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The 8 Biggest Chatbot Mistakes That Are Costing You Money

Posted on | by Harriet Norman

Whilst AI can be dated back to the 1950s when Turing famously invented the Turing Test, the first ever chatbot, ELIZA was introduced in 1966, followed by a series of ‘chatterbots’ in the 1990s. This wave of chatbot technology was not created to support customer service, rather they were tested by audiences to develop bot intelligence.

For a detailed history of AI chatbots, click here.

Today, chatbots are considered a must-have tool belonging to the wider customer service toolkit, facilitating not only customer support but lead generation, sales and growth. Therefore, it is not just customer service teams that value chatbots but also Sales and Marketing.

However, when it comes to selecting chatbot software and the subsequent implementation process, a surprising number of companies get it wrong. The results of which can be detrimental and include unnecessarily high overheads, poor CSAT and damaged brand reputation.

So, how can you ensure that the chatbot solution you select and your roll-out method works effectively for your company, achieving goals that surround:

  • Support cost reduction
  • CSAT and NPS improvements
  • Enhanced CX
  • Contact centre efficiency
  • Lead and revenue generation

This article addresses the common mistakes that all too many companies make when it comes to chatbots, offering advice as to how they can be avoided:

  1. Neglecting Objectives and Strategy
  2. Not Working with A Vendor Experienced in Your Field
  3. Not Giving Your Chatbot a Personality
  4. Selecting A Chatbot That Cannot Provide the Right Answers
  5. Choosing A Solution That Cannot Escalate to A Human
  6. Making A Decision Based Solely on Pricing
  7. Choosing A Chatbot That Has A Lengthy Implementation Process
  8. Settling for A Chatbot That Cannot Be Optimised

1. Neglecting Objectives and Strategy

Common mistake:
When companies do not discuss and define clear goals with measurable objectives regarding their upcoming chatbot initiative, a multitude of things go wrong. The result of which means that those involved have no clear direction as to what they’re working towards, with no benchmark to know if they are on track or if something has gone wrong. Without objectives, there is nothing to measure the success of the outcomes against.

Best practice:
Meet with everyone who will be involved with the deployment of your chatbot solution to ensure that everyone is on the same page with goals, expectations and outcomes. Not only does this allow for smoother execution but it helps to identify issues in their tracks. Having input from those involved provides a varied perspective, providing value to key decisions regarding chatbot selection.

2. Not Working with A Vendor Experienced in Your Field

Common mistake:
Many businesses choose chatbot software vendors that are inexperienced in the field in which they operate. Chatbots that have not been tested in a certain field lack intent sets and therefore do not know their audience. The result of which includes money and time spent with vendors who cannot produce the chatbot required to fit your customers’ needs.

Best practice:
Spend time and care when considering chatbot vendors. Compile a list of must-have criteria for your chatbot; its functions, capabilities and vendor experience. Ensure that the vendors that make it to your shortlist have sufficient experience in your field and can demonstrate tangible examples and results. Their experience will prove hugely advantageous during implementation.

3. Not Giving Your Chatbot a Personality

Common mistake:
Neglecting your chatbot’s personality results in a forgettable CX and denotes a boring brand reflection. Chatbots that are missing a name, icon, personality and tone of voice will appear outdated. Whether your chatbot solution is incapable of such configuration or it has been neglected, the result will not be positive for CX.

Best practice:
Ensure that the chatbot solution you choose offers flexible configuration; enabling the bot’s responses, language and grammar to match your brand’s personality and tone of voice. Certain chatbots include additional configurable search layers that ensure a conversational, on-brand response is always served.

4. Selecting A Chatbot That Cannot Provide the Right Answers

Common mistake:
When companies opt for basic chatbot solutions that are not powered by AI or utilise Natural Language Processing (NLP), customer satisfaction suffers significantly. This is because the bot relies on customer queries matching its records exactly in order to produce responses. Because it is highly unlikely that every customer enters the chatbot’s exact records, a correct result is rarely produced. Instead, the chatbot replies with “I’m sorry I don’t understand your question. Please try again.”

Best practice:
Always choose chatbots that are built on AI and harness the power of NLP. This ensures that regardless of how a customer phrases their query, a relevant answer will be produced. The way this works is through NLP’s sophisticated layers of search. By unpicking the query structure, it analyses each component including keywords, grammar, intent and popularity to understand context and therefore return relevant results.

To find out more about the difference between simple and AI-powered chatbots, click here.

An images to represent the 4 layers of Natural Language Processing: search keywords, intent, grammar and popularity.

5. Choosing A Solution That Cannot Escalate to A Human

Common mistake:
Whilst chatbots are effective in dealing with routine questions, there will always be customers who require help with issues that are more complex and warrant human rather than artificial intelligence. When a chatbot cannot firstly identify when escalation is required and secondly, cannot provide a smooth escalation to an agent, then CX and CSAT plummet. When customers have serious issues they need resolving, having to repeat the process and their query over and over again is not ideal.

Best practice:
Ensure that the chatbot solution you select offers seamless escalation options. Make sure that the chatbot can detect when it is not capable of resolving the query and that escalation to other agent-assisted channels such as live chat is stress-free, taking place in the same window. It is also key to the customer’s experience that transcripts are carried over to avoid unnecessary repetition, reducing Average Handling Times (AHT).

An example of a chat bot widget with Live chat offering

6. Making A Decision Based Solely on Pricing

Common mistake:
Too many companies focus solely on finding the cheapest chatbot solution and unfortunately have to pay the price in the long run. Such solutions might seem tempting but generally end up costing you more over time. This is because lower-priced chatbots don’t utilise NLP which leads to the laborious process of manually programming lots of questions and answer pairs. Further, poor escalation provided by such solutions result in higher contact volume reaching contact centres and therefore unnecessary high support costs.

Best practice:
Ensure that during your chatbot software selection process, you stick to a comprehensive list of essential requirements. Include attributes such as NLP utilisation and escalation capabilities that facilitate the escalation from chatbot to cost-effective agent-assisted channels such as live chat. This not only improves CX but minimises telephone and email queries which prove significantly more expensive to handle.

7. Choosing A Chatbot That Has A Lengthy Implementation Process

Common mistake:
Commonly, the software implementation process is not discussed with the vendor. This results in companies waiting significantly longer than anticipated to have their chatbot up and running. When expectations are not discussed, the implementation process can become lengthy and this affects your customers and operational costs negatively.

Best practice:
Always make sure to discuss and agree on the chatbot implementation timeline with your vendor. This avoids any confusion, manages expectations and ensures you will be up and running on time. It is also crucial to choose a chatbot that utilises low-code implementation, this makes setup simple, using one line of code and minimising the need for cross-departmental involvement.

8. Settling for A Chatbot That Cannot Be Optimised

Common mistake:
Companies that opt for a chatbot solution that does not include a built-in analytics function removes the opportunity for optimisation. Without analytics that demonstrate how well your chatbot is performing in terms of resolved queries, escalation and trigger management there is no way to identify issues or plan for improvements. In other words, whilst competitors are constantly optimising their chatbots, yours remains stagnant.

Best practice:
Ask your prospective chatbot vendors for a demo. Ensure that one of the areas that they show you is the software’s analytics function. Choose a chatbot that offers a comprehensive range of visual analytics that can be downloaded or viewed in a dashboard aesthetic. Such metrics will help you to identify any gaps or errors that emerge and focus on areas for optimisation and improvement so that your chatbot is working to its full potential.

An image showing chatbot metric / analytic data

If you enjoyed this article and would like to know more about chatbots, read our guide here or for help regarding your organisational requirements, please

Get in Touch
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How to Roll-out A Successful Internal Knowledge Base

Posted on | by Harriet Norman

Why Is an Internal Knowledge Base Fundamental for Business?

An internal knowledge base is essentially a company’s central library of knowledge, packaged in an agent-friendly interface. It contains all the fundamental information that employees and other stakeholders require to work effectively. This includes anything from returns policies to product specifications and troubleshooting videos to decision trees.

Access to this sort of information is vital for agents whose roles are to facilitate customer support. With customers expecting fast answers to questions and quick fixes to their issues it is important that agents can find the correct knowledge articles efficiently in order to satisfy customers.

The internal knowledge base interface that agents utilise everyday works by with your company’s wider knowledge base to retrieve relevant and accurate knowledge articles that help to solve customer issues. Powered by AI and by harnessing Natural Language Processing (NLP), agents benefit from quick access to results regardless of how a query may have been phrased.

An image showing the knowledge interface

Once an agent types the customer query into the system, NLP unpicks the sentence using sophisticated algorithms, analysing components such as keywords, grammar, intent and popularity to understand context and produce relevant results.

