The majority of businesses have far more data than they are able to do anything with.
The CRM can store thousands of contact records, hundreds of deals in various stages, years of activity logs, email histories, call notes, pipeline snapshots, and more, dating back to when the system was first installed. It is all in there.
For most businesses, most of it is sitting idle. Reports are run at the end of the month. Pipeline Review is on Fridays. A couple of standard charts are pulled for the leadership deck. At the end of the meeting, the data returns to the system quietly until the next reporting cycle.
This is the difference between CRM data and business intelligence. The data exists. But intelligence needs more.
Why most CRM reporting falls short
The standard CRM report will reliably answer a few questions. How many deals are in the pipeline? What is the total value of the pipeline? How many activities did each rep run this week? What was closed this month compared to last month?
These are handy numbers. They are not Business Intelligence. They tell us about what happened. They are not giving any reason for it, a prediction of what might happen next, or a suggestion of what a business could do differently in response.
This gap is due to the fact that the majority of the reports being generated in a CRM are based upon the data that can be easily captured, like deal stages, activities count, close date, etc. instead of the data that can be used to make decisions.
If a pipeline report indicates that fifty deals are in the proposal stage, then a sales manager would know that the proposal phase has been accomplished. It does not let them know which ones are likely to close, which ones are at risk due to another competitor being mentioned on the last call and which ones are "cold.
Report vs intelligence is the same as "description" vs “understanding well enough to do something about it”.
The data quality problem nobody talks about enough
Prior to the discussion of converting CRM data to intelligence, there's a more basic problem that many businesses aren't fully addressing.
The truth about the quality of CRM data is rarely pretty.The reality of the quality of CRM data is almost always ugly. Records showing full records have incomplete fields. Deal stages have been revised to meet the reporting requirements and not necessarily reality.
The activity logs that appear complete lack large categories of interaction because no one was regular in logging them.
Whether or not the analysis layer on top of it is sophisticated, bad data isn't business intelligence. An inconsistent contact data set will result in unreliable segments. A deal stage forecast that is not based on appropriate stages is not a forecast, it is a spreadsheet guess.
Before you get serious about CRM data as a business asset, get serious about data governance first! That includes a set of agreed field definitions, strict rules for data entry, periodic checks for data completeness, and an on-going sense of the team about the importance of clean data and how it can benefit them. It is not the sexy side of the intelligence discourse. It is the part that decides if the following conversation is going to get anywhere.
What actual business intelligence from CRM data looks like
With a solid data foundation, the next question is what type of useful intelligence to build.
One of the most actionable conversion rates to analyse is the conversion rate by source, segment and rep. If a business can see not only how many deals it has closed, but the conversion percentage of each stage of the deal, and how that rate is different depending on the lead source, industry, deal size and individual salesperson, then they are working with something useful. Patterns emerge.
Some sources of lead are twice as likely to convert as others. Deals above a certain size have a significantly longer average sales cycle. One rep converts a lower per cent of inbound leads and a much higher per cent of outbound leads. These patterns are indicators of decisions. They are worth looking for.
Another area where good use of the CRM data creates real intelligence is churn prediction. Patterns leading to churn are identifiable before customers churn when volume, response time, satisfaction, engagement in renewal communications, and product usage signals are all visible in a single customer view.
This is a pattern that has been seen in cases where a customer has been raised 2 times over the last month, has not opened the last 3 renewal communications and the primary champion is listed as a former employee in the CRM.
Predicting that trend and acting on it in a timely fashion is what can either make the difference for retention or a surprise loss.
When used wisely, CRM data can greatly enhance the accuracy of revenue forecasting. A forecast model that weights deals by stage conversion rates, time in stage, and historical accuracy by rep generates more accurate and realistic estimates of what will close as opposed to an approach that simply aggregates all pipelines by expected close date, which would lead to optimistic numbers and high miss rates. It is not perfect. It's quite a lot more valuable than the other one.
The role of connected data in making this work
CRM data can't be everything. The intelligence becomes much more useful when the CRM data is linked with other data from other neighbouring systems.
When CRM is tied to billing data, the business can see which customers are most profitable, not only in terms of buying more often, but also paying on time, growing the relationship over time and at the lowest service cost. The acquisition and retention numbers inform where acquisition effort should be targeted.
Once coupled with support data, the business can identify the product combinations that result in the highest volume of tickets, the groups of customers that need more hand-holding and the types of problems that keep recurring with different customers. This information is useful for the product team, support team and sales team at the same time.
By aligning CRM with marketing data, the business can determine which campaigns are actually responsible for revenue, not just the number of leads they generated, but which ones turned into customers, how big the purchase was and how long the sales cycle was. Most businesses can't answer this question because the marketing system and CRM haven't yet been connected in a way that makes this answer appear.
This is one of the fundamental features that makes everything possible and so useful for businesses that work with Zoho Consulting Solutions, which seamlessly work together in a native integration that includes Zoho CRM, Zoho Books, Zoho Desk, and Zoho Campaigns. This is already a connected data layer. With no custom integration project blocking the way, the intelligence work can commence.
Building a reporting practice that people actually use
If there are reports that no one reads, then it's not business intelligence. There are additional costs of running the show.
To actually get the CRM intelligence to do its job, you must consider the individuals who need the information to make a specific decision and design the reporting to those individuals' needs rather than the needs of the CRM system.
The sales manager wants to see what sales deals need to be followed up on today, not last month's sales. A customer success team should gain visibility into accounts that are exhibiting early signs of churn, rather than a churn rate. An executive team should have confidence in the revenue figures, rather than a pipeline value that is not necessarily accurate.
Reports are used for specific decisions, in a specific context and by specific people. All generic dashboards not specific to who uses them and what they are for are ignored.
It's easy to be clear, to tie each report to a decision: What would we do differently if this number were up or down? If it is nothing, there is no value in keeping the report.
What separates businesses that get value from those that do not
These are the characteristics of the businesses that are able to turn their CRM data into business intelligence, and it's not about the platform they use.
They have worked on data quality as a core discipline and not as a final task to be completed before a presentation. They've integrated their CRM with other nearby applications to ensure that the data they're analysing is complete. They have developed reporting, not around a decision, but around specific decisions.
They have joined forces with partners who know the platform, but also have a strong understanding of the business processes and know how to configure the system so that the intelligence is accessible.
The latter is more important than most businesses realize prior to experiencing it. The reporting features of Zoho CRM and the reporting layer of all Zoho applications are truly capable, and so is Zoho Analytics.
They must be configured in such a manner that they generate intelligence and not noise, and this is only possible by having a clear understanding of the data model, and how it relates to the actual business operations.
This is what experienced Zoho Consulting Solutions teams are equipped with when it comes to implementation, which is why the businesses that invest in it, always get better results than those who set up Zoho CRM without the help of a consultant and then wonder why the reports aren't helpful.
The honest bottom line
CRM data becomes business intelligence when it is clean, connected and configured around the decisions that are relevant to the business.
None of them occurs naturally. All three must be deliberately invested. Those businesses that make the investment discover that the CRM is not just another record-keeping system, but also one that truly guides the business.
Those that aren't usually running the same report each month and asking themselves why all their data isn't telling them anything that they didn't already know.
The data is there. The usefulness of it is a decision.