Like with any aspect of a successful business; having chatbot analytics in place allows you to keep track of your goals and make continuous improvements along the way. With chatbot analytics, they are developed to employ a self-learning process that takes systemic records of data, metrics, preferences, and trends which eventually help chatbots in monitoring user interactions, and adapt relevant responses accordingly.
This allows chatbots to play a significant role in data analytics, and for these reasons are development companies always on the lookout to leverage best strategies, tools, and technologies in business.
In today’s world, chatbot dynamism has become more vital while the eruption of data that keeps moving towards higher endpoints.
According to an IDC Digital Universe study, for example, the amount of data to be created in the digital space is projected at 35 zettabytes by 2020.
That number was further bumped to 40 ZB and now is at 44ZB due to recent AI improvements and IoT factors. Another study by the IDC estimates that business transactions (B2B and B2C inclusive) done on the internet will get to 450 billion per day by 2020.
As a result, both the technological and business world are exploring the opportunities of data analysis targeted towards the generation of actionable insights that will lead to strategic business decision making. The extended functions of bots as transactional and insightful parts of the analytics field make bots even more phenomenal.
The first business objective of chatbots in regards to data and analytics is that users get to interact with data themselves by leveraging bots conversation interface directly.
This efficient bot architecture incorporated with data analytics capabilities will deliver exceptional business values to both users and companies by providing operational experience, better customer experience, and instrumental data analysis.
The second benefit has to be automated data gathering. Bots can easily collect information that clarifies the user’s inclinations and preferences toward a product or company in real-time conversations. Several financial organizations have started to leverage in on this by introducing bots software to automate interactions with customers.
This attribute is usually extended to information and feedback collection and is then processed by theses financial institutions and used for different purposes.
Chatbots, analytics are often overlooked and underappreciated. Most companies ignore this embedded benefit of chatbot analytics. Although chatbot analytics are unlikely to impact the success of a chatbot campaign directly, they can provide valuable data into growth and improvement opportunities. If appropriately designed, chatbots can get into the minds of its users and identify various pain points users experience with your products.
Based on the mode of the chatbot deployment, its functions and the demographics of the user base, The KPIs (Key Performance Indicators) that you need to track will often vary; however, we have highlighted some key metrics that you can monitor to extract valuable insight for just any available chatbot.
Monitoring the data on active users is a must on any chatbots. This KPI provides knowledge on the overall popularity of your chatbot and serves as a proper measurement for success. If you notice a downward trend in this metric, this could be an indicator to redesign your chatbot use cases.
To measure how often users come back to use your chatbot, you can set up a monitor for the number of recurring active users of your chatbot. A poor KPI in this category may lead to high dissatisfaction rates amongst first-time users.
Session Lengths are critical to assessing the ability of our chatbot to carry out extended meaningful conversations with users. An ideal session length will vary from use cases and the context of the conversation. A shorter session length can be a good thing; however, it is usually more indicative of some form of failure with your chatbot program. Also, endeavour to program period for timeout into you bot, to avoid inflation of sessions through idle periods.
Lengthy sessions are usually a positive indicator of your bot being able to have extended conversations with human; however, take note of the number of steps taken to solve a customer’s query per conversation. chatbots should aim to resolve each question in as few conversation steps as possible.
Users may leave a chatbot conversation if there is an unnecessary lengthy back and forth chat without any perceived progress. It is vital to make a good early impression on users through engaging conversation flow. A warm, welcoming message is an excellent way to achieve this.
Direct feedback from users is pivotal to chatbot success. Providing an avenue for users to rate your chatbot based on their satisfaction and productivity level is an excellent way of identifying your chatbot strengths and weakness. It is advisable to get program feedback inquiries on a per chat basis, rather than a chatbot basis. So that you can quickly identify weak points in your chatbot’s flow of conversation.
Got a poor rating? Don’t fret poor ratings are an indication that users aren’t entirely pleased and that there’s still more work to be done. Examples of common chatbot flaws include incorrect answers, poor conversation design, repetitive responses, and knowledge gaps.
Your chatbot should be able to invoke fallback responses when unable to find proper responses to user messages. Instead of giving inaccurate answers or going mute, your chatbot should respond by letting users know that it couldn’t get a match. Monitoring how often failed replies occur and the messages invoking these replies will help you in identifying faulty Natural Language Processing (NLP), knowledge gaps, and if expectations are unclear from the user’s end.
If your chatbot gets asked questions they can’t answer, this is an indication in a gap in knowledge that needs filling, or it could just be that your chatbot does not provide such service, and you have to make it clear to users. If you, however, notice a frequency in fallback response from your chatbot when they are asked questions they should know, this is probably an NLP failure and an indication that you need to train your chatbot’s NLP to recognize better variances in which users can phrase their queries.
After a set period of chatbot and human interactions, you must start looking at critical stats to get a good idea of your chatbot usage. Average session duration is an excellent metric index; however, remember to be specific in your application to both industry and circumstances.
For instance, if a chatbot is designed to answer questions and help clients only, session durations should be on a shorter scale. On the other hand, bots who specialize in placing orders or telling a story should engage users in longer, more meaningful interactions.
Ensure you keep an eye on the number of sessions and the flow of conversation of each customer, as this could be an indication whether your chatbot is doing its job correctly or not.
Endeavour to go through conversations where users interacted with your bot the least to establish which issues it was unable to solve.
You should also look out for users who have had multiple sessions with your chatbot in order to see what it’s doing right. Using this blueprint, you can then aim to recreate the various conversation types and adequately program the chatbot to serve users better.
CTR currently isn’t a significant metric, in the nearest future; however, predictions are that it will become an important KPI to look out for. Remember, if an interaction is meant to be self-contained, (sales or service processes should be accomplished entirely through your bot) high CTRs can be a significant red flag.