Because this is handled using AI, the problem-solving process takes place automatically and within the same window. The results of which is a large accumulation of time saved that would otherwise be spent searching for answers or transferring customers to supervisors. By reducing Average Handling Times (AHT), contact centre costs are significantly reduced and agents can deal with more queries, impacting positively on CSAT.

According to a report by Gartner, support costs can be reduced by 25% when a knowledge management discipline is in place.

When an internal knowledge base is effectively deployed companies benefit from:

  • Significantly reduced contact centre costs
  • Greater agent productivity
  • Improved CSAT and NPS ratings
  • Enhanced CX
  • Empowered agents

Choosing Effective Internal Knowledge Base Software

The success of the roll-out, including user adoption and buy-in, will be heavily determined by the software that is selected. The software selection process for your internal knowledge base is crucial and requires time and careful consideration.

To ensure a smooth and successful roll-out of your internal knowledge base, start with the software, ensuring it includes:

  • Ease of access through AI
  • Real-time article updates
  • Integrations that help serve customers
  • Features that promote productivity

Ease of Access Through AI

Choose internal knowledge base software that is built on AI and that utilises NLP. With these forces as work, agents have access to a rich library of knowledge articles at their fingertips using a simple search function. As NLP takes care of identifying, retrieving and producing relevant results rather than the agent doing this manually, significant time is saved and efficiency is boosted. For some contact centres the result of which is up to 25% reduction in Average Handling Times.

Without an AI-powered tool in place, First Contact Resolution (FCR) rates suffer. Without a tool that helps agents quickly find that critical piece of information that will satisfy a customer’s issue, often customers are transferred or a call back is arranged for when the information has been found. However, internal knowledge bases that utilise NLP’s intent-based search features increase FCR rates considerably. This is due to its capabilities to understand what is being asked and matching queries with their most relevant results.

Real-Time Article Updates

When it comes to the distribution of information, whether that is internally to agents or externally to customers and other stakeholders, consistency is key. Distributing inconsistent, inaccurate or outdated information can prove detrimental to companies, damaging reputation and worse in some cases. Ensure you choose internal knowledge base software that enables the straightforward editing and updating of knowledge articles in real-time. This means that once an article has been amended, the changes will instantly be reflected through whichever channel your knowledge base connects to.

With circumstances frequently changing and therefore the way many businesses operate, changes must be quickly made available to those who have direct contact with your customers.

A simple knowledge base editor allows those with permission to easily add, edit and update knowledge article whilst agents can make article suggestions and flag those they suspect require updating.

An imaging showing how agents can flag articles in Synthetix Knowledge for your Team

Integrations That Help Serve Customers

Including an agent-facing knowledge base is hugely beneficial to your business operations. However, what happens when you introduce a customer-facing knowledge base, or in other words, an online FAQ or self-service tool is of significant value.

By choosing knowledge base software that powers both an internal interface for agents and an external interface for customers, not only are you providing consistent information across channels, but also contact reduction. By including a self-service option on your website, the level of contact that would otherwise reach the contact centre – most of which including routine queries – is significantly reduced. This not only improves CX but allows agents greater bandwidth to effectively deal with customers’ more complex issues – resulting in higher CSAT scores.

Internal Knowledge

Customer Facing Knowledge

An image showing an example of a self service tool for Lexus

Features That Promote Productivity

Some internal knowledge base tools. offer additional features designed to further promote agent productivity.

For instance, AI-predictive suggestions use AI to recommend relevant knowledge articles on every agent keypress. These suggestions are displayed within the internal knowledge base and can easily be opened, then copied and pasted over to the customer to further reduce AHT.

When integrated with your live chat solution, features such as the live keypress feed help agents deal with chats with optimal efficiency. It lets agents see what customers are typing with every keypress, often allowing them time to solve and prepare a resolution before the customer has hit “send”.

Ensuring Successful User Adoption

Once knowledge has been harvested from sources such as employee insights and reports, it can be contextualised and transformed into bitesize knowledge articles that make up the knowledge base’s content.

For more detail on knowledge contextualisation and the knowledge management process, read this.

The next step of the roll-out includes user adoption. Ensuring that the internal knowledge base is well received and accepted by its users and stakeholder that are involved is critical to the roll-out’s success. The aim is having employees fully on board with an understanding as to how the initiative will benefit them and the overall business. This can be achieved through a number of methods.

Embed into Culture

Introduce employees to the idea of knowledge sharing and the internal knowledge base well in advanced to prepare them for the roll-out. This might involve weekly company meetings or even tasks that help them become familiar with the initiative. When practised over time, this will become engrained in your company culture.

Assigning Advocates

Before the roll-out takes place, assign several advocates whose responsibility will be to coach others on the upcoming internal knowledge base deployment. Have them act as other employees’ first port of call if they have any questions regarding the roll-out.

By having people of influence within your company advocate the internal knowledge base, it encourages the wider team to get excited about change. If employees are prepared and championing new technology it is likely that other stakeholders will also buy-in.

Employee Involvement

Involve everyone in the initiative as much as possible, as early as possible. This not only helps employees get used to the idea of any changes, but it’s also an opportunity for any employee input.

Organise company-wide surveys and smaller in-depth meetings for those directly involved, for example, agents. This will help with the internal knowledge base’s effectiveness by enriching the content from those with first-hand experience but also helps with user adoption. By familiarising agents with how it will work and having them contribute to the roll-out, the more likely they are to support the new initiative once it is deployed.

Rolling Out and Maintaining Your Internal Knowledge Base

Once the content is finalised and your employees are prepared, the roll-out itself shouldn’t be a complicated or long process. Once contracts are agreed, the deployment of your internal knowledge base, depending on your requirements is completed in days or weeks, providing your software vendor uses low-code deployment methods.

When it comes to maintaining your internal knowledge base, how can you ensure that it continues to be utilised, optimised and proves effective?

Encourage Engagement Through Gamification

To keep agents motivated and engaged, gamified visuals are available to champion user wins and encourage healthy competition in the contact centre. User scoreboard metrics such as most queries solved and top searches are visible to all and displayed in graphics to incentivise and create transparency.

An image that shows Knowledge Base analytics

Measure Effectiveness Against Objectives

To make sure that your internal knowledge base is proving effective in its intended areas, it is good practice to have its core goals and objectives always in mind. This way you can easily measure the system’s metrics against objectives to assess whether you remain on track or not. From this, appropriate changes can be made if necessary.

Measure metrics that reside in the tool’s analytics suite, such as search result metrics which reveal how many queries were successfully dealt with using the internal knowledge base and how many are optimisable.

An image that shows Knowledge Base analytics

Optimise Your Internal Knowledge Base with Analytics

Keep your knowledge articles up to date and your internal knowledge base accurate by assessing top query analytics that identifies any gaps in your existing content. This is also an opportunity to discover trends as they emerge.

To optimise your content, you can also carry out regular employee surveys and agent interviews to capture new insights and information, continuing to develop your internal library of knowledge.

An image that shows Knowledge Base analytics

If you enjoyed this article and would like to know more about knowledge management, you can read our guide here, or for advice on software selection, please

Get in Touch

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Executing a Knowledge Management Strategy

Posted on | by Harriet Norman

What Is Knowledge Management?

Knowledge management is a key business discipline that is utilised by a multitude of businesses to optimise knowledge, transforming the way it is perceived and dealt with. When executed effectively, knowledge management alters company culture so that it is centred around knowledge. Businesses that achieve this share the understanding that knowledge is a powerful asset which is everywhere and should be harnessed should they wish for growth, efficiency and improvement.

Operationally, knowledge management involves the harvesting, analysis, conversion, organisation and sharing of knowledge with the relevant audiences. This could be:

  • Internally for employees to use as a central library of information
  • Externally for customers to access knowledge through self-service tools
  • In contact centres for agents to use when helping customers

By embedding knowledge sharing into the company culture, the capture and collection of knowledge becomes an everyday practice for employees. This helps to remove knowledge silos within your business, promoting transparency and collaboration.

Companies that execute a successful knowledge management strategy also benefit from:

  • Operational efficiency
  • Agent productivity
  • Reduced support costs
  • Empowered employees
  • Better decision making
  • Improved CSAT / NPS ratings
  • Growth and innovation

Knowledge Management Objectives

Like with any new initiative, your knowledge management goals and objectives must be carefully considered and finalised before anything else.

Without knowing why you are implementing knowledge management or what you aim to achieve through its impact and having everyone on the same page, the execution is likely to fail. With a clear view of the purpose and desired outcome, you ensure that everyone has the same vision and is working towards a common result. It also helps everyone involved measure their progress and question if they are on track as expected, if not, then appropriate changes can be made – but at least there will be transparency.

