A Product Manager’s Guide to Product Optimization with Reviews
A Product Manager’s Guide to Product Optimization with Reviews

A Product Manager’s Guide to Product Optimization with Reviews

Raz Michaeli

Raz Michaeli

Jul 7, 2024 ‧ 7 MIN.

The key to any successful innovation strategy is being consumer-centric. This means actively listening to consumers, allowing them to guide the innovation process. What product features are most desirable to consumers? Look no further than consumer reviews, the ultimate source of truth, where no one is shy about speaking their mind. Feedback ranges from  product performance, defects, to wish list items. 

However, product managers don’t have infinite resources at their disposal. That means they often need to prioritize new product features and adjust their product roadmap. There are many ways that product managers can analyze the costs and benefits of innovations.

This blog will use one product management framework, RICE, to explore the different ways that consumer insights from online reviews can help product managers develop a road map for product optimization.

Product Manager Best Practices and RICE

One of the most common best practices used by product managers to prioritize features is the RICE formula, which stands for Reach, Impact, Confidence, and Effort. By leveraging consumer sentiment and discussion volume, product managers can align prioritization with generally accepted best practices. Consider that Reach and Impact can be determined by discussion volume: the higher the discussion volume, the greater the reach and impact. This volume can pertain to an entire vertical or specific product topics. Confidence aligns with consumer sentiment and star rating drivers. For example, if sentiment is low compared to competitors, it can provide confidence in the value of developing the feature. Drivers of one and two star ratings are ones that should be tackled to improve a brand or product’s average star rating.

The final parameter, Effort, reflects a business’ resources to develop a feature. Effort can reflect both monetary and manpower resources. This parameter must be assessed by each organization on its own.

Category Insights

The journey begins with category insights, offering product managers unparalleled flexibility to drill down into consumer sentiment and discussion volume. This is achieved using generative AI, which performs textual analysis of reviews with large language models. Product managers can then easily access positive and negative consumer rankings for:

  • Vertical benchmarks
  • Competitors’ products
  • Their own brand’s products
  • Product topics

As mentioned earlier, these parameters align with the RICE formula: Reach and Impact are reflected in the discussion volume of vertical benchmarks and product topics, indicating what features have broad interest and significance. Confidence is derived from the consumer sentiment and rankings, guiding decisions on feature development. By exploring the discussion volume and sentiment for the overall category, product managers can determine the features that can be developed. 

Let’s put it to the test by exploring the vacuum category.

Vacuums

Here’s a snapshot of the data from the last 24 months for vacuums. The average sentiment for the entire vertical for the past 24 months is 65%. The category has 479,487 reviews that have been distilled into 1.4M opinions of various aspects of the category.

Insights for the entire vacuum hierarchy.

As a product manager, you might focus on a specific subcategory that presents a different picture. For instance, honing in on the wet/dry vacuum subcategory, consumer sentiment rises to 68%, with over 87,000 opinions. 

Metrics for the wet/dry vacuum subcategory.

Here’s how you can apply this data to the RICE formula:

  • Reach: Use the total number of opinions (87,000 for wet/dry vacuums) to estimate how many consumers your feature will impact.
  • Impact: Measure the difference in sentiment (68% for wet/dry vacuums vs. 65% for the overall category) to gauge the potential improvement your feature can make. It’s an opportunity to check specific topics to see what’s trending up or down for an additional dimension.
  • Confidence: The consistency in sentiment across a large sample size provides confidence. Analyze how sentiment changes with features, and use this to back your decisions.
  • Effort: Identify which product aspects (e.g., Suction, Size, Price/Value) are driving sentiment. Estimating the effort needed to address these specific issues will help prioritize tasks effectively.

Beyond priorities in accordance with RICE, the numbers indicate that category-wide, consumers are somewhat satisfied with their purchases but there is definitely room for improvement. 

Ask yourself, are your own products above or below the benchmark? A below average benchmark is indicative of rampant consumer dissatisfaction begging the question of why do consumers feel the way they do. If your product is above the benchmark, you know that your product is on the right track and you should be looking for opportunities to innovate.

For example, if your product’s sentiment is below the benchmark, it indicates areas needing improvement. Conversely, if it’s above the benchmark, it suggests your product is well-received, and you should explore further innovations. In our case, Shark and Ryobi’s wet/dry vacuums are above the benchmark, while Bissell is below. The lower sentiment for Bissell highlights areas like Suction, Power, Size, and Price/Value for Money that require attention. By addressing these, product managers can enhance their roadmaps to better align with consumer expectations and improve overall satisfaction.

Comparative view of leading vacuum brands.

By exploring consumer sentiment around various product topics, we gain a clearer understanding of why Bissell is below the industry benchmark. Topics that stand out include Suction, Power, Size, and Price/Value for Money. This data highlights the challenges Bissell faces with their wet/dry vacuum line. These areas for improvement are crucial insights that any product manager can integrate into their roadmap. Note that overall, the brand has a relatively low discussion volume at 181 reviews compared to competitors like Ryobi.

