LivePerson to Google Data Studio

This page provides you with instructions on how to extract data from LivePerson and analyze it in Google Data Studio. (If the mechanics of extracting data from LivePerson seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is LivePerson?

LivePerson promotes conversational commerce on digital messaging channels including SMS, Facebook Messenger, Apple Business Chat, and WhatsApp, as well as on websites and mobile apps. It lets businesses create AI-powered chatbots to handle consumer messages alongside human customer service staff.

Getting data out of LivePerson

LivePerson provides several APIs, including a LiveEngage Data Access API that lets developers retrieve data stored in the platform about agent activity, engagement, web sessions, and surveys. For example, to retrieve information about agent activity, you would call GET https://{domain}/data_access_le/account/{accountID}/le/agentActivity.

Sample LivePerson data

Here's an example of the kind of response you might see with a query like the one above.

{
     "dataAccessFiles": {
       "@id": "28045150",
       "link": {
         "@href":
    "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity",
         "@rel": "self"
       },
       "file": [
         {
           "@name": "Agent.1461387600000.1461391200000.part-00001-0",
           "@scopeStartDate": "2019-04-23T01:00:00-04:00",
           "@scopeEndDate": "2019-04-23T02:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461387600000.1461391200000.part-00001-0.gz"
         },
         {
           "@name": "Agent.1461391200000.1461394800000.part-00001-0",
           "@scopeStartDate": "2019-04-23T02:00:00-04:00",
           "@scopeEndDate": "2019-04-23T03:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461391200000.1461394800000.part-00001-0.gz"
         },
         {
           "@name": "Agent.1461394800000.1461398400000.part-00001-0",
           "@scopeStartDate": "2019-04-23T03:00:00-04:00",
           "@scopeEndDate": "2019-04-23T04:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461394800000.1461398400000.part-00001-0.gz"
         },
         {
           "@name": "Agent.1461398400000.1461402000000.part-00000-0",
           "@scopeStartDate": "2019-04-23T04:00:00-04:00",
           "@scopeEndDate": "2019-04-23T05:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461398400000.1461402000000.part-00000-0.gz"
         },
         {
           "@name": "Agent.1461402000000.1461405600000.part-00000-0",
           "@scopeStartDate": "2019-04-23T05:00:00-04:00",
           "@scopeEndDate": "2019-04-23T06:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461402000000.1461405600000.part-00000-0"
         }
       ]
     }
    }

Preparing LivePerson data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The LivePerson documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Keeping LivePerson data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From LivePerson to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing LivePerson data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites LivePerson to Redshift, LivePerson to BigQuery, LivePerson to Azure SQL Data Warehouse, LivePerson to PostgreSQL, LivePerson to Panoply, and LivePerson to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from LivePerson to Google Data Studio automatically. With just a few clicks, Stitch starts extracting your LivePerson data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.