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  • Writer's pictureDarren Cody

Influential Buying in Social Marketplaces

What Influential Buying Is Not

Imagine if shopping online was like getting advice from your neighbour across the fence, not a billboard. That's Influential Buying: it's not about whose ad is flashiest on your social feed but about genuine recommendations from real people. Unlike Social Commerce, where endorsements are often as authentic as a reality TV show script, Influential Buying is the genuine article. It's like recommending a winter jacket in Canada — if it doesn't keep you warm, the community's feedback will be as chilly as the air outside. So, let's dive into why not all endorsements are created equal, especially when a 'harmful insight' can leave you out in the cold.

The Social Marketplace

When building a marketplace, social proof, trust & safety will likely be at the forefront of your design and development requirements. It may be because more and more users are becoming aware of disingenuous reviews flooding social media. We understand that we can only trust those we know or follow online. 

A social marketplace takes, as you guessed it, a social-first approach by establishing a collaborative community as the foundation and primary growth lever that leads to transactions or other monetization avenues for the company. It brings like-minded communities together in a central platform to shape opinions and, ideally, buying patterns. 

The social marketplace allows community members to build a reputation online, which holds value to the marketplace itself. The community becomes the defensive moat, protecting it from a funded competitor spinning up overnight and repeating the battle of Wimdu and Airbnb. If you haven’t heard of that fascinating true story, please do a deep dive and learn how persistence is critical to starting a marketplace. 

In “The Impact of Social Influence and Trust on Customer-to-Customer Online Shoppers’ Purchase Intention: An Empirical Study in Indonesia,”; it was uncovered that social influence is a contending factor that plays into the purchase patterns of new and existing customers above Trust. 

Some seemingly obvious statistics:

  • If recommended by a peer, you’re x4 more likely to convert on a purchase - Nielsen

  • Comparing peer endorsements against online advertising, 92% trust the endorsement while only 33% believe an ad - Nielsen

  • Word-of-mouth recommendations convert between 3 to 5 times higher than other traditional channels

The social marketplace is exactly that, at scale! We are turning digital strangers into people with trustworthy insight about a product, service, or other members of the site. 

The Influential Buying Ripple Effect

Have you ever read a review of something you would buy and wanted to ask the reviewer follow-up questions? What about when you’re working in the backyard or have an ongoing project in your apartment and need a recommendation for a tool to complete the job? What about choosing the adoption analytics tool to integrate into your marketplace? What do you do? 

We (Marketplace Studio) are willing to offer a 2-hour session on Product Strategy if you don’t typically follow this sequence. 

You will first ask someone in your network who is knowledgeable on the subject, and if that yields little to no results, you’ll go to Google to find ‘authentic’ reviews. You inherently trust a person’s opinion first, followed by the words you read from a stranger. Why is that? We believe we can shift this into accepting a digital stranger’s recommendation for the truth if you know there is a ripple effect to every action in the marketplace.

Imagine a world where every review and every recommendation sends ripples across the marketplace pond. With a clever Authority Scoring system, your marketplace becomes a realm where trust is the currency, and every ripple amplifies the community's voice. Suddenly, the opinion of the underdog reviewer, once overlooked, becomes as golden as a trusted friend's, thanks to the transformative power of the ripple effect.

Imagine planning the ultimate party and needing the perfect playlist to set the mood. You'd likely ask a friend with impeccable music taste for recommendations. If their suggestions fall flat, you turn to the digital world, seeking out playlists with glowing reviews. This is the Ripple Effect in action. In your marketplace, it's like throwing a party where every guest's opinion shapes the vibe. The Authority Scoring system acts as your DJ, ensuring the recommendations that get the most nods or shakes influence the party's success. Every review every shared experience, contributes to the collective energy, making your marketplace the place everyone wants to be.

Community Regulation

How could you stop a particular vitamin company from hijacking product reviews for the sole benefit of the company? Is $600,000 fine enough? What if there was some self-regulation in the marketplace? Where people can control the authentic impact a bad or good product/ brand has on the marketplace with more than just a thumbs up or down, which has no reflection on themselves? 

Enter community regulation established by Wikipedia. Did you know that in 2023, “812,635 registered editors made at least one edit?” One out of 85 editors in 2023 made changes to an AFD (Articles for Deletion), demonstrating the effectiveness of a self-regulating community through a unique scoring system—source. Being a registered author on Wikipedia allows you to create new articles, edit semi-protected pages, and upload images and other media files. Registered users can also rename pages, customize the appearance and features of the site through preferences, maintain a watchlist of articles of interest, and interact with the community through user talk pages. Moreover, contributing under a username can help build a reputation within the Wikipedia community based on your contributions and edits.

Unregistered or "anonymous" editors on Wikipedia face several restrictions to maintain content integrity and security. They cannot create new articles, edit semi-protected and fully protected pages, or upload files. Their ability to move (rename) pages is also limited, and they cannot customize the interface or maintain a watchlist as registered users can.

