Customer Relationship Management (CRM) software is currently the most prominent software market in the world. This fact stands to reason, as businesses increasingly embrace digitization to better tap into a vast potential online audience. Moreover, CRM has demonstrable merits that span across marketing personalization and automation, customer segmentation, profitability analysis, and more. However, CRM statistics outline both benefits and challenges that business owners need to consider. A prime example of the latter lies in defining the most concrete, actionable types of data to have in your CRM. Thus, let us devote this article to exploring this subject.
Types of data to have in your CRM
What are the main types of data to have when building your own CRM?
There are likely as many answers to this question as there are marketers. Furthermore, exact categorizations of data types will also differ quite notably. Thus, we will divide the types of data to have in your CRM into 4 main, commonly accepted categories. Then, we will explore some ways to use each category in practice.
1. Identity data
First and foremost, CRM gathers identity data. As the name implies, this type of data serves to uniquely identify customers and facilitate contact. Identity data typically includes the following:
- Name; first name (forename) and last name (surname), title or post-nominal (designatory) letters
- Personal information; date of birth, gender
- Job information; company name, job title
- Contact information and postal address; phone number, email(s), physical address
- Social network information; social media profiles, addresses, handles, etc
Using identity data
Identity data is arguably the most useful type of data to store for any kind of customer interaction management. In turn, it is arguably the most straightforward data type to use.
Fundamentally, identity data allows businesses to identify, and get in contact with, customers. Thus, you may use it to consolidate a thorough customer database, where every customer can be uniquely identified. It may also help inform the following practices:
- Marketing personalization; adding a customer’s name to marketing emails, etc
- Customer segmentation; segmenting customers by demographics, etc
- Customer support; personalizing customer support communications, referring to interaction history, etc
However, identity data cannot thoroughly inform such practices by itself. Instead, it typically requires descriptive data to truly provide actionable insights beyond basic identification and contact information.
2. Descriptive data
Descriptive data, then, builds on identity data to provide said actionable insights. It typically intends to provide a deeper level of customer profiling by exploring such aspects as lifestyle choices.
Specifically, descriptive data typically includes the following:
- Familial details; marital status, children or lack thereof, age and number of children if present
- Career details; profession, education level
- Lifestyle; property, car, and pet ownership or lack thereof, and other notable interests and hobbies
As such, it is not a standalone data type. Rather, it is an expansive data type that directly synergizes with identity data to yield marketing and interaction management benefits.
Using descriptive data
Descriptive data, in tandem with identity data, offers a plethora of uses. To outline a few, consider the following:
- Deeper customer segmentation; psychographic, technographic, behavioral, needs-based, and value-based segmentation
- Deeper marketing personalization; enhanced personalization through deeper, more accurate customer profiling
- Enhanced lead scoring; deeper insights that inform lead status in conjunction with historical company data and interaction history
More specialized uses of descriptive data may also include targeted lead acquisition for B2B companies and tailored eCommerce product promotions. Where identity data offers a basic customer profile, descriptive data expands on it to inform customer journey mapping.
3. Qualitative data
Similarly, qualitative data seeks to delve deeper into descriptive customer data. Therefore, it explores customer attitudes, opinions, and purchasing motives, usually by directly asking for such information. Typical means of acquiring such data include feedback forms, questionnaires, and surveys.
Qualitative data thus includes:
- The customer journey; details of the journey that led customers to a website or online store
- Purchase satisfaction; the satisfaction rating provided for a completed purchase
- Customer service satisfaction; the satisfaction rating given for customer service interactions
Examples of questions that can produce such information are, respectively:
- “What led you to our website/store?”
- “How satisfied were you with your purchase?”
- “How would you rate our customer service?”
Fortunately, while qualitative data is less objectively measurable, it is typically accurate – the majority of customers eagerly provide honest feedback.
Using qualitative data
Similar to the other types of data to have in your CRM, qualitative data can see many uses. Such uses may include:
- Enhanced customer journey mapping; using customer journey data to refine mapping and buyer personas
- Enhanced outreach; personalized outreach accuracy based on purchase satisfaction
- Enhanced customer service; customer service improvements in response to feedback
Other practices qualitative data may inform may include deeper lead scoring and psychographic and behavioral segmentation.
4. Quantitative data
Finally, quantitative data shows measurable operational data, the tangible numbers that relate to their interactions with your business. Having identified customers as accurately as possible, quantitative data is the definitive type of data to have in your CRM.
Specifically, then, these include:
- Transactions; the number of completed purchases, products purchased, order value, cart abandonments, product returns, etc
- Communications; communication history, dates, and channels, click-through rates (CTRs), etc.
- Online activity; social media engagement (impressions, likes, shares, comments, etc.), website visits and time on page, online registrations and product views, etc.
- Customer service history; number, nature, and dates of customer queries, interaction history, etc.
Thus, quantitative data is arguably the most actionable type of data to collect. It hinges entirely on recording hard, measurable data to apply to various interaction management practices.
Using quantitative data
Naturally, quantitative data can see considerable use. Applications of such data can include:
- Deeper value-based customer segmentation; precise insights into average order value (AOV) and average lifetime value (ALV)
- Enhanced online presence; deeper data into website and page effectiveness, actionable social media metrics to inform outreach and content creation
- Enhanced customer support; customer support interaction history, data on recurring inquiries
Notably, applications of quantitative data can also include automated marketing outreach, product recommendations, cart abandonment prevention, and others.
