No Lead Left Behind: Bolster Your Customer Acquisition Strategy with CRM Data Analysis

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Doing analyses on CRM data is a skill that most technology companies (and any self-respecting growth strategist sleuth) should master.

In our work with OpenView’s more than 20 expansion-stage technology companies, we often find ourselves diving into a company’s CRM databases to conduct extensive data analyses, either as part of a stand-alone, in-depth review of the company’s customer characteristics, or as part of a larger study commissioned on customers acquisition productivity or sales process optimization.

The modern technology sales force is extremely reliant on software-as-a-service CRM systems (most often salesforce.com) to record, track, and report on all customer interactions throughout the sales cycle. Adoption don’t always go 100% smoothly. For many companies, their sales processes do not always match exactly with out-of-the box configuration. There is also an enormous amount of variation in the level of discipline and sophistication in how companies input and manage customers data in the system. That said, the CRM database is usually the richest repository of customers data a company has throughout its growth cycle.

Why CRM Data Analysis is So Important (and Sometimes So Daunting)

CRM data is the first port of call for any sales and marketing analysis. The broader the use of CRM in a company, the more uses it can provide — if it is part of the marketing automation system, then it can help with marketing funnel analysis. If it is also used as part of the customer support system, then it can help with customer retention and success optimization. To make this more concrete, consider some of the questions that a well-structured CRM analysis can answer for your business:

  • What is the profile of the most attractive customers?
  • What marketing channels are generating the most number of leads/opportunities?
  • What are the conversion rates at each stage of the buying process?
  • Where are the current sales opportunities by their stage in the buying process, and how does that compare with historical trends?
  • Are there bottlenecks in the sales process, where opportunities are stuck longer than expected?
  • Which sales rep or team of reps is most effective at closing deals? Most efficient? Most productive?

In later stage or larger companies, customer usage patterns, customer transactions, user logs, or website or apps analytics data might eventually generate larger volume of data that can now be extensively mined with Big Data tools (consumers oriented tech companies have really pioneered this, but given that their customers interact directly with their services, web analytics and customer transactions database are their CRM system). Yet, compared to those structured data stores, the fact that CRM data is typically unstructured and activity and stage-oriented means it is better at capturing the diversity and complexity of the two-way human interactions of selling a product. That’s especially true when the product is complex technologies and customers represent large businesses with multiple stakeholders.

But it is also exactly because of that complex combination of structure and unstructured data fields, the endless variation in configuration, and the virtually intractable problems of data hygiene and standardization, that CRM analyses can also be daunting.

A Word to the Wise: Look (and Plan) Before You Leap

There have been instances working with our portfolio companies when, in our excitement to learn and analyze this treasure trove of information, we have sometimes jumped straight into the CRM and found ourselves hopelessly overwhelmed with the volume of the data and the complexity of the data structures, exceptions, and limitations. We quickly found that our initial analyses, while providing some great insights, were very sensitive to basic assumptions such as data consistency, data coverage, and record’s uniqueness. When we dug deeper into the datasets, the skeletons kept coming out and forcing us to review and revise our analyses, or even redo them to ensure that our insights were still true. Often, the insights and data you uncover point toward conducting new analyses, which will require even more data preparation work. This becomes extremely frustrating, and I know that it is a common issue that happens with CRM data analyses everywhere.

So, instead of drinking from the proverbial fire hose and learning as much as you can right away, we have found that it is important to spend time in advance to plan out a more systematic and strategic approach. That way, you can handle and verify a lot of your assumptions before actually diving deep into the data and starting to make conclusions.

A New Series: How to Structure and Execute CRM Data Analysis

In this series of blog posts, I would like to introduce our approach to structuring and executing a CRM data analysis, with a strong emphasis on the pre-analysis work of structuring and data preparation. I hope that this can be a useful guide for anyone who is interested in analyzing customer data deeply and comprehensively. That, after all, is essential for truly understanding your customers and knowing how to best serve them.

In the next posts, I will discuss the high-level steps involved in a CRM analysis and then go more in-depth for each of those steps. Knowing that CRM analysis is not the only way to learn about your customers, I also plan on discussing alternatives to CRM analysis, and when they should be used and for what purposes.

In the meantime, please drop me a note in the comments to let me know any specific questions and topics you would like me to cover!

 Photo by: umer malik