The Lettria solution
That’s why Lettria is uniquely positioned to address this particular use case. Not only does the platform offer the benefits of an off-the-shelf solution combined with the accuracies and strengths of a fully customized NLP, but we’ve also developed specific integrations and functionalities that make enriching your CRM with data from your call transcripts and other recordings…well…sort of easy.
We know. Easy is never a term that you want to hear applied to fairly complex technology or software and it might even be a red flag for some of our readers. But trust us. We’ve really worked hard on this and tried to make this process as simple as possible. We’ve built a platform that allows you to turn a foundation into a customized NLP and found every possible way to save you time without reducing your capabilities.
Don’t believe us just yet? We understand, but keep reading and you’ll find out just how Lettria solves this particular problem and some examples from some use cases that we’ve worked on with clients.
So, let’s get to the bit about how Lettria handles the problem of enriching your CRM with your unstructured data.
By now you should at least have some understanding of how natural language processing works. What you might not yet be aware of are the two major challenges that make enriching a CRM with unstructured data difficult.
The first is creating a specialized NLP that is capable of processing unstructured textual data to your requirements and using that to populate fields within your CRM. The second is having an NLP that automates the two other key steps within this workflow: those being automatically processing the call transcripts and then transmitting that data to your CRM once the processing has been completed.
Lettria handles both of these challenges by making the process as simple as possible. For the first issue, our platform approach means that you get to start with a sort of NLP foundation that you can fine-tune for your specifications.
As we’ve already discussed with any technical software decision, you always have to ask yourself whether it is more effective to build or buy what you need. The major advantage associated with building your own NLP solution would be the fact that it would be specialized for your industry, the terms used by your leads and employees, and any specifications that might be unique to your company, project, or sector. The issue is that most companies don’t have the resources necessary to successfully build their own NLP.
Those lack of resources automatically forces most companies to look at off-the-shelf solutions. These require fewer in-house specialists and can be much quicker to get up and running, but that sometimes means that you sacrifice some level of specialization.
Lettria’s platform approach means that you get the best of both worlds. With 15 pre-trained multilingual models to start from, you get access to an AI model that you only need to focus on fine tuning in order to get the best results. This makes starting a project with Lettria 4 times faster than building one your own, but much more accurate than other off-the-shelf solutions.
The Lettria platform also allows you to connect to third-party software via an API and solve any integration problems. This means that you can automate the importation of call transcripts into your NLP platform and automatically enrich your CRM with the data that it processes.
In short. You get a specialized NLP platform that requires less time to set up and less time to manage on a daily basis. It’s a win all round.
But the advantages don’t stop there. This might be sounding a bit like an infomercial, but this is the section where I show you that bonus feature that you might not yet have realized that you needed. What is it you ask? Collaboration.
Most NLP projects are managed exclusively by data scientists and developers and for some projects that might be OK, but it isn’t the case for a lot of them. In fact, 85% of NLP projects fail because of the challenge of moving from development into production.
The reality is that, whilst your data scientists and developers might be more interested and comfortable with training an AI model and improving accuracy, your sales or customer success teams will be the ones more interested in the actual results.
That is clearly the case when you look at a problem like getting more value out of the unstructured data from within your CRM. Sure, your data scientists will fall in love with how accurate the Lettria platform is, but it’s your sales and customer support teams that will benefit from the trends that are identified, the processes that are automated, and the time that is saved.
Where other NLPs fail to actively involve non-technical profiles in the project, the Lettria platform encourages participation and makes it simple enough for anyone to use. The no-code approach means that you don’t need to have advanced technical skills in order to use the platform and your non-technical teams will be able to be involved in the project from the very start.
This has a number of benefits:
- It encourages project buy-in, which is often the downfall of new initiatives. I’m sure that we’ve all seen projects that have failed almost before they’ve begun because of skepticism or an unwillingness to change or adapt from some of the stakeholders.
- Everyone understands what the project aims to do. Non-technical teams are often presented with complicated projects where they are told how incredible the results will be, but they often don’t really understand what is going on or where the data is coming from. By introducing a collaborative platform you ensure that your sales and customer success teams (or whoever it might be) understand what is going on from the very start.
- The project benefits from a range of different skill sets and knowledge. Natural language processing might be the natural domain of data scientists and developers, but they can still benefit from the knowledge that your non-technical teams have. Whether that’s helping them to train the AI model for the right terms or improving how data can be recorded in your CRM, their knowledge can make the project far more efficient and effective.
Collaboration really is the final step in ensuring that you can enrich your CRM using the unstructured data from your transcripts. If you’re looking at NLP solutions that don’t encourage and allow non-technical profiles to take an active role in a project then you are significantly reducing your chances of success.
