CRM Business Intelligence Best Practices
Many companies are data rich and information poor. They know data should be their most valuable company asset but struggle to transform it from a byproduct to an information advantage.
There are three overarching best practices to remedy this situation. The combination of the right use cases, industry benchmarks and analytics tools can be used to convert data into your most valuable asset.
Start with Role-Based Use Cases
To be effective, decision support must be timely, relevant, contextual and role-based. That last item is your starting point.
For example, use cases for salesforce analytics apply data to uncover revenue insights, such as which leads or sale opportunities are most likely to close. Equally important, insights can predict which sale opportunities cannot be won. For sale managers, analytics 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 can surface process breakdowns such as a quoting or order entry problem so a swift response can prevent the loss of short-term and long-term revenue.
Marketing analytics perform tasks such as look-alike modeling to show which customers will grow customer lifetime value (CLV) the most if actively engaged. They then show the most effective campaign component assemblies. The technology applies multivariate propensity models to show the combination of offers, content, channels, and call-to-actions by customer type or target audience to optimize lead conversions.
Our experience has shown that using analytics to show what combination of content (i.e., flight, offer, call-to-action, channel) to deliver at each state of the buyer journey and for each buyer persona increases conversions by double digits immediately.
Customer service analytics can identify the high volume of low complexity calls that are well suited for customer self-service channels. They also detect those customers at risk of churn and suggest retention levers to save them. This technology can even deliver proactive customer support by resolving customer issues before they happen. It does this by detecting the correlation between new incidents and existing products, extrapolating to other customers with those products and proactively implementing solutions before they incur the failure.
Some use cases are trivial, so apply benchmarks (more on this below) to prioritize by payback. It's also very helpful to document each use case in an agile user story format so the effort and payback can be measured. This will show how some use cases contribute to others and enable you to prioritize to deliver the biggest gains first.
Apply Industry Benchmarks
Operational benchmarks show us what good looks like. They bring context to performance measures.
For example, if your sales win rate is 46 percent, is that good? Well, not if the average for your industry is 48.5 percent. And when multiplied by the number of sale opportunities this could result a big deviation that would otherwise go unnoticed and unresolved.
Performance benchmarks provide a comparison to show where the company stands and most needs to improve. They also facilitate CRM predictive analytics. Many managers like to apply pro forma models to show how a 1 percent improvement in any key performance indicator (KPI) impacts company revenues or profits.
Other clients with KPIs below the industry average prefer to see the revenue impact by improving their performance to the median level. Knowing the financial upside impact allows managers to know how much they should invest in programs to achieve that upside.
Without industry benchmarks you may be setting goals in the dark. Without benchmarks, you really don't know what the results should be, don't know where the finish line is, and don’t know when to stop spending because the investment is greater than the payback. No decision to use the company's limited resources should go unmodeled.
The 3 Most Effective BI Tools
Decision support tools are effective at churning through large volumes of customer, sales and service data and delivering precision 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. Below are some of the most useful tools to turn data into assets that drive improved customer outcomes and company financial performance.
CRM Dashboards
The goal of decision support is to get the right information to the right person at the right time, so they deliver improved customer experiences and make better decisions. And dashboards are the top tool for this job.
Good CRM dashboards focus on the most essential KPIs and prioritize information based on what is most important to each user. They display what should be done, in a prioritized order, to aid time management, create a work rhythm cadence and maximize staff productivity.
Great dashboards go further to shift information from being interesting to actionable. They deliver data-driven and fact-based insights that answer important questions, help staff make better decisions and encourage action to lift performance. They include comparison points, such as industry benchmarks, to provide a relative ranking of what’s working and what needs improvement.
The best dashboards support real-time predictive modeling to show how changes in behaviors or actions impact business results. They display forward-looking KPIs and show the financial impact of both action and inaction. It's this level of analysis that shifts information visibility from hindsight to foresight. It's also this level of reporting that connects metrics with prescriptive recommendations, such as guided selling, next-best-action, or links to Playbooks.
Predictive Analytics
Data becomes more actionable when it advances from historical to predictive. In fact, without predictive analytics, the view for every person in your company is entirely backward looking.
CRM predictive analytics perform pro forma modeling to compare alternatives and plot the shortest path to a defined destination. This is a technology to increase customer profitability at scale. That's because this technology can examine customer data to identify transactions, events, occurrences or patterns of behaviors that predict whether a marketing offer will convert, a sale opportunity will close, or a customer will churn.
Or they can go a step further to forecast the financial impact of reducing customer churn.
Or they can go yet another step to engineer financial results. For example, the below company growth model is depicted in a pyramid and supported by data that rolls up from the lower levels of execution to achieve the company's revenue goal.
There are several potential routes to your goals but using a model such as the one below shows the path that can be accomplished in the least time and cost and with the least risk. Unless there is holistic alignment from lower-level execution to company results the company's top business priorities will be delayed, degraded or just not achieved.
The predictive pyramid is useful because no action or business process lives in isolation. Each has cascading effects that impact many areas and those impacts must be considered when making tradeoffs. This holistic visualization is helpful in determining where to invest your limited time to achieve the biggest financial uplift.
Artificial Intelligence
CRM systems hold a treasure trove of customer, sales, marketing and service data. However, there is such a thing as too much data, or at least more data than can be manually processed.
AI creates precision insights based on its algorithms that sift through large volumes of customer-related data. It essentially identifies information that correlates with desired business results and makes recommendations to achieve those results.
That makes AI the technology that transitions CRM software from a data depository to a predictor of customer behaviors and facilitator of customer and company objectives.
When CRM users integrate AI into their daily work routines, they become more efficient and effective. For example, sales managers can apply AI to surface the leads being neglected, the sale opportunities that need attention and the forecasted deals that are at risk.
Call center managers can use AI to improve customer experiences, increase customer satisfaction, deliver faster resolutions, lower cost to serve, and scale customer service operations.
Market leading CRM applications such as Microsoft Dynamics and Salesforce have removed technical barriers with simpler tools, such as Azure Machine Learning and Salesforce Einstein. This puts AI capabilities into the hands of business analysts and power users.
AI is no longer in the early days. We crossed that chasm and any company not using this technology is clearly substituting labor for technology.
Data is the fuel, AI is the engine, and precision insights are the destination.
Remember This
The most successful companies are defined by their ability to collect and curate the right data, apply data to deliver improved customer engagement, and use analytics to make insights actionable at every customer interaction or customer decision point; that is those points where customers choose whether to do business with your company.
They use CRM business intelligence to create a 360-degree view of their company.
Our experience shows that implementing CRM business intelligence need not be a complex journey, and that harvesting even small volumes of key data can drive significant improvements and sustained financial benefits.
In fact, BI is one of only four sustainable competitive advantages because improved customer engagement and better decision making never loses value, is not easily copied by competitors, and is not displaced by disruptive technologies.
CRM software is a tool. Information is an asset.