center and start treating it as a measurable, accountable growth engine.

How can our readers further follow your work online?
Follow our insights and posts on LinkedIn (https://www.linkedin.com/company/evidnt), and feel free to connect with me on LinkedIn (https://linkedin.com/in/alexandreyev).

This was very inspiring. Thank you so much for joining us!

This was a great conversation — thank you for having me! Marketing is evolving faster than ever, and I love discussing how brands can stay ahead, optimize their budgets, and drive real sales impact. Looking forward to continuing the conversation!

Alex Andreyev Of Evidnt On 5 Things You Need To Create a Successful Food or Beverage Brand

https://medium.com/authority-magazine/alex-andreyev-of-evidnt-on-5-things-you-need-to-create-a-successful-food-or-beverage-brand-54a6134636d9
Martita Mestey Sep 20, 2024

Look for Inspiration in Unlikely Places — Buying behaviors change rapidly. Look across categories for inspiration, like how Doritos developed their Ranch flavor by recognizing the popularity of Ranch dips in grocery deli sections. Constant innovation and drawing insights from unexpected places can lead to new, winning product ideas.

Asa part of our series called “5 Things You Need To Create a Successful Food or Beverage Brand”, I had the pleasure of interviewing Alex Andreyev.

Alex Andreyev is the CEO and Co-founder of Evidnt, a data and analytics platform that provides deep sales insights and analytics for CPG brands. Alex is a known innovator and leader, having developed the first multicultural data platform for Gravity, which was later acquired by Dentsu. He also contributed to the development of IPG’s maturity canvas and led data, analytics, and programmatic teams for major companies such as Coca-Cola, Johnson & Johnson, Sara Lee, Este Lauder, Darden, IBM, and AMEX. Alex is recognized as one of the foremost thought leaders in the space of retail and CPG data development and management.

Thank you so much for doing this with us! Before we dive in, our readers would love to learn a bit more about you. Can you tell us a bit about your “childhood backstory”?

Iwas born in Kyiv, Ukraine, and moved to the U.S. when I was nine. Both of my parents were entrepreneurs, and I caught the entrepreneurial bug early. In high school, I started my first business — a Christmas Tree company in NYC, which is still run by a childhood friend today (https://nycchristmastrees.com/). Since then, I’ve immersed myself in the world of marketing and advertising, working on iconic brands like Amex, IBM, and Coca-Cola. When I noticed a gap in real-time data for offline brands, I launched Evidnt in 2020 to help brands plan, target, and measure more effectively.

Can you share with us the story of the “ah ha” moment that led to the creation of Evidnt’s analytics platform?

Coming from performance marketing, I saw a massive gap in data for consumer packaged goods (CPG) brands. While working at Coca-Cola, I noticed that data from couponing and retailer sales was often limited, delayed, or inaccessible, making it difficult to measure real-time impact. I realized that 40% of CPG sales came from small and independent retailers who didn’t have access to the same level of insights as large chains. The “aha” moment was realizing we could help smaller retailers get access to these insights and aggregate their sales data to provide near real-time analytics to brands. That’s how Evidnt was born.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?

There were plenty of missteps along the way that helped shape our platform. Initially, we thought we could help small and independent retailers buy everything they needed from our marketplace through data and analytics. In hindsight, it was naïve to expect retailers to switch long-standing partnerships. Instead, we pivoted by introducing them to brands and distributors, providing more choices while still delivering top-notch analytics. This shift allowed us to refine our strategy and stay focused on supporting smaller retailers.

What are the most common mistakes you have seen people make when they start a food or beverage line? What can be done to avoid those errors?

Distribution is one of the biggest challenges. Many brands, especially those that started direct-to-consumer (DTC), try to scale too quickly, often at the expense of quality control or managing retailer relationships effectively. Without strong market support, brands struggle to meet demand and damage their chances of getting into larger chains. The best approach is sustainable growth — starting slow in key markets with strong online sales, building brand recognition, and expanding strategically through independent retailers. It’s not the fast track some brands expect, but it’s the most reliable way to build a lasting brand.

Let’s imagine that someone reading this interview has an idea for a product that they would like to produce. What are the first few steps that you would recommend that they take?

Test the market, innovate, and keep refining. One great example is GNGR Labs, which continuously improved their Ginger Shots — tweaking flavors, packaging, and marketing based on customer feedback. They used data and analytics to drive sales, identify key markets, and work directly with independent retailers to test their products in stores. This approach led to steady, sustainable growth while maintaining product quality.

