Categories
ReadWrite

Data Integration: How Connecting Business Apps Boosts AI Capabilities

data integration apps

Artificial intelligence (AI) and machine learning (ML) have become omnipresent in our personal lives. The same is true in the business world. Data integration can help businesses take their AI applications to the next level.

In many respects, AI is now a mature business technology. Consequently, it’s no longer the sole province of pioneering companies. Not only have advanced analytics powered by ML become popular. Chatbots responding to customer requests through a Natural Language Processing (NLP) AI are also on the rise.

However, many are leaving AI’s full potential untapped.

More than anything, AI needs data. ML algorithms ingest large data sets for training. AI is at its best when it extracts actionable information from a myriad of data points that no human could conceivably make sense of.

Businesses collect ever-larger amounts of data. Yet, most fail to harness all the data they have for their AI applications.

In 2021, a massive section of business operations happens in apps. These are now usually cloud services by Software as a Service (SaaS) companies. There are accounting platforms like FreshBooks. Customer Relationship Management (CRM) systems like Salesforce. Marketing powerhouses like HubSpot.

According to recent statistics, the average business uses 73 different apps. Furthermore, employees conduct over 80% of their work in the cloud. In 2020, the pandemic additionally magnified these trends.

Enormous amounts of data live on each of these platforms. With integrations, these services can take advantage of each other’s data. This enables countless business processes to take place in the cloud, from marketing to financial reporting.

Yet few businesses take advantage of all the possible integrations to unify their data and put it at the disposal of AI and ML.

Having a data integration strategy matters. Here’s why, what the challenges are, and how your business can benefit.

Why Strive for Data Integration? To Create and Harness Synergy.

In business, AI has three central tasks: process automation, generating-depth analyses, and engaging customers and team members.

For all this, data from connected business platforms is invaluable.

Let’s take a core office technology as an example: business phone services. Modern business telephony is cloud-based, thanks to Voice over Internet Protocol (VoIP) technology. For businesses, this has countless advantages, some more obvious than others. VoIP has enabled multiple communication channels, from video conferencing to live chat.

But VoIP business telephony also enables AI applications.

For example, NLP AIs can transcribe voice interactions in real-time. This can be applied to every single conversation between company representatives and customers. Then, ML algorithms can run advanced analytics such as sentiment analysis on the transcripts. The result? Company representatives can receive an instant evaluation of their performance. They can go over the conversation and learn.

But by integrating with other platforms, this kind of AI-generated data becomes even more useful. Connect your cloud business phone system with a CRM, and you can add every transcript to a customer profile. This immediately boosts possibilities for personalization. In turn, this can increase your chances of conversion or retention.

You can also integrate the data into a personnel management platform. For instance, sentiment analyses of a company representative’s conversations might be frequently negative. You’ll see this in their performance evaluation. Then, you can tackle the problem. Targeted training could, for example, showcase positive examples from other transcripts.

Or imagine you’re using sentiment analysis algorithms to sift through online reviews. By itself, this will give you valuable insights into customer attitudes and expectations. But integrating it with data from your digital marketing platform, and you can go much further. AI can then correlate email campaigns or social media strategies with sentiment trends in online reviews.

Getting Crucial Information in Real-Time

Speed is another core advantage of integrating data to boost AI performance. Integration increases the amount of information you can access. What’s more, access becomes easier and much faster.

Let’s go back to the business telephony example. The transcripts from customer calls are in your CRM. Imagine someone calling with a problem they were having with one of your products. A few days later, the same customer calls again. Now, the AI assistant in your business phone service pulls the relevant CRM records. Before your representative even picks up, they get a full run-down of this customer’s case: How they’ve described their problem. Personal information from demographics to billing history. And even what conversational strategies in the last call worked best.

All this saves your customer from having to give a long-form recap of their problem. For one, this reduces frustration on their part. For another, it also allows your representative to address them in a personalized manner. Overall, the exchange will be more efficient and satisfactory for both parties. Your business saves resources and increases customer satisfaction at the same time. All thanks to AI integration.

Generate Positive Feedback Loops

A final significant advantage of business app data integration for AI is positive feedback loops.

This means that by integrating platforms, you generate new data. On this basis, you can refine your strategies and grow your platforms. Leading to more data.