Ensure all the key people involved in the knowledge management strategy are present to discuss goals and objectives, they will have valid views – all of which must be considered.

Common knowledge management objectives involve:

  • Reducing operational overheads and support costs
  • Promoting contact centre efficiency
  • Improving CSAT and NPS scores
  • Optimising the customer journey online
  • Supporting business growth
  • Supporting new product/service innovation

Once overall goals have been agreed, bitesize objectives can be decided alongside a timeline.

Knowledge Management Auditing

Once your team has a shared understanding of the purpose and outcomes regarding the knowledge management strategy, it is good practice to conduct a knowledge audit. This involves the evaluation of the current knowledge within your company to highlight any strengths, gaps, existing attitudes, opportunities and roadblocks.

Conduct the audit by considering the following:

  • What are your company’s requirements surrounding knowledge?
  • What current knowledge assets or resources exist within your company?
    • What format do they exist in?
    • Are they mainly explicit or tacit types of knowledge?
  • What gaps exist within your current knowledge offering?
  • How is knowledge currently shared around your company?
  • What are the roadblocks that are preventing knowledge sharing?
    • Do you have a dedicated and accountable knowledge executive?
    • Are there any processes already in place?
    • Do you utilise any software that enables sharing?

Once the knowledge audit has been carried out, you will have a clear understanding of where your strengths and areas for improvement are. This helps to influence the way in which you execute knowledge management.

People and Processes

Dedicated Knowledge Executive

Whether its a Knowledge Manager or dedicated executive from a division such as Customer Service, Customer Experience or Marketing, it’s fundamental that you have someone that takes ownership and accountability over knowledge management.

Ensure that your knowledge executive has the skill and experience to deal with knowledge. This includes knowing how to collect, curate, and harvest knowledge, but also involves the translation of tacit data into consumable knowledge.

Without a Knowledge Manager or equivalent in place, your knowledge will become ineffective, inconsistent and redundant. Without a dedicated individual updating, editing and adding to your bank of knowledge then not only does knowledge management not work as a function but inaccurate and therefore potentially damaging information can circulate.

Knowledge Management Process

Process is imperative when it comes executing your knowledge management strategy. Whilst there is no cookie-cutter approach to this and steps will differ from business to business, it’s important to follow the knowledge management process at the least in its’s simplest form:Discovery:The first step concerns identifying and capturing any explicit knowledge that already exists within the company. This can be found in intranets, DMS and shared company documents, for example, HR policies and Sales processes.

Capture: This focuses on extracting any tacit, undocumented knowledge which generally resides within the brains of senior employees. Interviews and reflection exercises are used to harvest knowledge that would otherwise remain subconscious.

Organise: Once all data has been collected, it must be analysed, grouped and translated into digestible content that is familiar with its audiences. This means communicating data as knowledge articles that are written in the company’s tone of voice.

Share: Using knowledge management software such as an intelligent knowledge base, the sharing of knowledge amongst colleagues and with other stakeholders such as customers is seamless and secure. Knowledge bases that utilise AI and Natural Language Processing (NLP) ensure knowledge is fully accessible.

Evaluate: This fundamental step focuses on the constant review and monitoring of knowledge to ensure it is always accurate, up-to-date and serving its audiences effectively. Such evaluation exercises can be carried out using analytics.

A diagram showing the process flow of knowledge management

This approach can be used time and time again for the effective flow of knowledge to a range of outlets. Ensure each step is given careful attention and that none are skipped or disregarded.

Read more about the Knowledge Management Process stages, here.

Knowledge Management Software

At the heart of your knowledge management strategy is knowledge management software. It is what essentially facilitates the discipline – which would fail without such systems in place.

Most commonly businesses utilise knowledge bases that are powered by AI to act as their centralised repository of companywide knowledge. It stores all knowledge articles surrounding your company, products and services and allows knowledge executives to add, edit and update in real-time to avoid any information inconsistencies.

With a knowledge base, knowledge sharing is simplified. Whether it’s your employees, agents or customers accessing knowledge, the utilisation Natural Language Processing (NLP) ensures that the right information is always delivered.

For instance, agents who are accessing the internal-facing knowledge base begin by typing a customer query into the search bar. NLP gets to work unpicking the query, analysing the keywords, intent, grammar used and popularity, so that no matter how a query is phrased, the best results are produced.

An image showing the knowledge interface

From a customer’s perspective, a filtered version of your knowledge base can be accessed via a number of online self-service tools. When a customer requires support or needs to solve an issue, they can either navigate to a knowledge article by choosing a category or NLP will produce relevant answers based on what is entered.

For effective knowledge management software, consider:

  • Is it powered by AI and does it harness NLP?
  • Is it built with both contact centres and customers in mind?
  • Does it enable seamless integrations with other key systems?
  • Is it implemented using low-code?
  • Does it use open RESTful open APIs?

Check out the Knowledge Management Software Buyer’s Guide

Implementation

The implementation of knowledge management software – that is from the SRS agreement to being fully up and running – does not need to be complicated or time-consuming. By choosing knowledge management software that utilises low code, depending on your business requirements, your employees and customers could be benefiting from knowledge within days or weeks. All it requires is a simple line of code that is installed on your website.

Your knowledge base seamlessly integrates with your fundamental customer service tools for the two-way sharing of knowledge. whether it’s self-service widgets , chatbots or live chat , all users can access consistent information fed from the same source.

Through its open RESTful API capabilities, an intelligent knowledge base can also connect to any key 3rd party applications that your company relies on, for example, your CRM.

An image showing how Self-Service, 3rd Party Tools and Contact Centres all integrate with Knowledge

Measurement and Maintenance

Your knowledge base’s analytical suite is how you will determine how effective your knowledge management strategy has proved in terms of achieving your overall objectives. Whether your objectives surrounded CSAT or agent efficiency, it is critical to the success of knowledge management that metrics are constantly analysed and reviewed.

Investing significant time and capital into the execution of your knowledge management strategy, only to fail at the final hurdle would be detrimental – and is why measurement is so important.

Just some knowledge analytics that are available to companies for analysis include:

  • Search results: helping you to determine the effectiveness of knowledge, revealing how many articles resolved queries and how many required further optimisation.
  • Top queries: providing an insight into customer behaviour, revealing what is being searched and whether knowledge articles were available to fulfil their requirements.
  • Triggers: demonstrating which tools have been triggered on your website and which have proved the most optimal at resolving issues.
An image that shows knowledge base and internal knowledge analytics

If you enjoyed this article and would like to more about knowledge management,t, you can read our guide here, or for advice on knowledge management software and implementation, please

Get in Touch
Image on lady on laptop for knowledge management processes

What Is the Knowledge Management Process?

Posted on | by Harriet Norman

What Is Knowledge Management?

Knowledge management, a key business function, is practised by a multitude of businesses to optimise knowledge, treating it an asset with significant monetary value attached. When implemented effectively, knowledge becomes embedded into a company’s culture so that the collection, analysis, sharing and evaluation of knowledge is second nature.

Companies that implement knowledge management understand the significant value knowledge holds when it comes to business. When we consider how fundamental intellectual property, processes and patents once stemmed from knowledge, it’s apparent why so many companies encourage the collection and sharing of knowledge.

When businesses have teams across multiple locations, knowledge becomes isolated and knowledge silos are formed. This can prove detrimental to business operations and innovation and only exacerbates as companies scale up. Knowledge management instead promotes a knowledge sharing culture which helps to tear down knowledge silos and retain important company knowledge. It also ensures that when an employee leaves a company, their tacit knowledge does not leave with them.

Further, knowledge management helps to bring new employees up to speed quickly, transforming them into experts through the knowledge that has been collected.

Businesses that implement and practice knowledge management benefit from:

  • A significant reduction to customer service support costs
  • Higher CSAT ratings
  • Efficient operations
  • Enhanced decision-making process
  • A cultural shift towards knowledge sharing
  • Empowered employees
  • Growth and innovation

For more on the fundamentals of knowledge management, click here.

What Is the Knowledge Management Process?

People, process and technology each play integral roles when it comes to implementing knowledge management. It’s crucial that your company not only has a dedicated knowledge executive, someone who manages knowledge as an asset and knowledge management as a working machine, but what is fundamental in executing the discipline is the process you follow.

Whilst every business is different and there is no cookie-cutter approach to the knowledge management process, most commonly the following structure is utilised:

  • Discovery: What existing knowledge can be collected?
  • Capture: What undocumented knowledge can be extracted
  • Organise: Is the knowledge consumable?
  • Share: Is the knowledge accessible?
  • Evaluate: How can the knowledge be optimised further?

