Articles | b'Hunted Hive'https://huntedhive.com/articles/2019-10-16T14:00:00+00:00ArticlesCounting down the Top 8 Factors in Building a White-label Marketplace2019-10-16T14:00:00+00:00Martin Rusishttps://huntedhive.com/articles/author/martin.rusis/https://huntedhive.com/articles/top_factors_white_label_marketplace/b'<p>Because there are already many white-label solutions out there for marketplace creation, this article outlines the things to consider in finding the right white-label solution for you, as well listing key advantages and disadvantages of each.</p>
<h3><strong>Marketplaces, matching and ecommerce today</strong></h3>
<p>Today, there are so many new matching-style marketplaces starting up. It’s like a new platform that matches empty car spaces with drivers looking for a spot pops up every month!</p>
<p>Not to say the market is crowded. Far from it. There will always be niches that can benefit from matching solutions. Some will be more profitable than others.</p>
<p>With this rise of the sharing economy, the corporate world is taking notice.</p>
<p>There is a clear demand for platforms that reduce the complexity and technology risk involved in creating matching marketplaces that capitalise on otherwise idle resources. Enter the white-label marketplace solution.</p>
<p>Shopify is a great example of a white-label solution. Using it, anyone can create their own personalised store for much less money and in much less time than building one from scratch. It has upended traditional ecommerce.</p>
<p>However, for a marketplace that truly matches need with resource there are added layers of complexity above what a white-label solution can do. These include:</p>
<ul>
<li>Facilitating multiple vendors</li>
<li>Managing multiple products from the same vendor</li>
<li>Geographical search</li>
<li>Different types of business models.</li>
</ul>
<p>Keep all this in mind, as we begin the 8-point countdown.</p>
<h3><strong>8. Testing the viability of your marketplace Idea</strong></h3>
<p>Your marketplace idea might work out great on paper, but you absolutely must test if there is a need for it.</p>
<p>You can research to identify a niche until the cows come home; however, nothing tests the market better than a<a href="http://en.wikipedia.org/wiki/Minimum_viable_product"> minimum viable product (MVP)</a>. What this means is you don’t build out the full scope of your idea upfront – the cost and risk are too high.</p>
<p>Instead, start with the minimum set of features your product needs to differentiate itself from its competition or which can solve the pain point your target market has.</p>
<h3><strong>7. Estimate the cost of your marketplace</strong></h3>
<p>Clearly define the budget for your start-up. This will immediately dictate your market-entry level when choosing a white-label solution. On this thread, there are companies that offer enterprise-level framework and others that offer DIY templates with expert help available as a premium. From bottom to top, the price range in the white-label platform market is massive.</p>
<p>One thing every white-label provider has in common is that their market-entry time and development costs are vastly lower than with developers building bespoke platforms from scratch.</p>
<p>The cost of some bespoke platform MVPs crests seven figures. With a white-label provider, the cost of your MVP will usually be in the five-figure range.</p>
<h3><strong>6. Think about intellectual property</strong></h3>
<p>Even though you are the client, software development IP usually vests with the developer. Instead of ownership, you are issued a perpetual license to use the source code for the intended purpose.</p>
<p>You can, however, pay a premium and sign an agreement to own the IP, but this has its own complications.</p>
<p>Your white-label developer can offer you these competitive prices because, often, they are re-using components they’d previously built.</p>
<p>Furthermore, when looking at a multi-tenant SaaS white-label solution, the IP can’t easily be transferred to you or anyone else because the core system also drives other marketplaces.</p>
<p>What doe this mean? Read the fine print in any agreement. Talk with your agency to understand what you can and can’t do with the product they are developing for you.</p>
<p>If IP is a big thing for you and your business, consider open-source options. Should that be unacceptable, you may have to look at building a bespoke platform, which will substantially increase project cost and duration.</p>
<p>Again, using Shopify as an example: it is so inexpensive because it is a multi-tenanted solution where each user runs on the same infrastructure. None of them has ownership rights over that infrastructure.</p>
<p>We often see clients who have outgrown these shared-IP options for whatever reason (usually because they need advanced custom features) and now want a completely bespoke ecommerce solution.</p>
<p>By choosing a white-label platform first and then being compelled to go bespoke later, they end up paying twice to develop one marketplace. Then again, had they not initially saved money through white-labelling, <a href="https://huntedhive.com/articles/how-to-create-marketplace-website-the-chicken-and-egg-problem/">they might never have been able to launch successfully</a>.</p>
<h3><strong>5. A marketing budget for your marketplace</strong></h3>
<p>Most new businesses and start-ups forget how expensive good marketing is. In fact, marketing often costs more (sometimes an order of magnitude more) than the development.</p>
<p>Why? Because getting a collaborative consumption marketplace in front of enough of the target market really is its own challenge. Do not rely on the build it and they will come mindset.</p>
<p>Some digital start-ups put every cent of revenue less costs into their marketing - that is, the staff (who are often shareholders) don’t even get paid for a fair while.</p>
<h3><strong>4. Do the features your marketplace needs match what the white-label solution offers?</strong></h3>