Take chatbots as an example. These days, countless companies use them in their online stores and on their websites. They are powered by NLP AIs. Their usefulness hinges on whether they can provide relevant information to customers.

For this, context is crucial. Customer behavior can provide that context. By supplying chatbot AIs with behavioral analytics, you can increase their usefulness.

For this, you can feed customer data from e-commerce and website analytics platforms and feed it to an AI. Have the AI find patterns and use these patterns to train your chatbot. On this basis, it will give suggestions and make recommendations to customers. Then, track the further behavior of these customers. It will give you even more data – to refine your chatbot.

This feedback training between chatbot AIs and e-commerce analytics helps increase communication efficiency. And seriously boost customer experience and thus retention and sales.

The Challenges of Data Integration

As the examples above make clear, data integration entails countless benefits for AI. But it also poses several challenges in its implementation.

First, businesses looking to integrate data face the hurdle of siloed technologies. Data silos are inaccessible for ML algorithm training. Nor can trained models analyze their contents for decision-making.

Second, the readability of data is a central issue. Many platforms have idiosyncratic data structures and lack metadata or context. They might even save files in proprietary formats.

Third, the trustworthiness of data is often in question. A study in the Harvard Business Review has highlighted this. In it, almost 50% of companies admit to data integrity problems. Issues with false, inconsistent, or stale data are frequent. And even more troubling, more than half of enterprise data fails to provide value altogether, due to siloing.

There are several avenues businesses can take to avoid these pitfalls and overcome these challenges to effectively harness integrated data for AI. The first of these is data mapping to establish a single source of truth.

Data Mapping and Single Source of Truth

Strictly speaking, data mapping is a subfield of data integration. It describes the process of connecting a data snippet in one system to a corresponding item in another. For example: Mapping the “name” field in a CRM record to the “caller ID” field in a business phone system.

The goal of this is to unify data while maintaining its integrity.

Crucially, this also helps businesses create single sources of truth. This refers to a single master record, which can boast high accuracy and sees frequent updates. Other systems will derive their data from here.

This doesn’t mean that all data needs to be stored in a single record. But for each item, only one ultimate source should exist. Especially highly sensitive data – like names, addresses, or social security numbers – should be treated like this. This makes it much easier to control and audit how this information is handled while still harnessing its value.

The good news is that modern AI-based cataloging tools can maintain such master records. They can parse information from all your platforms and deduce credible metadata. They can also automate data cleaning and highlight inconsistencies. This is a crucial first step in dealing with data complexity.

Dealing with Data Complexity

As mentioned above, data readability is a major hurdle to overcome. At its core, that means dealing with data complexity.

More and more raw data is available. This raw data is available in countless different formats. These formats range from social media posts over e-commerce statistics to server logs.

In addition, context is lacking for a lot of this information, especially natural language data. Take a thread of social media posts, which may include abbreviations and typos. Many statements might not make sense without the accompanying emoji, GIFs, or videos.

Plus, all this information is scattered across an ever-growing number of platforms. And these platforms, in turn, may have dozens, if not hundreds, of third-party integrations.

Using AI to Overcome Data Integration Challenges

The good news is that AI systems are becoming better and better at handling these challenges. This means you can use AI to generate input data for AI.

First off, AI can conduct data mapping automatically with precision and speed. In addition, the mapping mechanism can weed out issues such as duplicates and missing values. This helps maintain data integrity and speeds up data unification.

Furthermore, AI makes it possible to automate regular exports of data siloed in various places to central data hubs. It can infer data structures and parse uncommon formats. And crucially, it can propagate and consolidate data across systems, drawing from a single source of truth.

Recently, AI and ML algorithms have become adept at parsing situational context. If the schema structure for the incoming data is unknown or only partially known, they will either judge on a pattern basis, or parse the content to decipher a schema.

AI-Assisted Integration or Self-Service APIs?

One crucial question of data integration remains, though. How exactly can a business go about it?

It’s easy to say that AI can do this and that, without specifying exactly how it does so.

Generally, there are three ways in which businesses can approach data integration.

First, enterprise-scale businesses probably have the resources to hire and task data analysts with establishing a system of integration and analysis. They have the required knowledge in coding and data mining to set up a system that is custom-tailored to their employers’ specific needs. With the right access to the administrative space of various business apps, little should stand in the way of success.

