When it comes to sales data, there’s not much room for error. Sales data is the drumbeat of an organization and arguably among the most important to get right in order to understand its future.

Let’s dig deeper into what sales analytics looks like, some of the common metrics that organizations use, and the major benefits and challenges of this analytic practice.

What is sales analytics?

Broadly speaking, sales analytics is the practice of generating insights from sales data. The techniques and tools associated with sales analytics may vary—it can include everything from advanced predictive modeling techniques to simple spreadsheets—but the outcome is always a clearer understanding of the data, which should be used to set goals, metrics, and a larger strategy for the sales team.

Sales analytics seeks to answer questions like, “What are the trends we’re seeing in our sales data?” or “What is leading to upselling/cross-selling opportunities?” or “How are our sales reps performing in each region?”

At any given moment, sales teams might perform different sales analytics that relate to different goals. For example, meeting the current quarter’s target may require a sales pipeline analysis to determine which customers are most likely to close. Or, in order to set targets for the upcoming year, sales teams might conduct a bigger-picture revenue analysis at year’s end.

What are some examples of sales analytics?

There are several baseline analytics that sales teams need to perform in order to operate effectively. However, many of today’s sales teams are bolstering their sales analysis with more advanced initiatives that drill deeper into sales data. Some of those analytics efforts include:

  1. Sales channel analytics
    Many organizations will sell their product through a variety of sales channels, such as resellers, in-store retail, online eCommerce sites, or direct sales. Identifying and doubling down on the best-performing channel for the organization can have a significant impact on revenue.
  2. Pipeline velocity
    Every organization has a sales pipeline—some average days, others can average a year or more. In any case, understanding how sales prospects move through the pipeline is critical to repeating and improving success. What makes sales prospects move more quickly through the pipeline? What makes them stall or drop out of the process completely? A pipeline velocity analysis can spur new prospect conversions at a higher rate.
  3. Pricing analytics
    Though product marketing teams often lead pricing analytics, it’s not a bad idea for sales teams to get involved. Often, sales reps have first-hand customer knowledge that marketers don’t and they can provide feedback on new pricing strategies in real-time. Pricing analytics should provide insights on the pricing range that customers have felt comfortable paying for (and where there is room to grow), as well as what feature or service they find most valuable.
  4. Predictive analytics
    A sales rep’s greatest strength—or weakness—is timing. When should they send the next email? Is now a good time for a follow-up call? Understanding when to check in with prospects can mean all the difference between closing a sale or not. By leveraging huge volumes of historical sales data, predictive analytics can alert sales reps as to the best time to check in with prospects.

What are some of the important sales metrics that sales teams should be tracking?

Performing a sales data analysis just for the sake of it isn’t a worthwhile exercise. Sales analysts need to ensure that the purpose of their sales analysis—and the sales metrics they want to produce from them—are top of mind.

Sales metrics serve as a guiding light for sales reps, indicating exactly where goals are being met or falling short, and can help activate sales reps’ performance. Sales dashboards (which can be created manually, but more often are built through the help of sales analytics platforms) make these metrics visible to the entire team, and beyond.

Here are some sales metrics that many sales teams consistently track.

  1. Sales per rep
    Each sales rep should have their own quota and know exactly how they are performing against that quota at any given time.
  2. Sales by region
    This metric indicates how the product is selling in different regions. Understanding where your product is overperforming or underperforming can prompt questions about regional differences in sales messaging, the need for more/less sales reps in certain areas, etc.
  3. Revenue by product
    For organizations with a portfolio of products, this metric is essential. Understanding which product is raking in the most revenue shines a light on what customers are actually looking for and how to serve them. Plus, sales reps can double down on that product to boost revenue.
  4. Revenue generated from existing customers
    This metric differentiates revenue that is tied to new business vs. revenue generated as a result of cross-selling, upselling, repeat orders, expanded contracts, etc.
  5. Year-over-year sales growth
    This is a flagship metric, one that the whole company should pay particular attention to. Understanding how the company has performed year-over-year is a metric that underlines the company’s future ahead, successes, and challenges at stake.
  6. Deals lost to competition
    It’s great to meet your quota, but how much of that quota could’ve been increased had the company not lost deals to competition? Understanding which deals were lost to competition indicates the potential revenue gap that reps could fill.

What are the challenges of sales analytics?

There’s no doubt that getting sales analytics right is important. Inaccurate metrics like customer growth rates or quarterly revenue may not impact the organization in the short term, but their long-term effects can cause organizations to overspend or invest in the wrong markets or verticals.

However, performing sales data analysis is not without its challenges. Some of the common challenges that organizations encounter when performing sales analytics include:

  1. Ensuring that data is accurate and timely.
    In most organizations, it’s up to sales reps themselves to populate CRM platforms or other tracking documents with their most up-to-date prospects, upsells, deals closed, etc. However, sales reps have a lot on their mind—and filling data fields is usually last on the list. It can be a challenge to gather the necessary data from reps for sales analytics.
  2. Fielding data from prospective customers.
    In order to perform a sales analysis that involves customer opinions about the product, such as Net Promoter Scores (NPS), etc. it can be challenging to gather customer data easily and efficiently. Creating simple online forms that are as clear and direct as possible yields greater results in gathering customer information.
  3. Determining which historical data is relevant to include in your sales analysis.
    Comparing past sales performance can be like comparing apples to oranges. If the product or sales messaging has changed dramatically or if the amount of customers at the time wasn’t statistically relevant, it can be difficult to draw real conclusions.
  4. Integrating, cleansing, preparing data from different sources or applications.
    Sales analysis requires gathering data from a huge number of sources and applications—everything from CRM data to eCommerce data to product data might be considered. Integrating, preparing, and cleansing all of these sources is not only difficult but incredibly time-consuming and can easily eat away at the hours of your sales analysts.

Data preparation and sales analytics

Successful sales analytics is an orchestration between sales analysts, sales reps, customers, business stakeholders, and many different systems and analytic tools. But at the heart of it is data. And without data that has been successfully cleansed and prepared for analytics, any analytic outcome won’t be trustworthy.

Data preparation for sales data analysis used to take hours, if not days, under traditional spreadsheet tools. But now, sales analysts are seeking out more modern options to prepare data for analytics: data preparation platforms, which can accelerate the overall data preparation process by up to 90%.

Alteryx Designer Cloud has been routinely named the leading data preparation platform. Its machine-learning powered platform acts as an invisible hand during the data preparation process, guiding users towards the best possible transformation. Its visual interface automatically surfaces errors, outliers, and missing data, and it allows users to quickly edit or redo any transformation. Finally, it integrates with essential sales applications and can pull in data from anywhere within the organization.

Learn why organizations are incorporating Designer Cloud as a key part of their sales analytics today. Schedule a free demo from our team or get started right away with Designer Cloud on the platform of your choice.

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