How to Transform CRM Data into Actionable Insights
Highlights
- Most companies are data rich and information poor. Data is both their most unused and under-capitalized asset. Fixing this situation can make data the company's most valuable asset.
- Speed and quality of decision making used to be contrary but no longer with a data transformation program.
- Data transformation directly contributes to improved business intelligence, which is a sustainable competitive advantage. Making better business decisions never loses its value.
Data is the digital currency for 21st century.
The explosion in the volume, velocity and variety of data has made data a business phenomenon of our time. Data is now a natural resource and raw material that yields customer and business intelligence. In many ways, data is to the 21st century what steam power was for the 18th, electricity was for the 19th and hydrocarbons were for the 20th.
But here's the interesting thing about data, a data paradox really. Virtually every business decision and customer interaction benefits from more relevant data. But most companies have large, vast amounts of data that languishes, goes unused and dies on the vine. They are data rich but information poor. We have found three reasons for this.
- Many times, it is because the data resides in fragmented, disparate systems and is difficult to access and consolidate.
- Other times it is that companies don't know how to use the data they have.
- And in most cases, it's because management hasn't created a culture that emphasizes data-driven decision making. Information is just as important to the business as products, services and processes. But this is a difficult concept for some people.
See the 3 barriers that prevent companies from converting #data to insights, and the data transformation framework so they can.
Click to TweetCRM Data Transformation is Needed
Transformation systemically converts raw data into information, which is further refined into actionable insights and then delivered to the person or customer interaction point where it can be applied.
This transformation delivers customer insights to marketing, sales and customer service people to deliver differentiated customer experiences, impact customer moments of truth, increase marketing and sales customer conversions, increase customer lifetime value (CLV) or other customer facing business outcomes. This list of opportunities is endless but to achieve any of them a framework is needed.
A Data Transformation Framework
Data is an asset. But to yield value, the data must be converted from a raw material to a finished product of information or insight.
Data offers a use it or lose it proposition. The framework shows how to turn data into an asset and leverage business intelligence as a sustainable competitive advantage. The framework consists of six steps.
Information Discovery
This step shifts transformation from a theoretical exercise to practical use cases that deliver tangible results for staff and customers. A Design Thinking workshop is the quickest way to surface and prioritize the most important and highest impact data transformation opportunities.
Once target insights are identified, understanding how to deliver those insights pursuant to the analytics continuum will show how to get progressively more value from information. For example, even with the same data, predictive analytics are more powerful than historical reporting.
Industry benchmarking also helps by comparing your business process efficiency and decision-making effectiveness to your industry peers and taking action to improve weaknesses against competitors.
Data Requirements
This step defines the data needed for transformation. If your CRM data quality is good, you can simply define the data, its sources and extraction methods. However, if your CRM data is questionable, you should first assess data quality.
Even if you perform a perfect CRM data transformation project, poor data quality will torpedo the entire effort. If you need to improve CRM data quality, consider the CRM Data Quality Framework.
Data Assembly
Simple information assets may be created with queries and reports accessing CRM tables and columns. However, for more powerful insights such as self-service analytics with natural language processing (NLP), predictive models or prescriptive analytics you will likely need to create a data model.
Any good data model should conform to the CRM architecture and leverage performance techniques such as normalization to separate unrelated table data (i.e., no need for Case and Campaign data to be in same table.) Even dashboards like the Microsoft Dynamics analytics view below require the creation of data models.
You will also need to define the data extract, transform and load (ETL) process and supporting tools. Data extraction retrieves data from defined 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.
To engage users, you need to evolve from information content to insights and advance reporting from being merely interesting to inducing action.
The point of creating insights is key. Insights are not data, facts or statistics, these are all knowledge. Insights are the reasons, behaviors 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.
The three best ways to make CRM data actionable are to make the data highly visual in role-based CRM dashboards, increase data value pursuant to the analytics continuum and link data findings to recommended actions such as a Playbook.
The analytics continuum model shows how to deliver the biggest impact. Historical and descriptive data seldom induces action. However, when data advances from Descriptive to Diagnostic and more so to Predictive it becomes highly actionable.
AI and Machine Learning
Data is the fuel, AI is the engine, and insights are the destination.
In a CRM software context, artificial intelligence (AI) is most commonly used to improve customer engagement, deliver better customer experiences, increase staff productivity and make more informed sales, marketing and customer service decisions.
CRM software systems such as Microsoft Dynamics 365 and Salesforce have democratized AI with purpose-built tools, such as Azure Machine Learning and Salesforce Einstein, to put AI apps in the hands of business analysts and power users.
These AI tools can harvest, mine and structure data into algorithms and models to deliver logic apps, probabilistic models or answers with confidence levels. They can answer questions such as which target audiences will respond positively to which offers, which prospects will buy and not buy from our company, or which customers will purchase which products.
Information Distribution
Insights must be routed to the customer interaction point where they can improve a customer experience or to the employee who can leverage them to make a better decision. Insights may be contextual reporting or recommended next-best-actions. Or they may go further such as a CRM dashboard with interactive modeling and natural language processing that responds to human speech or typed questions.
Continuous Process Improvement
Data transformation is a journey. Progress is measured in terms of business outcomes.
It's critical to calculate the impact in financial terms. Only when an impressive ROI is achieved is the program sustainable. Any transformation only succeeds if it can achieve a business opportunity previously unachievable.