This is a bit of a no brainer:
Confusion triggers put into perspective how your chatbot behaves when faced with challenging tasks and requests and how users react to your bot.
There are thousands of ways users could ask a question or make the same request; a bots usual response to questions like this is “I don’t understand.” if you notice this rely trend from your bot the questions act as confusion triggers and you need to analyze each case individually to discover how best to prevent this from happening again.
One of my favourite marketing brands currently is Brain labs. They frequently mention that part of their astronomical growth (8218%!! YoY) is because any marketing they carry out is looked through and analyzed with a fine comb. They do small A/B tests and make data-driven decisions.
This very much applies to chatbots too.
Let’s cover some quick wins:
Even if you have the latest analytics set up correctly, your front line team who deal with the enquiries are a crucial part in the feedback and bot improvement loop. They can help give context with enquiries and provide recommendations to how the conversation should flow depending based on their experience.
On the note of personal experience, also take their insight with a pinch of salt. Humans obviously make bias decision and feedback should be taken into account with multiple other data points to be validated.
Customers are at the exchange end of the spectrum and know more about how your company could be better. Getting this data from customers can reveal the top things that keep customers from being your promoters. And sharing this information with your agents is one of the most potent ways of resolving issues.
Chatbots most valuable business asset is in answering random questions quickly. However, If the questions become more complex, and the bot unable to answer, then there is a need to have an option to transfer the interaction to a human representative.
Such a representative should be able to quickly pick up from where the conversation ended identify what is wrong and proffer solutions where necessary.
This escalation path is essential as it may just be what leaves the customer feeling if their query was handled adequately or not,
It’s therefore crucial that organizations in addition to investing in a chatbot, should also put sufficient escalation management resources in place to ensure customers get the best solutions to their problem and that waiting times are kept at an agreeable minimum.
Implementing and operating chatbots can be a significant commitment, although chatbots do fulfill an invaluable role within many organizations it’s not advisable to venture into chatbot operation if you’re not equipped or willing to continually manage them.
Contrary to what many people believe, chatbots can be expensive to run, as they are time-consuming to maintain and always in need of real-time information to stay relevant.
chatbots can quickly become redundant if an organization fails to continually provide their bots with sufficient data, to educate and give the bots what they need to learn and grow. chatbots work similarly to how a website operates you have to keep both of them updated.
Users understand that chatbots are computers and can be of tremendous help in a variety of cases. However, as with other automated systems, it quite common for customers to feel a lack of empathy or find difficulty expressing their emotions during conversations with chatbots.
Situations like these happen because chatbots aren’t programmed to be empathetic. There’s a solution though; developers could endeavour to make chatbots more empathetic towards users. Chatbots need the ability to capture and process customer sentiments from conversations, without explicitly asking.
Having the ability to adapt the conversation flow and respond to the mood, tone of voice and opinions of users will go a long way in providing a complete bot solution package.
To achieve this, critical bot training is required. Insights on how to tailor this development could be gotten from analytical bot data and various KPIs. The instructions should cover multiple emotional situations, sentiments, and nuances attributed to the average human.
This shouldn’t be a one-off process, as there should always be room for improvement, especially in areas where the chatbots are noted to be underperforming.
The secret to getting more information from users through bots is by knowing when to ask relevant questions.
For most organization, information collection stage usually starts with other live chat platforms, or social media messaging applications – like Facebook Messenger, after which bots are introduced.
The difference between these processes is human influence and interaction; humans can read conversation flow better and know how to extract relevant information from consumers during conversations. At the moment, bots are lacking in such regards and so, following a script for customer data retrieval is essential for optimal results.
You can make your chatbot a lot friendlier without seeming intrusive through the use of Natural Language Processing (NLP). The system recognizes and learns languages in real-time and produce a conversation flow that is human-like and friendly. This system allows the software to utilize data and provide consumers with personalized information.
The software is also able to recognize the customer’s tone, allowing the chatbot to interact and engage in a conversation manner.
In many of such cases, customers ended conversations unaware that they were receiving correspondence from a bot, rather than a real human. With a more relatable tone, customers get a more interactive experience with the brands’ software.
Another benefit of bots is that it is a continuous learning process. So, transcripts of conversations can be analyzed and used to improve the software capability for a better customer experience next time.
If you’d like to learn more about natural language processing, we have an in-depth guide about how it works and how it helps.
Its common knowledge that people communicate differently through a phone call compared to text messages, chats, or emails. As a developer or business, it is vital that you are able to identify these differences and train chatbots accordingly so that customers are always satisfied with interaction and outcome from your chatbot.
Ultimately, the goal of chatbots is to create a realistic imitation of human communications through the use of Artificial Intelligence.
To program and continually train bots to adapt to the way customers love to converse and communicate. As an organization, it is vital to have access to data analysis of chatbot interactions based on usage and industry relatable to you.
Chatbot adaptations should be based on the insights gathered from interaction analytics and should include provisions for adaptability to the changes in conversation types and flow of humans.
Our favourite 2 are:
Ok, we’re obviously a little biased here. While there are some good analytics tools out there, they don’t achieve everything that we know our clients need – a CMS for chatbots as well as a comprehensive bot analytics product.
How EBM excels over other platforms is that it doesn’t just provide in-depth analytics, but it also uses machine learning to provide recommendations on how you should build your conversation flows, intents and training phrases. Saving you a lot of build time.
If you’re in a situation where the bot building platforms you use looks like Frankenstein, (for example, using Chatfuel as the bot builder, Dialoglfow as the NLU and are looking for better analytics) Then ChatBase is likely your best choice.
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