Knowledge bases are a common component of customer support services. They’re designed to answer as many questions as possible, which eliminates the need for human agents in some situations. This can offer better customer support at lower costs, but there’s a big problem with this kind of solution: they lack real-world solutions.
The Problem with Knowledge Bases
Knowledge bases might be useful in letting humans find answers to their questions, but they’re not very efficient at it. Natural language questions require more than a boolean lookup. Humans can do this by matching the question in their mind to concepts they have stored.
Computers, however, struggle with this problem, which makes them unable to answer such human-like questions. Natural language AI helps solve the biggest problem with knowledge bases (the inability to match meaning) by working fluently with human language and concepts.
The biggest problem with today’s knowledge bases is the same one that existed when they were first invented: they’re boring. The facts and figures in a knowledge base are devoid of anything that might resemble a personality. They lack the spark of life.
Details about products, articles, or events are often presented in a list format. When you ask a question, you get an answer from a list. For example, if you ask Siri to find you a pizza place nearby, it’ll respond by listing off some local places. It’s up to you to figure out which one is closest or best for you.
In fact, that’s exactly how most people use Siri—to get quick answers to specific questions. The reason is simple: it takes too much effort to learn how Siri works and then apportion your requests appropriately.
It’s also very hard to remember every command and every nuance of the system; users have already complained about this issue in Amazon Alexa conversations. While we like to think of Siri and Alexa as super-powered AI assistants, they do act more like standard knowledge bases.
Using the Power of AI and NLP to Improve Knowledge Bases
Software doesn’t understand the nuances of language like we do. That’s why we’re often disappointed with responses from search engines. Sometimes they’re just not specific enough to help us. Other times, they lead us down a rabbit hole of irrelevant information that doesn’t answer what we want to know at all.
That’s where natural language processing (NLP) comes in. In its most basic form, NLP makes software more human-like by allowing it to understand and respond to the way humans actually speak—the grammar, the slang, and so on. It makes it easier for people to interact with devices without having to use specific commands—like “Hey Siri” or “Okay Google.” The technology is rapidly improving as well, enabling more complex interactions between humans and machines without having to be so literal in our speech patterns.
This is important because it means computers can better understand our needs in order to provide relevant answers and actionable information that actually helps solve human problems.
Cognility’s Approach to AI Knowledge Bases Are the Future
Businesses know that maintaining an accurate knowledge base is important for their success. However, keeping them up to date is often a very time consuming, manual process.
With Cognility, this problem is finally going to be solved; AI will do most of the work for you, without requiring a team of data entry people to keep your knowledge base relevant and up-to-date. Our technology is the future of insight engines, providing direct answers based on content within documents like PDFs, videos, audio tracks, and more.
Find the answer you’re looking for in seconds using natural language input, the power of AI, and propriety SaaS technology found nowhere else.
Insight engines and connected search platforms offer businesses better visibility and boost traffic. What’s the problem? SMBs can’t benefit from these offerings in the same way enterprises can–if at all.
Small businesses deserve the ability to provide their employees and their customers with immediate access to the answers they’re looking for. It shouldn’t be treated as hand-me-down technology simply due to cost, complexity, and a lack of integration.
That’s where Cognility takes shape, providing connected search that clears to the pitfalls of current insight engines specifically for SMBs.
What’s Wrong with Today’s Connected Search Options?
Today’s connected search options are great for big companies who can easily overcome the hurdles that insight engines usually come with. These include price, resources, integration, and more. But these hurdles are shaped more like crevasses for SMBs, looking eagerly across as big business grows.
Current Insight Engines are Too Expensive for SMBs
Simply put, most small businesses cannot afford current connected search platforms. The price of cutting-edge technology is often out of reach.
The good news is that beginning your journey with AI doesn’t have to cost a fortune. When current insight engines are too expensive for small and medium businesses, there are still options available. There are some innovative AI platforms, like Cognility, that offer unique, powerful, and affordable SaaS platforms for SMBs.
Today’s ConnectedSearch Options are Time-Consuming
The vast array of data available from search, social media and other resources clearly has the potential to increase insight and drive business growth. However, most small businesses are struggling to mine the value from their data. In reality, accessing and analyzing this information takes time.
The problem is that current insight engines are too time-consuming for a small business. Small businesses don’t have the time or resources to sift through heaps of data to discover hidden gold.
We believe that every small business needs an insight engine that provides real-time insights on demand, in any industry, at a low cost.
They’re Way Too Resource-Heavy
These solutions, which analyze large document repositories, can give companies an unfair competitive advantage. Truly effective connected search insight engines for SMBs require enterprise-grade hardware and algorithms. For example, the same Hadoop cluster that would run a large company’s AI workflows might prove too resource-intensive for smaller businesses with fewer resources.
Furthermore, it takes expertise to design and implement these systems. Companies must consider their internal resources before choosing insight engine software. If they don’t have an in-house data scientist or open-source Hadoop specialist, they may have to hire externally or use a managed service to get insight engines up and running.
