Customer relationship management (CRM) systems are widely used in many businesses and organizations today. While it’s great to have all your customer information compiled and accessible in one source, you may not be maximizing the value of your CRM. In particular, big data, business intelligence (BI) and analytics packages offer an opportunity to take your customer data to the next level. Let’s take a look at how you can achieve this with your customer data.
How to Leverage CRM as a Source for Analysis
Most CRM systems operate on top of databases that have the necessary capabilities to feed data into analytics and BI systems. While skilled database programmers can get a lot of mileage out of writing complex queries, the reality is that most of the data can simply be pulled into the big data pipeline by way of database connectors. These are small components of applications that are used to talk with databases in languages like MySQL, MongoDB, Fox Pro, and MSSQL.
Once you’ve pulled the data into your analysis engine, a host of functions can be used to study it. For example, a company might use the information from their CRM to:
- Perform a time-series analysis of customer performance
- Analyze which appeals from email marketing content seem to drive the greatest returns
- Determine which customers are at risk of moving on to competitors
- Find appeals that reinforce customer loyalty
- Spot customer service failures
- Analyze social media postings by customers to assess their experience with the company
What Methods Are Used?
Suppose your business wants to determine which email campaign appeals are worth reusing. Working from copies of email content, you can conduct a word cloud analysis that shows which concepts were strongly featured. Response data from the CRM can then be used to identify which words and phrases have performed best.
These items can then be organized into a BI dashboard widget that tells those writing the emails how to structure their content. For example, a market research firm might find that case studies drive more users into the marketing funnel than news about the practice. Marketers can then write new campaign emails based on that provided data. Almost as important, they can also access email performance and refine their approach until the material is exemplary.
Tools that are ideal for projects like this include:
- Sentiment analysis
- Marketing data
- Pattern recognition
- Word cloud analysis
Such analysis will also require a constant stream of data going from the CRM into the analytics engine and onward to the BI dashboards. Done right, this sort of big data program can convert something you’re already accumulating, such as customer relationship data, into insights that drive decision-making at all levels.
How Much Data is There?
Data for analysis can come from a slew of sources, and it’s important to have a CRM that allows you to access as many potential data sources as possible. For example, a company shouldn’t draw the line at collecting email addresses. You can also ask customers to include their social media accounts, such as handles from Twitter, LinkedIn, and Instagram.
Server logs should also be mined for interesting data points. You can, for example, study IP addresses and user logins to determine where a prospective customer might be in the marketing funnel. If you see that a lot of leads are dropping out at a certain stage, such as after signing up to receive your email newsletter, you can then start to analyze what’s misfiring at this stage in the process.
Once the problem is corrected, you can even use the CRM data to identify which customers you should reconnect with or retarget. You might, for example, send an offer for discounted goods or services to increase your customer lifetime value.
Conclusion
At many businesses, the CRM system is a highly underutilized resource. By coupling it with big data and an effective BI package, you can quickly turn it into a sales-driving machine. Team members will be excited to see the new marketing and sales tools at their disposal, and customers will value the increased engagement.