It’s important that this process, or a variation of this process is followed for the successful deployment of knowledge management. Skipping or neglecting a step could prove detrimental to the final result.

Step 1: Discovery

Before Discovery, it is good practice to discuss knowledge management goals and objectives to provide your knowledge manager with clear direction as to what your company needs to know and what knowledge needs to be found.

Following this, the first step of the knowledge management process concerns the collection of any explicit knowledge that already exists within the company. Explicit knowledge is codified, consumable and can be easily communicated, some examples include:

  • Policies such as HR or returns policies
  • Processes such as Sales or product launch processes
  • Documents such as product specifications or supplier agreements
  • Reports such as marketing effectiveness and customer research

Due to its nature, the collection of explicit knowledge is straightforward but can take time.

Knowledge managers might discover explicit knowledge in shared company DMSs, CRMs, intranets and other records. Whilst this knowledge already exists, it is likely to be highly fragmented and therefore requires dedicated time to data-mine. Effective knowledge management software assists in semi-automating this otherwise time-consuming step.

Step 2: Capture

The Capture stage of the knowledge management process is responsible for extracting and capturing any tacit knowledge that exists within the company. Unlike explicit knowledge, tacit knowledge is generally difficult to identify, articulate and remains below the surface.

Tacit knowledge is learnt over many years, is usually utilised subconsciously and resides within the minds of senior employees, often becoming second nature to them. Because tacit knowledge is not easily communicated, specialist skills and methods are used in order to extract this incredibly valuable type of knowledge. Such methods include:

  • Observation
  • Interviews
  • Surveys
  • Retrospect (reflection meetings that take place after the completion of a project)
  • Knowledge harvesting (often involving senior employees)

Once tacit knowledge, for example, expert opinions on competitors or salesperson intuition on customer behaviours has been captured, it is the role of the knowledge manager to transform it into a digestible form.

Step 3: Organise

The purpose of knowledge management is to embed, share and teach knowledge to relevant audiences, therefore it is paramount that the knowledge itself is presented in a comprehensive yet simple and digestible way.

This means that your knowledge executive must translate the tacit data collected into consumable knowledge.

Deep analysis and skill are required to collect, unpick, combine and rebuild tacit knowledge into comprehendible articles that match the company’s tone of voice and the entity in which the user is searching for.

Most businesses use knowledge base technology to simplify this process, ensuring control and organisation whilst knowledge is imported to one centralised repository.

Knowledge base providers that utilise Natural Language Processing (NLP), can even advise knowledge managers, flagging poorly structured titles and offering recommendation to improve them.

An imaging showing how agents can flag articles in Synthetix Knowledge for your Team

Step 4: Share

This stage is particularly important to knowledge management, it is what makes knowledge accessible and available to the right people at the right time, whether it’s:

  • Internally for employees searching for critical documents, acting as a central knowledge library
  • Externally for customers in the form of self-service software , using a filtered view they can access relevant articles

The most effective way to share knowledge throughout a company is through a knowledge base. Once articles are imported, they can easily be updated, reviewed or added to using the straightforward editor. Any changes are reflected in real-time to ensure information consistency and accuracy, no matter how or who is viewing it.

Built on AI and harnessing NLP, intelligent knowledge base technology, users have access to results fast. By unpicking sentence structure and analysing each word, NLP can comprehend what a user is asking regardless of how they have asked it. Such knowledge bases offer article recommendations on every keypress to further boost efficiency.

Users can also navigate to knowledge articles using categories, sub-categories, filtered views and favourites tabs.

For more on how to successfully roll-out a knowledge base, click here.

Step 5: Evaluate

The final stage of the knowledge management process is one that should be continuously carried out. It is responsible for ensuring that the knowledge stored and distributed is proving effective to its audiences. Without this step your knowledge becomes stagnant and knowledge management has no space to optimise.

For constant operational and knowledge improvements, your knowledge base analytics should measure:

  • Search result metrics:analytics that demonstrate the effectiveness of articles based on resolved queries, subsequently revealing the effectiveness of your knowledge base
  • Top query metrics: these provide key insights into customer trends and requirements, revealing where any content gaps are and areas for optimisation
  • Trigger metrics: these metrics show where certain tools trigger on your website, revealing effectiveness and how customers are interacting with your brand
An image that shows knowledge base and internal knowledge analytics

Facilitating Knowledge Management Through Software

Whilst people and process are fundamental to the implementation of knowledge management, without effective software in place the facilitation of the discipline would not be possible.

Most commonly a knowledge base is utilised to store, share and measure the effectiveness of your companywide knowledge, from HR policies to product troubleshooting videos. It acts as the nucleus of knowledge management and feeds all key business and customer service tools. This means that the internal knowledge base your agents use will produce the same articles as the self-service tools that your customers use, regardless of the way a query is phrased. It ensures consistency.

For smooth implementation and reliable maintenance of knowledge, look for software that:

  • Is powered using AI: This ensures your knowledge base is intuitive, delivering the fastest, most relevant results possible.
  • Harnesses powerful Natural Language Processing: Critical to CSAT and agent efficiency, NLP understands what is being asks and can therefore deliver the best answers.
  • Is built with the contact centre in mind: The knowledge base’s agent interface includes features such as AI-predictive suggestions and integrated knowledge to save operational costs.
  • Is built with customer experience in mind: Customer facing tools such as self-service offer intelligent search and categories, as well as escalation to agent-assisted channels if necessary.
  • Includes seamless integrations: Connect with self-service tools, chatbots and live chat channels for consistent knowledge sharing.
  • Is implemented through low-code: With low-code implementation, knowledge management as a functional discipline can be up and running quickly.
  • Uses open RESTful APIs: In order to connect to your key 3rd party applications such as CRMs or email management tools.

If you enjoyed this article and would like to know more about knowledge management, you can read our guide here. Or if you would like any advice about knowledge management software or the process, please

Get in Touch
Image of lady on Laptop using Knowledge for Teams

Essential Knowledge Management Tools

Posted on | by Harriet Norman

What Is Knowledge Management?

Knowledge management is a discipline utilised by businesses to optimise knowledge and the way in which it is treated. It concerns the extraction, collection, analysis, sharing and development of companywide data to promote operational efficiency and enrich both customer and employee experience.

The global knowledge management market is estimated to reach a size of $1.1 Trillion by 2027, growing at a CAGR of 16.8% over the analysis period 2020-2027, as companies continue to recognise the value of the practice.

By embedding knowledge management into a company, a knowledge sharing culture is created whereby knowledge is perceived as an asset that has a monetary value attached. Companies who effectively implement knowledge management experience:

  • A significant reduction to customer service support costs
  • Higher CSAT ratings
  • Improved operational efficiency
  • Greater accuracy and consistency of information
  • Faster, more informed decision making
  • Enhanced CX
  • Empowered employees

It’s important for businesses to stay on top of knowledge management if they want to remain competitive, satisfy their stakeholders and avoid stagnancy.

For successful implementation, companies must utilise a variety of intelligent knowledge management tools, with the more important being a knowledge base.

The Importance of a Knowledge Base

A knowledge base is ultimately what facilities knowledge management within any organisation. Without this essential knowledge management tool, knowledge sharing both internally and externally would prove ineffective, inaccurate, and cumbersome.

A knowledge base works as your company’s centralised repository of knowledge, containing everything from product specifications to brand guidelines and support tutorials to returns policies. Its purpose is to make the right information accessible to the right people at the right time, this could be employees, agents or customers.

As your sole source of knowledge, the risk of inconsistent or inaccurate information being distributed is significantly reduced. Your knowledge base powers all your key customer service and knowledge management tools, feeding them with accurate and up-to-date information. This contributes positively to CSAT and mitigates the chances of brand reputation being damaged.

A knowledge base helps to improve efficiency companywide. Not only does it integrate seamlessly with all your key business and customer service tools enabling smooth, 2-way knowledge sharing, but it tears down knowledge silos. Knowledge otherwise kept within teams or locations is stored in one place for all to benefit from. Now equipped with more, richer knowledge, decision making at both an operational and strategic level is optimal.

Further, as Sir Francis Bacon said, “knowledge is power” – it is an asset and the more you harness it the more competitive you become.

At the core of any effective knowledge management strategy is a knowledge base, the function cannot be successfully implemented without this powerful tool.

What Are Knowledge Management Tools?

