What makes good sales data?

There’s no underestimating just how important data is to your sales department.

It’s essential for a number of reasons, including helping with decision making, improving future performance, and understanding your customers better.

In other words, a quality data set leads to accurate sales insights. But how do you determine whether or not the data you have is good?

To help you answer this question, we’ll first outline different kinds of sales data and then explain the basic tenets of good data, all of which are keystones of our sales platform, Sell.

The three types of sales data

Types of Sales Data (1).png Three foundational pillars of sales data include rep metrics, deal metrics, and business metrics.

Simply measuring revenue or the gross number of sales isn’t enough. To fuel growth, you need to be tracking as many relative metrics as possible. The right metrics will depend on your company, but are valuable to learn more about your company, customers, and sales process.

We have an in-depth article on sales metrics, but let’s quickly take a look at the different categories and the data required. Although not exhaustive, below are nine examples of sales data in the form of metrics and the purpose of each:

Rep metrics

10.png Rep metrics help you track your sales reps’ performance and also see areas where they need improvement in the sales pipeline. These are basically productivity metrics.

 

  • BDR capacity:
    • Formula: Max. leads worked per day x working days per month x # of BDRs
    • Purpose: Determines the capacity of your sales team and if certain sales tools/training are needed
  • Lead yield:
    • Formula: Sales revenue for deals generated from leads / Total # of leads generated
    • Purpose: Allows you to see the value of a lead in your pipeline
  • Stage conversion rates:
    • Formula: # of opportunities that ever existed in the later stage / # of opportunities that ever existed in the earlier stage
    • Purpose: Helps you “quickly spot and correct issues like process roadblocks, ineffective BDR/AE handoffs and poor lead quality”

Deal metrics

9.png These types of metrics give key insight into the deals you are closing and whether or not you need to focus on different deals.

 

  • Win rate:
    • Formula: # of deals won / # of deals created
    • Purpose: Generates more accurate sales forecasts and helps you gauge deal quality
  • Average deal size:
    • Formula: Sum of the value of won deals / # of won deals
    • Purpose: Helps you determine the types of deals to focus on
  • Average sales cycle:
    • Formula: Total # of days to close deals / # of closed deals
    • Purpose: Provides insight into the effectiveness of your sales process

Business metrics

8.png Business metrics help you measure overall company performance. You can determine if your sales process or strategy needs improvement based on the money you are spending or bringing in.

 

  • Monthly recurring revenue (MRR):
    • Formula: Average monthly revenue per customer x # of customers
    • Purpose: Tracks recurring revenue and helps you understand future revenue
  • Sales formula:
    • Formula: Number of leads x % of leads worked x % of leads worked converted to opportunities x % of opportunities worked x % win rate for worked opportunities x average deal size
    • Purpose: Measures and evaluates your sales strategy
  • Customer acquisition cost:
    • Formula: Total sales & marketing expenses / # of new customers
    • Purpose: Use to determine how your customer spending compares to your sales.

For each metric, notice the data required in each of the formulas. Make sure you are collecting this information to turn into actionable sales insights.

The tenets of good sales data

Now that you know what to use your data for, you need to make sure you are collecting good sales data— enough data that is accurate and uniform.

As we have discussed in a previous article, strategic thinking is first required in data collection. A plan should be in place that answers the following questions:

  • How does the data align with your overall company goals?
  • How will each piece of data be analyzed?
  • How will the data eventually contribute to the customer experience?

Understand what is first needed to improve your business. After that, you can begin collecting data (transparently of course— don’t be sneaky with data collection)—whether through surveys, email forms, research, etc. The best way to manage it all in one place is through a customer relationship management system (CRM).

To know what type of qualities you are looking for, here are three tenets of good sales data.

Quantity: Why too much is best

Types of Sales Data (1).png To summarize HBR’s above statement—to turn data into actionable insights, you first need to have enough of it.

As previously stated, you shouldn’t collect data just to collect it (think strategic data collection and ethical data management), but you can’t analyze if you don’t have enough information.

It’s called big data for a reason. By taking a quantitative look at your sales process, you can gauge true performance. If some data is missing for a few clients, your roadmap is already way off.