Many people have good ideas all the time. But some people seem to struggle in taking a good idea and translating it into an actual business. How would you encourage someone to overcome this hurdle?

The difference between a good idea and a successful business is execution. It’s about starting small, doing the non-scalable, boring tasks — planning, testing, refining. You need to test, learn from your data, and constantly improve. Some people just need a co-founder or partner who complements their skillset. That’s why we love working with partners, helping with the marketing and analytics while they focus on building their brand and product.

There are many invention development consultants. Would you recommend that a person with a new idea hire such a consultant, or should they try to strike out on their own?

It depends on the business, but I don’t think patents solve all problems. The best brands grow despite competition by focusing on their unique value, brand promise, and customer experience. There will always be competitors who can copy your product, but they can’t replicate your brand or what it stands for.

What are your thoughts about bootstrapping vs looking for venture capital? What is the best way to decide if you should do either one?

In today’s market, it’s tough for startups to meet the high growth expectations of VCs. Unless you find a VC who truly shares your vision, I recommend bootstrapping as long as possible. It forces you to build a healthy, profitable business model. With high interest rates and rising consumer acquisition costs, it’s harder than ever to scale quickly, so sustainable growth is key.

Can you share thoughts from your experience about how to file a patent, how to source good raw ingredients, how to source a good manufacturer, and how to find a retailer or distributor?

While we’re not specialists in this area, the fundamentals are research, testing, and starting with things that don’t scale. It’s about putting in the work to find reliable partners and building a solid foundation.

What are your “5 Things You Need To Create a Successful Food or Beverage Brand” and why?

1. Test Early — Test your product with key audience groups, gather feedback, and refine. GNGR Labs continuously improved their Ginger Shots by tweaking flavors, packaging, and marketing based on customer feedback, leading to better product-market fit.

2. Find Your Tribe — Once you understand what your customers love, scale through communities that resonate with your brand. Rap Snacks, for example, started in Philadelphia but grew by aligning with rap celebrities who were fans, which expanded their reach and led to new product lines.

3. Scale Strategically — Don’t rush into big retail deals before you’re ready. Cosrx, a skincare brand, found success by starting small with eCommerce and regional retailers. They scaled when they had the right systems in place, eventually landing in Sephora and Target.

4. Leverage Data — CPG brands often lack real-time data, but you can use insights from your website, eCommerce, and distributor sales to understand customer behavior. Brands like Bibigo analyze buying patterns to optimize marketing strategies and tailor promotions for different regions.

5. Look for Inspiration in Unlikely Places — Buying behaviors change rapidly. Look across categories for inspiration, like how Doritos developed their Ranch flavor by recognizing the popularity of Ranch dips in grocery deli sections. Constant innovation and drawing insights from unexpected places can lead to new, winning product ideas.

Can you share your ideas about how to create a product that people really love and are ‘crazy about’?

Test, test, and test some more. Not everyone will love your product right away, but through constant testing and improvement based on customer feedback, you can find your brand’s unique flavor profile. As the advertising legend said, “The customer isn’t a moron; she’s your wife.” Listen to your customers and keep refining your product.

Ok. We are nearly done. Here are our final questions. How have you used your success to make the world a better place?

At Evidnt, we believe there’s room for both big brands and small businesses to thrive. By leveraging our expertise in data and insights, we aim to democratize access to actionable analytics so that everyone — from the smallest retailer to the biggest brand — can succeed. A rising tide lifts all boats.

You are an inspiration to a great many people. If you could inspire a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger.

I’d encourage people to start taking action on their ideas. Entrepreneurship is a rollercoaster, but the first step is always to start. You never know where it will lead, and even the smallest idea can spark something much bigger.

Thank you for these fantastic insights. We greatly appreciate the time you spent on this.

Alex Andreyev of Evidnt On The Future Of Retail Over The Next Few Years

https://medium.com/authority-magazine/alex-andreyev-of-evidnt-on-the-future-of-retail-over-the-next-few-years-12ae4cec3f7c

It’s about the experience — Consumers do not buy products; they buy product benefits and the feelings associated with them. Make the buying experience enjoyable.

Aspart of our series about the future of retail, we had the pleasure of interviewing Alex Andreyev.

