Customer industry segments

We have thousands of customers in PostHog, many of which are in similar industries. As CSMs having an understanding of our customers' industries can help us better be an expert on PostHog works best for their specific use cases. This page serves as a resource for us to be able to collect and share industry specific vocabulary, important metrics, PostHog best practices, etc. that allow us to quickly ramp up on the industry to better engage with those customers.

Industry segment list

These segments can change as our customer data evolves, but the following serve as a starting point:

• AI & Data
• Consumer software
• Developer tools
• E-commerce
• Education
• Enterprise software
• Finance
• Healthcare
• Logistics
• Marketing

Template for industry playbook

Eventually each industry listed above will be linked to its own playbook with details its specifics. The following is a template that can be used to create the playbook:

### Description (general overview of what the industry is and the businesses it consists of)
### What they care about (i.e. what is most important to their business success)
### Industry terminology
### Common software used
### Important business metrics and data
    #### Metrics
    #### Data (event taxonomy, person profiles, groups)
### PostHog products they should be using
    #### Product
    	##### Best practices
    	##### Common challenges
    	##### Cross product use cases

Segments in Vitally

Industry segment is a custom account trait in Vitally. You can find and edit your customer's industry on the side panel of their account page as a pinned trait. You can add a value or edit current value directly on the account page or add the industry segment as a column to any custom tables you have in Vitally.

<summary>E-commerce playbook</summary>

Ecommerce description

Online retail businesses including direct-to-consumer brands, marketplace platforms, and omnichannel retailers selling physical or digital goods through web and mobile.

What they care about

• Conversion rate optimization across the entire funnel
• Cart abandonment reduction
• Customer acquisition cost (CAC) vs lifetime value (LTV) balance
• Site performance impact on sales
• Mobile vs desktop performance disparities
• Seasonal traffic and sales patterns
• Inventory turnover and demand forecasting
• Return rates and reasons
• Cross-sell/upsell effectiveness

Industry terminology

• AOV (Average Order Value): The average dollar amount spent each time a customer places an order.
• PDP (Product Detail Page) / PLP (Product Listing Page): PDP is the individual product page with detailed information, images, and add-to-cart button. PLP is the category or search results page showing multiple products in a grid or list format.
• SKU (Stock Keeping Unit): A unique identifier code assigned to each distinct product and its variants (size, color, etc.) for inventory tracking.
• Drop-off rate / Abandonment rate: The percentage of users who leave a process (like checkout) without completing it. Cart abandonment specifically tracks users who add items but don't purchase.
• Retargeting / Remarketing: Advertising strategy that shows ads to people who previously visited the company's website or app, aimed at bringing them back to complete a purchase.
• Attribution window: The time period after a user clicks or views an ad during which a conversion (purchase) will still be credited to that ad. Common windows are 1, 7, or 30 days.
• ROAS (Return on Ad Spend): Metric measuring ad campaign effectiveness by dividing revenue generated by the cost of ads.

Common software used

• Platforms: Shopify, WooCommerce, BigCommerce
• Analytics: Google Analytics 4, Contentsquare, Hotjar
• A/B Testing: Optimizely, VWO, Shoplift

Important business metrics and data

Metrics

• Conversion funnel: Homepage > Category/PLP > PDP > Add to Cart > Checkout Started > Purchase Complete
• Key rates: Browse-to-buy rate, PLP>PDP rate, PDP>Cart rate, Cart>Purchase rate
• Revenue metrics: Revenue per visitor (RPV), items per order, repeat purchase rate
• Engagement: Pages per session, bounce rate by landing page, search-to-purchase rate
• Performance: Page load time correlation with conversion

Data

Event taxonomy

• Core events: product_viewed, product_added_to_cart, checkout_started, order_completed
• Detailed spec for Ecommerce event taxonomy
• Key event properties: product_id, product_name, price, currency, quantity, category, brand, variant (size/color), cart_value

Person profiles

• Often anonymous until purchase or email capture
• Limited utility for one-time purchasers but valuable for subscription/replenishment businesses
• Key properties: customer_type (i.e. new/returning), total_orders, total_spent, last_order_date, preferred_product_categories

PostHog products they should be using

Product analytics

Best practices

• Build conversion funnels for each major product category
• Create cohorts based on acquisition channel to compare quality
• Track micro-conversions (newsletter signup, wishlist adds)
• Monitor search query performance and null results

Common challenges

• Shopify and other ecomm website builders can make installing PostHog properly difficult and cause unique bugs related to plug-ins, etc.
• Cookie/privacy restrictions affecting attribution

Cross product use cases

• Use session replay to identify issues > Create experiment to test fix > Monitor with analytics
• Feature flag for seasonal promotions > Track performance in analytics > Watch customer interactions via replay
• Identify drop-off points in funnels > Watch those specific sessions > Run experiments on improvements