In addition to your knowledge base, other knowledge management tools that involve customer relationships and analytics can help to improve operational efficiency. With the knowledge base at the centre of knowledge management, other 3rd party applications such as CRMs and analytical tools increase cohesion across your entire technology stack.

Intelligent Knowledge Bases

Built on AI and harnessing powerful Natural Language Processing (NLP), an intelligent knowledge base is the nucleus of knowledge management. It stores and shares valuable information using AI, intelligent search systems and filters to ensure the correct knowledge is served to the correct audience.

Its editor system allows knowledge articles to be added, updated, edited and linked to one another with ease, all reflecting in real-time to ensure the consistent distribution of answers. Not only is it easy to maintain, but users can also find what they’re looking for through categories, views and the system’s sophisticated NLP.

Integral to CSAT

An intelligent knowledge base’s NLP helps customers find quick and relevant answers through the customer-facing tools they interact with, positively contributing to CSAT. Self-service and chatbot applications for example that integrate with your knowledge base utilise NLP to understand the context and intent behind customer queries. By unpicking sentence structure and analysing keywords, intent, grammar and popularity, relevant results can be delivered regardless of how a query is phrased. This allows a large proportion of routine queries to be solved simultaneously and at scale using AI, removing the need for an agent and providing a smooth customer journey.

Essential to Agent Efficiency

For the contact centre, an intelligent knowledge base is essential when it comes to agent efficiency. With all information intuitively available at agents’ fingertips, they do not have to toggle between windows and resources to find the right knowledge articles. The result of which means that more customers can be served, reducing Average Handling Times (AHT) by 40%. Additionally, knowledge bases that harness Natural language processing (NLP) are shown to consistently deliver accurate information to the agent, increasing First Contact Resolution (FCR).

Your knowledge base helps to reduce training times, up to 30% in some cases. Less time and fewer costs are spent on onboarding and training as the knowledge required to train new user has already been extracted and stored in the knowledge base. The provision of your knowledge base ensures that starters don’t have to know an answer to a query or process in your contact centre, but only to know how to find that information.

An image to indicate the different in training times using Synthetix Vs not using Synthetix

Customer Relationship Management

Your CRM plays an important role in knowledge management, in particular complementing your knowledge base. Whilst a CRM’s purpose surrounds maintaining customer relationships, it acts as a repository of customer information which is valuable in informing knowledge creation.

The vast data that is captured inside you CRM – everything from demographics to buying patterns, pain points and objections – is highly valuable for decision making but would not be stored in your knowledge base, similar to how articles would not be stored in your CRM.

Analytical Tools

Analytical tools, those that capture, collect and present metrics surrounding knowledge management are imperative to continual operational improvement and knowledge optimisation.

Not only can these metrics tell you how effective knowledge management is proving in relation to serving customers, but it also reveals any content gaps or roadblocks in existing knowledge articles. Specially curated graphs and charts provide insight into how many queries were successfully answered and how many were not, revealing areas for improvement.

Top search query analytics help not only give provide insight into customers’ search behaviour but also help to influence decision making in other areas of business such as product development. If there are patterns in what customers are asking for, this may reveal their current motivations and requirements, ultimately giving you a competitive advantage.

An image that shows knowledge base and internal knowledge analytics

Effective knowledge base software will have a comprehensive analytics suite integrated into the product itself, working seamlessly to provide insights.

Integrating Your Knowledge Management Tools

To further optimise the power of your intelligent knowledge base, integrate it with key business and customer service tools. Effective knowledge bases ensure the seamless integration between other customer service tools such as self-service, chatbots and live chat to facilitate 2-way knowledge sharing. They also utilise open RESTful APIs to connect with key 3rd party applications such as your CRM and email management software.

Such knowledge management integrations allow:

  • Customers access to relevant knowledge through self-service tools
  • Agents to efficiently answer customer queries with knowledge at their fingertips
  • Chatbots to guide customers through their journey whilst answering their routine questions
  • Agents to see what a customer is typing via live chat, recommending articles on each keypress
  • Customer information to be added and updated automatically in your CRM
  • Email and automation to be triggered based on customer interactions
An image showing how Self-Service, 3rd Party Tools and Contact Centres all integrate with Knowledge

If you enjoyed this article and would like to find out more about knowledge management, you can read our guide here. Or, if you would like any help implementing knowledge management tools, please

Get in Touch
Image of laptop showing knowledge for your customers

Customer Self-service: 8 Ways to Get It Right

Posted on | by Harriet Norman

What Is Customer Self-Service?

A fundamental support tool, customer self-service provides customers, visitors and prospects with the option to serve themselves online. This means users can find answers to their own questions, resolves their own issues and discover critical information by themselves.

Not only does this contribute significantly to contact reduction in the contact centre – as AI-powered customer self-service tools automatically handle routine questions – but customers love it too. By cutting out the need to find contact information, get in touch with an agent and by giving customers the tools to help themselves, things become much easier, quicker and more convenient, which of course helps to improve the customer experience.

67% of customers prefer self-service over speaking to a company representative.

The global self-service technology market size is expected to reach USD 46 billion by 2027, registering a CAGR of 6.7% from 2020 to 2027. The growing demand for self-service solutions amongst businesses can be attributed to their successes within customer satisfaction and operational efficiency.

Customer self-service reduces contact volumes by up to 50%. When we consider the sheer volume of routine queries that make their way to contact centres to be dealt with and the costs associated with handling them, this contact reduction translates into huge cost savings. The AI-powered self-service tools can recognise multiple variations of questions, matching them with relevant knowledge articles and providing customers with the most relevant results.

When configured correctly, customer self-service can intercept customers when they need help, removing the need for agent assistance and therefore reducing the volume of routine questions received. This means that agents have more bandwidth to deal with customers’ complex issues.

Through the utilisation of Natural Language Processing (NLP), customers are always delivered the most relevant results. This is achieved by NLP unpicking sentence structure and ultimately understanding context to successfully answer questions. This and the freedom to serve themselves helps to increase CSAT and positive associations with your brand.

For more on the fundamentals of self-service software, click here.

Different Forms of Customer Self-service

Depending on your business, website and how customers interact with your brand you might choose to offer self-service on its own page, as a widget, or both.

Whilst an FAQ-style self-service page offers customers a hub to which they can independently search for what answers they want, a widget can be configured to display when certain conditions are met. Using trigger management, customer self-service widgets can offer help when, for example, when certain pages are visited, or a certain amount of time is spent on one page. This increases the chances of customers reaching their destination and can even contribute to revenue.

An AI-powered chatbot is also a form of self-service, providing an additional conversational element to CX.

An image of a screen showing our web self service tool

1. Underpin Customer Self-Service with A Knowledge Base

Behind any good self-service solution is an intelligent knowledge base.

As your centralised source of company knowledge, your knowledge base powers your self-service tools. Through seamless integration and sophisticated AI, the software identifies relevant knowledge articles based on what a customer has entered into the self-service tool. Natural Language Processing (NLP) helps to understand the query intent and subsequently retrieves adequate answers.

To ensure you get customer self-service right, select knowledge base software that:

  • Is powered by AI
  • Harnesses NLP
  • Is built with the contact centre and customer in mind
  • Includes seamless integrations
  • Is implemented through low-code
  • Uses open RESTful APIs

2. Plan Around Customer Experience

When discussing goals, objectives and possible outcomes for any self-service project, it is crucial to remember the end-user and consider their needs and expectations with every decision that is made.

Perhaps your underlying goal for implementing customer self-service is one of the following:

  • Contact reduction in the contact centre
  • Reduction of support costs
  • Better CX
  • CSAT or NPS score improvements

Even if your main goal surrounds contact or cost reduction, the self-service tool must first cater to your end-user in order to function successfully. Surprisingly, the end-user and their needs often get lost within projects such as this, so keep them in mind always.

Ensuring that the customer is at the forefront of every key decision that is made around self-service keeps you on track to implementing the best solution for your company.

3. Always Choose Natural Language Processing

Customer self-service without Natural Language Processing (NLP) cannot effectively handle queries or deliver CX.

Why? Basic self-service solutions that do not utilise NLP rely totally on the customer query matching its records exactly in order to provide an answer. When a variation is entered instead, the customer is generally served an unhelpful and frustrating response.

AI-powered self-service software on the other hand,, utilises NLP so that multiple variations of the same query are successfully understood and therefore customers are served relevant answers. NLP unpicks queries, analysing keywords, intent, grammar and popularity to comprehend intent, increasing the chances of the correct results being served and thus great CX.

An images to represent the 4 layers of Natural Language Processing: search keywords, intent, grammar and popularity.

4. Map Your Customer Journeys

Having a clear, holistic view of your customer journeys encourages cost-efficient routes whilst enhancing experience.

