What is CRM Analytics?

The Evolution of Database Marketing and the Birth of CRM Analytics

Robert Kestenbaum sat in his office, staring out the window, contemplating a recent discovery he had made for one of his clients. He had found a way for a company to generate tens of millions overnight and was just about to draft a letter to the CEO with the details.

For the last five or so years, he and his wife Kate had been working on something called “database marketing.” They provided services to British Airways, which had more than 10 million existing customers, as well as to Barclays and other major players.

They discovered how to analyze customer data to gain insights into which potential customers might respond to a marketing campaign. Using this information, they could tailor the message sent to each client.

As he sat there, considering the immense opportunities these tools could unlock in the business world, he had no idea how right he was.

Just a few years later, a revolution would begin—one we now call CRM: Customer Relationship Management.

Nowadays, it seems that merely thinking about buying something prompts Facebook to start showing you recommendations. That thought both scares and amuses me.

This opens great opportunities for businesses that can leverage data effectively, but mastering one crucial element is essential: CRM Analytics.

From Lists to Actionable Insights

At the dawn of the digital era, the term “CRM Analytics” didn’t exist. What we had was called database marketing.

Database marketing had many meanings, but at its core, it was an attempt to consolidate all available contacts and related data into one place, making it more useful and practical for outreach.

Large retail chain stores were among the first to adopt this approach. They implemented loyalty cards linked to POS systems, allowing them to gather data on customer lifetime value, seasonal purchase preferences, and even the hours best suited for shopping.

Essentially, businesses were building lists with very primitive purchasing patterns, such as “these customers are women because they buy hygienic products for women.”

Some were a bit more sophisticated, like “razors are usually bought together with toilet paper, so we can place them on the same shelf.”

Yet, these methods lacked simplicity and agility.

As computers evolved, so did databases and analytical capabilities. Today, we can collect almost infinite amounts of data and make decisions based on it.

Better data leads to better decisions, except when it doesn’t. – Rory Sutherland

One of my favorite authors, Rory Sutherland, loves to challenge conventional wisdom, pointing out that data doesn’t always lead to better decisions because humans aren’t entirely rational beings.

Still, we gain valuable insights from CRM systems and use them for the betterment of our businesses.

In this article, I’ll lay the foundation for understanding CRM Analytics, equipping you with essential insights for further exploration, and sparking ideas on how to enhance your CRM journey.

Understanding CRM Analytics

CRM Analytics is a broad term that encompasses everything from reporting tools to AI-driven predictions within the domain of the Management of Customer Relationships.

The term serves as an umbrella for all aspects of a business’s customer interactions during the pre-sales, sales, and post-sales stages.

For larger companies, CRM Analytics plays a critical role in informing management about business performance and providing data-driven insights to impress investors.

Smaller companies can leverage this data to compete with industry giants.

While different companies may use various datasets, they all share one common goal: leveraging data science in some form. In the world of customer management, this data science is known as CRM Analytics.

CRM Analytics tools help you achieve a holistic view of your company’s customer performance.

Customer Lifecycle

CRM Analytics covers the entire customer lifecycle, from a website visitor to an Opportunity, and even to measuring the Net Promoter Score (NPS).

Initially, database marketing focused on answering a few simple questions, such as “Which products could be cross-sold together?” or “When should we offer Christmas discounts?”

Today, the approach has evolved. Instead of answering one question at a time, businesses now build dashboards and reports that can be accessed regularly to address ongoing queries.

The focus has shifted from answering individual questions to tracking near real-time performance.

With the advent of computer technology and e-commerce, companies now gather much more information about customers at the early stages of their decision-making process.

For example, try searching for tools like Leatherman or looking up BMW. Then, open your Instagram, Facebook, or any social network you use. Chances are you’ll start seeing ads from those vendors.

This demonstrates how advanced CRM analytics have become. We’ve moved from a time when someone would sit and analyze vast amounts of consumer data to real-time actions like predictive advertising.

But let’s walk through the different stages of the customer lifecycle and see how they are reflected in CRM Analytics.

Pre-Sale Stage

The pre-sale stage is where the CRM Analytics solution can provide some of the most immediate and tangible results.

Regardless of your industry, you likely have a company website. This means you can use tools like Google Analytics to monitor how many visitors your site receives, which pages are the most popular, and where visitors spend the most time.

Since a website is like digital real estate, you can analyze your traffic to identify areas for improvement. Enhancing these areas can lead to more leads, sales, phone calls, or whatever your website’s target action is.

During the pre-sale stage, companies typically rely on data from their marketing teams, such as social media post reach, website traffic reports, lead generation reports, conversion rates, and campaign performance statistics.

The primary focus of CRM Analytics at this stage is to evaluate the effectiveness of marketing campaigns and the Return on Marketing Investment (ROMI). This CRM data analysis helps businesses decide where to make improvements or changes to boost the efficiency of their marketing efforts and increase the likelihood of converting more prospects into paying customers.

It’s also important to note that pre-sale CRM Analytics doesn’t always include data from your CRM system, which can cause some confusion since the term “CRM” is part of the name.

Sale Stage

The sale stage is where most of your business activity takes place. This stage is familiar to any company that engages in commercial transactions.

If you’re in B2B sales, you’ll want to monitor your pipeline, assess its value, and track which Opportunities are expected to close this week, month, or quarter.

In retail, you might focus on monthly sales figures, the number of visitors in your store (often considered part of the sales stage), and the average transaction value.

In other words, this stage is where you typically have the most information about your customers and the sales process, even if you don’t have one of those CRM systems in place. You still gain valuable insights that guide your actions.

