Starting the analytics journey: Where you can find sales growth right now

Starting the analytics journey: Where you can find sales growth right now

Starting the analytics journey: Where you can find sales growth right now

By getting five basic elements right, sales teams can drive new revenue while building analytics muscles for the future.

If data is the oil of the digital age, then analytics is the engine that turns it into energy. What excites the most forward-thinking executives today is analytics’ strategic value: the ability to enable and inform broad commercial growth and transformation, not just incremental efficiency gains.

The temptation of many executives is to charge into the fray, invest a lot of resources, and build a state-of-the art sales analytics capability. But that’s a little like trying to switch to a Formula One race car when you could be getting a lot out of your current model.

Successful sales analytics is a journey, but the first step is making sure that you’re getting the most from your existing data (Exhibit 1).

Building analytics capabilities in your commercial organization

In our experience, sales organizations can generate new revenue growth quickly by consistently collecting existing “core sales” data and metrics across the sales process and acting on the insights they produce. This approach also helps build “data-driven muscles” in the organization, so that it’s ready to take advantage of the more sophisticated sales analytics that come later in the journey.

Getting the basics right

Generating sales growth quickly requires getting five basic elements right:

  • Use what you have. Most sales organizations already have the data and tools to derive significant value from analytics. Developing simple lead-generation forms that feed into existing customer-relationship management (CRM) tools or updating CRM with accurate pricing books that feed discount requests are examples of foundations that must be in place to enable regular collection of core sales data at various points in the sales process. It’s also important to use commonly available tools across the company to collect data, keeping customization to a minimum. The more standardized the tool, the cleaner and more consistent the data will be, and the more understandable and valuable the insights that result.
  • Know what you need to measure. To focus their data collection, sales leaders need to set a vision for what they want to achieve and then work out the best data and metrics to help them do it. Most companies have dashboards that feature up to two dozen metrics, when often, only one or two really influence business outcomes—the others are there just because they’re easy to calculate. The best companies prioritize metrics that are specific to the outcomes they want and adopt universal sales-performance metrics that define the health of the business, such as deal-cycle length or customer churn rate.
  • Focus on data hygiene. Yes, companies often have plenty of data, but it’s usually hard to use, out of date, or inconsistent. Companies can remedy this by installing processes to tag, scrub, and rationalize data (i.e., make sure the data are comparable), and even report on the cleanliness of the data to ensure they’re reliable.
  • Create accountability. Organizations need to identify a leader who is both responsible and accountable for clean, consistent data that sales leaders can rely on to support salesforce performance.
  • Invest in a data-driven sales culture. All the data and insights in the world won’t matter if the salespeople don’t use them. Sales leaders can encourage salespeople to base their decisions on data rather than on “experience” or “gut” by putting tools in the hands of the right people. Those are often the front-line managers whose job it is to forecast the business and who need quality data to do it.

While we know this “focus on the basics” may sound obvious, we still see many companies neglect it, which keeps them from getting full value from more sophisticated sales-analytics solutions.

Getting started: How commercial organizations unlock value in existing sales data and processes

Significant value can be unlocked by mining core sales data that is trapped in reps’ heads or in IT systems. Understanding who the real decision makers are or which products a company previously purchased can be invaluable, but this kind of data can be easily collected at each step of the sales process (Exhibit 2).

Checklist for starting commercial analytics

The very act of collecting data creates value and lays the groundwork for an advanced sales-analytics program. Here are the areas where we’ve seen sales organizations get the most value from their data:

Sales planning

Best practice organizations are using software to accelerate the process of assigning accounts and territories by feeding a range of variables (customer potential or organization revenue targets, for example) into one place to define coverage models rather than relying on the traditional blunt instruments of “industry sector” or “geography.” The act of putting the data in one place for analysis enables the whole sales planning process to be faster, more efficient, and ultimately more effective.

A global technology firm needed to speed up and improve the accuracy with which it matched sales reps to opportunities. It relied on spreadsheet models to conduct sales planning, sending multiple files between different country teams, and had no way to compare sales plans with top-line revenue goals and strategy at the start of its analytics journey. The company began to use sales-coverage planning software to collect relevant coverage variables and automate the territorial assignment of more than 3,000 sales reps. This tool created a single cloud-based source of data so that the sales-planning team and regional sales-management teams could work together at the same time without sending files around. It also enabled the organization to compare sales plans across regions and against targets, model the outcomes of different account segmentation and coverage decisions, and track decisions on quotas based on account segments.

Rather than rolling out a new coverage plan three months into the new sales year, the company could now kick-start the year with the plan already in place. Not only that, but having gone through the exercise once, the company could then easily plan the following year’s coverage using updated variables because the data was in one place.

This approach saw a 5 to 7 percent improvement in productivity. The new coverage model also made it much easier to track what worked and what didn’t. In areas where coverage didn’t work, it was now possible to change the approach on the fly because the data was available in the coverage tool.

Pipeline management and forecasting

Organizations need to know what the steps of “good selling” look like, and they should require reps to capture opportunity data at every stage of the sales process, rather than just entering a deal on the day it closes.

A global logistics company was struggling with this. The COO was desperate to create transparency in the sales pipeline and to bring consistency to reporting worldwide. This would lay the foundation for more reliable decision making and accurate forecasting.

