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Here are 10 things to consider.
In today’s competitive landscape, brands rely heavily on consumer feedback to make informed business decisions. Many are considering building their own consumer feedback analytics platform to gain full control over their data and insights. However, this approach introduces several challenges, both in terms of initial setup and ongoing maintenance. Before you decide, here are 10 critical factors to consider.
Scraping data from multiple retailers presents a unique set of challenges, especially at the SKU level, where product listings can vary significantly between sites. Each retailer has its own distinct website structure, URL patterns, and product categorization methods, which means that scrapers must be tailored for each individual site. These scrapers need to accurately capture data like product descriptions, brand model, ratings, and reviews, all while navigating constantly evolving site architectures.
Even minor changes in a retailer's website, like updates to URL formats or product categorization, can break scrapers, requiring sophisticated monitoring, constant adjustments and re-testing, making SKU-level scraping across multiple retailers particularly challenging. This labor-intensive process is prone to downtime, and without real-time maintenance, companies risk missing critical product data updates, leading to incomplete analyses and poor business decisions. While outsourcing scraping may seem like a quick solution, it often lacks the flexibility to address changes swiftly, creates dependency on external timelines, and can also result in delays or inaccuracies in the data.
Matching the same product across different retailers is a nuanced task, and it's even more challenging for competitor products. While you may have internal systems to track your own SKUs, competitor product details aren't always readily available. Retailers often use varying SKUs, product names, and descriptions, making automated matching difficult without sophisticated algorithms. This requires advanced product-matching algorithms that rely on tailored models to deal with ambiguities and inconsistencies in product listings across retailers and competitor products.
Example: A competitor’s 12-ounce bag of "BBQ Chips" at one retailer might be listed as "Barbecue Chips Family Size" at another, even though it's the same product. These slight variations make product matching difficult, and require a sophisticated approach..
Creating accurate filters for relevant product attributes is a nuanced task that demands significant effort and deep domain expertise. Data science teams must design systems that can sift through vast amounts of data, identifying and prioritizing key attributes such as "durability," "taste," or "usability" across thousands of SKUs. This process is not just about identifying obvious features but also involves understanding which attributes truly influence consumer sentiment within specific categories. Given the complexity and variation across product lines, developing these filters requires extensive time, iteration, and specialized knowledge.
In addition to the initial work, filters must be flexible enough to adapt as consumer preferences evolve. If the system lacks precision in capturing the right attributes, critical insights can be missed, leading to incomplete analyses and suboptimal business decisions. Without robust filters in place, data science and insights teams may find themselves struggling to extract the insights necessary for timely product improvements or innovation, slowing down their ability to respond effectively to market needs.
Example: Pet care brands need to capture key attributes like "allergy relief" or "joint health," which may not always be explicitly stated in reviews. For instance, pet owners might say, "My dog isn't scratching as much" or "My cat is moving more easily," and without accurate filters, these critical insights into product effectiveness could be missed.
Handling syndicated reviews, promotional content, and duplicate reviews across retailers requires a sophisticated approach to tagging and de-duplication. Reviews often appear on multiple platforms or within syndicated content, making it essential to correctly identify and categorize them to avoid inflating positive sentiment or skewing analysis. This task involves building a system capable of automatically detecting and flagging duplicate or incentivized reviews without manual intervention, which can be both time-consuming and error-prone.
An additional challenge is that not all promotional reviews are explicitly tagged, meaning systems must be able to detect certain language patterns or clues within the review itself to identify incentivized content. Advanced algorithms are required to recognize these subtle indicators while also parsing through vast amounts of review data, often presented in different formats across retailers. Without robust mechanisms in place, companies risk drawing faulty conclusions from the data, which can mislead decisions on product performance, marketing strategies, or customer satisfaction.
Example:
In the beauty industry, a skincare product sold across multiple platforms like Walmart, Sephora, and Ulta may have syndicated or duplicate reviews that, if not properly de-duplicated, can inflate positive sentiment and distort insights. Additionally, promotional reviews with subtle language like “I received this product for free” need to be accurately flagged, or else they risk being misclassified as organic feedback, leading to skewed analysis.
To make sense of unstructured reviews, you need accurate NLP models trained specifically for consumer feedback in your product category. While off-the-shelf large language models (LLMs) like GPT can quickly identify themes or provide summaries from data, they fall short in several key areas, especially for ongoing, large-scale consumer feedback analysis. Off-the-shelf LLMs can’t deliver the depth, accuracy, and consistency needed for nuanced, long-term insights.
