Customer support is a key to the success of any SaaS business. Nevertheless, language barriers may significantly impair the support capacity and make customers frustrated. In the case of SaaS companies that have customers all around the world, different language needs render the process of scaling customer support costly and operationally difficult.
It is thus crucial to do away with language differences. Although human translation remains the most preferred approach, its exorbitant prices are out of reach of most small and mid-sized SaaS companies, prompting them to explore AI-driven tools to learn English with AI and enhance multilingual support internally. Fortunately, quality translation is becoming possible even at a low cost through technological developments.
It is a practical, resource-economical article that addresses how resource-limited SaaS companies can eliminate language barriers in customer service.
The Rising Significance of Multilingual Support
The SaaS market will mushroom into a 428 billion dollar industry in 2025, and the geographies of high growth are Asia and Latin America. The demand for multilingual customer support will accelerate as SaaS enters various geographical locations across the world.
Forrester forecasts that over 40% of developer teams creating cloud software will be located in emerging markets in 2025. Consequently, English-only customer service will not suffice even among technical users. Already, 76% of software buyers demand support in native languages regardless of English proficiency.
Additionally, regional data residency regulations are making localized service indispensable. The European Union’s GDPR policy, for instance, requires that customer communications be in official EU languages.
Given that more nations are coming up with similar legislation, the capacity to offer customer support in different languages will cease to be a choice. That will require additional agents, which will jeopardize an increase in operational costs for SaaS companies.
The High Costs of Multilingual Support
The provision of phone and chat support in various languages is a costly affair. In North America alone, an on-site agent is going to cost upwards of $43,000+ per year, not to mention Europe, where salaries are even higher. It is not practical to employ full-time representatives to cover languages such as Danish and Finnish that are not used frequently.
Other overheads for in-house multilingual staff include recruitment costs, benefits, attrition costs, management costs, workspace costs, and others. The expenses are multiplied with each additional language provided.
Outsourced human translation is exorbitant, too. Professional services charge $0.15 per word on average. For context, translating a thousand customer tickets per month in just Spanish will cost $15,000.
Legacy translation software relying on rules-based machine translation struggles with nuanced human conversations, resulting in poor-quality output. This frustrates users and compounds support costs.
Advancements in AI-powered Translation
Thankfully, recent advances in artificial intelligence have made it possible for a new caliber of translation solutions that deliver great quality with little human involvement.
This technology combines neural machine learning, trained on vast datasets, with custom models that adapt to a client’s unique vocabulary and style. It continuously improves through active learning.
The latest cloud-based services like Unbabel, Deepl, and Lingvanex now translate specialized customer service conversations with over 85% accuracy. They understand diverse linguistic nuances and even handle tricky tasks like transcribing support calls.
Vetted professional translators polish the machine output where necessary, but this human effort is minimized. This allows the services to be priced affordably on a pay-per-use model. Unbabel, for example, charges just $0.06 per translated word.
How SaaS Firms Can Utilize AI Translation
AI-powered language services provide on-demand multilingual support at reasonable pay-per-use rates for companies of all sizes. Their flexible, usage-based pricing prevents the exorbitant overheads of large in-house teams.
SaaS firms have multiple options to integrate such solutions into their customer service workflow based on their scale and needs:
Translation Middleware
Platforms like Unbabel and Deepl offer lightweight API-based middleware that can plug into a company’s existing help desk system, like Zendesk. All support tickets raised in foreign languages get routed through this translation layer before reaching agents.
The middleware automatically translates inbound queries and user responses in two directions. This allows agents to support customers in native languages without actually knowing those tongues.
Dedicated Portal
For larger-scale requirements, SaaS firms can utilize a dedicated multilingual customer portal from vendors like Smartling, Gengo or Transifex. This consolidates all translation needs across support, product UI, marketing, etc., into a single portal.
Machine translation handles bulk requirements like knowledge base articles, while professional linguists focus on critical human touch tasks. Everything stays in sync even as content changes.