Under the company’s existing pipeline-management process, 20 percent of sales opportunities were created in its CRM system without a sales stage being defined— they were simply added into the system as an opportunity—and a third weren’t entered until the deal was halfway to completion. This meant sales leaders had no idea where in the cycle a deal truly was, or even if the deal was a qualified rather than just a prospective lead. This limited the organization’s understanding of the true length of a deal cycle, how a deal initially came together and, most importantly, how to push a deal through the sales process to close. Having a clear pipeline-management process and standards for managing data within the pipeline are both enormously helpful for training reps to sell successfully as well as for accurate forecasting and financial planning based around when revenue can be expected.

To create transparency and consistency, the company set out to simplify its CRM design, reducing the number of sales-pipeline stages from nine to five, reflecting the sales process on the ground, and making it easier for the sales force to enter data about the deal as it went through the cycle. In parallel, the company standardized the definition of each stage, so everyone from a junior sales rep to the head of sales shared the same perspective and captured data accordingly.

Under the new system, a rep had to start each opportunity in sales at stage 1. To drive adoption of the new system and the correct input of data, weekly pipeline calls were held using the CRM dashboard as the source of truth: if it wasn’t on the dashboard, it wasn’t discussed. These small steps simplified CRM usage, helped reps better evaluate prospective versus qualified deals, and improved the tracking of critical data, giving reliable visibility to deals in every stage of the sales process.

By standardizing the sales process and its definitions and then using the CRM to capture the right data elements at every sales stage, sales organizations can lay the groundwork for developing valuable insights into customer buying behavior and improving rep performance outcomes.

Pricing, discounting, and order management

A global payments company had multiple pricing books in its CRM system. Some books were out of date, while others had prices missing. The upshot was that the front line was either accessing inconsistent information or reps were relying on spreadsheets saved on their hard drives to generate proposals. In the end, everyone from sales reps to finance staff to product development had a different definition of a “good deal.”

The company decided to end ad-hoc pricing and instead updated prices in the books for thousands of items and made sure all the pricing books were available and up to date in the CRM.

Reps were then required to use the CRM pricing books to access the latest optimized price lists, and to organize, create, and submit price quotes based on the price books. Requests for discounts had to be sent to management using the CRM. Any discounted pricing had to be based on the up-to-date price books so that quotes and discount data could be linked to current pricing. Pricing and discounts could then be tracked in real time rather than after deal close. The result was a 2 percent increase in gross margin.

By populating CRM tools with the most up-to-date prices and pre-approved discount levels, pricing and discounting becomes more efficient and accurate, allowing reps to respond to customers faster, spend less time on manual pricing, and have confidence they’re offering a good deal for their customers and their company. It also lays the groundwork for organizations to track and address pricing and discounting levels much more accurately over time.

Customer success and postsale account management

Many companies sit on troves of data that relate to existing customers. When this data is integrated into a “360-degree” view of the customer, it can be the basis for more effective cross-selling and upselling, or for companies to deliver better services and improve customer satisfaction. This data is usually spread across disparate data systems, often in separate organizations. Integrating, synthesizing, and reporting it is complex and labor intensive. To realize the potential of their data, companies must automate as much of the process as possible and democratize access for teams across the company.

A leading chemicals company took this approach as it sought to unlock greater revenue growth and share of wallet in its largest accounts. It began with a four-week sprint to manually identify, clean, and aggregate relevant data sets into a master table, and then used this learning to automate the process for other data sets. The company also incorporated external data sets, such as Dun and Bradstreet’s, which added more depth and insight to its analytical abilities. Once the table was ready, sales operations could mine the existing customer data for insights about growth opportunities, then link those insights to cross-sell or upsell opportunities in the CRM system. Even with such basic steps, the company was able to lift its organic growth 6 percent above the market rate.

This 360-degree view of the customer also helps identify customers at risk of leaving and enables sales teams to address their dissatisfaction proactively. Software-as-a-service (SaaS) vendors such as Salesforce.com often set up “customer success” organizations. These teams pull together multiple sources of reliable customer data into a common view to create “customer-health” scores that can guide optimal customer service—predicting when a customer might leave or be receptive to hearing about a new product.

One SaaS company halved its customer churn rate by incorporating health scores into its customer-success practices. Despite facing a challenging internal data environment with disparate data sets and colleagues reluctant to grant each other access, it started small by creating basic health scores with easily accessible, clean data. As these scores yielded early wins, thus proving the value of using basic customer information, the company provided more data and refined the health scores to improve their impact over time.

These initial steps are the key enablers for a more advanced set of analytics that are more predictive of customer behavior with even greater potential to unlock value.


Simply getting the right data in the right place is the first step in building robust commercial analytics capabilities. Sales leaders can begin to make better and more informed decisions when the data available in existing tools is of high quality and trackable throughout the sales process. This gives them a 360-degree view of their business and their customers—and lays the foundation for more robust analytics and insight generation down the road.

About the author(s)

Charles Atkins is an associate partner in McKinsey’s Silicon Valley office, where Mitra Mahdavian is a partner; Katelyn McCarthy is a consultant in the Chicago office, and Michael Viertler is a senior partner in the Munich office.

The authors wish to thank Sinem Hostetter and Nate Shilling for their contributions to this article.

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