At Lettria, we’ve not tried to develop an NLP for every use case, but we have tried to develop the best NLP platform for the use cases that we want to address. And we think that we’re pretty good when it comes to handling unstructured textual data and enriching your CRM with the information contained within your call transcripts.
But don’t just take our word for it. Here are a couple of real-world examples from clients that have used Lettria for some very different projects. What do they have in common? The huge challenge of taking large sets of unstructured textual data and extracting key insights from it that can improve processes, increase resource efficiency, and allow them to manage by exception.
Now, before we get there, we’ll address the elephant in the room in the fact that both use cases are from French organizations. Lettria is a French company and, whilst we take pride in our advanced multilingual models and our international footprint, some of our most advanced projects are from companies that started using us early on.
Let’s take a closer look.
Solving the problem of unprocessed data in CRMs
Created in 1991, La Poste started as the French national postal service but is now part of Groupe La Poste, which has expanded to included a bank and insurance company (La Banque Postale), a logistics service provider (Geopost), and a mobile network operator (La Poste Mobile).
With annual revenues of over €34 billion, the group now employs around 250,000 people across its various businesses. La Poste has more than 17,000 branches and post offices in France and relies on delivering a consistent level of customer service and experience across its business.
The nature of their business means that they communicate with customers and leads via phone calls on a daily basis. Their call centers and sales teams form the backbone of their business. Like many other organizations, they’ve implemented policies and introduced software that allow them to ensure consistency across those calls, but extracting key data from the calls themselves was a problem that they were struggling to solve.
La Poste’s problem is in no way unique. Any company handling thousands of calls will know how big of a challenge it is to extract key insights from each exchange and incorporate them into their CRM, knowledge base, and business strategy. Only 3% of their calls were being processed and reported to their CRM, so there was little doubt that valuable data was being missed.
When they started to look for a way to solve this problem, they not only found that natural language processing was the only viable option, but actually the perfect solution. After benchmarking various solutions and consulting with Illuin Technology, La Poste decided that they needed the customization that might be typically associated with an open-source solution combined with the advantages offered by a platform approach.
The Lettria platform was exactly what they were looking for and they started their NLP project in September of 2021. Whilst most natural language processing projects take a lot of time to get off the ground, Lettria’s platform helps users to reduce their time-to-market by 75% and La Poste’s proof of concept was released after just 4 months.
Training your own AI model can be incredibly time consuming and resource heavy, but Lettria has focused on optimizing this process by allowing you to customize the pre-trained models and this meant that the La Poste project only required an average of 2 hours of workshops per week during the development phase and under 100 hours of work to develop a fully customized AI model.
To make things even simpler for La Poste, Lettria developed connectors for both their speech-to-text provider (Allo-Media) and their CRM (Microsoft Dynamics) to simplify integration. This allows Lettria to automatically extract data once it has been processed by Allo-Media and push structured data directly to Microsoft Dynamics.
Although many NLP projects are handled exclusively by data scientists and development teams, the Lettria platform encourages collaboration and the inclusion of business profiles that helps users to get the most out of their projects.
In the case of La Poste, this meant that project managers, call center supervisors, and salespeople were all included in the process to draw on their knowledge and ensure that the platform was used by every stakeholder that would benefit from the insights that it identified.
In just a matter of weeks, 32 labels had been created and identified within conversations. Those labels refer directly to CRM fields allowing the AI to train itself in very precise classification patterns. This classification language model combined with an API means that raw text can be analyzed and key elements (like topics and entities) can be extracted.
Even with the reductions in time-to-market that Lettria offers, it can be easy to focus on the heavy-lifting that can be required at the start of any NLP project, but the advantages are clear to see once a project is up-and-running.
Whereas La Poste were previously only processing 3% of their calls, 75% of calls now have at least one label, and, going forward, Lettria will allow them to automatically process every phone call and input that data directly into their CRM.
This invaluable information has allowed La Poste to develop a more complete and sophisticated understanding of its customers, improve consistency and standardization across phone calls, and adapt its business offerings as a result of business insights that were identified through the analysis.
The La Poste use case is an excellent example of how a business can benefit from enriching their CRM with data from call transcripts. But, more than that, it is also an example of how important it is to choose an NLP option that allows both technical and non-technical profiles to take an active role in the project.
By choosing Lettria, La Poste was able to ensure that sales and customer success teams could contribute to the project, understand its value, and benefit from the insights that were being identified.
After all, as we discussed in the section above, although NLP projects are often thought of as the exclusive domain of data scientists and developers, the end-users of the information that they produce are often those with business profiles.
So that’s one example of how Lettria has been used to extract key information from the unstructured data of call transcripts, but not every company uses our platform for exactly the same use case or challenge.