Second, you can opt for self-service analytics platforms. A growing number of platforms aims to make analytics easily available for non-specialists. Many of these come in the form of cloud APIs. Their aim is to democratize AI analytics. Following this philosophy, some platforms even offer chatbot-like features. These help you create an integration procedure conversationally.

And finally, you can – at the very least – harness the integrations that are already at your disposal. Many major business apps already offer native integrations with each other. Even more, they are compatible with bridges such as Zapier. In many cases, taking the time to explore the options of integrations that you have in your existing app ecosystem can already pay off big time. Prioritize platforms that offer AI analytics and automation capabilities, and then proceed to map out the nexus of available integrations.

Final Thoughts

Data integration is a challenge. But it’s a challenge worth tackling to reap the rewards.

Harnessing all the data at your business’ disposal is vital in 2021. It will help you understand your customers better, meet their needs, and increase your sales.

The best way to go about this is to use AI to improve AI through data integration.

You can either manually explore your existing integration options, find an easy-to-handle cloud API, or approach the task with the help of a professional analyst.

Putting the required systems and automated processes in place will take some time and effort. At the end of the day, it will be worth it.

You will be able to boost the quality of your algorithms, establish positive feedback loops, and gain insights at an unprecedented level. With decisions based on the insights, you’ll be able to steer your business through the times to come.

The post Data Integration: How Connecting Business Apps Boosts AI Capabilities appeared first on ReadWrite.

Categories
AI Analysis Digital Strategy Marketing Startups

How to Use AI to Create a Data-Driven Digital Marketing Strategy for Your Startup

ai digital marketing

When you’re growing your startup, there are countless things to consider – from product design to hiring your team. One of the most crucial aspects, though, is digital marketing.

No matter how amazing your product, you’ve got to get the word out to create an audience for it. This is where digital marketing comes in. And AI is rapidly gaining ground here. 

It’s an open secret that AI is transforming almost every aspect of our lives. This includes marketing strategies. In today’s business world, harnessing AI is essential to keep up with competitors or even get a step ahead. 

This article is for you if you’re working on setting up a digital marketing strategy for your startup. It outlines what data-driven digital marketing is, where AI comes into play, and how you can make the best use of this technology’s potential. With what you’ll learn here, your startup’s digital marketing strategy can excel from the beginning. 

What is a Data-Driven Digital Marketing Strategy? 

The biggest mistake you can make in digital marketing is to base your strategy solely on assumptions. Assumptions about your product, your performance, or your target audience can end in disaster. 

With a data-driven strategy, you gather and analyze as much information as possible. Then, you base your decisions on these quantitative insights. 

Building a digital marketing strategy from scratch involves four steps, with or without AI involvement.

1 – Market Research 

First off, thoroughly investigate your audience and your competitors. 

Find out who your customers are and where their interests lie. 

Then, learn what you can from your competitors. Which audiences do they target? What are their marketing strategies? Which messages of theirs resonate with customers?

2 – Drawing Up a Buyer Journey 

When a potential customer first sees your product, they won’t instantly buy it. Not even if you have the world’s best marketing strategy. 

Broadly, customers go through three phases in their buyer journey. First, they become aware of your product and its advantages. How does it benefit them? What problems could it solve? Then, in the consideration stage, they compare alternative solutions. Finally, they decide to go with your solution – or not. 

When you draft a digital marketing strategy, figure out how to optimize this journey.

3 – Choose the Right Digital Marketing Channels 

Next, you have to decide which marketing channels to use. Typically, this includes content, email, and social media marketing. Search Engine Optimization (SEO) and Search Engine Marketing (SEM) also factor into it. 

4 – Launch, Analyze, and Refine 

Finally, you can launch your digital marketing strategy. Consider having a try-out phase. When you do put your finished digital marketing plan into action, keep a close eye on performance and make adjustments as you go along. 

How Does AI Factor Into It? 

Artificial intelligence (AI) and machine learning (ML) have massive potential for data-driven digital marketing. They excel at rapidly gathering and processing vast amounts of data. This means they can offer almost instant insights into complex realities. 

Another bonus: You can use AI and ML to automate processes. Tedious tasks like content research no longer need to bog down your marketing team. Algorithms can take care of these tasks faster and with more in-depth results. 

Here’s a detailed guide on how to leverage AI and ML during every step of creating a data-driven digital marketing strategy.  

