CRM Business Intelligence Research Findings and Best Practices
Highlights
- CRM software offers many information reporting tools. However, time and budget are limited so prioritizing the most effective decision support tools will lower investment and accelerate results.
- Research found that 5 CRM business intelligence (BI) tools we're ranked as the most effective by the Best-in-Class performance archetype.
- The Best-in-Class cohort was also 4 times more likely to leverage a CRM Analytics strategy to plan and manage their CRM decision support journey.
Research Shows How the Best-in-Class Leaders use CRM BI Tools Differently than their Lower Performing Peers
The goal of CRM BI, often called CRM analytics or decision support, is to get the right information to the right people at the right time, so they deliver improved customer experiences or make better decisions.
Timely variance notifications permit course corrections before performance problems exacerbate. Detailed information reporting identifies the causes and linkage among performance problems to accelerate their resolutions.
To be effective, BI must be timely, relevant, contextual and role-based.
For example, salesforce analytics can uncover revenue opportunities, such as which clients, leads or sale opportunities are most likely to close. They can identify variances in real-time, such as revenue leakage, which may include leads not being followed-up or sale opportunities not being followed-through. Or they may surface business process breakdowns such as quoting or order entry problems. Swift course corrections prevent the loss of short-term and long-term revenue.
Marketing analytics can identify the most effective campaign component assemblies. The technology can apply multivariate propensity models to show the combination of offers, content, channels, and call-to-actions by customer type or target audience to optimize lead acquisitions, lead conversions and sales pipeline growth.
We have used marketing BI to increase sales lead acquisition rates by double digits within a few weeks with analytical models that show what engagement (i.e., flight, offer, Call to Action, channel) to deliver at each state of the buyer journey and for each buyer persona. This level of specificity is required for precision marketing.
Customer service analytics can identify the high volume of low complexity calls well suited for customer self-service channels. They can aid proactive customer support by resolving issues before they happen. They can apply data patterns to detect the correlation between new incidents and existing products, extrapolate to other customers with those products, and proactively outreach with a solution before they incur the failure. Customer service decision support can also detect customers at risk of defection and suggest retention levers to save them.
Analytics tools are effective at churning through large volumes of customer data and delivering insights and recommendations that are just not possible otherwise.
But to achieve these types of insights at scale, a mix of information reporting tools are needed.
But nobody can implement every CRM decision support technology well.
So, the question is which decision support tools are the most effective?
Decision Support Research Findings
The CRM Benchmark Report published findings that shared the frequency of use (i.e., application utilization) and effectiveness of CRM decision support tools as rated by users. The results are shown below.
The above table compared the total respondents to the Best-in-Class performance archetype (the top 15 percent) to understand what the top performers do differently. The data discovered the decision support tools ranked as the most effective by the top performers were different than their lower performing peer groups. And as shown below, the differences in ranking of effectiveness were statistically significant.
The data also found the two performance archetypes of Laggards (lower 35 percent) and Medians (middle 50 percent) revealed a disproportionally higher use of 'CRM Lists and Views' and 'Exports to Excel'.
That suggests these users are limited to the default or out-of-the-box CRM configurations or manual exports to Excel to uncover their own insights for improved customer engagement and decision making. This effort may work for isolated users but cannot scale, which means most CRM users cannot apply information for improved performance.
There's one more research finding that can accelerate performance results.
A CRM analytics strategy defines the CRM information use cases needed for each type of user or role and then identifies the data transformation process and CRM decision support tools to deliver the information that will satisfy those use cases.
However, it’s a continuous journey as once users are empowered with more information, their needs evolve to even more empowering use cases. That kind of journey is most efficiently delivered pursuant to a CRM analytics strategy.
The CRM Benchmark Report found that only 21 percent of respondents advised they have a documented CRM analytics strategy. While that figure is low, the data also revealed that the Best-in-Class CRM leaders adopted a CRM analytics strategy 4 times more frequently than their lower performing peers.
The disproportionately higher adoption among the top performance archetype should not go unnoticed by others seeking to make better use of CRM decision support.
From Research Findings to Best Practices
The research findings were scrutinized to determine if statistically significant patterns existed. The analysis found that the Best-in-Class archetype adopted several different behaviors and programs. Those programs were reverse engineered to produce 7 evidence-based best practices that collectively maximize CRM BI performance results. See the CRM Business Intelligence Best Practices post to learn how to replicate the actions of the top performers.