Its good practice to understand customer journeys, from start to finish – especially those that are common. By mapping these out, it quickly becomes obvious any roadblocks users might be experiencing, as well as opportunities for automation to take place.

For instance, if you found a pattern in a journey where users were requesting a particular form or task, you might look at ways in which to automate this task through self-service opposed to introducing an agent. This would cut support costs and prove smoother for customers.

This is also a great opportunity to configure custom triggers to pages or events that warrant additional help.

Customer Journey with Roadblock

  1. Customer identifies an issue
  2. They use a search engine to find a resolution
  3. No results are found, this is a roadblock affecting CSAT poorly
  4. Customer continues research on company website
  5. They contact Customer Service
  6. Issue is resolved
An image that shows an example customer journey.

Customer Journey with Roadblock Resolved

  1. Customer identifies an issue
  2. They use a search engine to find a resolution
  3. An SEO result is found quickly and efficiently contributing to CSAT
  4. Issue is resolved
An image that shows an example customer journey.

5. Configure Escalation Points

Not every customer query can be dealt with using AI, sometimes human intelligence and empathy are required to effectively serve a customer.

Of course, companies know this to be true, but what is fundamental here is the way in which customers are escalated from self-service to agent-assisted channels. It can be the difference between a repeat purchase and a poor CSAT score or review.

This transition must be seamless, without obstacles or the customer having to repeat themselves. Ensure you select a customer self-service tool that offers smooth escalation to live chat and other agent-assisted channels. These escalation points can be configured based on keyword triggers, such as “cancellation” or naturally when AI cannot find a resolution.

6. Choose A Low-Code Deployment Option

Customer self-service software deployment shouldn’t be time-consuming or complicated, nor should it require a multitude of employees from different departments.

By selecting self-service software that utilises low-code, implementation is effortless and tools can be up and running quickly and efficiently. Unlike traditional deployment methods, low-code involves the simple installation of one line of code, removing the need for hours of developer work or toolkit configuration.

During the software selection process, make this key criterion, it will ultimately help you hit your goals and improve customer service far quicker than alternative, dated methods.

7. Know the Power of Seamless Integration

Companies understand the importance of having not only a strong collection of key business and customer service tools, but a collection that cohesively communicates and works together to serve a shared purpose.

If your knowledge base, customer self-service tools, CRM, email platform and other key apps cannot share data amongst each other, then efficiency, CSAT and costs will suffer dramatically.

Select self-service software that effortlessly integrates with your internal, customer-facing and 3rd party applications for optimal efficiency.

An image showing how Self-Service, 3rd Party Tools and Contact Centres all integrate with Knowledge

8. Measurement Is Key for Constant Improvement

The measurement of self-service analytics is integral to the continual optimisation of customer service.

The frequent measurement of self-service metrics helps to identify areas of success, behavioural patterns, customer preferences and any errors. It ultimately determines the effectiveness of your tool, revealing gaps that can be improved and trends that can influence key decision making in other areas.

You can’t manage what you can’t measure, so ensure that the customer self-service software you choose offers a comprehensive analytics suite.

An image that shows self-service software analytics dashboard.

If you enjoyed this article and would like to know more about self-service you can read our guide here or for advice on software and implementation, please

Get in Touch
Image of macbook showing a Web Self Service solution

3 Types of Online Self-Service That Boost CSAT

Posted on | by Harriet Norman

This article explores the benefits and use cases for the 3 types of online self-service.

What Is Online Self-Service?

Online self-service software provides customers with a platform to which they can serve themselves; finding answers to questions, sourcing important information and resolving issues.

Within a company’s wider customer service ecosystem, self-service plays a crucial role whilst complimenting other applications. Not only does its capabilities facilitate the handling of mass routine queries at scale, but it also intuitively escalates contact to agent-assisted channels where necessary to ensure that customer needs are always effectively dealt with.

Built using AI, online self-service automatically retrieves results from your centralised knowledge base depending on what a customer has entered. This means that a vast range of routine questions can be dealt with simultaneously without the need for an agent.

For contact centres, this means significant savings on operational and staffing overheads that are associated with processing and handling large volumes of routine queries. Online self-service also creates a greater bandwidth for contact centre agents by handling routine questions, with more time to deal with more complicated customer queries, CSAT and NPS scores thrive.

For your customers, whose expectations of service and digital experience are higher than ever, self-service tools enable convenience and the independence they require to access what they need. Today, customer prefer to do it themselves, it proves far more efficient sourcing their own information rather than waiting in an unnecessarily long queue for an agent to send a simple routine answer to them.

70% of customers now expect a company’s website to include a self-service application.

Further, through the utilisation of Natural Language Processing (NLP), online self-service enhances overall CX. Capable of dissecting customer queries, analysing keywords, intent, grammar and popularity, NLP understands the context of each question. The value here is that regardless of how a question is typed, NLP will understand what is being asked and subsequently produce a relevant answer. This removes the frustration associated with unhelpful, dead-end responses and enhances CX.

Companies deploy online self-service to help reach the following goals:

  • Reduction in support costs
  • Decrease in routine contact levels
  • CSAT/ NPS score improvements
  • Enhanced CX
  • Increase in contact centre productivity

3 Types of Online Self Service

Online self-service is essential when it comes to effectively delivering CX. There are a number of aspects that will determine what type or types of self-service is right for your company, for example, the ways in which your customers interact with your brand or the nature of your company.

The 3 most common types of online self-service include:

The FAQ-style Portal

The Floating Widget

The Chatbot

The FAQ-Style Portal

Perhaps the most recognisable form of online self-service is the FAQ-style portal. It lives on your website as its own page and is easy to navigate to. Think of this online self-service tool as a knowledge hub that customers actively seek out when they want to find answers.

Unlike static FAQ page solutions, this type of online self-service harnesses powerful AI and integrates with an intelligent knowledge base to produce the most relevant, accurate and up-to-date answers. When your knowledge base is your sole source of knowledge, you can be assured that any information served to customers, regardless of the channel, is consistent.

Read: Why an FAQ page is no substitute to a knowledge base. Here

In the self-service tool’s ‘backend’, the knowledge base editor, knowledge articles are easily added and updated to reflect in real-time. Articles are also added into categories to help customers navigate and find answers quickly.

It works like this:

  1. Customer either select a relevant category to browse FAQs,
  2. Or, the customer types their query into the search bar
  3. Natural Language Processing unravels the query, analysing each keyword, query intent, grammar and popularity
  4. With query context understood, NLP recognises the multiple ways in which the query might be phrased and identifies the most relevant articles based on this
  5. The online self-service tool displays these articles
  6. The customer chooses the article that fits their query
  7. The customer is satisfied with the article and leaves positive feedback

Key Benefits

  • Easy to find and navigate
  • Aimed at those who want to self-serve
  • NLP utilisation for accurate answers and CSAT
  • Knowledge base integration ensures consistency
  • Contact reduction for the contact centre

The Widget

The widget is another popular form of online self-service that is structurally the same as the FAQ-style portal, in that it is powered by AI, connects to an intelligent knowledge base and harnesses NLP. However, in terms of appearance and function, it’s different in many ways.

Firstly, the widget’s look is completely different from the FAQ-style portal’s. It is considerably smaller, more compact and only takes up a section of the page. It is essentially a condensed version of the FAQ-style portal, whilst retaining all of the same information.

This is because of its function.

Unlike the FAQ-style portal that offers self-service in one dedicated destination, the widget can be present across a number of pages, available to help customers self-serve regardless of the stage they’re at or what page they’re on. The key difference in functionality between the FAQ-style portal and the widget is this: one is for your customers to actively find and the other is designed to intercept those who might need help – often users don’t recognise they need help until the widget offers it.

This form of online self-service can be configured to trigger when certain conditions are met by users. For instance, a custom trigger may be set up for when a visitor lands on a certain page that has historically led to users asking for help. By bringing help to the visitor, their journey becomes much smoother and more enjoyable, in some cases it helps them act more efficiently.

Not only is the widget persistent, staying with the customer through multiple pages, but by doing so it can help to encourage lead and revenue generation. For instance, a custom trigger set up on your cart page displays the widget after a certain amount of time has passed, the widget prompts the user, asking “Need any help?”. The user does need help, they need a question answering before committing to the sale, so instead of leaving the page to find answers and consequently reducing chances of conversion, online self-service comes to the user.