Improving processes and structuring patient data
The applications for using natural language processing to analyze transcripts of spoken interactions actually goes far beyond calls from sales teams and customer support. There are instances where processing the unstructured data from other recordings and voice-to-text can also benefit your organization.
Similar to La Poste, Assistance Publique Hôpitaux de Paris (AP-HP) found themselves in a situation where they had more unstructured data than they could handle. With nearly 40 hospitals it is the largest university hospital in Europe and with 100,000 employees, it is also the largest employer in the Paris region.
All of those hospitals add up to a lot of patients. And a lot of patients means a lot of data. All of those patient reports contain crucial information that, in this instance, could very much mean life or death. But you don’t want doctors and nurses to be tied down dealing with generating reports when they could be focusing on the patients themselves.
Since 2020, AP-HP has been running the BoPA innovation chair that is focusing on identifying problems from the operating room in order to find human and technological solutions. This innovative project is based on 5 major blocks: the Human Factor Block, the Viz Block, the Bot Block, the Light Block, the Touch Block, and the Box Block (a name derived from the black box that is used for flight recordings on airplanes).
Their focus include the fields of surgeon-patient communication, surgical image capture, natural language analysis in the operating room, augmented reality using digital twice or fluorescent light, collaborative robotics or cobotics (design of collaborative robots), and the protection of operating room and patient data.
The project includes collaboration with a number of other organizations and institutions, including INRIA, Institut Mines-Telecom, and Université Paris-Sacaly and brings together leading researchers from the fields of virtual reality, digital twins, and artificial intelligence.
One of the problems that this initiative identified was the fact that patient reports were very difficult to manage. They are generated at every stage of a patient’s journey (pre, peri, and post) and constantly changed in format and in the usage of technical terms.
Even something simple as a voice dictation required 3 steps:
- The dictation had to be transcribed by hand by an assistant
- The text then had to be put into a report on the hospital’s letterhead by a separate department
- and then the report could be analyzed by a medical secretary to detect key variables about the patient which could then be added to their Electronic Health Record (EHR).
This process required 25 minutes of human time per report and this was an area where the BoPA innovation chair felt that technology could speed up the process and reduce the amount of resources involved in the analysis and data input.
So, in February 2020, right before the world was about to be plunged into a global pandemic that would put an unprecedented strain on healthcare infrastructure and medical resources, AP-HP decided to work with Lettria to create an AI model specific for the healthcare sector.
This process took only three months, which was an incredible achievement given the circumstance in which it was being developed. With tens of thousands of reports generated each month, natural language processing and automatic language processing (ALP) stands to save AP-HP an incredible amount of time by automating the processing and analysis of each report.
The problem isn’t unique, but the use cases can be
This only goes to show how the application for natural language processing to enrich CRMs and other software with the information from unstructured data can be found in virtually every industry. This article has very much focused on the call transcript use case as it is the most prevalent, but there are many situations in which it can save an organization resources and help them to improve their processes.
It’s actually part of the reason why we create content like this. We know how powerful our solution is, but we don't always know the problems that it can help our users to solve. By putting some examples out there and letting you think about your own business, there’s a good chance that new users will come to us with new use cases that we can help them with.
As with any good software, we’ve tried to create the best tool possible and we’re always happy to see the creative ways in which our users apply it to help them address their needs. But one thing is for certain, by specialiazing in the processing of unstructured data and simplifying the integration with all of the third-party tools that you already use, we’ve made the Lettria platform an incredibly powerful solution for finding key information that you’re currently missing.
Start enriching your CRM with information from unstructured data
We’ve explained the problem and shown you the solution, so what are you waiting for? If your company handles a large number of phone calls with leads or customers then it is absolutely certain that they can benefit from enriching their CRM with the information from unstructured data.
All too often we fall into the trap of thinking that processes and software solutions are good enough. They’ve got you this far, so why look for something better? But in this instance this is about turning the data that you already have into data that you can actually use.
All of those call transcripts contain business insights and trends that could make the difference for your next campaign, your next quarter, or your next product. And whilst you might already be using some form of natural language processing for some of your business needs, it’s unlikely that it has the capabilities required to make this process simple and automated.
Lettria’s decision to specialize in textual data processing makes it different to many of the off-the-shelf NLPs on the market. Its platform approach and functionalities also make it uniquely suited to use cases like this one.
So if you think that you could benefit from this type of project, then you should be clicking on one of those helpful links that we will have put into this article and getting in touch with our team. We can help you to develop a fully customized NLP platform in a fraction of the time that it would take to develop your own solution or even get a different NLP up and running.
We know, this is a long article with a lot of information in it, but that’s just the kind of thing we specialize in. You’ve handled the analysis on this occasion, let’s automate that process for your call transcripts.