[For B2B businesses] Specific examples of successful results achieved through offline CV
Posted: Wed Dec 11, 2024 5:03 am
This is a case study of a major manufacturing company, Company A.
Online CV is a document download. Sales staff visit and negotiate with the acquired leads, and the flow after lead acquisition is managed with a CRM tool.
Operational challenges
Company A had the following issues in its operations:
Each product line has a budget kuwait phone number library and target CPA
We don't know the sales rate after online conversion for each product group.
I don't know the conversion rate from online conversions to sales negotiations depending on the type of advertisement
What we did to solve the problem
[Introducing offline conversions to advertising operations]
We used the following two methods to visualize the results after introducing offline conversions and acquiring leads.
①Import the number of deals to Google using GCLID as a key
Import the number of sales opportunities into Google Ads as offline conversions using the GCLID as a key.
This makes it possible to check the number of sales opportunities per keyword and per ad, and also makes it possible to optimize Google Ads for the "number of sales opportunities," which is closer to closing a deal than "lead acquisition (online conversion)."
② Organize by unique parameters for each ad group
By assigning parameters to each ad group, a table linking media data and sales negotiation data was created.
This made it possible to visualize the number of sales negotiations for each media, ad type, and each ad group on a product group basis.
result
Before offline conversion was introduced, ads could only be evaluated based on the CPA of lead acquisition, so that CPA was set as the KPI. After offline conversion was introduced, ads could be evaluated based on the number of sales negotiations, that is, based on the contribution at points closer to closing, and KPIs could be set with figures that are highly relevant to achieving goals.
In addition, it became possible to manage advertisements by weighting the numerical values of points that are closest to closing a deal, which enabled us to improve the cost per deal by approximately 60%.
Online CV is a document download. Sales staff visit and negotiate with the acquired leads, and the flow after lead acquisition is managed with a CRM tool.
Operational challenges
Company A had the following issues in its operations:
Each product line has a budget kuwait phone number library and target CPA
We don't know the sales rate after online conversion for each product group.
I don't know the conversion rate from online conversions to sales negotiations depending on the type of advertisement
What we did to solve the problem
[Introducing offline conversions to advertising operations]
We used the following two methods to visualize the results after introducing offline conversions and acquiring leads.
①Import the number of deals to Google using GCLID as a key
Import the number of sales opportunities into Google Ads as offline conversions using the GCLID as a key.
This makes it possible to check the number of sales opportunities per keyword and per ad, and also makes it possible to optimize Google Ads for the "number of sales opportunities," which is closer to closing a deal than "lead acquisition (online conversion)."
② Organize by unique parameters for each ad group
By assigning parameters to each ad group, a table linking media data and sales negotiation data was created.
This made it possible to visualize the number of sales negotiations for each media, ad type, and each ad group on a product group basis.
result
Before offline conversion was introduced, ads could only be evaluated based on the CPA of lead acquisition, so that CPA was set as the KPI. After offline conversion was introduced, ads could be evaluated based on the number of sales negotiations, that is, based on the contribution at points closer to closing, and KPIs could be set with figures that are highly relevant to achieving goals.
In addition, it became possible to manage advertisements by weighting the numerical values of points that are closest to closing a deal, which enabled us to improve the cost per deal by approximately 60%.