Analytics
Alpine IQ's Analytics section gives your team the power to analyze custom audiences, segment sales to uncover key performance insights, develop data-driven customer personas, and more/
Audiences are at the core of data analytics. Without an audience to analyze there will be no data to observe. The audience builder is an imperative part of data segmentation and can used to get detailed insight into your business operations
- Predicted rank: machine learning predictionson future performance of product, category, or brand
- Predicted order value: machine learning predictions on average order value from individuals within the audience being analyzed
- Predicted liftetime value: machine laerning predictions on the lifetime value of individuals within the audience being analzyed
- Geo insights: heatmap showing the location of customer transactions. This feature can be enabled via Connect Data > Extensions and clicking Enable next to the Geocoding option. Pricing available on Geocoding card
- Budtender insights: tab showing how your budtenders are performing at the retail level
- Data analytics becomes more refined as more data is digested so much like flower, let it bloom to see all the beauty inside
- Data analytics can be a powerful tool when deciding how to segment audiences and who to send campaigns to
- Data analytics can be used in inventory planning using our predictive analytics tool to see what product you need to scale up or down on
Follow the steps below to analyze your very first audience
1.In the left-hand navigation bar, click Data Analytics then click Analytics
2. Select an audience by clicking on the Audience(s) analyzing field. For a high level analysis of all your customers, select All tracked users but this can be used to analyze any audience in the system. Optional: select a specific date range and compare against another date range which will product a side-by-side comparison
3. Click Run Analysis to generate the report. If you edit the audience or date range, be sure to click Run Analysis again to produce a new report
Tab | Description | Available insights |
Order Insights | Data pulled directly from transactions in your point of sale |
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Predictive Analytics | Machine learning-driven data that can be used in marketing and sales planning |
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Member Club | performance metrics of your loyalty program |
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Actions and conversions | Performance metrics on any/all messaging tools and revenue generated therein |
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Geo Insights | Heatmap showing where your customers are coming from Enablement of this feature is required to see data and can be turned on by heading to Connect data > Extensions > Enable |
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Budtender insights | Individual budtender sales metrics pulled directly from your point of sale The Avg Rating column must be enabled to see data and can be turned on by heading to Quick setup > Budtender post order review > More details > Install |
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Demographic insights | Data pulled from ID scans or provided by integrations/signup forms/etc as well as general census data |
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conversions occur when a customer takes an "action" on a campaign and makes a purchase within 14 days of taking that action. Examples of "actions" can be:
- text_received
- text_click
- Google_review
- Open
Find a list of all your top actions by scrolling down to the Top actions before converting table on the Actions & conversions tab
You can decrease the size of the conversion window at the individual campaign level. When creating a new campaign, scroll down to Advanced settings and adjust the conversion window via Set a custom attribution window for conversions
Revenue from that conversion is calculated from the first purchase the customer makes after taking that action. Find a snapshot of your all-time revenue from conversions at the top of the Actions & conversions tab
You can also find revenue metrics on your Campaign reports page. Find a global snapshot of the revenue generated from campaigns at the top of the page or look at individual campaign metrics by clicking the "+" symbol to the left of the campaign
Use the Customer Lifecycle tab to gauge how sticky your customers are and reengage customers that aren't purchasing as frequently as they once were
Toggle the Timeframe to see metrics over different periods of time
Customer Lifecycle metric | Description | Use case |
Leaky Bucket | the ratio of new customers vs customers that are likely gone | If the ratio of Likely Gone customers is higher than New active customers, focus on win-back and retargeting efforts to get those customers back to an active purchasing habit |
Customers won back | the number of customers that became active again from being likely gone | |
Lifecycle distribution | Alpine IQ segments customers into different stages of our Lifecycle Distribution chart depending on how much time has eclipsed from their first two tracked purchases
| If your Active customer base decreases over time, consider win-back campaigns targeting audiences built around Chilling, Absent, & Highly Absent customers. Create this audience using the below traits: Contact trait / Lifecycle stage / contains / [lifecycle stage] |
