Companies today are quickly realizing that they are installed in large data sources and therefore want to integrate analytics and intelligence into their CRM operations.
It is easy to see why. Effective data collection and analysis are essential for a better relationship with the customer.
Data + Insights: this is the company
Using the information collected from the data to create a single, unified view of each customer provides an organization with the information it needs to make decisions about customer acquisition, interaction, and retention, enhancing the offer, messaging, and customer experience. Data analysis can segment buyers and predict customer behavior to better tailor marketing and sales activities and develop more effective customer acquisition strategies.
In short, data and insights eliminate assumptions and assumptions and support all marketing and sales decisions and activities with solid numbers and statistics.
But getting to that point requires much more than just getting basic contact information. Ultimately, you need to transform your organization’s culture and resources to build trust in big data and fully harness the power of analytics.
Step one: find the right strategy
Mountains of digital data are generated every day. How can you be sure that the data you collect and analyze is truly valuable to your organization at a time when data decisions are being made at almost every level of the company?
- Ask and answer this question (if possible; it is more complicated than you think): What is the overall strategic business problem you are trying to solve?
- Understand how the data will be used to determine project goals, specific needs to be met and how and by whom the data will be used.
Step two: get the data right
- Check your data sources, whether market research, internal or external data. Know the pros and cons of each source and the ways to overcome these limitations.
- Use technology that provides the most up-to-date data possible (as a reference, consider how much revenue your company has earned in the past six months).
- Get help from IT or your data science team to identify key data to be used for analysis.
Work with them to ensure that the data is “clean”, that is, correct and structured, and that outliers are removed to make them useful, including frequency, geography, statistics, etc. Think. It checks and checks even small data errors that can affect processing.
- Once you have the opportunity to obtain “raw” data from the general data science team, you must confirm that the data is correct, which is necessary to achieve your marketing objectives. Work closely with marketing and sales activities to ensure that the selected data provides the sensational information you want about the customer.
- Don’t forget the details, including filtering, sorting data by importance, summarizing the data, and how vivid it is through visual aids.
Step three: get the right intelligence that can be detected from the data
- Performs three types of analysis: descriptive (summary of data), predictive (trend lines, sentiment analysis), and prescriptive (useful information for optimal decisions).
- Make sure the data is followed by the context to provide the right insight.
- Generate specific perceptions, communicated clearly, relevant to the person who receives them, and closely aligned with business objectives and strategies.
- Focus on trends instead of individual data items so you don’t miss out on major changes in movement or direction.
- Get insights by looking for strong correlations between variables.
- Ask other analysts about their perspectives. By keeping the same data in mind, unique insights can be generated.
Play the role of the devil’s advocate to view data from different angles.
- Get the results of the right data for the right people. For example, C-level employees need high-level insights and clear data, such as global prices or market trends, while marketing teams demand individual criteria from customers and influence marketing efforts.
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From achieving true personalization to predicting behavioral patterns and creating ongoing conversations, improving customer engagement begins with this process.
May the data be with you.