Key Benefits

  • Is designed to proactively offer help
  • Can be configured to trigger when certain conditions are met
  • NLP utilisation for accurate answers and CSAT
  • Knowledge base integration ensures consistency
  • Contact reduction for the contact centre

The Chatbot

Although inherently different to the FAQ-style portal and widget, the chatbot is still a form of online self-service. The chatbot takes on the role of the digital concierge, it intercepts the customer early on in their journey to essentially guide them, ensuring they reach their destination and answering any routine questions.

The fundamental difference between the chatbot and other types of online self-service is the conversational manner in which they communicate. The primary search layer that is used to retrieve answers is that same as it’s self-service counterparts, utilising NLP to unpick and analyse queries. But the chatbot also harnesses an additional search layer. Synthetix’s, “Jabberwocky” for example, is the additional search layer that is designed to understand the conversational quirks and colloquialisms used by customers when engaging with the chatbot. If the knowledge base cannot understand these, the search layer kicks in to ensure a conversational response is always delivered through your brand’s tone of voice.

The chatbot facilitates self-service and delivers an experience similar to that of an agent-assisted channel like live chat , all whilst negating the need to involve agents for a large volume of customers interactions.

Key Benefits

  • Delivers CX through conversational self-service
  • Acts as a concierge, intercepting and guiding customers
  • NLP utilisation for accurate answers and CSAT
  • Additional search layers deal with quirks and colloquialisms
  • Knowledge base integration ensures consistency
  • Contact reduction for the contact centre

Which Is Best for Your Customers?

Every company is different, with a number of factors that will influence your self-service decisions, therefore there is certainly no cookie-cutter solution.

Read: The Best Self-Service Software For Customers

For your customers, perhaps just one type of online self-service will suffice, or perhaps a blend is more suitable in order to complement other tools and subsequently the whole customer journey.

Check the benefits listed for each type of self-service against your customer profiles and customer journey maps. You will then have a clearer idea of requirements and solutions.

If you enjoyed this article and would like to know more about self-service you can read our guide, here or for advice on software and implementation, please

Get in Touch
Image of man using a chatbot on an ipad

Conversational Chatbots: The Fundamentals For CX

Posted on | by Harriet Norman | Leave a Comment on Conversational Chatbots: The Fundamentals For CX

Chatbots: Taking CX By Storm

Just decades ago, chatbots were considered futuristic or gadget-like, they were innovations with a huge untapped potential for CX. The chatbots we are familiar with today, however, are functional customer service tools that have taken CX by storm, particularly in recent years.

Today, approximately 1.4 billion people use chatbots worldwide – accounting for a significant chunk of the overall population that has digital access.

For many businesses, chatbot are now deemed essential – if they aren’t already part of the existing technology stack, they are quickly making their way onto CX roadmaps across industries. According to one study, 77% of executives have already implemented and 60% plan to implement chatbots for after-sales and customer service.

For customers, chatbots provide familiarity, convenience and instant access to relevant information on your company, products or services. This not only enhances CX but drives demand as the global chatbot market is expected to grow from $2.6 billion in 2019 to $9.4 billion by 2024 at a CAGR of 29.7%. For companies who wish to remain competitive but are yet to implement chatbots into their current offering, they are worth considering.

Simple Vs Conversational Chatbots

There are major differences between simple and conversational chatbots that can affect your customers considerably. Whilst simple chatbots often seem the more cost-effective option, when it comes to fulfilling your long-term CX strategy, this is where they fall short.

Types of Chatbot

There are 3 common chatbot types used in customer service, these include:

  • Menu-based chatbots
  • Keyword recognition chatbots
  • Conversational chatbots

Menu-based
chatbots

Keyword recognition
chatbots

Conversational
chatbots

An image to demonstrate the differences between basic and advanced AI chatbot software.

Menu-Based Chatbots

Using menu buttons to help customers navigate to the answer required, this chatbot type has basic functionality. Menu-based chatbots are built on rule-based automation as opposed to AI, which means that they can only respond to queries that match their pre-loaded responses exactly. The limitation here is this chatbot will not recognise even the smallest query variation, this results in a dead-end response without the capability to further attempt to understand what a customer is asking.

Whilst this simple chatbot type might be adequate for very small or start-up businesses with extremely basic needs, for example serving a fixed set of FAQs, it is not sustainable for larger companies with more sophisticated CX goals.

Keyword Recognition Chatbots

This type of chatbot utilises basic AI to break down the query at hand, analysing each keyword to deliver the most relevant result. By focusing on different word classes, keyword recognition chatbots can determine the most suitable response. For instance, by isolating keywords “renew” and “subscription” or “setup” and “account”, the chatbot can assume the customer’s requirement and send a response based on this.

Although keyword-recognition chatbots harness AI to some extent, they are not effective at recognising and conversing with multiple query variations.

Conversational Chatbots

When it comes to delivering CX, conversational chatbots are by far the most effective type of chatbot. These advanced tools utilise AI, harnessing Natural Language Processing (NLP) to understand the context and intent of the question that is asked. This means that multiple variations of the same query can be asked and an identical answer is delivered seamlessly. Even if a question is not immediately obvious, conversational chatbots use decision tree technology to ask a series of questions until a resolution is found.

Conversational chatbots will never serve the response: “I’m sorry I don’t understand the question. Please try again.”

Unlike its basic alternatives, this chatbot type can be configured to naturally converse with customers, adding character to the experience whilst conveying brand personality. Through machine learning principles the CX is further enhanced as customer journeys are personalised; conversational chatbots can learn, store and use customer information for future sessions. By remembering certain details and preferences CX is of optimal efficacy.

Basic, rule-based chatbot

AI-powered chatbot

An image to demonstrate the differences between basic and advanced AI chatbot software.

Why Conversational Chatbots?

A report by Gartner reveals that 91% of organisations plan to deploy AI by 2022. Another report suggests that by 2025, 80% of large enterprises will need to have a “conversational-technology-focused-centre” implemented.

AI and the tools in which it powers are rightly viewed as game-changing technologies.

While AI has been around since the 1950s when Turing developed the Turing Test, followed by the debut ‘chatterbot’, ELIZA, that was developed in the 1960s, why is it that the conversational chatbots of today are experiencing such rapid adoption?

The answer is completely customer-centric; conversational chatbot adoption is customer-driven. As more and more businesses compete for customers, more and more new and innovative technologies are used to drive a high level of service. As they become accustomed to this, customers expect more from businesses in general. They want less effort and a better experience. They want the ease and convenience of using an online contact channel and the depth and personality associated with agent-assisted channels.

Chatbots do not only provide a familiar instant messaging interface for customers, contributing to an enjoyable experience, but they assist in guiding customers through their journey, from start to finish. When configured correctly, chatbots are your customer’s first port of call when looking for advice, they can be guided to their destination in a number of ways:

  • Most commonly, conversational chatbots solve customers’ routine questions instantly using AI-powered automation
  • For instances when customers don’t yet know the resolution they require, but know there is a problem, conversational chatbot utilise decision tree technology to take care of the problem-solving process until the correct resolution is found
  • Chatbots detect when a query is non-routine and therefore escalates the customer to an agent-assisted channel such as live chat where the adequate help can be provided – all of which takes place in the same window

Conversational chatbots are not only a hit with customers but with customer service and contact centre teams alike. Their capability to automatically handle significant contact volumes allows agents to focus on the queries that are complicated by nature, boosting CSAT and agent satisfaction. As a Result, Average Handling Times (AHT) are reduced by 25% and First Contact Resolution (FCR) is increased by 80% (Synthetix research).

Read: 8 Reasons Why You Need A Chatbot for Your Website

NLP, NLU, NLG & Machine Learning

How do these products of AI impact conversational chatbots and CX?

Natural Language Processing

Natural Language Processing (NLP) is a branch of AI that deals with linguistics, its main purpose is to help machines understand the human language. NLP comprises Natural Language Understanding (NLU) and Natural Language Generation (NLG) to naturally interact with customers, simulating a real conversation.

Natural Language Understanding

Natural Language Understanding (NLU) uses algorithms to isolate and analyse the contents of a customer query. By identifying word classes and detecting sentiment, topics, entities and intent, NLU is essentially capable of comprehending context and what a customer is asking.

Natural Language Generation

Natural Language Generation (NLG) is the process of taking the structured data that has been produced as a result of NLU and transforming it into consumable, natural language. Algorithms that understand the construct of a naturally phrased sentence build responses based on the understanding and processing of the interaction.

Machine Learning

Machine Learning, a subset of AI surrounds the idea that computers can automatically learn and improve based on experience opposed to human intervention. Conversational chatbots adopt Machine Learning principles to personalise and enhance CX. By identifying trends in customer information, storing it and them remembering it for future interactions, chatbots create a positive and efficient experience for customers.

