NLP vs. Predictive Analytics: do you really need an AI chatbot?

By 2022 chatbots are expected to cut business costs by $8 billion. Not only will it reflect on a business’s bottom-line, but it will also impact the way we communicate –  in just 2 years, chatbots will become a more frequent chat companion than our partners! 

But are chatbots an ultimate solution to every business problem and should you be misled by the buzz around this tech solution? More importantly, if not chatbot, then what tech solution can help you outperform your competition? 

In this article, you will learn more about chatbots and what they offer, as well as some other AI solutions that can outperform chatbots and bring an even better business outcome. Let’s first look at why chatbots have become a buzzword tech tool and what it constitutes.

Types of chatbots

There are two types of chatbots businesses use – rule-based and AI-powered. Rule-based chatbots react to sentences that contain specific words. In simple terms, they serve as an extension to a F.A.Q. library or knowledge base on a company’s website and can answer simple questions such as queries about your company’s working hours, office location, or provide solutions to simple problems. 

Rule-based bots don’t require any tech knowledge and are usually off-the-shelf solutions.

Custom bots are based on NLP (natural language processing) and can learn and understand the situational context of a question. They can also read human emotions and complex metaphors. Custom bots require specific skills, sometimes a team of data scientists and developers. Custom bots are a better solution for high-traffic websites, where many users need to be served at the same time.

You can read a more detailed comparison of an off-the-shelf solution and custom AI-powered bots later on in this article, but let’s first take a look at how AI bots can contribute to business and why it makes more sense to build an NLP-based bot than grab an off-the-shelf solution. 

What can an AI bot do?

There are multiple tasks that AI bots can help businesses with.

 

  • Increase sales

As bots are available at any time, they are right there when a customer needs help – it can help choose a relevant item in a shop, or inquire about price options, as well as adjust to a customer’s mood and understand their way of describing things. 

Unlike pre-built bots, AI-powered chatbots are able to answer more precisely in a short period of time as they can take into account the context of a question and won’t need to ask additional questions to uncover intent, which would be the case for pre-built, off-the-shelf solutions. 

 

  • Keeping lead response time low

According to Harvard Business Review, lead response time is a critical metric for businesses to convert leads into clients. The customer will wait no longer than 5 minutes before losing interest in a provided solution. Bots help answer customer questions in no time whatsoever! 

 

  • Human-like interaction

PayPal decided to pass on a pre-built solution and implemented its custom NLP-powered bot to provide more precise answers to its clients straight away. Normally, a rule-based chatbot would ask additional questions to narrow down the context of the question asked.

A NLP-based bot can identify user intent and provide an answer much faster in comparison to its less advanced off-the-shelf substitutes. 

 

  • Scale your customer service effort (chatbots work 24/7)

Around 265 billion support tickets are submitted every year resulting in $1.3 trillion direct costs. While bots will not replace a support department fully, they will certainly reduce costs as human assistants maintain their focus only on the most crucial tasks – those where a human being is required. 

 

  • Personalized experience

AI-powered bots are able to collect user information from various platforms to later build a comprehensive 360° customer profile. The information collected can be used to make more personalized offers. As a result, the relevance of in-bot product suggestions increases, so does customer satisfaction from online shopping. This is also a starting point for brands to build their customer loyalty. 

 

  • Source of customer data

The more information you collect about your customers, the more accurate understanding of their needs you and your employees will get. As a business owner, you can use the information bots collect to derive some insight and build a hypothesis on the demand for your products. 

Your bot will also benefit from interacting more with customers, and as a result, collect more data – it will identify new patterns and provide more precise answers in the future. Please bear in mind – this applies only to NLP-powered bots, pre-built rule-based bots are not able to derive patterns from previous answers.

Use Cases of AI Bots:

PayPal (Financial)

PayPal uses machine learning to identify fraud as well as answer clients’ questions related to declined payments, unauthorized charges, account hold limitations, charges, processing time of a given payment and more. Thanks to NLP capabilities, the bot is able to deduce the context for the asked question and give the answer right away.

NLP vs. Predictive Models

So you might think, if PayPal made it, so should I! However, if you don’t have a huge user base there is no point in such a solution at all. Instead, you can be smarter and choose a less “sexy”, but not less efficient AI solution – a recommender system. 

In fact, if you have been gathering data about your customers’ behaviour before, you don’t have to overcomplicate it and make use of what you already have – data – to boost conversion and customer satisfaction. 

With a recommender system, you would be able to motivate customers to buy more by offering items they have considered before but haven’t bought or suggest complementary products to already purchased ones. This way, you are not asking customers what they need to solve or find – you already know what they want! 

Amazon is one of the companies using predictive analytics to increase purchase volume. Check more about recommender systems in this article.

 

Hugo Legal AI (Legal)

HugoLegal has democratized access to legal support in Estonia – thanks to HUGO, people who can’t afford access to legal support now access it for free through a bot. However, more complicated cases are passed over to law firms – it helps lawyers decrease customer acquisition costs and for clients, to match them with the right profile of a lawyer. Hugo.legal has already matched 21,5k cases with law firms and at the same time, has generated over 1000 appeal documents for those who can’t afford legal help. 

A fair enough chatbot is a great tool to establish communication with a user and match them with the right lawyer. It is the core of Hugo.legal business. But if you already have information about your users, you don’t need a chatbot to make a perfect match that would be based on multiple data points. Instead you can rely on predictive analytics and build your first predictive model in a few months. 

