Tag: analysis

Omnichannel & Sentiment Analysis (II)

I’ve previously touched upon sentiment analysis within Omnichannel in several articles (https://thecrm.ninja/omnichannel-sentiment-analysis/ and https://thecrm.ninja/omnichannel-supervisor-tools/). It’s really a great feature that allows agents to quickly & easily see how the customer is interacting. It also allows for supervisors to see at a glance how interactions are going overall.

With all of that, I thought it would be helpful to take a further look into how sentiment analysis actually works, so that we can understand it a little better.

Now, the actual nuts & bolts for sentiment analysis are provided by Azure Cognitive Services. There are a wide range of tools available through this, but we have no need to go into Azure to configure this. It’s a simple setting within Omnichannel to get it working, rather than needing to fiddle around with many different things:

However, what’s actually going on during a conversation, and how is the sentiment analysis worked out/calculated? We see the pretty little face icons (with the different colours), but how are these actually being set?

Well, there are two ways in which algorithms are used to calculate the sentiment that’s shown:

  • Natural language processing (NLP)
  • Machine learning (ML) algorithms

With these two ways methods, it’s possible to not only see what the current interactions are showing, but also to enhance the model to understand sentiment better.

Note: In a session that I presented recently, one of the attendees asked if it’s possible to train the model, to result in a custom algorithm. Unfortunately this isn’t possible to do – the machine learning that takes place is the general Azure one, rather than one for a single company or customer

The following diagram shows the sentiments that are used. They’re nicely colour-coded, for ease of reference as well:

When a customer interacts through Omnichannel, the sentiment shown is based on the last 6 messages received from the customer. As a result, the sentiment shown can very well fluctuate & change during the conversation, based on how it’s going.

The Sweetest Languages in the World - | Beyond Exclamation

Obviously, customers aren’t just going to use English to communicate. Companies are based around the world, and will use their native/local language when providing support. Omnichannel allows for this without an issue, utilising the Azure Text Translator API behind the scenes to provide this. If you’re interested to see which languages are supported for this, head to https://docs.microsoft.com/en-us/azure/cognitive-services/translator/language-support which is the latest source of information for this.

There are some interesting things to know around how this actually works:

  • When a language other than English is used, the Text Translator API translates the text to English, and then it’s analysed/scored for sentiment
  • If a language isn’t supported by the Text Translator API, it won’t be scored
  • If profanity (eg a swearword) is detected, the sentiment will automatically be shown as Negative or Very Negative, regardless of the rest of the last 6 lines of conversation

Some people have expressed their concern to me around how accurate the Azure translation actually is, but to date I haven’t seen any major concerns resulting out from it. As with the other Azure services, Microsoft is continually refining & improving it. That being said, there are several languages with very nuanced terms. I’d like to think that these would be supported without issues.

There is, however, somewhat of an interesting behaviour when starting off the analysis at the beginning of the conversation:

  • If the initial language is detected as English, it’s assumed that all of the subsequent conversation will be in English. As a result, if the customer switches away from English, the system won’t recognise this, and a Neutral sentiment score will be shown
  • If the initial conversation is not in English, then the system will check every conversation line & re-detect the language as necessary.

This seems somewhat strange to me, as I’d have thought that the system would automatically check the language for each conversation line. I can think of plenty of scenarios where different languages are used in a single conversation, even if it does start with English being used. I’d like to think that this will be updated at some point, to make the experience better.

Omnichannel & Sentiment Analysis

In general, it’s usually quite useful to be able to see how customers are engaging with your company, and how they’re feeling about things. If customers are disgruntled, annoyed, or complaining, it’s important to be able to understand the root cause/s of their issue/s, and resolve them as soon as possible.

One of the tools available in Omnichannel is Sentiment Analysis. What is this?

Being able to identify how customers see/interact with your brand, accurately, is vitally important. Using people to manually trawl through your data to attempt to identify this has many drawbacks:

  • Lack of consistent approach
  • Large amounts of time needed
  • Many manual touchpoints

As a natural follow-on from this, being able to identify & categorise the sentiment in customer communications through using machine learning can unlock many business use cases that can then result in immense value for your company.

Microsoft provide the ability for this through Azure Cognitive Services. It’s really quite interesting in how this actually works. You can go to https://azure.microsoft.com/en-gb/services/cognitive-services/text-analytics/, put in a sentence, and see what results come back. It can be quite amusing to see what different colours come out as!

As part of the analytics around chat (and by chat, I’m not referring to just a chat bot – anything within Omnichannel can be referred to as ‘chat’, from an agent perspective), sentiment analysis can be used.

This is quite easy to set up. To do so, open the Omnichannel Administration Hub, go to the Settings area in the left-hand menu, open ‘Sentiment Analysis’, and click to enable it. Remember to save it to apply it!

This will then result in the agent interface showing the following:

Now, this isn’t static. The sentiment will update in real time as the conversation continues, and will change based on what the customer is saying.

Now, obviously we’d expect agents to be able to judge the tone of the conversation based on what’s being said (at least I’d personally expect it). So for this, the sentiment that shows within the chat isn’t that helpful.

However, it does come into its own in a slightly different place. This is the Omnichannel Sentiments Analysis Dashboard, which is served through PowerBI.

Through this, supervisors can understand how their company is measuring up to their KPIs & necessary trends. They can also understand the overall support experience that omnichannel is having, along with tracking the sentiment of customer interactions. As a result of having this to hand, better understanding of customers can take place, resulting in improvement of the overall customer experience.

Once the dashboards have been configured within PowerBI (I’m going to do a separate post on this), it’s then possible to surface these within the Omnichannel Customer Service Hub (which users with the Supervisor role will be able to see). This means that supervisors won’t need to open a separate place to see these; it’s all available through the same interface.

There’s also a more detailed view into what’s actually happening, through the ‘Omnichannel Insights – Sentiment Analysis Report’. This displays a lot more information, drilling down & splitting the data up into agents, queues, channels & trends. Here’s an example of this:

With all of this information as the fingertips, it’s now really possible to drill down into the details. Through this, we’re able to carry out full & proper analysis on what’s actually causing customer interactions. From looking into what’s occurring, it’s then possible to review the current state of things, and see what can be improved. This will then result in more positive sentiments shown by customers, and drive their loyalty to the company!