A product manager can focus on the sentiment and volume of each topic to help prioritize product features. For instance, the share of discussion for Suction is 24%, with a 70% average consumer sentiment. In this case, Ryobi falls short of the benchmark. A product manager for the company might decide to prioritize optimizing this feature because it clearly impacts nearly a quarter of consumers. This feature demonstrates significant Reach, Impact, and Confidence.

Comparative view highlighting the consumer sentiment for topics.

Delving into Topics

In the earlier example, we examined specific brands to identify areas for product improvement. However, exploring topics across the entire vertical also holds tremendous value.

The graph below reveals a different story, showing that consumer sentiment across several topics is low or negative. These topics range from attachments, price/value for money, and battery life to product lifespan. Each topic with low consumer sentiment presents an opportunity for a brand to innovate and optimize its product offerings:

  • How can product attachments be improved?
  • Can battery life be enhanced?
  • Can the product lifespan be extended?

By considering these aspects, product managers can apply the RICE formula to prioritize improvements—evaluating Reach and Impact from the breadth of topics discussed and assessing Confidence from sentiment data. This approach helps develop a more robust product aligned with consumer needs.

Topics for the wet/dry vacuum subcategory and their respective consumer sentiment.

To get a handle on the feedback for each topic, a topic summary for positive and negative sentiment can be generated. For this example, the summary has been generated for the Attachments topic. The positive opinions highlight what consumers love but the negative ones are areas where a brand can enhance their product to align with consumer demands.

Summary of positive and negative options for the Attachments topic which can help product managers with their innovation roadmap.

Spotlight on Wish lists

When writing a product review, consumers often express features they wish they had in the product. The generative AI engine is quickly able to aggregate the data to provide a succinct summary of what consumers want in the product. This is low hanging fruit that any product manager can incorporate into innovation strategy.

The example below showcases a summary of points from the AI engine for the consumer wish list: 

  • Consumers wish for a better hose, preferably one that is quieter and more suitable for shop or spot vacuuming.
  • They would like better storage options for tools and fittings.
  • Some consumers wish for a pressure relief valve on the floor attachment and a longer power cord.
  • There is a desire for the vacuum to come with a helmet and a longer hose or an extension wand.
  • Consumers also wish for a longer power cord and a longer suction hose.
  • Some consumers would like a longer hose and better attachments for the price.
  • There is a desire for a longer cord, a longer hose, and more suction power.

All these points can be tackled using consumer feedback to optimize the product line. It demonstrates how a brand can actively listen to consumers to launch a better product in response. 

Spotlight on Defects

Consumer wish lists are valuable to take current product offerings to the next level. However, understanding product defects can help fix features or issues with the product line.

Similar to the wish list, the generative AI can produce a summary of the most common defects. As we can see from the list below, much of the feedback has to do with product quality:

  • Dented body
  • Broken foot
  • Missing pieces
  • Broken filter holder
  • Missing screws
  • Defective motor
  • Damaged foam filter

A brand that wants to move the consumer sentiment needle to a more positive place would need.

Star Rating Drivers

Another factor a product manager can explore is the star rating driver, which specifically looks at the topics driving five-star ratings as well as one- and two-star ratings. In the chart below, the following topics bring down the star rating average for the entire category: lifespan, hose, suction, filters, and price/value for money.

There are 6,044 reviews with 1-2 star ratings across the hierarchy. In 11% of the cases, consumer sentiment for the lifespan topic was the top contributor. As a product manager crunching the numbers to prioritize features, star rating drivers can be used as part of Impact. Note that the lifespan topic appeared earlier in the piano chart with all the topics. Given the recurrence of these topics, they can influence the Confidence score in the RICE formula.

Star rating drivers for the wet/dry vacuum category which can help support innovation priorities.

Conclusion

In today’s consumer-driven market, the secret to successful product innovation is a consumer-centric approach. Actively listening to consumer feedback from reviews provides invaluable insights into product performance, defects, and desired features. By applying the RICE formula—Reach, Impact, Confidence, and Effort—product managers can effectively prioritize features and adjustments, ensuring that their roadmap aligns with consumer needs and expectations.

Using consumer sentiment and discussion volume, product managers can pinpoint which features to enhance or develop. This approach not only helps address critical areas for improvement but also ensures that innovations are based on real consumer data, leading to more satisfied users and a competitive edge in the market. Integrating these insights into the product development strategy allows brands to meet consumer demands more effectively and drive long-term success.

Learn more about how Revuze can help with Product Innovation & Optimization here.

Raz Michaeli

Raz Michaeli

Emily Louise Spencer is an in-house content writer at Revuze. She is a graduate of the University of York with a master's degree in Chemistry. A published scientific author, she now works as a content writer and copy editor.