How deep-diving into Product & SKU Intelligence helped our client to improve ROI by 45%

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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) 

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:

1. Got a list of all the products that were attracting high-quality customers
2. Understood the category wise CLTV
3. 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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