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Calculating ROI from Online Campaigns with Seamless Data Integration – Part 1

Probably the most important rule in performance marketing is: decisions should not be made on gut instinct, but should be based on a sound foundation of facts (data) and empirical values (analyses). It is therefore all the more surprising that a large proportion of companies attribute very inadequate importance to the crucial tracking parameters when setting up their online marketing campaigns. This leads not only to considerable inefficiencies, but also to a “blind spot” in the control of lead nurturing and thus to a costly misallocation of marketing capital.

Communication network concept. Network business.

Seamless data integration is the basis of performance marketing

According to a study published in the Marketing Automation Report 2021 by the ZHAW School of Management and Law, more than 30 percent of the companies surveyed face the following three biggest challenges in online marketing:

  1. The collection of high quality customer data
  2. The limitations of currently existing systems
  3. The internal skills and know-how of the employees

Interestingly, the two biggest challenges, i.e., the collection of high-quality customer data and the limitations of existing systems, result from a lack of in-house expertise.

Therefore, the first step is to create awareness within the company for the importance as well as for the pitfalls of data integration, which many companies fail to overcome. Successful data integration is the solution to much of the challenges in online marketing.

High quality data integration helps to collect good quality customer data and can overcome limitations of current systems. This makes it essential for performance measurement!

The data integration problem

In the majority of companies, the success of marketing campaigns in performance channels can be measured only inadequately or not at all. Actions that are not measurable are also not controllable. As a result, online marketing unfortunately often resembles a “Blind Man’s Buff” game at the expense of valuable customer data.

A large part of the data required for performance measurement is insufficiently or not at all taken into account when setting up campaigns. This would require adequate technical know-how and complex integration of various tracking parameters in the campaigns and databases.

The classic integration problem usually consists of several challenges and can be divided into the following technical problems:

  • Insufficient data quality: Outdated, incomplete or inconsistent data cannot be used effectively and does not provide a good basis for decision-making.
  • Insufficient system structure: The established data systems do not have the right structure to pass the data smoothly. The APIs of the systems cannot communicate without API middleware.
  • Data silos: Essential data is not passed between multiple interconnected systems, but is used only within one system, shielding it from other systems.
  • Distributed data management: Data is not consolidated, but distributed across different platforms in a decentralized and confusing manner. Often there is no centralized platform that brings all data together.

Collecting and maintaining data is often time-consuming and costly. To unlock the full potential from the data collected, data integration must be done with meticulous precision. However, if a company dedicates itself to this challenge, the potential for performance marketing is enormous. So how can this problem be addressed?

Data integration via distributed and centralized platforms

Distributed and centralized platforms can solve problems such as inadequate system structures, data silos and decentralization, and simply make the widespread “blind spots” in the performance measurement of marketing campaigns disappear. Thus, they represent a solution tailored to the integration problem.

With the help of API middleware tailored to the systems, such platforms allow various data to be gathered and provide a single view of all customer data. The information is consolidated, structured and made available there from all systems used by the company. In addition to data transformation, a centralized platform also handles clear and orderly data visualization.

The use of distributed and centralized platforms eliminates the cost of information gathering and provides marketing and sales decisions with a sound basis of high-quality customer data for their decisions.

Schematic diagram of the system infrastructure

In order to measure the marketing accountability of campaigns, the processes must be modeled end-to-end, i.e., the data must be seamlessly consolidated. For example, measuring ROI works like this:

Via Google Ads the campaigns (with UTM parameters) are transferred into the DataEngine. UTM parameters are saved in Evalanche for each form submission. All profiles are synched to the DataEngine where purchases are entered (or synched from the ERP-system). Based on all these information the ROI of the campaigns can easily be calculated.

First, a campaign is placed via Google Ads. As soon as a lead is redirected to a company’s website via the campaign and fills out a form there, all campaign parameters (UTM) can be sent to the centralized platform (cf. bidirectional plan between Evalanche and the platform). For example, this can be information about the campaign through which the customer accessed the company website, or which keywords he used for the search. These data records are compared with the existing customer profiles and supplemented in the case of existing profiles. Otherwise, new lead records are created. If the lead is sufficiently developed, it is converted to a contact. As soon as the contact buys a product, this is recorded in the platform (either directly or via synchronization from the CRM/ERP system). The campaign parameters of the contact can now be assigned to the purchase. In addition, the campaign data from Google Ads is synchronized unidirectionally into the centralized platform. This data can be linked to the purchase data, as this also contains the campaign data of the customers. Now the ROI of the campaigns can be determined.

Part 2 coming soon.

Author: Tabita Lukjanovic

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