3 ways AI will increase your sales

sasha uritsky

Nov 06, 2017

Many of us get the understanding of artificial intelligence from the film industry. It creates an image of smart, humanized machines that are helpful, efficient and omnipresent. It is true that AI has seen rapid advances in the past several years, to the point that it became an integral part of our everyday life. In real life, however, AI is far away from the level portrayed in sci-fi movies. And yet there are affordable AI tools and solutions that can make a significant impact on your business.

Here are three main reasons why a company, especially if it is a B2C company, should consider integrating AI into their business process.

AI makes your sales process scalable

AI solution dealing with your prospects and customers works 24/7 without sick days, holidays and breaks. It can handle any level of traffic, incoming inquiries and conversations. It does not need to be trained. It does not have personal issues or bad days. It is always polite and uses professional jargon. It is fast.

AI creates better user experience

Some might find it surprising but this is only because they have experienced low quality AI solutions. A professional AI solution makes customer experience better primarily because it delivers the results with a minimum of fuss and maximum efficiency. A good AI eliminates bureaucracy, makes customer experience speedy and seamless, and that’s what consumers are looking for today.

AI offers sustainability

Adding AI to your business model creates long-term sustainability for the business. It allows your business to grow while controlling, or even minimising the costs. More importantly, it ensures that the business remains competitive in providing the level of customer service consumers became accustomed to. Lastly, it creates platform for future technical improvements and integrations which, without a doubt, will be based on Artificial Intelligence components.

Interested in reading more? Check out our other blogs:

Why Keywords Do Not Cut It on Social Search

Most of the online search is keywords-based. Same in social domain, a vast number of analytical tools, networking platforms and mobile apps use keyword-based technologies as well.

There is a difference, of course, between traditional internet search and social search. The former finds websites. The latter finds conversations, messages, posts. Keyword-based internet search is doing a decent job for us for over 20 years. Keyword-based social search is not doing a decent job at all.

Consider a basic example: finding on Twitter who is interested in buying jeans. We can start by typing ‘jeans’ but that brings up too much noise. Maybe ‘need jeans’? Less noise but then we people who use expressions like ‘looking for jeans’ or ‘want jeans’ or shopping for jeans’. Not to mention those who use ‘denim’, or brand names. So we have to run multiple searches or create a complex search string using logical AND and OR and hope it works. Neither option is simple, or convenient, and certainly not efficient.

The above example highlights the major flaw with keyword search - it does not capture the meaning of social conversations, and therefore cannot be a reliable source of information about conversations.

It does not provide too much of correct information. And it does provide lots of incorrect information. But the biggest problem is that it has extremely limited potential for improvement.

So as long as we stick with keyword-based social search the results are destined to be limited.

Why, then, we stick with keyword-based search in social search? Simply because there is no good alternative. Until recently, that is.

The advanced semantic technologies capable of capturing the meaning, or intent, of conversations are now offering an exciting alternative.

I will discuss these technologies on my next blog.

Artificial Intelligence Chat Is Evolving Faster Than IVR

Although it doesn’t feel like all that long ago, way back in the 90s one of the most important factors to a call center’s success was the ability to route a customer to the right support agent with the IVR (Interactive Voice Response). Countless hours were spent identifying the most efficient call routing patterns and expert agent capabilities to ensure that your request reached the right person quickly. This technology is still widely used today and there are still teams in the largest companies programming IVR systems to accomplish pretty much the same goal.

As the standard for customer support evolved there have been many attempts to improve the function and the customer experience associated with IVRs to reduce hold times and provide more relevant support faster. Even today some companies will use their IVR system as a way to keep a customer on hold, rather than provide a solution, when agents are inundated with calls.

For those of us who’ve worked in the voice industry for some time, we’ve seen first-hand the attempts to accomplish a customer’s need before reaching an agent. First there was expert agent routing that delivered your call to the agent most qualified to help you. Then came advances in voice recognition, which today has evolved to be a very effective tool to increase containment rates and deflect calls from reaching a live agent. My two favorite examples of the power of voice recognition are Cox Communications and Capital One, two examples of great voice recognition and routing.

Our memory, however, is short. It wasn’t so long ago that we were all pulling our hair out punching digits into the phone or constantly repeating “agent”, “Agent”, “AGENT”, AGENT!!!!!”.

Whether it was a limit of computational power or the sheer cost of developing and implementing advanced call center technology, it took decades for phone systems to be able to front end the customer support process as efficiently as they do today. Thankfully we all survived to see it without boiling over from the hypertension usually associated with calling with a customer service department.

Bad customer experience is definitely not the case with Chat Artificial Intelligence (Chat AI). While we seem to hear about the shortcomings of Chat AI like the disconnected conversations and the robotic like responses, these experiences are usually the product of Chatbots with limited AI functionality or early stage deployments. The increases in both computational power and the massive advancements in machine learning are driving excellent customer experiences that improve over time.

When was the last time you heard of technology actually performing better, on its own, without a ton of additional development work or continuous updates? Well, that’s the case with Artificial Intelligence. Like a person, the more experience it has interacting with customers and information, the better it performs with little need to be manually improved or fine-tuned.

Today, AI Chat can be used to answer a large majority of customer requests and because Artificial Intelligence learns as it is used, customers prefer to interact through AI chat to avoid all of the frustrations commonly associated with calling a contact center agent.