7 CRM Business Intelligence Best Practices
CRM business intelligence research published in the CRM Benchmark Report revealed the Best-in-Class performance archetype (the top 15 percent) adopted different CRM BI tools and programs than their lower performing peers.
That research produced 7 CRM Business Intelligence best practices that can be used by others to replicate the results achieved by the top performers. Below are the best practices.
CRM Analytics Strategy
As referenced in the prior research article, the Best-in-Class leaders overwhelmingly lead with a CRM analytics strategy.
That strategy shows how to connect data, insights, actions and outcomes. It identifies what outcomes matter most and need to be pursued first. It calculates your budget and expected ROI from the investment.
Every good CRM analytics strategy and program starts with a common question. Most business managers and even many CRM consultants think the question is "how do I best implement an analytics strategy or program?" But that's not right. The first question isn't how, it's why.
Before implementing CRM BI tools, you need to first answer the question, 'why should I implement analytics?' Why invest the time and money? How will this change staff behaviors, customer relationships and company profitability? Because if adoption does not do these things you don't need to consider it further.
But for those seeking improved customer relationships and business performance, the short answer to the essential question is that CRM analytics provide the data-driven, fact-based information to make more timely decisions that aid customer experiences and business performance improvements.
Here's the interesting thing about data, a data paradox really. Virtually every decision and customer interaction benefits from more data. Most companies have large, vast amounts of data. But for many it languishes, goes unused and dies on the vine. They are data rich but information poor.
Data offers a use it or lose it proposition. It is a powerful asset if converted to information and made actionable. Otherwise, it is an ongoing cost without benefit.
Simple information reporting can be created with queries and reports accessing CRM software tables and columns. However, for more powerful insights such as self-service analytics with natural language processing (NLP), or predictive models, or prescriptive analytics you will need to create a data model.
You will also need to define the data extract, transform and load (ETL) processes and supporting tools. Data extraction retrieves data from source locations. Data transformation filters, cleans, modifies, normalizes, appends, formats or otherwise processes data. Data load inserts transformed data to a destination, normally in a presentation-ready format, so it can be acted upon.
To engage users, you need to advance data from broad information to purpose-built insights and evolve reporting from being merely interesting to inducing action.
The point of creating Insights is important. Insights are not data, facts or statistics, these are all knowledge. Insights are the causes, reasons or learning behind the data, facts or statistics. The dictionary defines the word Insight as "seeing below the surface". It's new learning, something that teaches and induces action.
Three ways to make data actionable are to make it highly visual in role-based software dashboards, link the data findings to recommended actions such as a Playbook, and allow the data to be interrogated, manipulated and used for predictive analytics. For companies that can convert data to insights, data will become their most valuable asset.
Industry performance benchmarks show us what good looks like. They bring context to performance measures.
For example, if your sales win rate is 46%, is that good? Well, not if the average for your industry is 48.5%. And multiplied by the volume of deals this could be a significant deviation that would otherwise go unnoticed and unresolved.
CRM benchmarks deliver some unique insights. They provide a relative comparison to identify where the company stands and most needs to improve. And they enable predictive analytics. Many managers like to apply pro forma models to show how a 1 percent improvement in any key performance indicator (KPI) impacts revenues or profits.
Other clients with KPIs below the industry median may prefer to see the revenue or profit impact by improving their performance to the median level. Knowing the financial upside impact allows the company to know how much it should invest in programs to achieve that upside.
No decision to use the company's limited resources should go unmodeled. Without benchmark data, you do not know what the results should be, do not know where the finish line is, and do not know when to stop spending because the investment is greater than the payback.
360˚ Customer View
You cannot systemically improve customer relationships and profitability without customer analytics.
Customer analytics are needed to understand, engage and predict customer interactions. They allow staff to deliver relevant, personalized, contextual and predictive customer experiences that delight customers.
The best starting point for customer analytics is a 360-degree customer view. This view should go well beyond firmographics and demographics and identify what's most important to each customer. It should advise which channels customers prefer, when they like to engage, who they prefer to talk to, the issues they consider important and what content or products they are most interested.
This view is essential to using data to deliver differentiated customer experiences and knowing what actions do not contribute to an elevated customer experience. Only with data can companies deliver differentiated customer experiences at scale.