Get to Know Your Customers With AI 

First, figure out who your customers are and what they want. AI can help you by finding patterns at macro-scales and providing actionable insights. 

You can use AI pattern recognition to analyze the data you’ve bought from market research providers. 

Alternatively, you can deploy algorithms to pull enormous amounts of data from social media or discussion forums. Using natural language processing, algorithms can sift through countless posts, reviews, and articles.

The goal is to run a sentiment analysis. With this AI tool, you’ll be able to identify the major questions, trends, and gaps related to products like yours. It will also make it easier to pinpoint your audience’s demographic details. 

Suppose you’re running surveys or focus groups to get a more in-depth understanding of your potential customers. In that case, AI and ML will help you make sense of feedback. Using voice recognition, it can even transcribe and analyze the results of oral discussions. 

Use AI Tools for Competitor Research 

In parallel, you can use AI-driven tools to give you an edge in competitor market research. 

Use these tools to track competitors on social media and find out what their strategies are. Get statistics on posting frequencies, types of posts, and identify high-performing content. Gather insights into their followers’ demographics. Figure out how these followers interact with the brand through comments, shares, and likes. 

AI-based platforms can also help you reverse-engineer your competitors’ website analytics. Once you launch your own website, keeping a close eye on page traffic and bounce rates will be crucial. Before you get there, though, check out what your rivals are up to. Think of it like peeking over your friends’ shoulders during a math test. Analyze what they’re doing, then do it better. 

Also, AI and ML can mine your competitors’ link profiles. Having links to your website on other pages is a crucial factor for high rankings. That’s because search engines see such links as proof of trustworthiness and authority. By understanding which pages link your chief rivals, you can draft a strategy to stay competitive.

Set Up an AI-Optimized Buyer Journey 

When you envision your customers’ buyer journey, know that you can use AI at every turn to improve their experience. 

In fact, an outstanding user experience (UX) largely depends on AI. Machine learning algorithms can provide every customer with a personalized experience. From the first time someone comes in touch with your brand, your AI will keep tabs on them. It will identify which stage of the buyer journey they are at. Which aspects of your product interest them? What problems are they aiming to solve? 

These insights will then curate website content, adjust paid ads, or send personalized recommendations. If you use AI for nothing else, use it for personalization. Statistics show that 91% of customers are more likely to shop with brands that tailor content and communication to their interests. 

AI can also improve customer service, another critical element of the buyer journey. Especially during the comparison and decision phase, swift customer support can be the clinch. AI can help in the form of chatbots that answer routine questions and auto-generated email responses. This will free up your team to respond to complex or urgent queries. 

Harness AI for Content Marketing

Creating the right content has become crucial to digital marketing. Search engines reward comprehensiveness and high information value and penalize superficial, spammy posts and articles. 

AI can help you to produce high-quality content that will drive organic traffic to your website. 

Based on your target audience’s interests and your competitor’s performance, AI tools can generate content ideas. They will tell exactly which topics resonate with potential customers. 

To go further, deploy AI-driven tools to create content profiles. When writing any piece of content, research is the most tedious part. It can take hours to go through similar blog posts and analyze how they performed. 

AI will take this load off your back. The right tools can scan through all existing articles and posts on any subject. They will then tell you how to approach the topic, structure your content, and which gaps other articles left for you to improve on. 

More directly, AI can improve the quality of your writing. Deep-learning algorithms will polish your grammar and improve your vocabulary. 

Leverage AI Tools for Social Media 

Tools based on AI and ML can boost your social media performance. 

They will help you analyze past performance to pinpoint the right content to post. On a larger scale, they can help you draft a messaging strategy. One size does not fit all: You have to tailor your brand voice to different platforms. Overall, this will help you develop a social media strategy for maximum effect and reach on each channel.

Also, you can automate processes like analytics. Gain cross-platform insights by having your AI assistant regularly deliver comprehensive reports. 

Take Email Marketing to the Next Level With AI  

When you’re starting out with email marketing, you might be tempted to default to Mailchimp and then focus exclusively on building an email list. Instead, carefully check out the various AI and ML tools that Mailchimp alternatives (appsumodotcom) offer. These tools can reduce the time and money you have to invest and increase your ROI. Building an email list is important, but no longer everything. 