We, at Cognility, shrink the necessary resources down ten-fold by providing you with a SaaS platform that integrates wherever you need it.
Most Require High Levels of Technical Expertise
Transforming data into valuable business insights requires a lot of time and sophisticated knowledge. This means that insight engines are usually limited to large enterprises with teams of data scientists, who can use this information to identify trends in the market. The major challenge that SMBs face is finding a way to incorporate analytical tools without having a deep knowledge of IT.
When choosing Cognility, you don’t need to be a data scientist. We do the work for you for connected search that simply works. Not only can clients easily find answers to their questions, but you can find which question did not get a proper solution, and act in seconds, no matter how complex or deep into the data it may be.
Current Connect Search Engines Don’t Integrate Well
Enterprise search engines has difficult and IT-Intensive integrations into the programs and services most SMBs prefer – like Dropbox for file storage and sharing, Vimeo for video tutorials and more, or Zendesk as a user-friendly and SMB-friendly help desk.
Instead of being forced to migrate your data across, Cognility combines it all into a single interface where answers can be found no matter where the solution is stored. All you have to do is connect it and our SaaS platform will do the rest.
Next Steps: Start Using SMB-Friendly Cognility Search
Cognility is a powerful tool for support teams of any size working within SMBs that are looking to boost their level of service and take their customer support to the next level.
There’s no doubt that connected search has incredible potential, but you shouldn’t be left on the wayside simply due to company size. Cognility solves that problem. Get a demo of your business’ future insight engine today.
Whenever we need to approach the internet, we use Google, Bing or DuckDuckGo. It is so natural that we don’t give it a second thought. We also don’t give a second thought to the fact that when we enter a support site, we dont think its search would yield any relevant results. Why is that?
Evolving the Insight Engine
As search evolves, the concept of a search engine rooted on the traditional ten blue links becomes less and less relevant. The days of the “I’m feeling lucky” button and links to click on are over. The problem with the technology internal search engines use, is that it is limited by its focus on query-based results and lack of semantic analysis. This calls for major changes in the way we think about search engines – or insight engines as they should be called – as they will continue to become more of our primary interface in web sites and intranet.
As we know, text is not the only thing that helps us find answers. In fact, in recent years the insight engine has been evolving far beyond simple text processing. We’re exploring video search and how that can benefit our lives as consumers and organizations.
The State of Insight Engines Today
Tens of millions of queries are asked to Google everyday and the search engine provides insights to users in the form of results. These results are designed to help users find information they are looking for. Despite this, as many as 20% of queries typed into search engines go unanswered. This poses a challenge to the design and implementation of insight engines. Can we build efficient, dynamic, user-friendly, and accurate insight engines that can provide more answers?
Insight Engines are changing the way we do research, uncover insights and make decisions. The new approach is more human centric and relies on creating a relationship with the user.
The Insight Engine is the next step in search and is built on the premise that we need to understand intent before providing answers. If you use a search engine, you’ll notice that you do not always see what you were searching for. This is because search engines still rely on people typing in keywords or using Boolean logic to find what they are looking for. There’s no context or meaning behind the words, which makes it difficult for a machine to provide relevant results. The Insight Engine can solve this by understanding the intent behind your question and providing results based on your goals and interests in mind.
Why the Current Approach to Insight Engines Won’t Cut It in the Future
Traditionally, insight engines have been built on keyword-based systems that try to understand consumers’ intent as they search for products across multiple channels like search engines, e-commerce sites, and social media. While this has worked well for the past 20 years, it will not continue to be effective in the future. With the emergence of conversational interfaces, the role of insight engines is shifting towards helping businesses create a superior user experience through a more natural interaction with consumers. Successful insight engines will need to evolve into a deeper understanding of what consumers want from the commerce experience and how to serve them within an increasingly competitive landscape.
A Better Approach to Delivering Answers is Needed
Conversational interfaces are rapidly changing the way we interact with devices, from voice assistants like Siri and Alexa to chatbots on messaging apps like Facebook Messenger and WeChat. These interfaces allow us to express our intentions and needs in conversational language rather than search keywords. Rather than instructing Siri to “search for an iPad Pro store” or asking Alexa if Amazon sells shoes, we can simply ask “Where can I buy an iPad?” or inquire “Do you sell shoes?” This shift away from keyword-based search may seem minor at first, but it has major implications for commerce businesses of all kinds.
A better approach to delivering direct-to-answer solutions is needed. The power of cognitive computing allows us to move beyond just generating keyword-based results expanded a bit with synonyms, toward a new frontier of training models on vast amounts of data available on the Internet, in order to deliver actionable insights.
To get there, we need more powerful computing capabilities and new algorithms that will allow us to do things like learn from knowledge already in our data repositories, reason over unstructured content in order to make accurate inferences, understand sentiment (i.e., whether content is positive or negative), and extract entities from a variety of content sources (e.g., video, audio, text).
