If you've read up on Daasity's Data Model, you know that there are several small data models that live in your data warehouse, and each has one or more Explores.
Explores are a way to view the data that resides in the database.
The standard Daasity data model for Growth Customers has 12 different explores that are able to be queried from the Reports page of the Daasity app.
Daasity's Standard Explores
|Customers||Customer Flags & Info||The customer models provide cumulative information on customers, including total orders, sales, gross margin. RFM rank, First and Second order info, type of buyer (omnichannel, ecom, retail) and customer demo info such as address fields, gender, email address. Customer tags & notes from Shopify.|
|LTV Time Series||Customer repurchase and Lifetime Value metrics including gross sales, gross margin and first purchase SKU. Also contains pre-defined customer segments and subscription flag, if applicable.|
|Google Analytics||PDP Performance||The PDP (Product Detail Page) performance explore contains similar information as other GA explores, but it focuses on product pages. GA identifies product performance by product SKUs, so it is always a good idea to have 1 SKU per unique product; if a product is re-purposed, create a new SKU. Detail here includes information like Add to Carts from PDP.|
|Shopping Stage||Shopping stage illustrates an aggregated view of your customer's journey on your website. You may easily see the drop off points at various stages in the funnel, we have broken the stages into: Visit, Product View, Add to Cart, Enter Checkout, Convert. Data includes GA collected data such as device type, visitor type, GA and Modified Last Click Channel attribution and measures such as time on site, bounce rate and transactions.|
|Traffic||Aggregated session website metrics such as conversion rate, time on site, new vs. returning, avg. pageviews. Also can be viewed by Modified Channel which updates GA channel based on Daasity's custom channel mapping. Note that channel attribution based on discount codes alone cannot be viewed at the traffic level.|
|Marketing||Marketing Performance||This explore combines performance metrics from marketing platforms all in one place. Compare clicks, impressions, spend, orders, revenue, ROAS. Note that the data in this explore is at the vendor reported level; to view order data by vendor, use the Order & Order Line Revenue Explore|
|Multi-Channel Transactions||This explore combines your first click data about visitors with the last click data that shows the channel the user converted in and is typically how many companies report. It allows measurement of days from first touch to last touch, # of interactions from initial to final conversion (based on Google cookie data).|
|Orders||Orders & Order Line Revenue||Given the amount of detail around orders, this is our largest explore. Here you will find information about counts of orders, totals including sales, discounts, and more. It contains fulfillment info (if available through integrations), channel attribution info and customer info.|
|Shopify Reports||Sales Report||There are a few key differences in how Daasity reports metrics and how Shopify does. For example, Daasity records refunds differently and also eliminates fraud and cancelled orders from reporting. If you are seeking to match your Daasity data to Shopify, this explore allows you to do it. We've recreated the logic used by Shopify in their reporting to allow customers to see it here as well.|
|Subscriptions||Subscribers||This explore is available for ReCharge customers only. The information found here is on your total subscribers and subscriptions. Information on start and end dates of subscription, products and frequency can be found here.|
|Subscription Monthly Churn Rates||This explore is specifically built to calculate monthly churn rates based on subscribers and subscription status at the start and end of each month.|
How to choose the correct explore
Explores are grouped together based on the subject area, i.e., Marketing, and by the type of information contained within them.
Q: Why isn't all the data related to a particular topic housed in the same explore?
A: Not all data is created equal. Some data or sources cannot be joined to others, and some elements would cause issues like duplicate data. To avoid those types of issues, we've separated them for you.
The first step to choosing an Explore is to identify the subject area you want to learn more about.
The main subject area will lead you to the right data model, and from there you can pick an explore. The trickiest part may be determining if you are seeking information on Customers vs. Orders. One way to think about it is that the Customers Explore is aggregating information at the customer level, so if you are looking for total lifetime value or how all customers compare to each other, that would be the explore to use.
If instead, you are seeking information about Orders that involve order metrics or are in a certain date range, you will want to investigate the Orders & Order Line Revenue Explore. We have included several pieces of customer data in the Order & Order Line Revenue Explore to facilitate answering questions about groups of customers and orders.
Email email@example.com if you're having trouble finding or using Explores.