What is Interaction Analytics?
Interaction analytics allows you to take advantage of your customer interaction data. Through machine learning (ML), natural language processing (NLP) and artificial intelligence (AI), interaction analytics solutions can unify your customer interaction data and reveal meaningful and actionable insights.
What is Interaction Data?
Interaction analytics takes raw customer interaction data from across several channels and turns it into structured data that is then analyzed for trends and insights. The data and insights that come from interaction analytics help you understand the strengths and weaknesses of your customer service, products and processes. Interaction data can also be analyzed for customer journey mapping, pointing out leads, making predictions, and identifying training opportunities for staff. Seeing your customer interactions from different channels means you can gain a clear picture of your customer feedback.
Understanding Interaction Analytics
A deeper understanding of customer interaction analytics means you can better understand how and where to implement it in your business and the benefits it can bring.
Characteristics of Interaction Data
Interaction data comes from the various channels you might use for customer interactions, such as chat transcripts, social media posts, emails, customer service calls, and more. The problem that businesses may find when attempting to analyze this is that this data is unstructured and can be difficult to see in one clear view. This is where interaction analytics comes in.
How Interaction Data is Collected
The process of collecting interaction data starts with the raw data from multichannel customer interactions. ML and NLP are used to search, structure, transcribe and filter relevant customer interactions. Unifying your interaction data gives you a stronger data set to work from and means you get more accurate insights and predictions. AI is used to analyze customer sentiment, identify trends, make predictions, and produce insights.
Industries Where Interaction Data is Used
Interaction analytics is used to improve customer interactions in a variety of industries:
- Healthcare: Particularly in the wake of COVID-19, interaction analytics can help healthcare companies scale up their customer service and find and improve any inefficiencies. It can also help ensure a high level of patient care is maintained in phone or video consultations.
- Insurance: Interaction analytics can help insurance companies guarantee they are meeting regulations and isolate any keywords or phrases in customer interactions that could be a potential risk.
- Public sector: Central or local government agencies can use interaction analytics to better understand the wants and needs of citizens and improve their public feedback processes.
Pros of Using Interaction Data
Interaction analytics provides a deeper understanding of your customer, enabling a variety of business benefits.
Powers Personalized Experience
Personalized experiences are great for connecting with customers and developing loyalty. Customers don’t want to feel like they’re one of many. Rather, they want to feel messages and offers are personalized to them. When personalizing the customer experience, analytics has a big role to play. By structuring and analyzing customer interaction data, you can gain a better understanding of who your customers are and what they actually need. From this, you can create personalized flows and deliver messaging tailored to customers – serving up the products & services they need when they need them.
Enhances User Engagement
The more that customers engage with you, the more likely your business is to remain top of mind.. Interaction analytics can help you understand any roadblocks in the customer journey and eliminate them, thereby improving customer retention, engagement and loyalty. Understanding your customers better – creating a platform tailored to their actual needs and desires – goes hand in hand with growing user engagement.
Helps Improve Products Based on Feedback
Keyword and sentiment analysis allow you to get clear, specific feedback on your products. Whether positive or negative, interaction analytics ensures that you can understand how your customers are reacting-to and feeling-about your products – using those insights to make improvements across channels.
Cons of Using Interaction Data
While interaction analysis can help improve your customer service interactions and processes, there are challenges you’ll want to consider to make the most of your investments. Here are just a few:
Privacy Concerns
Taking the necessary steps to ensure your customer’s data privacy when collecting and analyzing interactions is important. Irrelevant data doesn’t need to be collected, and you can check that any sensitive information is anonymized. Data breaches can lead to a lack of trust and possibly even losing customers to your competition.
Data Quality Risks
Analytics is only as good as the data used. If the initial data that is collected is of poor quality, then any insights or predictions gleaned from it will be the same. This could potentially lead you down the wrong path, driving changes to systems or processes that don’t need it.
Resource Requirements
Whilst interaction analytics can reduce your operating costs, it is more than simply adding AI capabilities to legacy solutions. To fully benefit from interaction analytics, you need a robust AI-powered solution. This includes looking for a solution that is domain-specific, updating legacy systems, resourcing the right data analyst(s) to work with the output, and providing the right technical support.
Implementing Interaction Analytics
Here are a few things to consider to help ensure a smooth adoption of integration analytics:
- Scoping the analytics you need. How many interaction channels/platforms do you have/want to analyze?
- Establishing your goals. What kind of answers are you looking for? Which processes, products or behaviors do you want to look at most?
- Maintaining your systems. How can you ensure your current systems are compatible and up-to-date? Will your interaction analytics solutions integrate easily with your existing systems?
- Readying your team for the shift. Insights are pointless if they aren’t used. Are you taking the time to ensure your team knows how to use them to advantage?
The Future of Interaction Analytics
As with most solutions combining analytics, AI and business intelligence, interaction analytics is growing in usage and popularity. Along the way, systems are becoming easier to implement, navigate and understand. This includes making insights and interfaces more accessible so that less training and high-level analytics understanding are needed. ML also ensures that interaction analytics will improve as it gets used – delivering better, more accurate insights over time.
About Sutherland CX360
Sutherland CX360 is a CX intelligence platform that uses AI to analyze 100% of customer interactions, rather than the mere 3% that gets audited manually. CX360 works to analyze the customer journey, monitor quality and give you predictive outcomes.
Sutherland CX360 features:
- Rich insights into agent behaviors.
- Sentiment, topic and DSAT analysis.
- Omnichannel interaction analysis into customer expectations.
- Automated QA reports.
- Predictive models for outcomes by interaction, agent and team.