Integrate LivePerson with Amazon Kinesis
LivePerson’s artificially-intelligent chatbots automate approximately 70% of customer inquiries, allowing you to scale your business without the overhead of hiring new staff. As an AI-powered chat platform, LivePerson offers a comprehensive service that simplifies the process of building, managing, and finetuning chatbots to support your business goals.
About Amazon Kinesis
Amazon Kinesis is a powerful analytics solution that overcomes the batch-processing challenges of Hadoop — and similar solutions — which don’t allow real-time precision in decision-making because they can’t rapidly process high volumes of streaming data. With its ability to process hundreds of terabytes of streaming data per hour, Kinesis allows you to develop apps that rely on real-time data to fuel AI analytics, machine learning insights, and other applications. Kineses enables instant responses by eliminating the delay associated with batch processing.
Popular Use Cases
Bring all your LivePerson data to Amazon Redshift
Load your LivePerson data to Google BigQuery
ETL all your LivePerson data to Snowflake
Move your LivePerson data to MySQL
Bring all your Amazon Kinesis data to Amazon Redshift
Load your Amazon Kinesis data to Google BigQuery
ETL all your Amazon Kinesis data to Snowflake
Move your Amazon Kinesis data to MySQL
Integrate LivePerson With Amazon Kinesis Today
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LivePerson's End Points
LivePerson Messaging Channels
LivePerson's AI chatbots engage your customers via the messaging channels they're already using. You can embed LivePerson into your website and mobile apps to answer customer questions while they're using your service or browsing your products. Your LivePerson chatbots can also communicate with your customers via SMS, Apple Business Chat, Facebook, Twitter, WhatsApp, Google RBM, Email, Line, and Google AdLingo.
LivePerson AI-Powered Chatbots
LivePerson chatbots can address approximately 70% of your customer's questions by using a powerful AI engine that was taught with decades worth of consumer data. The platform includes AI templates with prebuilt dialog flows and advanced natural language processing features that get your chatbots up and running in no time. LivePerson makes it easy for non-tech-savvy employees — including your customer service reps and content creators — to develop and finetune chatbots from scratch.
LivePerson Call-to-Message Features
LivePerson includes a feature to turn the phone calls you can't answer into messaging conversations. If customers call and need to wait on hold, this function gives them the chance to start a messaging conversation and get help immediately rather than wait on hold or wait for a returned phone call. According to LivePerson, 8 out of 10 customers choose to message when offered the chance to stop holding. In this way, LivePerson reduces the number of telephone reps your business requires, as chatbots can answer frequently asked questions, schedule appointments, and more — without the need for human employees.
LivePerson Advanced Management Tools
LivePerson includes features that put managers at the center of messaging conversations, like a web-based workspace where you can monitor and control all conversations between customers, bots, and agents. Meanwhile, with bot-assisted messaging, agents can manage multiple customer-bot conversations at once from a desktop computer, laptop, or mobile device. This allows agents to intervene when required for more positive customer experiences. LivePerson even provides real-time feedback on conversation health, so your agents know which conversations most urgently require their attention.
LivePerson Analytics and Metrics
The LivePerson platform tracks and analyzes all aspects of customer messaging conversations by monitoring information like chat duration, customer intent, conversion stats, and customer satisfaction levels, so you can gain key insights from your customer messaging conversations. These insights will help you improve chatbot interactions while providing ideas for improving other areas of your business, products, and services.
Amazon Kinesis's End Points
Amazon Kinesis Video Streams
Amazon Kineses Video Streams allow you to safely ingest streaming video data from millions of linked devices into AWS for machine learning, analytical, and other processing purposes. The platform then encrypts, stores, and indexes the video data so you can access video with simple APIs, play live video streams, and offer on-demand playback. When incorporating this technology with Amazon Rekognition Video, TensorFlow, ApacheMxNet, and OpenCV, Amazon Kineses Video Streams makes it possible to build video analytics and computer vision processes into your applications.
Amazon Kinesis Data Streams
By capturing, processing, and storing data streams, Amazon Kinesis offers a real-time data streaming solution to ingest large amounts of information from hundreds of thousands — even millions — of sources at the gigabytes-per-second scale. The massive scalability of this solution lets you capture and produce immediate analytics on data pertaining to financial transactions, database event streams, location tracking data, clickstreams, social media activity, and more. Since the availability of streaming data happens in milliseconds, the platform enables real-time analytics of this information for instant detection of anomalies, dynamic price adjustments, precise dashboard metrics, and more.
Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose provides a simple and durable way to pull your streaming data into data warehouses, data lakes, and analytics solutions. Due to its compatibility with Splunk, Amazon Redshift, Amazon S3, and Amazon Elasticsearch Service, Kinesis Data Firehose empowers real-time data analytics for the dashboarding and BI tools you've come to trust. Fully managed and automatically scaling, you can use Firehose to encrypt, batch, transform, and compress your information before ingestion to boost security and save on disk space.
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics helps users without programming knowledge to analyze data streams with SQL or Java. For team members who know SQL, an SQL editor and templates are available for creating streaming applications or querying streaming data. Meanwhile, those with Java knowledge can develop more nuanced streaming applications that perform real-time data transformations and analytical processes.