Artificial Intelligence (AI) has a rich history of optimizing business outcomes. With CXLink AI can be used as user-driven AI recommendation engines to improve the customer experience (CX).
For example, use AI algorithm to improve the accuracy of predictions about which product or service was selected by most customers?
Leveraging AI in CXLink Customer Experience Management (CXM) application may feel complex, but using predictive analytics has now become essential to drive customer loyalty and increase revenue.
Here is how we do it:
(1) Gather Data from Multiple Sources
To build the foundation for a great CXLink application, we first need to gather data from various sources and listen to their customers on a regular basis.
CXLink AI works by learning models of individual or group behaviour from transactional data (history of business interactions with the organization), behavioural data (other interactions with the organization & its products and services), research data (profiles, opinions, expressed or implied desires), and even from the online or social presence of consumers (social postings, reviews, customer service messages). Combining data from all these sources will increase the accuracy of CXLink predictive models.
Then CXLink can use AI with different data to predict outcomes and recommend actions:
Quantitative data can be used to drive pricing and packaging of products. If you know that John took a Visa card in September, CXLink may be able to suggest a loan for him in October.
Time series data can be used to optimize the timing of interactions with the customer. For example, data shows you that Khalid paid school tuition for his kid in November. So the best time to present him with credit facilities would be October.
When you combine quantitative, time sensitive and categorical data and use predictive models to understand your customers, the resulting model will be more powerful than the sum of its parts.
(2) Apply AI Across Customer Journey Maps
At the core of CXLink CXM application, modern AI algorithm are incorporated inside its scenarios execution. This enable CXLink to orchestrate and predict the next best experience for your customers. For example, you are aware that Latifa has been going over her visa credit limit for the past couple of months. CXLink will be able to offer her an increase in her monthly limit before she even makes a request or call the service center.
This kind of predicting create happier path of customer experience. For instance, you are able to predict that Salim is interested in a real estate invest. You can offer Salim positive experience by guiding him to this type of investment even before he made a request.
Through CXLink machine learning capabilities, you can continuously improve customer experience across all touch points along the customer journey, even can do A/B tests or short surveys integrated into the customer experience. To keep up with evolving customer needs, using CXLink will provide the tools required to allow these dynamic models to keep learning and predict better outcomes.
AI can also be used to enhance combinations of transactional aggregate metrics, such as customer acquisition cost and revenue. It can also include softer metrics, yet strong predictors for future outcomes, such as satisfaction, brand sentiment or engagement. Optimizing these will lead to positive outcomes, such as increasing Customer Lifetime Value (CLV) and reducing churn.
(3) Trigger Actions to Improve Customer Experience
Once you’ve gathered data and given it a context with customer journeys, AI enables your CXLink application to take actions that impact outcomes. For example, let’s take identifying parts of the journey that contribute to churn. CXLink can predict customers who may be at risk of churn, and enable you track customer insights and trigger a personalized action to retain them. CXLink is performing this through listening to customer needs and finding a solution to meet their needs.
Using CXLink machine learning and data science, to listen to your customers, to understand their needs, and then act on those needs, which will provide the best value for your customers, and the value for your brand.
Leveraging AI in CXLink
Artificial Intelligence (AI) has a rich history of optimizing business outcomes. With CXLink AI can be used as user-driven AI recommendation engines to improve the customer experience (CX).
For example, use AI algorithm to improve the accuracy of predictions about which product or service was selected by most customers?
Leveraging AI in CXLink Customer Experience Management (CXM) application may feel complex, but using predictive analytics has now become essential to drive customer loyalty and increase revenue.
Here is how we do it:
(1) Gather Data from Multiple Sources
To build the foundation for a great CXLink application, we first need to gather data from various sources and listen to their customers on a regular basis.
CXLink AI works by learning models of individual or group behaviour from transactional data (history of business interactions with the organization), behavioural data (other interactions with the organization & its products and services), research data (profiles, opinions, expressed or implied desires), and even from the online or social presence of consumers (social postings, reviews, customer service messages). Combining data from all these sources will increase the accuracy of CXLink predictive models.
Then CXLink can use AI with different data to predict outcomes and recommend actions:
Quantitative data can be used to drive pricing and packaging of products. If you know that John took a Visa card in September, CXLink may be able to suggest a loan for him in October.
Time series data can be used to optimize the timing of interactions with the customer. For example, data shows you that Khalid paid school tuition for his kid in November. So the best time to present him with credit facilities would be October.
When you combine quantitative, time sensitive and categorical data and use predictive models to understand your customers, the resulting model will be more powerful than the sum of its parts.
(2) Apply AI Across Customer Journey Maps
At the core of CXLink CXM application, modern AI algorithm are incorporated inside its scenarios execution. This enable CXLink to orchestrate and predict the next best experience for your customers. For example, you are aware that Latifa has been going over her visa credit limit for the past couple of months. CXLink will be able to offer her an increase in her monthly limit before she even makes a request or call the service center.
This kind of predicting create happier path of customer experience. For instance, you are able to predict that Salim is interested in a real estate invest. You can offer Salim positive experience by guiding him to this type of investment even before he made a request.
Through CXLink machine learning capabilities, you can continuously improve customer experience across all touch points along the customer journey, even can do A/B tests or short surveys integrated into the customer experience. To keep up with evolving customer needs, using CXLink will provide the tools required to allow these dynamic models to keep learning and predict better outcomes.
AI can also be used to enhance combinations of transactional aggregate metrics, such as customer acquisition cost and revenue. It can also include softer metrics, yet strong predictors for future outcomes, such as satisfaction, brand sentiment or engagement. Optimizing these will lead to positive outcomes, such as increasing Customer Lifetime Value (CLV) and reducing churn.
(3) Trigger Actions to Improve Customer Experience
Once you’ve gathered data and given it a context with customer journeys, AI enables your CXLink application to take actions that impact outcomes. For example, let’s take identifying parts of the journey that contribute to churn. CXLink can predict customers who may be at risk of churn, and enable you track customer insights and trigger a personalized action to retain them. CXLink is performing this through listening to customer needs and finding a solution to meet their needs.
Using CXLink machine learning and data science, to listen to your customers, to understand their needs, and then act on those needs, which will provide the best value for your customers, and the value for your brand.