CRM. The key element of analytics is determining the source of sales. In order for a business to optimize and scale advertising activities, it is important to track which advertising format and channel brings in a converting and loyal audience, rather than empty applications. To do this, it is necessary to transfer the clientID from the analytics system (Yandex Metrica or Google Analytics) to the CRM system.
Stage 3. Development of technical specifications
We describe what we expect to receive as part of analytics 1000 cell phone number list automation. The TOR should include a description of the metrics involved, parameters, formulas for calculation indicators, as well as a description of how data from different systems will be linked. At this stage, we regulate the technologies used and the resources required to support the performance of analytics.
Stage 4. Implementation of the analytics project and technical support
We start collecting data and developing a prototype in accordance with the technical specifications and customer requests. This may require several iterations of improvements based on feedback from specialists who will ultimately use analytics as a working tool. And it is normal when, during the implementation process, it is necessary to adjust the visualization or supplement the data, the main thing is that this does not contradict the agreements at the previous stages.
Stage 5. Implementation of automated analytics and training
To ensure that the work is not in vain, it is important to train employees in analytics and implement business processes based on automation. The developed analytical solution should become the center of decision-making in the field of marketing and customer acquisition. For example, you can regulate that at a sales department meeting, data on the effectiveness of managers is tracked specifically by an automatic report, other manually collected data is not considered indicative.
To successfully implement automated analytics and train employees, several key steps must be taken:
Staff training: Train your staff on new analytics tools. Conduct training sessions and workshops to ensure your staff understands how to use data to make decisions.