The main difficulty of the task was that between the day when the order was received and the date when you can take the profit, there were 40-60 days on average. Because of this, it was impossible to assess the return on investment in advertising using standard e-commerce tools. The site does not have a traditional e-commerce way of buying a product, as this is a custom product that is made for the consumer. The final cost depends on the quantity, material, invoice, shipping cost and other characteristics of the goods.

It was decided to integrate e-commerce in two stages. Stage 1 1) Setting up Google Analytics, streaming cost data using OWOX BI. 2) Setting up the transfer of data on completed orders to Google Analytics via the Measurement Protocol. 3) Implementing the SourceBuster scrip Stage 2 Combining data based on exports from CRM and Google Analytics ROAS Summary Report Architecture 1) Once a week, sales data is uploaded from CRM using a connector, in which there are fields by traffic sources (marked under SourceBuster). 2) Similarly, once a week, Google Analytics uploads data on traffic sources with the following fields: date, source/channel, clicks, sessions, costs. 3) With the help of massive transformations, 2 tables are formed, which we connect by the key “date/source/channel/campaign/» - from GA, we take data on costs and traffic for the date - from CRM, we take data on applications (leads) on the date and subsequent completed orders with data on profit.

Before contacting the agency, the Client was only accounting. He took the amount spent on marketing, divided it by the profit received, and approximately saw the total marketing payback. Based on this data, it was impossible to make decisions at the source or campaign level. After we have introduced the changes described above, the Client began to conduct high-quality optimization of his advertising campaigns according to parameters that he had not tracked before: Cost Per Lead, Сost Per Order, ROAS and the share of the advertising campaign in the total profit received. By connecting end-to-end analytics, the Client was able to refine the analysis to the level of an ad group and a keyword. The Client started working on optimizing campaigns in Google Ads. He saw, for example, that within the same ad group there is a mixed demand — commercial (B2B) and end-user (B2C) requests. It was decided to cut bids on campaigns and groups that did not pay off in the quarter. The client received a structured data warehouse, a system for end-to-end business-specific analytics, which is an accurate tool for deciding on traffic sources and costs.