Hybrid Model
Companies anticipating a substantial support volume in select languages can consider a hybrid model. The bulk can be serviced through lower-cost channels like chatbots and self-help portals translated via AI.
Human agents step in for complex, high-touch conversations, but only in 4-5 core languages kept in-house. This caps headcount while still providing human interaction. Additional languages get covered through AI translation piped into the agent dashboard.
Effective Multilingual Chatbots
In addition to translation services, AI-powered chatbots present another affordable avenue for SaaS companies to manage multilingual support at scale.
Advancements in natural language processing now enable bots to handle customer conversations in various languages with ease. Their capacity to address a wide range of basic queries and assist users through knowledge bases significantly decreases the number of human support tickets. According to Gartner, chatbots will power 85% of all customer service interactions in 2025.
The natural language capabilities of today’s chatbots go beyond basic FAQs. Using techniques like intent recognition, they can understand nuanced customer questions and handle conversations spanning multiple exchanges.
Bots become smarter over time through machine learning on real dialogues. They can seamlessly hand off complex issues to human agents when necessary.
With multilingual bots and translation middleware, an affordable substitute for huge in-house language departments is produced. The total cost of the two capabilities is only a fraction of human support.
SaaS players are able to implement chatbots in the communication channels, such as pop-ups on websites, in product communications, and on social media. Having the capacity to learn quickly, bots effectively handle regular customer matters in different languages at any time of the day. This leads to reduced operation costs and a smooth user experience.
Best Practices for Implementation
To maximize success, SaaS companies should adhere to the following best practices when deploying AI translation:
- Centralize multilingual support instead of fragmenting by region to maximize efficiency and translation engine training.
- Enforce support documentation standards to ease machine learning. Template common ticket types.
- Identify a default dialect per language in order to reduce the number of variations that the engine will need to support.
- Give constant feedback and train the translation engine to recognize the terminology you use about your products and the way your company writes.
- Translate memory tools can be used to recycle past translated material, such as FAQs and articles.
- Use a small number of in-house linguists to tailor the engine and certify the quality of the output, particularly of critical content.
- Integrate with analytics tools to identify the top languages needed and prioritize those in the initial rollout.
- Localize self-help portals via AI to contain support volume; use human translation only for very sensitive content.
- Consolidate translation spending across the company to maximize bargaining power with vendors.
Overcoming Internal Resistance
Despite proven benefits, many SaaS firms encounter internal team resistance to adopting AI-based language solutions due to key concerns:
- Subject matter experts often question the accuracy of machine translation, leading to a lack of confidence in its quality.
- Fear that automation will make linguists and community translators obsolete.
- The belief that machine translation hampers brand voice consistency.
- Recurring expenditure on translation, even at lower cost, appears expensive to budget owners.
Such concerns are related to the past experience with older technology in the field of translation and the false ideas about modern solutions. Leadership has to take initiative in solving issues by:
- Being open about what machine translation can do, what it cannot do, and the plan to achieve trust.
- Emphasizing the productivity of an increased number of languages per agent.
- Communicating actual cost savings from reduced outsourcing and containment of headcount, recruitment, and management expenses.
- The idea is to make a certain percentage of the translation budget variable depending on usage in order to link the costs to revenue increases.
Conclusion
To sum up, the use of AI-based language services will enable SaaS companies to provide cost-efficient multilingual customer support needed to expand internationally. Even lean startups can afford to use multiple languages without the bloat of headcount at a cost of less than 100 dollars a month.
Machine translation is not flawless, but due to rapid innovation, its performance has become acceptable for use in customer service. Incorporating machine translation into help desk procedures and self-help websites provides SaaS companies the best of both worlds: technology and human.
With the increasing struggle in the global competition, it is no longer an option to eliminate language barriers. The cost-effectiveness of AI translation enables the company to satisfy various user demands and speed up the expansion into new non-English-speaking countries. SaaS companies must take action now.