<p>Building a specific marketplace with search criteria or other features unique to your target market will probably involve custom development.</p>
<p>This often automatically puts you at your white-label provider’s enterprise-level plan. If not, there will be a large additional fee for custom development.</p>
<p>You could consider removing said features from the MVP to save on cost and get it to market; however, if those features are what sets you apart from your competition, your cost saving removes your USP. Further, you’d need to talk with the white-label providers to see if they even offer customisation services.</p>
<p>In the very least, conduct an audit of what features are available for the client marketplaces on each white-label solution.</p>
<p>If they are offering a SaaS product and it is built on<a href="http://en.wikipedia.org/wiki/Multitenancy"> multitenancy</a>, then they might not find it cost-effective to dedicate the resources to do the custom development for your features.</p>
<p>In other words, they might decline unless the features you’re paying for will serve to underwrite improvements to the platform they can on-sell to future clients.</p>
<h3><strong>3. Does your marketplace need a mobile app?</strong></h3>
<p>A recent trend is for these collaborative consumption marketplaces to be targeted towards tradespeople and other skilled workers or service providers.</p>
<p>They let the offerer stay mobile through providing flexible employment opportunities and utilisation of idle assets and resources. For these sorts of marketplaces, a mobile app that can be easily downloaded from an app store is crucial.</p>
<p>Not all white-label platforms offer a mobile solution beyond basic responsive mobile-browser solutions.</p>
<h3><strong>2. What are your technical requirements?</strong></h3>
<p>Although an entrepreneur who isn’t tech savvy may not know what their technical requirements are, they should still seek to understand the risks present in the underlying infrastructure that will support their marketplace.</p>
<p>One of the biggest attractions to white-label solutions is that the risk of technology failure is significantly lower compared to a bespoke platform.</p>
<p>Fast forward 5-10 years and the solid foundations of a white-label solution could become critical to your continuity.</p>
<p>While there are many foundational things to think about, some of the most important are:</p>
<ul>
<li>What error-detection mechanisms are in place?</li>
<li>What security-monitoring mechanisms are in place?</li>
<li>Does your provider conduct regular penetration tests?</li>
<li>Does your provider develop using<a href="http://www.agilealliance.org/glossary/continuous-deployment/"> continuous deployment</a> for a testing environment (also called ‘staging environment’) and a production site?</li>
<li>Does your provider apply version upgrades to the underlying technology stack as needed?</li>
<li>How quickly does your provider respond to and correct bugs and vulnerabilities after they are detected?</li>
<li>Does your provider offer a certain number of hours of technical support each month?</li>
<li>What is the uptime of your provider’s framework?</li>
<li>What happens if a server goes down? Is there any built-in redundancy?</li>
<li>What are your provider’s backup and restoration capabilities if some of the above mechanisms fail?</li>
<li>What happens if your site is flooded with traffic? Is there a load-balancing framework?</li>
</ul>
<h3><strong>1. Resolving user pain points</strong></h3>
<p>When starting a business like this, addressing and reducing the pain points of your demanders (users) and offerers (sellers) is priority one.</p>
<p>For example, Uber and Airbnb provide easier access to transport and accommodation. They also eliminate the need to handle transactions during the driving and occupying experiences. Pain points removed.</p>
<p>In smaller niche marketplaces, addressing the particular pain points may require increased customisation.</p>
<p>This is why the one-size fit all white label approach is not applicable for every marketplace.</p>
<h3><strong>Thinking about your marketplace concept holistically</strong></h3>
<p>It is undeniable that white-label marketplace software has advantages over building a bespoke marketplace. For a start:</p>
<ul>
<li>The cost is significantly less, the underlying technology risk is lower and the time to market is significantly less.</li>
<li>You are freed up to focus on bringing people to the marketplace, building trust and authority in your space.</li>
</ul>
<p>White-label solutions also present some difficulties:</p>
<ul>
<li>You cannot definitively own the IP.</li>
<li>You cannot easily transfer the hosting and maintenance to a third-party if your relationship with the developer deteriorates.</li>
<li>You might be limited in the custom feature options that you want to add in the future.</li>
<li>You are at the mercy of the underlying technology framework – along with all the other marketplaces using the same platform.</li>
</ul>
<p>This article has touched on a lot of the nitty-gritty of developing and launching a new marketplace.</p>
<p>White-label solutions are excellent for most new players, but they have their limitations. That should not be a discouragement though. Forewarned is fore-armed.</p>
<p>The takeaway is this: with the buzz around marketplaces high, the level of enthusiasm among start-ups is outstripping the level of knowledge.</p>
<p>This article is a primer in letting you know what to think about in the concept phase and what to look for as you begin bringing your ideas to life.</p>'7 tips about Machine Learning for Matching Platforms2019-10-13T00:11:58+00:00Martin Rusishttps://huntedhive.com/articles/author/martin.rusis/https://huntedhive.com/articles/7-tips-machine-learning-matching-platforms/b'<p>The topic of Machine Learning in matching platforms is still new to many. However, getting a grip on its fundamentals is actually pretty straightforward. This article outlines the main ideas, concepts and surprises that we at Hunted Hive often cover in briefing clients about how Machine Learning applies for their marketplace or matching platform concept.</p>