We Are Evolving Insight Engines to Create the Future of Search
As we look to the future, it is imperative that there is a focus on a new type of user interface for search. The experience of searching should evolve from a paradigm of searching for keywords to a paradigm of question answering.
Cognility delivers direct answers faster with a new approach to search with intelligence and analytics.
What you are seeing in the image below is the marketing technology landscape for 2021 There are 8000 companies, mostly SaaS, trying to help generate more money from customers.
If we look into the industry’s solutions, we can see a big leap in three major areas:
Marketing automation is a big thing, from social campaigns to lead generation and automation. Today’s tools let you track an anonymous visitor on a landing page and measure all her/his action on your website with a scoring mechanism to optimize the sale opportunity and even predict what is the right moment to engage a sell offer. With the growth of eCommerce over traditional physical stores the impact is clearly shown in the number of marketing services. The amount of solutions is so vast that it is hard to point here the best solution, but I will take a chance here to introduce you to Mautic which is an open-source platform with very high capabilities and great supporting community. You can explore more solutions at the Customer service automation guide that we will update from time to time.
CRM platforms and sales management tools
The big change in the industry accrued when Salesforce introduced a new SaaS CRM to the world in 1999. It was a huge change as companies no longer needed to buy very expensive software and manage their customers on expensive data centers (I remember the days when I managed a small center with Alpha computer running Unix). This change made life much more simple at the beginning and evolved much further to today’s solutions such as Hubspot, Pipedrive and many more. Actually, there are so many solutions these days, each one is very good at a specific task, it makes it harder to know what is the right solution for your company. So if you have good tips and experience from a specific tool, please share in the comments.
The new companies’ approach in customer support and the big change in omnichannel communication, brought new and better solutions. For good several years, the CRM platforms were good enough to handle the customer relations and ticket any event within CRM. It was common when most communications were handled over the phone or mails. In 2011 Facebook released its messenger and changed the game again. Solutions have evolved with great live chat solutions like Intercom to a full help-desk solution by Zendesk and Freshdesk. Communication moved with the millennials to chat channels and companies needed to adjust and talk with their customers on Facebook Messenger, Whatsapp, webchat client, “the super app” – Wechat, email, and SMS. It was very natural for companies in this segment to evolve as the need increased.
The landscape is evolving and changing rapidly. New solutions are being introduced to adapt to the customers’ eco-system. Try not to tie yourself to a solution you can’t replace or upgrade quickly. Make sure you can use a solution that you owe the data and migrate easily. Check how a service can connect and integrate with others (A great solution for connecting different services with automation is Integromat). DO NOT USE tools with poor analytics.
One of the documents that always accompanies any product is the dreaded user manual. Writing a great manual is not an easy task and gets harder for a complicated product. But we do want to ease the knowledge transfer from the product creators (that is, us) to the product users. The following tips will guide you on building one:
- Know the product you are building the manual for
- Use the product
- Ask questions about it
- Take part of the design process if possible
- Know your audience
- Test the product on a focus group to see how they use it
- Take note of misunderstandings and failures in product usage; These will give you important ideas on what’s needed to be emphasized in the manual
- After having a user manual draft, have users read it and evaluate their understanding. Improve iteratively based on their feedback
- Make it simple
- Write it as you speak to your users
- Use short, clear sentences
- Do not assume your users understand the parts’ names. Make sure you redirect them with visual indications and images when needed
- Use visual materials
- People understand and remember visuals much better and faster than text
- Always add explanations for images. Never use images with no text attached to it
- Inside the image, use clear and large enough fonts
- Make a clear logic for the user manual steps
- Build a user journey map before creating the user manual, to understand what the user is doing on each step. For example, should he or she make sure no hidden parts are lost in the package? Or, do some parts arrive in a second box? you have to know that.
- Always indicate if some steps need tools or third party product
- Make sure you meet the safety regulations
- Indicate hazards prior to a step and bold it for the user
- Always follow the product regulations. In some cases, regulations will indicate what information should be added to your user manual. For example, it can be by CE or ASTM. Consult with the product manager and QA department, which usually hold this information.
- Document structure
- Plan your user manual layout. It can be a spread, a booklet, an accordion or anything that makes it stand out and easy to understand
- Make your layout simple. Use fewer columns as you to reduce confusion by the user
- Put the supporting graphics near the relevant paragraph. Do not let your users search for the right image
- Plan where your user manual is going to be published and how. Most consumer goods products, for example, comes with a printed user manual. This user manual is also presented online on desktop and mobile. If you can plan your user manual to be easily read on different platforms, you make it more accessible to your users
- Cognility automatic chatbot builder provides an automated tool based on deep learning technology, that turns any user manual into a chatbot. The chatbot focuses on understanding the user’s needs and provides the right information to the user quickly and efficiently.