Even before developing a dashboard, it is necessary to determine who will use it and what tasks it will solve. Only by knowing what questions the dashboard shall answer, Algorithm for successful it is possible to determine what metrics and parameters need to be collect. Also at this stage, it is necessary to define formulas for calculation indicators and standardize them. This will help to avoid a situation where the sales and marketing industry email list departments calculate and interpret ROMI, CAC and other similar indicators differently. Until there is an understanding between departments within the customer’s company, no automation of analytics will help improve the result.
Stage 2. Audit of data collection systems
It is important to analyze and improve Algorithm for successful the statistics collection systems even before starting data collection and developing an analytical dashboard:
- analytics systems (Yandex Metrica or Google Analytics). Statistics should be collected for all pages of the site, all advertising landings and domains. The analytics system should collect data for the entire funnel, including intermediate stages of application processing. If we are talking about an e-com project, the correct operation of e-commerce is also important.
- call tracking systems. It is necessary to collect data on the volume of calls from the site, mapping services and other sites tidio – top 5 alternatives Algorithm for successful where your business is presented. It is important to set up dynamic call tracking, which will allow you to track the effectiveness of contextual advertising right down to the search query.
- advertising accounts. It is necessary to check the correctness of the utm-markup and the transfer of such parameters as utm_source, utm_medium, utm_campaign, utm_term and utm_content.
- additional communication channels. It is necessary to check how requests to the company’s messengers are tracked, and at the stage of architecture design, provide for the necessary improvements so that none of the communication channels with clients remains untracked.
- 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 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 buy lead 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 track specifically by an automatic report, other manually collect data is not considere indicative.
To successfully implement automat analytics and train employees, several key steps must be take:
- 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.
- Implement analytics tools: Deploy the necessary software and infrastructure to collect and analyze data. Ensure that all systems are working correctly and interact with each other.
- Develop automated business processes: Create automated processes for collecting, analyzing, and reporting data. For example, set up automatic data downloads from various sources and generate reports based on them.
- Monitor and optimize: After implementation, monitor the system to ensure it is functioning effectively. Optimize processes as needed to improve the effectiveness of analytics.
- Measuring results: evaluate the effectiveness of the implemented changes. Compare the results with the set goals and analyze what can be improve.
- Continuous development: Develop your analytical capabilities and update your analytical tools in line with the changing needs of your business and the market. Implementing automated analytics and training your employees will allow your company to use data more effectively to make strategic decisions and achieve its goals.
Conclusion
Proper automation of analytics and employee training will increase the return on marketing investments, forecast business profits. A find growth points for a specific product, or choose the most successful strategy for launching new services. It is important to take a responsible approach to each stage of automation implementation. And carefully plan the process before starting work. Also, the secret to successful implementation will be active interaction with employees who will ultimately use the tool, because they are the source of valuable insights.