The right email marketing platform will offer AI and ML tools to increase email personalization for every single sub-group in your target audience. They will also offer analytical insights for data-driven email marketing. You’ll be able to check the effectiveness of different subject lines, and A/B test different newsletter layouts. Solid AI tools also help you increase your campaigns’ deliverability by optimizing send times and making sure your email list is clean.

Boost Your SEO Using AI 

When you think of digital marketing, chances are that Search Engine Optimization – SEO – tops your list of priorities. SEO has been key for years, and it should be an integral part of drawing up your digital strategy. 

Luckily, AI can give you a serious leg up. 

It’s important to know that search engines themselves use AI and ML algorithms. RankBrain, for example, is Google’s AI search algorithm. To get these algorithms to rank your site highly, using AI yourself is a must. 

To begin with, use AI to optimize your keyword research process. Deciding on the right keywords to target is fundamental for your SEO strategy. In 2020, trends like Latent Semantic Indexing (LSI) have gained momentum. AI-driven keyword research tools are an asset in harnessing these trends. 

You can also use AI to optimize your content for voice search. It’s estimated that 50% of searches this year will be voice-based. These voice searches use a natural language processing AI. Making sure that your content is compatible with them will improve your rankings.

Further AI applications in SEO are having an algorithm check how you appear in local searches, using it for A/B testing, and to optimize your site’s technical SEO. 

Optimize Paid Marketing with AI 

Finally, using the right AI tools can give a major boost to your SEM performance. As in other areas of marketing, AI helps you target the right audience and personalize content. In terms of analytics, it can show you the bigger picture. 

To be more specific, AI will also keep track of all your SEM metrics for you – from CTR (Click-Through-Rate) and CPA (Cost Per Action) to CPC (Cost Per Click). You can have periodic summary reports delivered and keep an overview of the success of different marketing campaigns. 

Conclusion 

Integrating artificial intelligence and machine learning into your digital marketing strategy from the get-go will give you a valuable advantage. It will help you base your marketing approach on data insights, rather than assumptions. 

AI now permeates every aspect of digital marketing. Setting up data-driven strategies and feedback loops is something to do sooner rather than later. 

Use AI-driven tools to gather and analyze data for market research. Make them part of your buyer journey. Harness them for content, social media, and email marketing, as well as SEO and SEM. 

Altogether, this integration of AI and ML will give your startup an edge from the start – and set you up on the path to success. 

The post How to Use AI to Create a Data-Driven Digital Marketing Strategy for Your Startup appeared first on ReadWrite.

Categories
AI AI in sales coaching sales coaching telephony VoIP

How AI is Revolutionizing Business Telephony from IVR to Sales Coaching

ai telephony sales coaching

Artificial intelligence (AI) and machine learning (ML) technology permeate every aspect of our lives, whether we realize it or not. It’s easy to surprise many people with the presence of AI in some areas. They’re baffled to learn that AI helps pollinate crops, brew better-tasting beer, and create new fragrances. Here is how AI revolutionizes business telephony, from Interactive Voice Response (IVR) to sales coaching.

Amazon stores using face recognition to keep track of your shopping basket contents is just a high-profile example of AI. In reality, AI is already everywhere. 

There’s another area in which AI has found an arguably surprising application: modern business telephony. 

The benefits aren’t obvious to most people. Many businesses still happily rely on legacy PBX systems for their communication needs. But in fact, companies using VoIP phone services benefit massively from AI and machine learning tools.  

Modern VoIP systems harness the potential of AI in many ways. Interactive Voice Response (IVR), smart assistants, and predictive analytics are just a few examples. 

How exactly does this, telephony, work? What are the most common applications of this AI that benefit business? And, come to think of it, what is VoIP in the first place? 

What is VoIP? 

The acronym VoIP stands for Voice over Internet Protocol. The technology does exactly what it says on the tin – it routes calls over the internet rather than traditional phone lines. 

At its most basic, VoIP – or IP telephony – converts audio signals into digital packages, then transfers them to the VoIP provider. There, they are routed to the recipient of the call and turn back into audio signals.

VoIP, like AI, finds use in more applications than many people think. From WhatsApp calls and Zoom meetings to smart speakers – if you’ve ever made a call via the internet, you’ve used VoIP tech. 

However, business-grade VoIP telephony is on a different level. There are dozens of business VoIP services that focus exclusively on meeting the communication needs of companies. All of them are in a tight race to win over customers by providing top functionality and efficiency. 