Furthermore, their contributions are tracked by IP address rather than a username, making their edits more scrutinized for vandalism or misinformation. In Wikipedia's deletion process, contributions range from "Speedy Keep," where an article is quickly retained due to clear consensus against deletion, to scenarios where outcomes are unclear due to parsing errors or lack of clear guidelines ("bot couldn't parse result"). Options include voting for deletion, proposing improvements, merging content into a more relevant article, or redirecting the page to a more appropriate topic. Each action requires careful consideration of Wikipedia's policies and the specific circumstances of the content in question.

A sense of community is vital to Wikipedia’s ecosystem. Editors are motivated by various factors, including the desire to share knowledge, improve the accuracy and quality of information, and the personal satisfaction of contributing to a global resource.

How Authority Impacts The Marketplace

If you have ten successful bookings with a 3.5+/5 review rating, and I have three successful bookings with a 3.5+/5 review rating and five recommendations deemed as ‘useful,’ who would be ranked with a higher score? 

At the root, it is based on genuine authenticity from the members; a person only has another’s best interest in mind when making a listing, ensuring it is high quality and accurate so that once sold, they are graded accordingly. Endorsing a brand they love because the product’s quality is at a level that meets their expectations. Flagging something as harmful to help others avoid conflict. It all follows into an Authority Score run by social pressure; nobody wants to be the bad person. 

If John is joining as an intentional bad actor, I’ll be weeded out quickly by the community. If Julie is a good actress who made an unintentionally misleading listing and corrected it once flagged, my reputation will remain as a good actress. The scoring system must be dynamically designed with a concentration on the desired outcomes of your marketplace. Is it weighted towards weeding out the Johns of the market, then the system may incorporate factors such as Time to Harmful Action from Signup Date, Average Number of Harmful Actions in 7 Days, Average Number of Negative Insights in 7 Days, etc. The negative flags will outperform the good ones, making the Authority Score tremendously lower than those like Julie. 

Designing and Building

This can undoubtedly be done in several ways and coded as such. 

There are many considerations in the short term, such as the six months post-launch when you’re fighting for traffic and adoption. Then, once you reach PMF (Product Market Fit), what happens at scale? What about bias or ‘gaming the system’?

It will be a fluid block of code that requires diligent tracking and data logging. I’ll describe (at a very high level) how I would build it for my MVP Marketplace, and below will be a direct quote from ChatGPT. 


  1. Make a fair and trustworthy environment for all community members, new and old

  2. Provide a friendly UX for members to understand with a complex backend to defend against harmful intentions

  3. Be as diverse as possible while focusing on purging bad actors, incorporated with redemption attributes


  1. Incorporate both “All Time” and “Average Over x Days” timeframe as measurement

  2. Implement a weighted system to rule each faucet

  3. For a Good action, there is a Bad action


  1. 20% = Transaction Outcomes

  2. 15% = Accuracy of Transaction-based Reviews

  3. 20% = Accuracy of Listing Description and Photos

  4. 10% = Recommendation Usefulness

  5. 10% = Endorsement Usefulness

  6. 10% = Overall Engagement

  7. 10% = Positive Engagement

  8. 05% = Negative Engagement

How ChatGPT Would Use AI to Build

Conceptualization and Design Phase

Predictive Analytics for Behavior Identification: AI can analyze vast amounts of user data to identify patterns and predict behaviors that correlate with positive and negative impacts on the marketplace. By leveraging machine learning models, you can determine which user actions should be encouraged or discouraged, helping to define the components of the authority score more accurately.

Customization of Score Components: AI allows for the dynamic customization of score components based on evolving marketplace trends and user behaviors. This adaptability ensures that the scoring system remains relevant and effective over time.

Development and Implementation Phase

Real-time Scoring: Implement AI algorithms that calculate authority scores in real-time, allowing for immediate feedback on user actions. This feature encourages positive behavior by showing users how their actions directly impact their scores.

Natural Language Processing (NLP): Use NLP to analyze reviews, comments, and other text-based interactions for sentiment analysis. This can help in assessing the quality of user-generated content and its impact on transaction outcomes and overall engagement, contributing to the authority score.

Fraud Detection and Prevention: AI can identify and flag unusual patterns that may indicate attempts to game the scoring system or engage in fraudulent activities. Machine learning models can be trained to detect these anomalies, protecting the integrity of the scoring system.

Maintenance Phase

Continuous Learning and Adjustment: With AI, the authority scoring system can continuously learn from new data, adjusting the weights of different score components and refining the scoring algorithm to better reflect the marketplace's objectives and user behaviors.

User Feedback Analysis: AI can process and analyze user feedback on the scoring system at scale, identifying common issues or suggestions for improvement. This insight can inform adjustments to the scoring algorithm and components, ensuring the system evolves in response to user needs.

Incorporating AI Responsibly

When incorporating AI into your authority scoring system, it's crucial to consider ethical implications, including transparency, fairness, and privacy. Ensure that users understand how AI contributes to their scores and maintain transparency about the AI's decision-making process. Regularly review and audit the AI models for biases and inaccuracies, adjusting as necessary to promote fairness and accuracy.

By leveraging AI, you can create a sophisticated, dynamic authority scoring system that not only scales with your marketplace but also continuously adapts and improves, fostering a thriving, trustworthy community.

Is Influential Buying Right For Your Marketplace?

Community is at the core of any marketplace with high positive engagement - so the question might be, when is the right time to implement something like that?


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