How to Select the Best CRM Reporting Tools

Highlights

  • Selecting the best CRM reporting tools is less of a technical exercise and more of an alignment among specific objectives, use cases and business intelligence apps.
  • Ease of use and simplicity are the two most influential factors in gaining adoption of CRM analytics software.
  • When purpose-built CRM reporting tools are used for their intended purpose, users can easily access, retrieve, route and apply information and insights. The right tools make users smarter, and that's a big deal.
Johnny Grow Revenue Growth Consulting

When selecting CRM information reporting tools, it's helpful to recognize that these tools sit between the analytics strategy and data strategy. It's the tools that bring both strategy and data to life.

Analytics Building Blocks

See the CRM analytics tools and the use cases that show how to choose the best tool for the job.

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CRM analytics tools, also known as business intelligence (BI) software tools, transform raw data from multiple sources into useful information and distribute insights to all who can use them, when they need them, in order to improve decision making timeliness and accuracy.

But that's easier said than done.

Many companies omit a CRM analytics strategy and simply use BI tools at random. When users apply the wrong information reporting tool for their purpose, they get inferior results, and they get frustrated, and the information reporting program deteriorates or fails.

When users cannot efficiently get data out of the CRM system, they have no reason to put data into the system, and user adoption deteriorates until the CRM program becomes marginalized or fails.

Fortunately, there's a better approach.

CRM BI, analytics and reporting tools are often viewed through a software lens and vetted according to their technical capabilities. That's not the best perspective to get the most value. When assessing CRM analytics software tools, the priority is to make sure they satisfy explicit business use cases by the people who will use them and with the least effort.

Also, rather than assessing a tool for one business need at a time, a better approach is to understand all or most of the use cases for each tool so the tool can be used more holistically. This will result in a shared architecture and more standardized configurations that are more extensible, more easily adapted to changing conditions and much easier to support.

The use cases below show how we lead with objectives and methods to identify the types of tools that are needed. Note that the types of tools are shown in italics.

Example Use Case 1:

Objective:

Align the buyer's buy cycle with seller's sales cycle.

Method:

Capture digital footprints and online behaviors to identify buyer intent and progress in their purchase journey. Marketing software such as Adobe Marketo or Salesforce Marketing Cloud captures digital footprints. Data is then transferred to CRM software which displays correlated online behaviors in Lead, Contact and Account records.

Tools:

Predictive analytics score online buyer behaviors to detect sales-ready leads.

Marketing software uses alerts to notify sellers of highly qualified leads.

CRM views or custom reports display customer insights for leads or sales prospects.

CRM dashboards display new or updated leads for the most immediate follow-up.

Artificial intelligence algorithms can automatically score new leads pursuant to the company qualification criteria or the company’s ideal customer profile (ICP).

Example Use Case 2:

Objective:

Improve customer engagement.

Method:

Deliver personalized, relevant and contextual communications at defined points in the customer life cycle.

Tools:

Social listening tools identify customer engagement opportunities.

CRM list builders or packaged reports display customer segments to deliver more relevant offers, promotions and communications.

CRM views display the 360-degree customer view.

Predictive analytics can perform What-If scenarios to model different promotions by customer segment.

CRM machine learning sifts through history to suggest Next Best Offers or other communication engagement for each customer.

Example Use Case 3:

Tools:

Custom reports can perform look-alike modeling to elevate more customers to high profit segments.

Alerts can monitor event conditions to trigger contextual outreach.

CRM workflows can schedule and automate a customer cadence for each customer type or segment.

Artificial intelligence (AI) can calculate customer health scores for each customer and apply alert triggers to notify when customer outreach is needed.

Predictive analytics forecast the most likely up-sell or cross-sell opportunities.

CRM machine learning or artificial intelligence (AI) can deliver guided recommendations for account relationships.

Self-service tools or Smartbots allow customers to help themselves on demand.

Objective:

Grow customer relationships.

Method:

CRM uses customer profile data, 360 degree customer views, customer insights and voice of the customer (VOC) data to deliver relevant, personalized, contextual and predictive customer experiences that improve customer relationships.

The business objectives will identify the types of tools needed. Most tools will be part of the CRM platform. However, some tools such as data lakes or data warehouses may not so they will need additional vetting.

Closed Loop Reporting

There's one more essential requirement to make the most of your BI strategy and supporting analytics tools.

Business processes need end to end visibility and measurement. From an analytics perspective we call this closed loop reporting. This type of reporting defines the correlation between an action and result, so that you can understand how every action impacts a downstream result and then improve the result by altering the action. Because customer processes span company departments, it's possible you may need different BI tools to achieve closed loop reporting.

The danger is that without an end-to-end reporting system, steps are not measured pursuant to the end goal, so process steps contain unknown variabilities and effectiveness, and are without proof of impact to the goal.

Without closed loop reporting, you're challenged to track end results back to the efforts and investments that created them. I'm reminded of a long standing analytics trend referenced by John Wanamaker who said, "Half the money I spend on advertising is wasted; the trouble is I don't know which half."