These providers have moved far beyond simple voice calls. Instead, they offer omnichannel communication. Usually, this includes voice and video calls, conferencing, internet fax, email, text messaging, and live chat. And, of course, countless additional features to make users’ lives easier. 

This is where AI comes in. 

VoIP is the time-and headache-saving-extra offered by business VoIP platforms — VoIP harnesses AI and machine learning. 

In fact, VoIP depends on a particular type of AI — natural language processing (NLP). 

Natural Language Processing and Conversational AI

For the longest time, computers couldn’t understand human language, neither spoken nor written. 

Not that people didn’t try.

As early as the 1950s, computer experts attempted to use machines to understand and translate texts. These were pressing efforts during the Cold War. Unfortunately, the technology of the time only allowed for simplistic approaches. Notably, they were often limited to word-for-word processing. 

The results could be hilarious. One famous experiment dashed Russian scientists’ hopes for machine translation success. How? Their computer had translated the Biblical, “the spirit is willing, but the flesh is weakâ€� as “the vodka is agreeable, but the meat is spoiled.â€� 

The basic issue: Natural language is fairly unstructured and very context-dependent, even as text. Dealing with voice data is even more difficult. Background noise, unusual speech patterns, individual pronunciation, and regional accents are just some of the problems. 

Nonetheless, artificial intelligence has made it possible for humans and machines by voice. Harnessing the computational power of the cloud, AI has become conversational. 

We’re not quite ready yet to take on Tony Stark’s JARVIS, but we’re getting close. Siri, Cortana, Alexa, and Google Assistant can attest to that. 

AI-based speech recognition already has countless applications, both in the business sphere and beyond. They range from digital PAs that take care of routine tasks to voice-based banking. 

The Internet of Things (IoT) is expanding at lightning speed. Fully 50 billion devices are expected to be connected to the Internet by 2020. Increasingly, people can interact with these devices using voice tech. Simply by talking, they can control anything from smartphones and tablets to fridges, ovens, and home security systems. 

Much the same is true for VoIP business phone systems. Here, NLP and conversational AI have opened up entirely new avenues for productivity features. 

AI Transcription: Voicemail and Entire Conversations  

Transcription simply means turning speech into text. It’s one of the most straightforward applications of AI voice processing in VoIP systems. 

Voicemail-to-email and voicemail-to-text are now standard business VoIP features. Either will transcribe the messages left when callers didn’t reach the person they were hoping to talk to. Then, they’ll send the transcript to your inbox or chosen messaging platform.

Users save time. Once, after returning from a vacation or business trip, they might have spent an hour listening to the backlog on their answering machines. Today, they can quickly glance through everything in their inbox, prioritize, and file messages away. 

What’s more, AI can transcribe entire conversations and file them away for future reference. This virtual paper trail can be incredibly handy in case of disputes or to follow up on individual clients. 

Transcribed conversations are also a boon for customer service. Agents can reach back to the details of past interactions with every caller and pick up the issue’s thread. Customers don’t like describing their problems or needs over and over. They strongly appreciate being able to get straight to the point. A 2019 survey found that 96% of customers agreed that “it is important being able to return to and pick up a customer support conversation where it left off.â€� 

Many modern business phone platforms also integrate with Customer Relationship Management (CRM) systems such as Salesforce or HubSpot. This means that all conversations’ AI transcripts can be added to the wealth of other data on every customer.

Data can also flow from CRM to connected AI-powered VoIP features. For fluid, real-time personalization, they can supply agents with the necessary details.

Smart Assistant Coaching 

Not only does AI enable sales representatives to make use of past, transcribed conversations. Some applications go as far as to offer sales coaching in real-time. 

AI interfaces can transcribe and analyze calls between customers and company representatives even as they happen. These AI interfaces can supply the agent with all the information they need, saving them from having to go rooting around for it.

Past purchase history? Got it. Date of the last customer call. Here it is. The serial number of the device the customer has been having trouble with. There you go. 

Every agent basically has a personal assistant handing them important information before they even have to ask for it. The specific information provided can make interactions with callers much smoother and more effective. In turn, this increases satisfaction on both ends of the line.

Plus: These virtual PAs can also take over routine tasks from taking call notes to scheduling follow-ups. 