<h3><strong>1. Search is the little brother of matching</strong></h3>
<p>While both search and matching connect user desires with provider offers, the method they go about doing this is categorically different. Moreover, the offers made by matching enabled by Machine Learning can be orders of magnitude more relevant and effective for the demander than conventional search results.</p>
<p>In a conventional search, the active user seeks a passive item or ‘asset’. The access to this is governed by a reactive rule-based system. The impetus and responsivity is all on the user, or ‘requester’, side.</p>
<p>Matching, however, has active users, active providers and active ‘assets’. While the user still does their part, the provider is likewise trying to find them and, most important, the system itself learns how to facilitate that for both sides most efficiently.</p>
<h3><strong>2. Machine Learning means small marketplaces can compete with bigger players</strong></h3>
<p>In the past, it took a lot of money to connect users and providers profitably. Now, you can use targeted Machine Learning to dramatically cut the overheads.</p>
<p>The key is that Machine Learning works best with large amounts of accurate data applied to defined qualitative systems. Therefore, small marketplaces that are highly targeted can sharply define their ‘system’ and deliver results at the same or better accuracy than larger and less-focused marketplaces.</p>
<h3><strong>3. Replaces the library with the data</strong></h3>
<p>The matching takes place in how the connection between demander and provider is made. This differs from traditional marketplaces where a match occurs when a requester query is processed centrally and filtered through a larger library system. In some ways with matching there is no library, just software that makes connections between active participants.</p>
<h3><strong>4. AI matching works forwards and backwards</strong></h3>
<p>‘Dumb’ matching systems just file providers and offerings in a passive library according to various attributes - they don’t actively seek out likely requesters.</p>
<p>An Artificially Intelligent matching platform understands not just what to do with user queries, but also what to do with the provider offerings.</p>
<p>This is a big advance for Machine Learning-enabled marketplaces over online ad listings or auction sites. In those, the seller uploads a lot of info that is categorised into a database and the user’s query is used to access that central resource. With Machine Learning matching platforms, the provider’s offering is ‘understood’ by the system rather than being filed in a database and then being accessed as a result of predetermined user filters.</p>
<h3><strong>5. Matching Platforms improve the signal to noise ratio</strong></h3>
<p><img alt="signal to noise" height="341" src="https://huntedhive.com/media/uploads/blog/.thumbnails/night-television-tv-theme-machines720.jpg/night-television-tv-theme-machines720-720x341.jpg" width="720"/></p>
<p>One of the big problems with putting the right requester and provider together is that there are usually many possible answers and paths a user can take. This issue gets worse the more the requester’s need is “obtuse”.</p>
<p>Netflix is such a fascinating recommendation engine because many of its requesters have a highly obtuse need: they are looking for the right ‘something’ to distract them from boredom. The target the recommendation engine must hit is vague and arbitrary. The offerings it can present to make a connection number in the thousands.</p>
<p>And yet, through iterative improvement, Netflix is getting quite good at delivering a “signal” in this noise. Each time one the recommendations generated by its matching engine is selected by a user, it is a success that the system learns from.</p>
<h3><strong>6. Matching algorithms use different types of data </strong></h3>
<p>There are many Machine Learning algorithms your matching platform could employ to analyse your data. In general, there are four main kinds of data they will use:</p>
<ol>
<li>Metadata about the listing itself</li>
<li>User behaviour data</li>
<li>Information the providers gives in their listing</li>
<li>Context data on the situation the users and offerers are in at the time of their interaction with the marketplace.</li>
</ol>
<h3><strong>7. Machine Learning is going to reveal things about your market you didn’t expect</strong></h3>
<p>You’re going to be surprised and it is up to you to respond. At Netflix, for example, the company is investing US$5billion into creating new shows - some of which seem <a href="http://www.eonline.com/au/news/880396/american-vandal-creators-reveal-how-they-convinced-netflix-to-let-them-satirize-their-greatest-true-crime-hits">really left field</a>.</p>
<p>You can bet that advanced algorithmic analyses of what performs well across the viewing behaviour of its 75 million users is the controlling factor into what programs are made and then picked up for a second season. The company is responding to what its relentless data acquisition finds.</p>
<p>In rule-based search systems, however, the parameters by which assets are categorised and by which results are returned are intentionally created by the software developers running the system.</p>
<p>The behaviour of the system is much less emergent and offers up far fewer surprises - in a way it can only show you what you are looking for, not what is actually happening.</p>
<h3><strong>Applying Machine Learning to help people and services connect</strong></h3>
<p>The short list above is just a few things that you have to know about Matching Learning as it applies to matching platforms and marketplaces. It’s a field that is developing very fast and, as you can see, holds great promise for those looking to find new ways and new sectors in which people with needs and people with offerings can find each other.</p>
<p>To find out more about matching, get in <a href="https://huntedhive.com/contact/">touch with us at Hunted Hive</a>.</p>'