One of our clients had hundreds of products on their website.
The products were of different sizes, quantity, price range, and category.
But they were not sure what products they should use on their Facebook and Google ads.
Or which products were bringing them, quality customers, with high Customer Lifetime Value (CLTV) at a low Customer Acquisition Cost (CAC)
Jump ahead to:
About The Client
- Leading E-Commerce Player
- 300+ products on the website
- Targeting luxury audiences around the world
Problem Statement
Improving the ROI of the ad campaigns while maintaining the scale. The client already had decently optimized their Facebook and Google ads accounts which were generating decent returns.
Key Analyses
Build an in-depth model to understand what products are suitable for acquiring high-quality customers. Also analysing product features to determine customer targeting options.
Sample Report and Metrics Used
From Easyinsights.ai we were able to fetch product wise data on
- Customer Lifetime Value (CLTV): CLTV is defined as the average revenue you make or expect to make from a customer by providing your services/products in return.
- Acquisitions: New users acquired by the business
- Repeat Percentage: Percentage of acquisitions coming back to the business (offline or online) to make a purchase
- Customer Acquisition Cost (CAC): The average amount of money spent by the business to drive one acquisition. Calculated as the total amount spent divided by the total number of acquisitions.
All these metrics were easily available once we connected the client’s ad accounts and CRM to Easyinsights.ai.
What is Easyinsights.ai?
Easyinsights.ai data driven digital marketing tool that connects your Google Analytics, Google Ads, and Facebook Ads and provides easy access to powerful monitoring, analyses, reports & dashboards
So, How did we do it?
First, via data stitching using Easyinsights.ai we found out the LifeTime Value, the Total number of acquisitions, and the Repeat percentage of all the products on the google sheet.
Sample data:
| Product | Acquisition count | CLTV | Repeat percentage |
| Product 1 | 2070 | 3177.15 | 44.59% |
| Product 2 | 326 | 1636.99 | 21.58% |
| Product 3 | 246 | 1337.33 | 19.49% |
| Product 4 | 1916 | 2156.87 | 22.55% |
| Product 5 | 277 | 2599.08 | 8.45% |
| Product 6 | 744 | 1863.47 | 3.23% |
| Product 7 | 940 | 1947.72 | 19.99% |
| Product 8 | 2170 | 2502.09 | 8.75% |
| Product 9 | 388 | 2650.6 | 14.81% |
| Product 10 | 1444 | 2391.95 | 8.03% |
| Product 11 | 119 | 1587.25 | 19.64% |
| Product 12 | 1137 | 1970.84 | 5.39% |
| Product 13 | 192 | 1850.12 | 6.87% |
| Product 14 | 767 | 2311.75 | 4.59% |
| Product 15 | 58 | 944.39 | 4.96% |
| Product 16 | 6723 | 3782.86 | 29.90% |
| Product 17 | 2766 | 2417.11 | 8.50% |
| Product 18 | 446 | 1376.23 | 5.75% |
| Product 19 | 28 | 561.14 | 6.51% |
| Product 20 | 3345 | 4133.98 | 82.21% |
| Product 21 | 3532 | 3468.53 | 51.94% |
| Product 22 | 102 | 982.82 | 2.76% |
| Product 23 | 154 | 1130.39 | 11.37% |
* This is a sample of the data, actual data had 300+ products.
Upon deep diving the above selection we can already see that there are certain products with higher CLTV and Repeat percentage.
Then, we divided these products into different categories and price brackets using the labeling feature in Easyinsights.ai .
And then we easily fetched the metrics based on these parameters.
Data fetched:
| Category | Acquisition count | CLTV | Repeat percentage |
| Category 1 | 2900 | 2275.8 | 10.56% |
| Category 2 | 767 | 2311.75 | 4.59% |
| Category 3 | 2396 | 3277.85 | 45.72% |
| Category 4 | 4519 | 3831.39 | 14.87% |
| Category 5 | 474 | 1395.37 | 6.09% |
| Category 6 | 2162 | 3023.17 | 24.66% |
| Category 7 | 256 | 1308.13 | 6.68% |
| Price Bracket | Acquisition count | CLTV | Repeat percentage |
| 50 – 100 $ | 2402 | 3303.08 | 8.35% |
| 100- 150 $ | 15264 | 4724.11 | 35.36% |
| 150 – 200 $ | 9730 | 5090.25 | 44.02% |
| 200$ + | 2503 | 2770.46 | 16.78% |
*so, we found the winning category and the right price range to target our customers with.
Insights we got from the above data
Now, we drove the following insights from the gathered data:
- Got a list of all the products that were attracting high-quality customers
- Understood the category wise CLTV
- Got an idea of the pricing preferences of the customers
Actionables
Finally, we used these insights to make the business decisions and drive up the net ROI of the business with the following actionable
- Passed the above finding as new attributes into the product feed for both Facebook ads and Google shopping ads
- Created separate product sets for products that were attracting high-quality customers and bid higher for them compared to the holistic feed
- Increased the use of high-quality customers attracting products in the top of the funnel campaigns
- Created a CAC benchmark for products relative to their respective CLTVs
- Provided insights on product team on what kind of product to source
Result
After fully incorporating the above actionables we were able to increase the overall ROI of the business by 45%.
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