Alex Andreyev is the CEO and Co-founder of Evidnt, a data and analytics platform that provides deep sales insights and analytics for CPG brands. Alex is a known innovator and leader, having developed the first multicultural data platform for Gravity, which was later acquired by Dentsu. He also contributed to the development of IPG’s maturity canvas and led data, analytics, and programmatic teams for major companies such as Coca-Cola, Johnson & Johnson, Sara Lee, Este Lauder, Darden, IBM, and AMEX. Alex is recognized as one of the foremost thought leaders in the space of retail and CPG data development and management.

Thank you so much for joining us in this interview series! Before we dive in, our readers would love to learn a bit more about you. Can you tell us a story about what brought you to this specific career path?

Inmy previous roles, I’ve spearheaded performance marketing initiatives in various industries and for well-known brands like IBM, AMEX, TD Ameritrade, Caesars Hotels, and UFC, among others. However, running the performance marketing team for Coca-Cola while at Universal McCann presented unique challenges due to readily available sales data. Anecdotally, I always joked that by the time we received performance insights on our Superbowl ads for Coca-Cola, my beloved NY Jets would already be out of the following year’s playoffs. This was due to data scarcity, delays, and fragmentation across retail sales data. This motivated me to seek a better solution. Thus, we launched Evidnt to bridge the gap and provide CPG brands with superior, quicker, and more profound sales insights to facilitate better business decisions.

Can you share the most interesting story that happened to you since you started your career?

I have been fortunate to work with incredible brands and individuals. One of the most exciting experiences was working with the Inter Milan soccer club to help expand their brand in the US and Asian markets. I had the pleasure of meeting iconic players such as Javier Zanetti and of sitting in the executive box to enjoy the game.

Are you working on any new exciting projects now? How do you think that might help people?

Yes, we’re currently working on incorporating AI’s power into our database to help brands answer business questions faster and provide deeper insights that are not easily caught. Retail space is dynamic and fast-moving and is a great indicator of the economy, brand perception, and buyer behaviors, using AI we’re hoping to unlock this for brands and marketers to make better decisions for their business.

None of us are able to achieve success without some help along the way. Is there a particular person to whom you are grateful, who helped get you to where you are? Can you share a story?

There have been many people who have been tremendous supporters in my career. Still, one person who has consistently given me opportunities to grow and has fueled my passion for creating is Sean Muzzy, the current Global President of Kinesso. Sean has always supported me, allowing intrapreneurial ideas to flourish within the large organizations he managed. He enabled me to help build the first in-house programmatic team at Ogilvy and to develop an innovative and consultative approach at Kinesso.

How have you used your success to bring goodness to the world?

We have had the privilege of working with numerous small and medium-sized businesses on both the retail and brand sides. SMBs are often left behind due to the lack of data and deep insights needed to compete with larger companies. We are proud to provide deep insights used by big box retailers to these smaller retailers who are instrumental parts of their local communities. We help them grow their businesses, regardless of size.

Ok super. Now let’s jump to the main questions of our interview. The Pandemic has changed many aspects of all of our lives. One of them is the fact that so many of us have gotten used to shopping almost exclusively online. Can you share a few examples of different ideas that large retail outlets are implementing to adapt to the new realities created by the Pandemic?

Online retail has grown, it still makes up a relatively small portion of total sales. Larger retailers are now using data and analytics to track sales and buyer trends, and are making more products available to customers through curated marketplaces. Like Amazon’s marketplace, retailers who understand their customers well use their brand as a platform to allow other brands to reach their customers while making money through marketing and marketplace fees. If every platform is now a retail platform, every retailer is now also a marketplace.

The supply chain crisis is another outgrowth of the pandemic. Can you share a few examples of what retailers are doing to pivot because of the bottlenecks caused by the supply chain crisis?

We are seeing many retailers using data and analytics to improve their inventory management. They want to ensure that they don’t have an excess of inventory, but also that they don’t run out of stock and miss out on sales. Retailers are also focusing on understanding regional differences and trends as demographics change, and as international and DTC brands drive growth.

How do you think we should reimagine our supply chain to prevent this from happening again in the future?

While no one can predict another pandemic or a run on certain products, we can track and provide insights into local buying behaviors to prepare retailers for changes in buying behaviors. By unlocking the insights from sales data, retailers should be able to better prepare and proactively make business decisions to limit interruptions to supply chains.

