"INTELLIGENT AGENTS" IN 2017
Artificial Intelligence (AI) has become an exciting buzz word that companies love to use when they are trying to describe their solution as "smart" technology. But, what does that really mean? Artificial Intelligence in B2B or B2C products, typically incorporates machine learning, deep learning or data mining techniques in order to automate a process; most popularly used for suggestive or predictive applications - "Customers who bought X also bought A,B,C and D, you should take a look," or "Viewers who watched Y also liked Z, you should watch that next."
In most recent years, the communication industry has embraced AI, with investments being spent on enabling call center agents to be more productive and effective with smart technology. When applied to telecom solutions, AI assisted agents can also be referred to as IA or "Intelligent Agents".
An Intelligent Agent, in the machine learning space refers to "...an autonomous entity that observes through sensory and actions in order to achieve it's goals..." However, an Intelligent Agent in the telecom world is a contact center agent that observes and learns from all data available in order to achieve their goal; whether that be a sale or customer satisfaction.
Now the question becomes - what data is available?
How else can businesses utilize speech analytics in the contact center?
- Top 3 Tips To Increase Sales Using Speech Analytics
- Elevate ROI Through Automatic Call Sorting & Churn Detection
- 7 Tips To Start Using Speech Analytics In Any Call Center
ARTIFICIAL INTELLIGENCE FOR WHAT'S SAID
Until recently, companies tried to determine customer sentiment or intent by energy, volume and pitch because using the actual content of the call (or voice data) was too difficult to capture. But with the latest R&D advancements in speech recognition, you can now surface and analyze rich voice data using artificial intelligence. By leveraging machine learning and advanced data mining, speech analytics solutions are able to identify patterns in voice recordings to gauge a speaker’s intent, and even predict future outcomes — be it a sale, account cancellation, or any customizable attribute a client might request. Using recorded call data to construct predictive models provides the means for automating call disposition, thus eliminating the need for costly human call scoring and giving management of call centers actionable insights to support their critical initiatives — and much more.
Agents can now be armed with valuable intel about each caller to better serve their immediate need, as well as preemptively make them an offer they are likely to accept based on similar customer's behavior.
With this new source of voice data, businesses are getting more data than ever and some struggle with how to make sense of it all. At VoiceBase, we help our customers with this pain point and work to automate a process around what's said on phone calls. Based on our experiences, we wanted to help you and your business out by sharing 3 strategies we've seen companies implement to take full advantage of their voice data.
INTRO TO THE VOICE DATA CUSTOMERS SEE
In order to take full advantage of these results, the first element to understand is that with every AI powered event detected, VoiceBase provides a confidence score. This is a calculated percentage to how closely this new call matches the profile created from past data. For example, we measure 10,000 columns of data in a phone call ,and for each data point there is a certain pattern we've identified that is present in every 'hot lead' phone call your company has had in the last year. Now we have a pattern to measure future calls against to see how closely those calls match this pattern, in order to tell your business this is either A. Hot Lead, B. Not a hot lead, and to what percentage does it match that existing profile. This percentage of confidence is the key to taking full advantage of all the data.
As I'm sure you can assume, not every call is so easy to say we are 90% sure this is not a lead, or we are 10% sure this is a hot lead (which means it is not!). With these three varying strategies, we help customers take advantage of the entire spectrum of results.
For the calls on either end of this spectrum; i.e calls we are SURE are hot leads and calls we are SURE are not hot leads, a business process can be automated. First, remove or flag the bottom percentage as NOT good leads. Don't waste your agents' time with these, now or in the future. Next, send the top percentage to your best agents to ensure the best odds at closing a sale!
Optimize your agent's time and help them meet their goals. Every good sales or marketing professional knows that the time it takes to reach out to a hot lead has a huge effect on how hot that lead still is. With confidence scores for each hot lead, your sales team can get a ranking of which leads to reach out to first, and they can work their way down the list.
There will of course be a certain percentage of leads that have a lower confidence level - let's say 50% confident or lower. With this group of leads that could go either way, you can evaluate how different agents are performing. You can also evaluate what is the lowest score that your team can convert into a lead? This will give you a bottom line to work down to, and is even an opportunity for internal motivation within your team.
By using AI powered call scoring and confidence scores, businesses can empower their sales team and improve ROI by turning your agents into Intelligent Agents! Our customers have seen a ton of value driven from these three strategies including;
- Increased Conversations
- Improved Agent Monitoring
- Less Customer Churn
- No Leads Left Behind
WHAT OTHER INSIGHTS CAN YOU GET FROM SPOKEN INTERACTIONS?
Optimizing your calls based on 'Hot Leads' is just one of many call dispositions we could automate processes around. Image what we would be able to automate if we dug into potential churn calls, upset customers, or appointments made? Harnessing the actual voice of the customer is the most effective way to improve your agent performance and power Intelligent Agents in 2017.