How do you show natural language processing value to clients?

I absolutely agree with the emphasis on identifying client problems before suggesting any AI or NLP solutions. At Miquido, we've seen a shift: clients increasingly approach us with explicit requests for AI consultations or solutions. This surge in AI awareness is likely due to its prevalence in media and conferences. However, the key remains understanding the client's challenges. It's crucial to have in-depth conversations about their most pressing issues, as these are the areas where AI can deliver the most value. Only then can we tailor solutions that genuinely meet their needs and drive optimal results.

?? Highlighting the benefits of NLP to clients requires a structured approach. Begin by pinpointing domains where NLP can be transformative for the client, then presenting tangible, quantifiable results to underline its effectiveness. Culminating your pitch with a well-defined call to action ensures clarity in the next steps. But why stop at just demonstrating NLP's value? Amplify your professional stature by curating a robust NLP portfolio, encapsulating a wide range of projects, maintaining clean code, and ensuring regular updates. Such dedication portrays expertise and positions you at the forefront of NLP advancements. ????

NLP can bring values to few areas below : 1. Data Handling and Extraction: ?Manual Data Entry and Extraction ?Content Classification ?Search and Retrieval Issues ?Compliance and Risk Management ?Legal/finance Discovery 2. Customer Insights and Engagement: ?Customer Feedback Analysis ?Language Translation ?Chatbot Integration ?Content Generation ?Personalization ?Competitive Analysis 3. Operational Efficiency and Security: ?Anomaly Detection ?Employee Productivity Understanding a client’s needs and desires is crucial for them to be receptive to potential outcomes. Clients are more likely to engage when they believe that you genuinely care about their concerns and aspirations.

As we are dealing with language there also needs to be an understanding on a company / brand's values and how it communicates with customers (as well as what it is trying to achieve). Where and how will both the brand and the customer see value? Is this going to be an augmented experience to help teams communicate or will it be the front line?

Unlike what many of us think, there isn't a generic NLP solution that can be applied to all solutions. The NLP solution must be built or tweaked for specific applications depending on the expected inputs and the problem space. E.g. a NLP solution for Shopping would require the NLP engine to product and brand names that may be multiple words, however, the words do not correspond to any specific language. On the other hand, a customer service NLP system for a specific product/company would need to be well trained in the questions (however technical they may be) that customers are likely to ask for that specific product/company.

Most customers don’t understand AI. Using the CRISP method can help explain complex processes to customers who want to use NLP

Devise a realistic use case and request time from the client. Prepare a presentation and give a demo. Beyond the demonstration, it's essential to do your homework on the costs involved. Determine how cost-effective the solution will be for the organization. There may be concerns about the costs associated with NLP; you'll need to justify the value it brings. Remember, the bottom line for most is cost savings. Accuracy is also a concern, so ensure you research this aspect thoroughly. Based on the organization's use case, you might recommend a simpler NLP model. This could be beneficial for small to mid-sized companies. They might not need advanced NLP that incurs high CPU costs and might be satisfied with a more streamlined version.

Offering a hands-on experience, such as a Proof of Concept (PoC), is exponentially more effective. Seeing and trying the solution firsthand allows clients to truly understand and believe in its potential, making the concept of ‘seeing is believing’ come to life.

If possible, quantify the impact. Let me explain: after explaining or detailing the solution, provide quantifiable metrics to underscore its effectiveness. For example, you can mention how implementing NLP can lead to a "20% increase in positive customer feedback." By presenting tangible results, you make the solution more compelling. Additionally, highlight the long-term benefits, such as adaptability and scalability. Try to emphasize that as the client's business grows, the NLP system will evolve with it, thus ensuring they get consistent value.

Businesses are seeing AI solutions and sales messages every day. The velocity of AI information that buyers receive is huge and therefore you need to understand how skilled organisations are in buying AI / NLP solutions. Yes, we need to keep the messaging simple, but we also need to be able to explain how a particular tool creates smarter, faster and better outcomes than alternative approaches - and that might mean getting into some of the details and data science. Everyone knows not all AI is created equal!

Most tasks that involve communications (written, verbal or both), reasoning, transcription, search, summarizing or any combination of the above are opportunities for NLP solutions to solve. The business value for efficiency, revenue generation, cost savings, time savings etc are well established and well understood. Always ensure that these fundamental metrics are presented well for long term sustainability of the initiative. Only after that, it makes sense to go into the technical metrics like accuracy, faithfulness and performance.

NLP is just one piece of a larger puzzle. The real magic happens when you bring NLP down from its techy pedestal and relate it directly to specific client pain points. How? Think pre-trained models tailored to niche verticals; we're talking custom-fit, not one-size-fits-all. While geeking out over metrics is great for us tech folks, it doesn't translate easily for the client. What does? Results and real-world outcomes. Forget explaining the high recall on extracting Dangerous goods Declaration data for example in T&L. Instead, paint a picture where their customs process sails through with 90% straight-through processing. The goal? Don't just show the tech's worth—show its worth to them.

