Genesys Predictive Engagement (CE37) for Genesys Engage on premises

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This topic is part of the manual Genesys Engage on-premisess Use Cases for version Public of Genesys Use Cases.
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Use machine learning powered journey analytics to observe website activity, predict visitor outcomes, and proactively engage with prospects and customers via agent-assisted chat, content offer or chatbot.

What's the challenge?

It’s challenging to identify the right individual, the best moments, and the optimal ways to offer assistance online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but it's expensive to always engage an agent.

What's the solution?

Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilities to identify the right moments for proactive engagement via a web chat or help content screen-pop.

Story and Business Context

One of the biggest challenges for the modern business is learning to work with the data available in a way that is both meaningful and easy to act on. The data generated by a website often goes unexplored, and as a result, the intentions and reactions of individual customers and prospects can be overlooked. Focus is often placed on the broad strokes–key metrics such as the number of conversions this month–and the ability to identify the potential customers who need engagement is lost. As a result, customers who may be on the verge of signing up for a trial, completing a checkout, searching for information regarding service or support, or any other desirable outcome, fall through the cracks. The high volume of website traffic makes it a challenge to identify the right individuals, best moments, and optimal ways to engage in real time. Expectations for time-to-respond are increasing but growing your staff is costly.

Genesys Predictive Engagement uses machine learning to observe the progress of website visitors toward defined business outcomes –such as purchase completion or requesting a quote – and enables the business to use real time observations and predictions rather than static rules, to trigger intervention only at the points when it is needed most.

For customers seeking service or support, a company’s website is often the first point of contact, even if it is only to find a phone number to call. But companies are challenged with making sense of and learning to use all the data generated by their website in a way that is both meaningful and actionable in real time. As a result, customers either end up calling into the contact center (an expensive support channel) or get frustrated with your business because they can’t find the help they need. Genesys Predictive Engagement prioritizes engagement with high value visitors and proactively offers chat to better utilize your staff and reduce your costs.

Examples of how the customer experience can be optimized by using data, context, and website behavior for a predictive engagement:

  • Use of machine learning to detect the progress of website visitors toward defined outcomes–purchase completion, requesting a quote–and enable the business to trigger intervention only at the points when it is needed most.
  • A customer who is recognized to be having trouble submitting a loan application is prompted with a proactive web chat enabling an agent to help the customer walk through the steps.
  • A customer needs to activate their new mobile phone, goes to the website, and searches for "device activation." A proactive chatbot is offered to help the customer walk through the steps.
  • A customer is planning a trip abroad and needs to notify their credit card company. They go to the company's website and based on a search related to "travel alert," a chatbot is offered to assist to prevent the need to call the contact center.
  • A customer is proactively offered self-help options to assist with a transaction, for example providing a link to a video to help with a Return Merchandise Authorization (RMA).

Use Case Benefits

Use Case Benefits Explanation
Improved Net Promoter Score Offer assistance only when needed to reduce customer annoyance.
Reduced Administration Costs Improve self-service rates by providing customers with the right information at the right time or proactively offering a chatbot to automate the conversation and prevent contact with an agent.
Reduced Handle Time When the engagement requires escalation from self-service to assisted service, the agent is provided context of the journey.

Summary

Understanding and leveraging knowledge of online activities and behaviors can provide context to better handle a follow-up digital or voice interaction to help customers who are shopping, buying, using the company's products across the full customer life cycle. This engagement intelligence can also be used for converting service requests to sales opportunities for cross-sell or up-sell. Genesys uses artificial intelligence to observe and analyze the progress of website visitors toward defined outcomes – service requests, pending transactions, application status - and allows the business to engage with customers using dynamic observations and predictions rather than simple static rules- creating happier customers, smarter employees, and better outcomes.

Companies have vast amounts of data within their CRM, marketing automation, contact centers and websites, and Genesys enables companies to unlock that data in real time to engage customers proactively, thereby eliminating the need for a voice call or contact without context. Genesys Predictive Engagement observes individual customer journeys on your company website and applies machine learning, dynamic (or audience) segmentation, and real-time outcome scoring to identify the right moments for proactive engagement with the right customer via chat, chatbot, or content offer.

Predictive Engagement's real time engagement sophistication increases customer satisfaction, improves conversion rate, and optimizes the use of agent resources for the highest value customers leading to improvement of key performance indicators such as call deflection, average order value (AOV), first contact resolution, and conversion rates.


Use Case Definition

Business Flow

Main Flow

Business Flow Description

  1. The customer starts browsing the company website.
  2. Genesys determines whether the customer is new or returning to the website, and associates data from previous journeys.
  3. The combination of segment and variations in outcome score can trigger an offer to chat with an agent or with a chatbot while the customer is browsing the website.
  4. An algorithm determines the predicted availability of agents to handle the interactions.
  5. If the customer accepts the invitation for chat, a registration window pops up where the customer can enter his data and the conversation with Genesys Blended AI Bots (CE31 Use Case) will start. In the registration form, customer can either manually enter his contact details (name, email) or contact details will be pre-filled if already known to Genesys.
  6. In Genesys Routing logic, a decision can be made based using context (for example, customer segment, customer lifetime value) and current agent availability


Business Flow

Routing


This diagram details the routing that takes place before and during the chat.

Business Flow Description

  1. Genesys routes the interaction to an agent based on the skills, media, language, and other ACD routing choices.
  2. An agent and customer are in conversation. The agent has access to full visitor context such as segment, journey information, and outcome score.
  3. After the conversation ends, the agent sets a disposition code within their desktop to record the outcome of the conversation.



For more details

For additional details, contact your Genesys Sales Representative by filing out the form or for immediate assistance call us: 1-888-Genesys.