Our post on the 100,000 registered customers milestone this summer included an infographic of sample use cases being explored by BigML users, which naturally span many different sectors and industries. Today, we’d like to start a series of posts that further highlight a subset of those business problems to give our readers some clues on how a comprehensive platform as ours can be utilized in different business contexts in case they’re considering new Machine Learning solutions.

There are many ways to organize use cases, e.g., by industry, function, geography. In this post, we will focus on startups and SMBs as we give you a glimpse of the motivation behind solving each reference use case. In a later post, we’ll concentrate on large multinational companies also finding success with the BigML platform.

Startups and SMBs have good reasons to prefer the BigML platform because it lets them to affordably step into Machine Learning with ample room to further scale efforts as data volumes and the number of use cases implemented grow over time. Some startups have products and services that cannot even be launched without Machine Learning at their core (e.g., sensor-based medical diagnosis), whereas others grow into Machine Learning as they realize they are sitting on top of a hard-to-replicate and/or completely unique dataset that can fuel high-value predictive use cases that help differentiate their existing products.

Once useful models are in place, the systems integration and deployment choices are multiple. On the lighter side, predictions can be served in real-time through the BigML REST API and be included in a customer-facing user interface, say for instance in a given module like the product or next-best-action recommendations. On the other hand, if the end-user is expected to interact with and interpret the models first hand (rather than just consuming their predictions), the visualizations from BigML models can be made available in the host application in a white-label manner.

Predictive use case examples at ML-driven startups

• Juriblox B.V. is a European startup active in the legal services space. Their SaaS solution takes care of an oft-overlooked aspect of legal contract review and management: non-disclosure agreements (aka NDAs).  The Juriblox service named NDALynn can quickly grade any NDA uploaded by its subscribers to let them know not only the overall aggressiveness of the subject-matter NDA but also highlights specific clauses that are likely to cause problems down the road. All of these predictive capabilities baked into their web user interface are made possible thanks to a number of BigML models tapped into via the BigML API. Juriblox achievement is especially remarkable given that they didn’t have a data scientist or other highly-paid dedicated analytical expert on staff. This example shows that a group of subject-matter experts with access to relevant data and armed with a good understanding of their customers’ context coupled with a developer team can deploy sophisticated Machine Learning systems that are core to their offering.
• Another BigML customer, Faraday.io, helps B2C businesses optimize demand generation by combining their customers’ CRM data with their national database containing key traits on over 125 million U.S. households. Faraday customers have been able to attribute as much as 1/3 of their sales to the ML-driven cross-channel campaigns addressing all stages of the B2C revenue lifecycle from customer acquisition to upsell and retention, e.g., social media advertisement performance comparable to the best targeting that Facebook ML models can support.
• On the other hand, Frogtek helps Mexican micro-retailers to better control and grow their businesses as the company’s point-of-sale (POS) systems register every transaction. This data is a boon for Consumer Packaged Goods companies that are starved for visibility into consumer behavior and preferences to optimize operational efficiencies such as inventory management with Machine Learning.
• The potential applications of ML to automate accounting are many. For example, Anfix, a Spanish startup, can help clients predict the correct expense account that a given invoice belongs to. Before Machine Learning, this process could only be performed by an accountant with an in-depth understanding of the company operations. The automation of such bookkeeping tasks allows financial professionals to use their time to focus on other activities that either result in more value to their customers or help them find new customers. Additional predictive efforts include determining in advance whether a given company will run out of money at some point in time, allowing scenario planning based on short, mid, or long term funding options. Knowing this information, the company can anticipate the negotiations with a bank to get a loan under more advantageous terms.

We hope these use cases give you some ideas about the wide range of Machine Learning opportunities in your setting. Please stay tuned as we will follow up this one with use case examples from large multinational companies in our next post.

Are you a manager or professional at a startup (or SMB) evaluating your options to better take advantage of your proprietary data sources by implementing Machine Learning systems to integrate predictions into your value proposition?  Be sure to get in touch with us at info@bigml.com. We can swiftly match you with a BigML expert, who can help you better formulate your approach by advising you on your data strategy, modeling (and evaluation) approaches, as well as your run-time prediction and deployment options.