Showing the value is the linchpin for convincing a client to invest in an NLP project. Target the most critical pain points or areas ripe for optimization. Clients are most impressed when you can provide concrete numbers, whether that's a projected 20% increase in revenue through customer sentiment analysis or a 15% decrease in operational costs via automated customer service. Visual aids like graphs or dashboards can significantly bolster your case, making the projected outcomes more tangible. Ultimately, it's about translating the capabilities of NLP into metrics that resonate with the client's bottom line. Numbers speak the loudest, and they're the language every client understands.

Knowing the metrics that the solution will be judged on is key. What are the 2 - 3 hard data metrics that matter most. Are there more subjective outcomes we need to create (how "easy" is the tool to use?) and if so how will this be judged. What are the benchmarks today and what threshold needs to be crossed for a project to be considered a success and roll-out approved?

The primary reason for an NLP solution is to help increase the breadth of services or support that you can provide the customer and the 24/7 time coverage. However, there needs to be a balance with the error rate, and the limitation of the NLP solution. You need to be transparent with the customer, and setup metrics that help measure the service provided, as well as the error rates.

Preemptively creating a "safety net" for the client is an effective way to resolve objections. While cost, complexity, and security are all prevalent issues, many clients are most concerned about the unknown aspect of installing new technology. Propose a step-by-step migration approach that will allow them to gradually implement the NLP solution. This allows them to monitor its efficacy and ROI in real time without making a large initial commitment, lowering perceived risk. This 'safety net' method combines facts, such as incremental performance measurements, with emotional reassurance. It's a balanced approach to dealing with both rational and emotional issues.

NLP has limitations, primary in addressing long tail queries. There needs to be transparency on the limitations of the solution being presented.

Objections aren’t roadblocks; they’re opportunities to clarify and reassure. 1. Anticipate Issues: Know common objections like cost or security and have ready answers. 2. Fact-Based Rebuttals: Use data and evidence to counter concerns. 3. Empathy Counts: Show understanding of their hesitations to build rapport. 4. Solution-Oriented: Explain how your NLP system can specifically address their concerns. 5. Emotional Appeal: Sometimes a well-placed story can turn the tide in your favor. By proactively tackling objections, you’re not just selling NLP—you’re building a partnership.

Clients may have concerns about the feasibility, cost, or data privacy. Anticipate objections and provide credible answers. For example, address data privacy with references to GDPR compliance and encryption. For cost concerns, clarify the long-term savings and scalability of NLP solutions.

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A strong call to action turns interest into action, sealing the NLP deal. 1. Be Specific: Clearly outline the next steps, whether it’s signing a contract or scheduling a demo. 2. Create Urgency: Highlight limited-time offers or exclusive features to prompt quick action. 3. Follow-Up: A well-timed reminder can reinforce the value proposition and swing indecisive clients. 4. Reiterate Benefits: Use the CTA to underscore how your NLP solution meets their needs and exceeds expectations. 5. Client Focus: Tailor the CTA to suit the client’s comfort level and preferred mode of engagement. By guiding the client through the final step, you make it easier for them to say ‘yes’ to your NLP solution.

End with a clear call to action. Offer a pilot project, demonstration, or a trial period, so the client can experience NLP's benefits firsthand. Provide a roadmap outlining the implementation process, timeline, and expected outcomes.

In my experience, it is key to remind customers that most of the value of your natural language processig efforts, as with any other machine learning project, will be achieved after the system has been on for a while, and has had a chance to collect additional data and to be trained.

I encourage you to give Microsoft Copilot 365 a try. While this article may become outdated soon, experiencing Microsoft Copilot is currently one of the easiest ways for clients to witness the value of Natural Language Processing (NLP) firsthand.

.. In addition, it's essential to highlight the importance of ongoing support and education. Even after implementing an NLP solution, clients may require assistance in understanding and using the technology effectively. Providing training sessions, resources, and continuous communication can help clients maximize the value of NLP and address any evolving needs or challenges they may encounter. Building a long-term partnership focused on their success will reinforce the value of NLP in their operations.

To showcase NLP value to clients, it's key to understand their business needs first. Demonstrate real-world applications like improved operational efficiency, enhanced analytics, and better customer interactions. Present measurable outcomes and success stories to make a solid case. Offering custom demos, educating clients on NLP, and ensuring legal compliance are also crucial. Highlighting competitive advantages and ensuring scalability shows foresight, while promising ongoing support builds trust for a mutually beneficial partnership.

NLP's value extends beyond immediate returns. Consider the long-term benefits of improved customer satisfaction, competitive advantage, and the ability to tap into insights hidden in unstructured data. Encourage continuous communication and feedback with the client to fine-tune the NLP solution to their evolving needs. Also, educate your client about ongoing advancements in NLP to ensure they stay ahead in leveraging this transformative technology.