AI chatbot Hugo Legal

BMW Bot (Automobile/Retail)

BMW has harnessed NLP to assist clients in the process of choosing a car – it guides them through a variety of models, trims, prices, specifications, and options. The bot also acts as an assistant for the less experienced drivers by describing the use of particular car elements and provides some explanation on how they are different from those available on the market. 

BMW AI bot

In this case, the chatbot is a nice-to-have tool to surprise customers. And let’s face it – BMW can do that on top of other AI and more traditional non-tech solutions. 

Apart from chatbots to give suggestions, companies can use AI to reduce churn. With AI, you can also predict when users are unlikely to pay a given price for the product – this is especially beneficial for subscription businesses where customers can opt out and choose a competitive solution in short instance. By using predictive analytics, you will be able to predict which users are likely to churn and provide them with some discount or special offer and simply give them more of your attention. AI goes much beyond chatbots! 

 

Lidl’s Winebot

The Winebot called ‘Margot’ helps Lidl’s clients make the right choice for wine. Additionally, it can provide a description of the origin of wine and its history and send some some recommendations on food pairing – far better than a shop assistant could! 

How would this bot differ from a simple pre-built recommendations bot? It constantly learns and identifies a sentiment. For instance, over time the Bot has learnt that users mean 5 pounds when writing £5, 5 quid, a fiver, and even “around five-ish”. 

The bot is available in multiple places of  Lidl’s ecosystem – not only is it available on Facebook, but it can also be discovered on website pages, where a user needs it the most.

Lidl Wine bot ai

So what are the alternatives? Let’s come back to recommender systems again. 59% of shoppers who have experienced personalization say it has a big influence on their purchase decisions. Instead of asking what wine your client wants to buy, you can use AI to analyze their previous purchases and impress them by suggesting their favourite Chardonnay they have bought before at some discount – to keep your loyal customers happy. 

Check out more use cases of predictive analytics and recommender systems here >>

predictive analytics in business

Air France’s Lucie

AirFrance has created an AI chatbot called Lucie to provide clients with their personal AI-powered travel agent. Lucie accounts for the context of asked questions and a customer’s mood to provide a wide range of recommendations for a trip. 

The bot asks customers about their dream holiday and after getting some description, it sends articles, videos, places of interest, and events happening at the destination. This is a whole new experience when booking a flight! 

Air France chatbot

This is a great solution to engage recurring customers and Air France did it the right way. 

However, if you want to be a step ahead, you would use predictive analytics and not ask, but suggest the most cost-efficient option. Such enterprise booking platforms as Hotailors have already disrupted the way business travel is made by introducing AI, in particular predictive analytics, to match business travelers with the most cost-efficient destinations and avoid high-season price boosts.

How to measure the success of your bot?

To track the progress of your bot, you should establish metrics of measuring the success of its performance. You could consider measuring the success of your bot based on these metrics:

  • faster response time (your bot solves a problem of an always busy support department and doesn’t leave potential clients waiting to break their objections)  
  • increased volume of interactions (the more customers interact with the bot, the more useful they find it)
  • higher rate of transaction success (this metric proves a bot can help manage transactions, apart from a finished transaction, you can also measure the increase of transaction volume in general, attributed to the bot)
  • more accurate responses (over time with more data processed, you should pay attention to how a bot can iterate on customer questions and responses it provides to them)
  • abandonment rate (this metric helps to measure how useful a chatbot is to your users and if they find what they have been looking for thanks to a chatbot, the lower the abandonment rate is, the more effective your chatbot is)
  • adoption rate (it helps identify what percentage of your users started interacting with a chatbot, it also proves your marketing effort directed at promoting the tool) 
  • interaction time (how much time users spend interacting with the chatbot)
  • resolution rate (how many tickets have been closed thanks to the chatbot) 
  • customer satisfaction (identifies how effective the chatbot is at solving customer problems and affecting their general impression of your service)

 

How much does it cost to build an AI bot? 

The cost of a AI-based bot depends on many variables. It will change depending on the number of capabilities you want it to have, number of platforms the bot will use, integrations, subscriptions, analytics, expected number of users, and more. 

You would have to find a reliable team of developers able to provide not only high-quality code, but  also help you build the product with the necessary capabilities. 

 

What to consider before developing an AI Bot?

Before  grasping the technical aspects of introducing a bot to your business, it is first worth  identifying the goal you want to achieve with your AI – it doesn’t matter if you are aiming at a chatbot or some other AI tool. 

The right question to ask in the beginning is “Does my company qualify for AI?” 

It could well be the case that you don’t need AI at all – your business problem can be solved with some other tools. It also can be that you don’t need a chatbot and you can build a recommender system or simply use statistics. To find out how ready you are for AI adoption, take this test >>

AI assessment

Finishing Notes

Even though more human-like assistants are already revolutionizing the way sales and customer experience activities are performed across industries, the majority of US (55%) customers still prefer to chat with a human being. This fact is often ignored by fans of chatbots. Moreover,  36% of survey respondents would only use chatbots for simple tasks.

Before choosing a chatbot over another tech solution, make an informed decision – consider your option in AI and start with verifying if your company qualifies for any AI solution.  

Connect with our team of data scientists here for initial scoping >> 

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Written by:

Margo Ovsiienko

Growth Marketer, Margo creates content that converts leads into paying customers for the SaaS industry. Before joining Neoteric, she had been building sales funnels in various marketing agencies and SaaS companies and thus helping companies make the process of lead gen and sales more predictive. In her free time she is a host of “Margo Leads”, a niche podcast on digital marketing.

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