What’s more, AI can analyze the best-performing company agents’ calls, from customer service to the sales team. By looking for patterns that had callers respond positively, it can suggest good responses.  Should an agent ever be at a loss for what to say or do during a call, AI can recognize the fact. It’ll then serve up a menu of time-tested phrases and actions to proceed. 

Overall, these smart virtual assistants can empower human agents – by giving them tools, boosting their performance, and making their work less monotonous. 

Smart Call Routing and Interactive Voice Response 

Another use of AI-powered business telephony tools is to allow callers to reach who they want to talk to with minimum frustration. 

After the fourth round of “Please press 1 for…â€� anyone calling a business – or rather, trying to contact the company – will be grumpy. Once a company representative finally does pick up, the call representative will have to deal with a customer who is already irritable and short on patience. Not the best of conditions. 

Businesses can avoid the initial unhappy customer aggravation condition,  thanks to Interactive Voice Response (IVR). 

Instead of forcing a caller to listen to a menu of choices, IVR allows them to state their call’s purpose in their own words. Next, AI analyzes the underlying intent. It then transfers the caller to the best-matching company representative currently available. 

AI can also make the lives of callers easier in other ways. 

Smart auto-attendants can prioritize calls with high urgency, classify them by topic, and even identify people’s dominant emotions from their tone of voice. They can give agents a heads up that they will be dealing with a ballistic caller and equip them with the tools to handle them. 

Finally, AI can eliminate the need for human action in the majority of customer service calls. Conversational AI interfaces can take care of common service needs. Examples are FAQ answers, troubleshooting, password resets, making or rescheduling appointments, refund requests, reservation adjustments, and ordering products. 

Reducing many of the interactions reduces the call volume human agents have to deal with. This allows them to focus on more complex requests. 

A 2019 article in the Wall Street Journal showcased TD Ameritrade’s experience with adding conversational AI ever since the company hasn’t had to hire any new agents to deal with calls. “Chatbots can answer basic questions about trade statuses and resetting passwords,â€� the WSJ reports, “while humans help with more complex problems related to taxes and beneficiaries.â€�

Research published in Marketing Science in September 2019 came to similar conclusions. One result was that “undisclosed chatbots are as effective as proficient workers and four times more effective than inexperienced workers in engendering customer purchases.� 

(Predictive) Analytics

Finally, AI can add invaluable analytics features to VoIP business phone systems. This is not really a surprise: AI analytics is everywhere these days, from car park management to automatically personalized content curation on websites.

VoIP phone systems tailor analytics specifically for the communication needs of businesses. 

Users can gain in-depth insights into aspects from agent performance to customer satisfaction. A core tool is sentiment analysis, used for objective analysis of customer feedback. Data can be combined from call transcripts, feedback forms, or even reviews posted elsewhere. Sentiment analysis isn’t limited to text, however. The tone of voice and – for video calls – even body language can be mined.

Such analytics can be done regularly. AI systems can automatically generate reports at fixed intervals for supervisors to assess team performance. They can also make them instantly available after each call, such as feedback to company representatives on how well they handled any particular call. 

Combined with CRM data, VoIP AI analytics allows deep insights into customer behavior. Grouping them into sub-audiences is just a first step. It can further predict their future engagement with the company, likely buying decisions, and communication patterns. 

Wrapping it Up 

Artificial Intelligence has become an integral part of numerous functionalities offered by VoIP business phone service providers. 

There is little AI involvement in the core VoIP services – voice and video calling. Nonetheless, it strongly affects which providers dominate the market. 

Not long ago, features like voicemail-to-text or advanced call analytics set a provider apart. Now, they are practically standard. To stay competitive, VoIP providers are racing to integrate cutting-edge, AI-based tech into their platforms. The stakes are high: VoIP, business phone services, are expected to keep on growing at a CAGR of 13.4% – and to hit $30 billion globally by 2025.

Whoever offers the most advanced features is likely to become a client favorite. 

It’s fair to say that AI now shapes how VoIP systems interact with callers just as it changes how customers interact with businesses in general.

Businesses, their agents, and callers all stand to gain. With AI tools, tasks, and issues can be dealt with effectively and efficiently, improving the customer experience all around.

The post How AI is Revolutionizing Business Telephony from IVR to Sales Coaching appeared first on ReadWrite.