Today’s cost of investing in data analytics depends on three main factors:
The human cost
The services you use
The tools you choose
In order to make data-driven decisions for long-term kuwait mobile phone numbers database growth, you’ll need to spend a good amount on data analytics.
However, simply investing in data analytics tools is not the entire solution.
There are many data analytics costs that need to be considered, and all of this depends on your company. The human cost, services, and tools are all part of the cost, as shown in the table below.
1. Human cost
You need people when conducting data analysis, as the end goal is to influence the willingness of your customers. It’s essential to look at how you source and enable the necessary talent.
The human cost means identifying employees or potential employees who can help integrate data-driven activities within the organization.
Those who already have analytical skills in your company can build their skills to reduce the cost of hiring experts. Adopt user-friendly training with tools that can be accessed by those trusted with the duties.
In doing so, small or medium-sized companies can reduce human costs in the long run. Nonetheless, you may still need to get experts to start the process and train your in-house employees.
2. Services
Your company’s data analytics costs need to include services. For example, there are agencies that do data analysis for other companies. Using an agency would be important in determining the cost for your SME.
For instance, you could contract a customer relationship management (CRM) agency to build some automated marketing workflows. In this case, the agency would spend time unifying some customer data sources. It would help develop a certain level of customer knowledge to aid the analysis or some RFM segmentation and then move on to email marketing workflows.
Just as the human cost, workflow-level costs also play a key role in your company’s data analysis budget.
3. Tools
There are some essential reporting tools that SMEs can use to do cost-effective data analytics reporting.