While these LLMs can identify themes, provide summaries, and enable conversations around your data, they come with significant limitations:
Purpose-built NLP models consistency, accuracy, quantitative comparison, and ongoing tracking—allowing you to ask deeper questions such as:
Example: In the fashion industry, the word "tight" might shift from being interpreted negatively to positively depending on fashion trends. Your models would need constant training to understand such shifts in context.
As your company expands into new markets or introduces new product lines, your system needs to scale. This includes handling different languages, regional retailers, and culturally specific nuances in consumer feedback. Building a system that can handle multiple languages, localization needs, and market-specific sentiment would require extensive development and maintenance.
Example: A product may perform well in North America but receive negative reviews in European markets due to cultural differences. Your system must be able to detect these nuances and adjust accordingly.
Building your own system introduces the risk of bias, both in how the data is collected and how the AI processes it. Without robust, fine-tuned models and quality checks, your insights could be swayed by poorly structured or incomplete data. There’s also the challenge of handling inconsistencies between retailers' data formats, review guidelines, and sentiment variances.
Example: If a retailer tends to show more positive reviews at the top of product pages, your system might unintentionally skew towards overestimating customer satisfaction unless this bias is accounted for in the design.
Building user-friendly dashboards that provide deep, customizable insights is a complex process that goes beyond simple data visualization. You’ll need to create dynamic tools that can adapt to the specific needs of various internal teams. For instance, marketing teams might want to see broader sentiment trends over time to assess brand perception, while product development teams require more granular, SKU-level insights to drive decisions on product improvements.
Creating these customizable views requires significant front-end development expertise, a deep understanding of UX/UI design, and the ability to make complex data intuitive and actionable for non-technical users. As business needs evolve and new questions arise, continuously building and adjusting these dashboards can become an ongoing and resource-intensive task, demanding constant refinement to ensure they remain relevant and effective across departments. Without a well-thought-out design, dashboards can become cluttered or difficult to navigate, making it challenging for teams to extract the insights they need to make informed decisions
Setting up real-time alerts for key events, like a sudden increase in negative reviews or a competitor gaining momentum, is crucial for staying ahead of potential issues and making timely, informed decisions. A well-designed alert system ensures that critical changes are flagged, allowing teams to respond quickly before problems escalate, such as addressing product defects or adjusting marketing strategies.
However, building an effective alerting system requires sophisticated logic to filter out irrelevant noise and prevent teams from being overwhelmed with false positives or trivial updates. The ability to detect patterns—such as a spike in reviews mentioning “broken” or “defective”—is invaluable, as it enables businesses to proactively address customer concerns, protect their brand reputation, and minimize the risk of lost sales or consumer trust. Without accurate, timely alerts, companies risk being blindsided by emerging issues, which can have long-term consequences for both product performance and customer loyalty.
Example: An alert should be triggered for a sudden decline in sentiment at a specific retailer, with reviews mentioning "damaged packaging" or "leaking bags." This quick notification enables your team to pinpoint and proactively address issues, preventing further customer dissatisfaction and lost sales.
As consumer language evolves, maintaining AI models to accurately capture these changes requires continuous updates and retraining. While this process is crucial for keeping sentiment analysis and insights relevant, it can also be labor-intensive and consume valuable time from data science teams. Instead of dedicating resources to manually updating models and refining topic classifications, data science teams can be far more effective when their focus shifts to high-level, business-specific projects.
By automating routine tasks like maintaining sentiment models and analyzing reviews, companies can free up their data scientists to work on strategic initiatives that have a direct impact on the bottom line. This could include using insights from consumer feedback to develop predictive models for product improvements, identifying emerging trends, or uncovering new market opportunities. When data science teams are not bogged down by repetitive maintenance tasks, they can deliver deeper, actionable insights that drive long-term business growth.
Bonus Consideration: Analyzing Multiple Data Sources
While reviews are a critical part of consumer feedback, many brands also rely on customer care interactions, search term data, and other unstructured sources to get a complete view of customer sentiment. Building a system that integrates these various data types—each with its own level of variability—adds even more complexity. Each data source requires unique handling to ensure it contributes meaningfully to the overall analysis.
Example: Customer care data may highlight issues like "broken parts upon arrival" that aren’t always reflected in reviews, but understanding and analyzing this alongside reviews gives a clearer picture of product quality and opportunities for growth.
While building an in-house consumer feedback tool provides full control and customization, it introduces numerous hidden challenges that require constant upkeep, specialized technical expertise, and significant resource allocation. For brands with thousands of SKUs and data coming from multiple retailers and sources like customer care or search data, the complexity of data collection, cleaning, analysis, and visualization becomes a major undertaking, with long-term maintenance costs that may outweigh the benefits of control. It’s essential to weigh these factors before deciding whether to build or buy a tool that offers AI-driven, turnkey solutions with industry-tested precision.
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