In your opinion, will retail stores or malls continue to exist? How would you articulate the role of physical retail spaces at a time when online commerce platforms like Amazon Prime or Instacart can deliver the same day or the next day?

The retail industry is changing, and although it’s hard to predict the future of large malls that may have been overdeveloped in some areas, online services like Amazon Prime and Instacart can’t provide the same experience as a physical store. The next generation of consumers will demand a more engaging shopping experience, and retailers that can combine entertainment and enjoyment with the act of making a purchase will continue to thrive. Customers will want more than just convenience and fast delivery. This also applies to brands, which are beginning to integrate their brand into retail stores, allowing customers to experience their products and brand promise beyond just using them. As David Ogilvy once said, you can’t bore people into buying a product, so in this sense, the shopping experience needs to be more captivating.

The so-called “Retail Apocalypse” has been going on for about a decade. While many retailers are struggling, some retailers, like Lululemon, Kroger, and Costco are quite profitable. Can you share a few lessons that other retailers can learn from the success of profitable retailers?

Just like AI won’t eliminate all jobs, eComm won’t kill all retailers. Kroger and Costco are masters at utilizing data to understand which products to stock, how to price them, and when to phase out products that aren’t selling well. They leverage their sales data to assist brands in improving their product marketing and driving higher sales, broadening their product offerings and opportunities for profit. By embracing data to gain a better understanding of their customers, retailers can take cues from Costco and Kroger to enhance profits and offer the right products to their customer base.

Amazon is going to exert pressure on all of retail for the foreseeable future. New Direct-To-Consumer companies based in China are emerging that offer prices that are much cheaper than US and European brands. What would you advise to retail companies and e-commerce companies, for them to be successful in the face of such strong competition?

As mentioned earlier, Amazon and DTC retailers, as well as brands that focus solely on low prices and convenience, will capture a portion of the market, especially during economic downturns caused by inflation and market uncertainties, which put pressure on consumers. However, retail involves more than just purchasing products and necessities; it’s about purchasing the value of the product and the associated emotions. Even for utilitarian products like a simple salt grinder that I can buy inexpensively on Temu, the experience and satisfaction are completely different when I can choose, touch, and feel a similar product while enjoying a Starbucks drink at Target.

Based on your experience and success, what are the five most important things one should know in order to create a fantastic retail experience that keeps bringing customers back for more? Please share a story or an example for each.

1. Know your buyers — Understand who they are, what they buy, and why. Look at the data and talk to them anecdotally.

2. Keep a close eye on trends — It is crucial for retailers to utilize trend trackers and monitor their in-store sales in order to stay current with the latest developments. As a side note, Evidnt’s analytics app provides retailers with product insights and trends from their own stores as well as similar ones in their markets. While keeping track of every TikTok trend may be challenging, closely monitoring changes in product sales is an effective way to understand the preferences of your buyers.

3. It’s about the experience — Consumers do not buy products; they buy product benefits and the feelings associated with them. Make the buying experience enjoyable.

4. Collaborate with brands — There are multiple ways to make brands stand out in your stores, whether through data partnerships, extra marketing opportunities, or simply by placing products that sell well together in high-traffic areas. Brands tell a story, and your store can be the perfect place for them to do it well.

5. Let your customers find you online — There are numerous solutions that can help you showcase your products online. Some of the easiest options, like Evidnt’s store locator, can help buyers find your store through advertisers’ ads. Online platforms provide an incredible opportunity for you to connect with both current and prospective buyers.

Thank you for all of that. We are nearly done. Here is our final ‘meaty’ question. You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)

I would initiate a movement centered around empathy and active listening. With so much division in the world, I believe that truly listening to our clients, partners, friends, and even those we don’t agree with is the initial step toward understanding their challenges, pains, and ambitions. Empathy involves making an effort to see things from their perspective and walking in their shoes.

How can our readers further follow your work?

Follow our insights and posts on LinkedIn (https://www.linkedin.com/company/evidnt)

This was very inspiring. Thank you so much for joining us!

Specific Media Deals Into The Programmatic Game, But Can It Play?

https://www.adexchanger.com/ad-exchange-news/specific-media-deals-into-the-programmatic-game-but-can-it-play/

Why Marketers Struggle To Link Online And Offline Customer Data - AdExchanger

https://www.adexchanger.com/data-exchanges/why-marketers-struggle-to-link-online-and-offline-customer-data/