During the sale stage, when a company already has customer contact information, reports such as Opportunities by stage, Sales reports, and New Business revenue are commonly used.

The main focus of CRM Analytics at this stage is to monitor the team’s sales performance.

The goal of analytics here is to improve sales efficiency and provide additional value through data-driven insights, such as probabilities that lead to more accurate sales forecasting.

Sales Forecasting

Sales forecasting is an interesting and sometimes controversial topic. I often hear from businesses that it’s not feasible or has limited value. However, I always argue that it provides valuable insight into how well the business is performing and allows for predictive actions. 

If forecasts indicate underperformance six months before it occurs, wouldn’t it be beneficial to act on that information in a timely manner?

Salesforce states on their website that 50% of their customers’ forecasts are within a 10% accuracy range. This essentially means that forecasts are accurate half the time. 

I tend to agree with this estimation, as I see similar results with my customers. It just takes a little discipline to make it work.

Accurate sales forecasting provides valuable information about closure rates and how sales departments are performing. Of course, companies dealing with consumers and e-commerce might use different metrics, like cart conversion rates, but the underlying idea remains the same.

After Sales

The after-sales stage is responsible for customer support, service, and feedback gathering for sales and customer service.

If you have a ticketing system in place, such as Salesforce Service Cloud, Zoho, or Zendesk, you’re likely familiar with common CRM metrics like “Time to Resolution,” “Percentage of Resolutions on First Contact,” and similar KPIs.

Less frequently, I encounter companies that collect feedback from their customers using systems like NPS (Net Promoter Score) to gauge satisfaction with their products or services.

However, those who do collect this feedback can observe interesting correlations between the performance of their customer service and the happiness of their customers.

Post-sale CRM Analytics can highlight areas where your product might need improvement.

For example, Leatherman, a well-known brand that produces multi-tools, recently launched their new flagship tool called the “Leatherman Arc.”

Initially, this tool came with two holes for a pocket clip that had to be purchased separately. Later, the company decided to include the clip with the product, so new customers didn’t have to buy it separately.

Customers who bought the product before this update were understandably upset and began flooding the company with complaints. It seems that Leatherman had a system in place to track the number of cases and their reasons, leading management to decide to distribute pocket clips to those early buyers free of charge.

This is a great example of how CRM Analytics can improve customer satisfaction and mitigate reputational damage from mistakes made during the product design phase.

CRM Analytics system provides an overview of customer retention, satisfaction, and the performance of your customer service.

This stage closes the loop of the customer journey and gives businesses a comprehensive view of overall performance.

Some companies even create unified dashboards to include all stages of the customer lifecycle in one place, providing an even more cohesive picture.

Challenges

The biggest challenge in implementing “true” CRM Analytics that covers the entire organization and provides a comprehensive, holistic overview of your company is consolidating all the data into one place.

Many vendors use the buzzword “single source of truth” as a mantra when selling their CRM software. However, the reality is that your data will always be scattered across multiple systems—Google Analytics, your website, Facebook ads, Google ads, LinkedIn ads, affiliate programs, CRM solutions, ERP—you name it. The list can go on and on.

The more data you collect, the more diverse your data sources become. The good news is that this challenge is solvable. There are tools available that can gather all the data in one place, known as Business Intelligence (BI) tools.

BI Tools

While I’m not advocating for any specific tool, I could mention one that stands out due to its widespread use: Microsoft Power BI.

In my experience, it’s not the best tool, nor the easiest to use, but it has the largest community of developers and is probably one of the most cost-effective and feature-rich options on the market.

Some other tools worth considering are: 

1. Salesforce CRM Analytics
2. Looker by Google
3. Google Data Studio
4. Qlik
5. Domo

Relying on Data Too Much

However, there is a drawback to relying solely on data—you may overlook what the data isn’t capturing.

One example I often mention in my other articles and presentations is the concept of survivorship bias.

During World War II, engineers were tasked with reinforcing parts of planes to prevent them from crashing. They observed that most of the planes they studied had damage to the wings and tail. Logically, you would think to reinforce those parts. However, the critical insight was that the planes with damage to other areas, like the engine or cockpit, didn’t make it back at all—so they weren’t included in the analysis.

Similarly, in business, it’s often what you don’t see that matters more than what you do see. So, you should be very cautious about how you use data in decision-making. It’s easy to misinterpret data, especially when correlations are found.

Remember, correlation doesn’t imply causation—a fundamental rule of statistics.

Best Practices

When implementing CRM Analytics, always start small. Trying to do everything at once is a recipe for failure.

Follow a well-established, step-by-step approach. Begin by implementing analytics on your website—this is likely the easiest step, as it often requires just a simple tweak of one line of code.

Next, set up tracking to see how users navigate your site and what actions they take, like leaving contact information or completing another key action. Experiment with these metrics.

If you don’t already have a CRM system, introduce one—but again, take it step by step. Start with Lead management, then move on to Opportunity management. Implement each component in manageable chunks that can be completed in a short time frame.

Once you have at least three sources of user information, consider hiring a consultant to integrate this data into the BI solution of your choice. From there, you can gradually expand the number of data sources.

When you find that you have more CRM data than you can effectively process, it might be time to hire a data analyst to help.

Remember, this is not a revolutionary process but an evolutionary one. You should proceed gradually, implementing one analytical tool and data source at a time.

Finally, if you need more information about available CRM Analytics tools and services, feel free to reach out to me or my team—we can definitely help.

If you’d like to chat, the first consultation is on us. In the meantime, don’t forget to subscribe to our mailing list. I send out monthly digests on the most interesting events in the industry to keep you on the cutting edge of corporate CRM technology.

Thanks for reading.

Cheers,
J.