An image showing the relationship of NLP, NLU, NLP and ML

Implementing Conversational Chatbots

How do these products of AI impact conversational chatbots and CX?

At first glance, the implementation of conversational chatbots might seem daunting, but with the correct tools, processes and support, it’s straightforward.

Following the vendor selection process and agreed SRS, your first step depends on whether you have an existing knowledge base or not. You will either:

A. Integrate seamlessly with the chatbot so that knowledge can be shared back and forth between the knowledge database and chatbot interface

B. Collect all existing and extract any new company knowledge in order to populate your centralised knowledge base – you can find out more about the knowledge collection process here

Conversation Chatbots - Image of chatbot being sourced by knowledge

Next, configure any secondary search layers that are responsible for understanding grammar and synonyms. For example, Synthetix’s system, “Jabberwocky” unravels customer query sentence structure to understand the meaning of any synonyms or quirks that your knowledge base is not familiar with. This ensures a conversational response is always delivered and increases accuracy. You can also configure this system to match your brand’s tone of voice so that personality is effectively conveyed during conversations.

Following successful implementation, it is good practice to closely monitor analytics for usage and trigger management data that can determine how effectively the conversational chatbot is working. Settings can be adapted and crucial decisions can be made based on such analytics for future CX improvements.

If you enjoyed this article and would like to know more about chatbots, check out our guide here. If you’d like any advice on conversational chatbots or help with implementation, please

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Contact Centre Chatbots: Why Are They Critical to CSAT?

Posted on | by Harriet Norman | Leave a Comment on Contact Centre Chatbots: Why Are They Critical to CSAT?

Why Chatbots?

Whilst they have been around since the 1960s, when the first chatbot, ELIZA was born, it wasn’t until recent years that the adoption of chatbots within business exploded. With the utilisation of AI and other intelligent functions, chatbots are no longer considered ‘futuristic’, rather a key component of the customer service ecosystem.

The global chatbot market is expected to exceed more than $10 billion by 2026 and will grow at a CAGR of more than 23% in the given forecast period.

It is also predicted that by 2022, chatbots will have saved businesses $8 billion per year, which is one of the key drivers for adoption and a fundamental reason why contact centre chatbots are fast becoming essential business tools.

So, how do they work?

Chatbots are a form of self-service that offer customer support through a familiar direct messaging interface. Built using AI and utilising Natural Language Processing, chatbots can facilitate a two-way conversation whereby the customer enters their query and the chatbot asks qualifying questions so that the most relevant answer can be delivered.

NLP utilises Natural Language Understanding (NLU) to dissect the query into keywords, search intent, grammar and popularity. Essentially understanding query context, an adequate and relevant answer can be generated through Natural Language Generation (NLG) using a conversational manner that matches your brand personality.

A image to demonstrate the 4 layers of search intent that is used by Natural Language Processing at Synthetix.

These mechanics help chatbots to understand multiple variations of the same question, meaning that they can successfully answer a query regardless of the language, grammar or idiosyncrasies that are entered. Chatbots that do not utilise NLP rely on customer queries matching their articles exactly in order to trigger a resolution, otherwise unhelpful responses such as, “I’m sorry, I don’t understand the question.” are delivered.

Basic, rule-based chatbot

 

AI-powered chatbot 

An image to demonstrate the differences between basic and advanced AI chatbot software.

Adopted by a multitude of businesses across many industries, chatbots can:

  • Reduce Average Handling Times (AHT)
  • Increase First Contact Resolution (FCR)
  • Cut operational costs
  • Boost CSAT
  • Enhance CX
  • Promote agent efficiency
  • Improve employee morale

Further, the role of the contact centre chatbot within the wider customer service ecosystem is an important one. Acting as an online concierge, it handles routine questions and tasks, whilst providing escalation to agent-assisted channels like live chat when human intervention is required.

If you’d like to know more on the fundamentals of chatbots, click here.

How Do Chatbots Benefit the Contact Centre?

No longer a perceived threat to contact centres, chatbots are now considered a vital tool for agents. Working in amalgamation with chatbots helps to combat the growing volume of routine contact queries associated with the explosion of digital transformation.

Contact centre chatbots have become increasingly popular in recent years, particularly in 2020 as a result of the pandemic. With a surge in support calls and emails and some organisations experiencing up to a 133% spike in contact , chatbots provide support to agents working remotely and in the contact centre.

Chatbots Help Your Contact Centre Boost CSAT

Through continuous AI improvements and NLP development, chatbots have evolved to better understand the context and intent of the customer queries they are asked. With advanced AI, chatbots can not only handle routine queries but many routine tasks, such as:

  • Taking a meter reading
  • Submitting a claim
  • Resetting a password
  • Booking and confirming a reservation
  • Checking a delivery’s location

Your chatbot can even be configured to match a certain tone of voice, providing the personal experience that so many customers require.

Whilst agents can only handle 1 call or email at a time, chatbots can automatically deal with large scale routine queries and tasks simultaneously. This, therefore, significantly frees up any channel congestion caused by large volumes of routine questions – the result of which means more customer queries are effectively dealt with and consequently, CSAT scores improve.

It also frees up agent resources, creating time and space for agents who can now provide their full attention and expertise to customers who require urgent attention due to their complex issues. If a customer is in a vulnerable position and needs urgent assistance, the last thing they need is to be waiting in a long queue to speak to an agent. This only reflects negatively on CX and CSAT but can be avoided by utilising contact centre chatbots.

An icon of an light bulb

Many customers prefer to self-serve if they can – around 73% in fact. By including a chatbot in your customer service offering you cater to your customers’ needs, improving CSAT.

Further, chatbots provide customers with 24/7/365 support, they can resolve queries and instruct customers outside of contact centre operating hours or during public holidays. AI chatbots can even assist customers through emergencies in real-time, successfully delivering relevant emergency contact details and instructions required.

Find out how a knowledge base can benefit the contact centre, here.

Chatbots Contribute to Agent Satisfaction

Contact centre chatbots, contrary to popular belief, don’t hinder agents, rather they have the capabilities to enhance their quality of work and job satisfaction.

When the vast majority of queries that come through the contact centre are the same mundane routine questions and monotonous tasks, this can take a toll on your agents – causing drive and morale to decline.

However, these routine questions can be dealt with at scale through AI, using chatbots, creating a greater bandwidth for your agents to which they can deal with more complicated, non-routine issues. When agents are able to dedicate their attention to helping customers with serious or complicated enquires it adds variety and purpose to their roles, which in turn contributes to job enrichment and motivation.

It’s no revelation that when employees are happy, this is reflected positively through their quality of work. So, when chatbots are introduced into the ecosystem, agents not only develop skills quicker but they are supported and happier at work, which has an impact on the service that customers receive, enhancing CSAT.

Chatbots Make Your Contact Centre More Efficient

Many company’s customer service and contact centre operations lack efficiency, particularly when it comes to the mechanics of the contact channels. Too often expensive agent-assisted channels are unnecessarily exhausted, or low-cost AI-powered channels are not utilised or difficult to find.

This is where chatbot solutions can assist the contact centre’s efficiency. Ultimately, when configured correctly, chatbots can sort queries into the correct channels, avoiding unnecessary costs. For example, if a chatbot is configured to be a customer’s first port of call for customer support it can either:

A. Deal with the customer’s routine query successfully

B. Or, detect that the customer’s query is non-routine and requires human assistance, automatically escalating the customer to an agent-assisted channel such as live chat

Through automation, chatbots can deal with routine questions significantly quicker than humans. There are many time variables when it comes to manually opening, replying, researching and resolving just one customer query compared to AI handling everything. This cuts your operational costs considerably, accounting for up to 30% support cost reductions.

Read: The 8 Biggest Chatbot Mistakes That Are Costing You Money

A chatbot not only escalates a query when it is non-routine, but it can also be configured to escalate to an agent-assisted channel based on the keywords the customer enters. Trigger management settings enable this escalation to take place based on certain keywords, for example, those that indicate urgency, such as “renew” or “cancellation”. By putting these customers in touch with a human that can efficiently deal with the matter, customer churn is reduced and revenue is increased.

Pair this with chatbots’ intelligent smart forms integration which collects targeted information from the customer throughout their journey and significant time is saved, slashing overall AHT.

Further, contact centres can become more efficient by utilising the data that chatbot technology produces. With detailed analytics revealing what customers are asking and article effectiveness, you can be in the know about current customer trends or issues. As a result, you can proactively better equip agents, building such intelligence into agent scripting and other learning tools.

If you enjoyed this article and would like to know more about chatbots, you can read our guide, here. or, If you would like any advice or assistance with contact centre chatbots

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