According to a McKinsey research report, "The impact of customer analytics on corporate performance is significant—and clearly underestimated" as "Companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment. For example, companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not."
Research results published in the CRM Benchmark Report found that the Best-in-Class analytics performers ranked CRM dashboards as the top information delivery tool.
To a first-time observer a CRM dashboard looks good. But if good looks were the factors for success my first marriage would have made it, and CRM dashboards would get much more utilization. The research shows that CRM dashboards achieve 30 percent utilization following a CRM software implementation go-live event, but within 3 weeks that utilization falls to 9 percent. Over time it falls further. The decline is due to dashboards not providing real help to users.
The good news is that the challenge can be remedied. For example, we know information must advance from being merely interesting to inducing action for dashboards to be effective. We also know the six ways to make data actionable include the following:
- Make the most important metrics highly visual
- Align performance metrics with company outcomes
- Show KPIs alongside budgets or industry benchmarks for context
- Shift from lagging to leading KPIs
- Allow the data to be interrogated, manipulated and used for predictive analytics, and
- Link the data findings to recommended actions such as a Playbook
Data becomes much 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.
Predictive analytics convert data to forward looking information such as next best answer, next best action or guided recommendations. Giving staff information they don't know or delivering insights which aid their productivity shifts their behaviors and improves business outcomes.
Predictive analytics are the tools to increase customer profitability at scale. They can examine customer history to identify events, transactions, occurrences or patterns of behaviors that predict whether a marketing offer will convert, a sale opportunity will be won, or a customer will churn.
Or they can forecast the financial impact of reducing customer churn.
Even better, they can be used to engineer financial results. For example, the below revenue growth plan is depicted as a predictive pyramid and supported by metrics that roll up from the lowest level of execution to achieve the company's top priorities (i.e., revenue growth).
There are many potential pathways to your goals but using a model such as the one below identifies the most direct route 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 tend be delayed, degraded or just not achieved.
The pro forma pyramid is helpful because no revenue process, program, or best practice lives is standalone. Each has cascading impacts that effect many areas and those impacts must be considered when making tradeoffs. This visualization is extremely helpful in determining where to invest your limited time to achieve the biggest financial uplift.
CRM applications hold a treasure trove of customer and sales data. However, there is such a thing as too much data, or at least more data than can be manually understood.
Artificial Intelligence (AI) creates insights based on its algorithms that sift through large volumes of customer data, identifies information that correlates with desired business results and makes specific recommendations to achieve those results.
AI is the technology to transition CRM software from a data repository to a predictor of customer behaviors, creator of customer insights and facilitator of customer and company objectives.
Research respondents that found CRM AI effective advised that the technology shifts customer and sales data from reactive to proactive, elevates data from a byproduct to an asset and creates a competitive advantage.
When staff integrate AI into daily work processes, 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.
Or contact center managers can use AI to personalize customer engagement, deliver faster resolutions, lower cost to serve, improve customer experiences, increase customer satisfaction, and scale customer service operations.
AI is no longer in the early days. We crossed that chasm and any company not using AI today is clearly substituting labor for technology. The question for business leaders standing the sidelines is not if AI will impact their business, but how and when.
Market leading CRM systems such as Microsoft Dynamics 365 and Salesforce have removed technical barriers with simpler tools, such as Azure Machine Learning and Salesforce Einstein, to put AI applications in the hands of business analysts and power users.
Data is the fuel, AI is the engine, and insights are the destination.
The Point is This
Most companies are data rich and information poor. They understand the value of data but struggle to transform it into actionable intelligence. Data is their most valuable but underused asset.
The most successful businesses are defined by their ability to maintain data integrity, collect and curate the right data, use data to create differentiating customer experiences, and apply analytics to make insights actionable at every customer interaction or customer decision point; that is those points where customers choose whether or not to do business with your company (i.e., moments of truth).
Adopting the right mix of CRM BI tools can convert data from a raw material to a finished good in the form of information or insight. That transformation will directly impact customer engagement and business decisions. And experience shows that even small improvements in either are shown to deliver significant and sustained financial benefits.
In fact, improved decision making is one of only four sustainable competitive advantages because better decision making never loses value, is not easily copied by competitors and is not displaced by new technologies.
CRM software is a tool. Information is an asset.