Here are the categories and examples of data you need to collect:

  • Contact info: Name, address, phone number
  • Demographic info: Age, gender, region
  • Behavioral data: Bounce rate, click rate, page views
  • Transaction history: Time of purchase, sale value, payment method
  • Communications: Emails, calls, live chats

Think back to the last 100 sales you’ve entered into your CRM. Of those, how many did you get full data from? Five percent? Twenty percent? In order to grow as a sales department, this number needs to fit the scientific threshold for data.

The point is to take your large data set and turn it into actionable insights. For example, Expensify accomplished this through Sell. The financial services company had the data but needed to make sense of the variety and volume.

Says Jason Mills, Head of Sales at Expensify, “I was searching for a solution that would provide deeper, more accurate insights to guide our growth strategy in a measurable and scalable way.” They needed a data-driven solution.

Using Sell’s scientific sales platform, the company analyzed millions of data points around their sales activities and quickly found opportunities to improve their sales process. One issue was tracking problems with a large segment of leads. Based on the information, Expensify was able to implement an initiative to fix the problem.

Don’t miss opportunities to improve— take your large data set and analyze.

Accuracy: Quality is vital

Data stat 5.png

The only thing worse than not having enough data is having plenty of data that you can’t trust. According to Forbes, “84% of CEOs are concerned about the quality of the data they’re basing their decisions on.”

Inaccurate data is created when reps either enter information incorrectly into your CRM or leave out important pieces of the puzzle. Both of these scenarios are guaranteed to lead to poor decisions and lost deals, especially in an age where your customers expect highly relevant service and experiences.

Think about it: just one transposed number or rogue keystroke and you have dirty data. That’s why minimizing manual data entry wherever possible within your sales tools can have a huge impact on data quality. An automated data capture process also helps your sales reps avoid mistakes that may occur when re-entering information into multiple systems.

The ability to pass lead and contact information into your sales platform directly from other business systems like Zendesk also helps eliminate data entry errors and keeps all relevant customer information in one place.

To make sure your data is clean, answer the following questions:

  • Perform a data audit. What information do you currently have?
  • Avoid silos. Centralize your customer data across departments.
  • Make sure that the company data format is consistent.

Another surefire way to fall prey to incomplete or inaccurate data entries is if your CRM isn’t mobile. By not giving sales teams the ability to easily enter data in real-time, businesses run the risk of reps forgetting to enter certain information once they’re back at their desks.

Uniformity: A Streamlined process

Data stat 4.png
Is your data being used the same way across your sales team?

Think of a huddle in football. The head coach relays the play to the quarterback, who spreads the message to the other players.

Though each player has a separate job, and may have learned the play differently, everyone knows the agreed-upon terminology. If even one player misreads the play, disaster can ensue.

That’s how your sales team needs to operate: in lockstep, based on uniform data. If your sales reps are using their own terminology or inputting data in their own way, the picture that gets painted will be blurred and distorted.

When you have uniformity in your data, the insights are simple to read — and complete.

One way to achieve uniformity is to use a CRM. Benefits include:

  • All of your data is in one place
  • You can track customer interactions
  • You have a unified view of the customer

One common problem with many CRMs is how they treat data related to deal failure. Think of all the synonyms for price points: “too expensive,” “competitor was cheaper,” “not ready for investment,” and so forth. Your company could have a problem with pricing, but without a uniform standard of quantifying this metric, it might not show up in analytics as the red flag it needs to be.

If you knew that a common complaint among missed customers was that your offerings are too expensive, maybe you could offer a more modest package or re-evaluate your pricing structure. Without that valuable data, you will continue to needlessly lose deals.

Our sales platform ensures that each sales rep is on the same page, enabling sales teams to create and enforce a standardized set of reasons through custom pipelines, fields and tags.

Put your data to good use

Based on the above information, ask yourself these questions: Do I have enough data? Am I generating it fast enough? Am I tracking more than just a handful of metrics? Is the data true? Is the data useful to our business objectives?

Ultimately, your data set is no good if you’re not turning it into actionable insights. That’s why we aim to make the process of gathering, reading and analyzing data within your CRM as efficient as possible.

For more information around how to get the most out of your sales data, download the free resource Shedding Light on Dark Data: How to Unearth Actionable Sales Insights.

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