Integrate Shoper with Data Octopus

#2

1. 01

   Why does it matter?

   Losing customers with high average revenue per user (ARPU) to lower-value ones means higher conversion costs and lower return on investment.

   The key is to identify the highest-ARPU segments and keep them coming back — or win them back.

2. 02

   How to check if the problem exists?

   Compare the average ARPU of active customers (last order within 365 days) with churned segments (last order over 365 days ago). Data Octopus shows this in the customer base report from your store engine.

   If churned segments such as Slipping Champions or Cannot Lose Them had ARPU clearly above today's average, you're losing your most valuable customers.

3. 03

   How to use the feed in your ad account?

   Data Octopus turns these customers into an audience list (Customer Match), available through a password-protected link and refreshed automatically. Upload it to Google Ads or Meta Ads.

   Run a Demand Gen campaign targeting the list directly, with ad groups by purchased category or brand and messaging that invites them back. You can also use the list as a signal for product campaigns or as a lookalike seed.

4. 04

   How to measure results?

   The number of people on the list should fall as returning customers move into active segments. Also track revenue and orders from the campaign targeting the list.

#8

1. 01

   Why does it matter?

   Customers who buy often and spend a lot drive high ROAS and campaign revenue.

   Your ads should reach them first — to keep them in your store and away from competitors.

2. 02

   How to check if the problem exists?

   Connect Data Octopus to your store engine. Customers are split automatically into RFM segments (how recently, how often and how much they buy).

   Pick the 2–3 most valuable segments — highest total revenue, average order value and order count.

3. 03

   How to use the feed in your ad account?

   For each segment Data Octopus builds a Customer Match list behind a protected link, refreshed automatically. Upload it to Google Ads or Meta Ads — review takes up to 24 hours.

   Use the list as an audience signal in product campaigns, for direct targeting (e.g. Demand Gen with ad groups by category or brand) or as a lookalike seed.

4. 04

   How to measure results?

   Track revenue and ROAS of campaigns using the list, and list size in Data Octopus and your ad account. Fewer customers in at-risk segments means they're becoming active again.

#10

1. 01

   Why does it matter?

   Returns cut revenue, raise storage and handling costs and blur real product profitability.

   Advertising products that keep coming back means paying for sales that don't exist.

2. 02

   How to check if the problem exists?

   With a store connection, Data Octopus shows the products with the most returns and the change versus the previous period. Look at at least the last 90 days.

   Most worrying are products with the highest return counts and those whose returns grew by tens of percent.

3. 03

   How to use the feed in your ad account?

   Data Octopus tags products above your return threshold with a feed label. Send them to a separate PMAX or Shopping campaign with a low budget and exclude them elsewhere.

   If returns still cause a loss after a few weeks, pause promotion and fix the cause — a defect, wrong description or sizing — before advertising again.

4. 04

   How to measure results?

   The number of products in the label should fall. Judge results after 4–6 weeks, since customers usually have 14 days to return.

#9

1. 01

   Why does it matter?

   A full picture of marketing results is key to judging what works and finding areas to strengthen.

   Without a single source of truth each platform reports its own result and real product performance stays hidden.

2. 02

   How to check if the problem exists?

   If you look at product data from your feed, GA4, Google Ads, Meta Ads and your store separately, in different dashboards, the problem exists.

   Also compare revenue and orders in GA4 with your store engine. A gap above 15% means segmentation should rely on store data.

3. 03

   How to use the feed in your ad account?

   Data Octopus joins all sources on a common key — the product or variant ID. In the product performance view you analyse impressions, clicks, cost, revenue and ROAS from several channels at once, by product, brand or category.

   Insights flow back into the feed as labels — e.g. products strong in one channel and weak in another — and steer campaigns right away.

4. 04

   How to measure results?

   The result: visible gaps between ad-platform metrics and between GA4 and your store — and budget decisions based on one consistent data source.