Summarize Articles #3

SPONSOR CONTENT FROM TABLEAU

Power Sales Performance by Harnessing Analytics OCTOBER 30, 2018

By Brian Selby, Senior Vice President, Worldwide Sales Operations, Tableau Software

How often is your sales team making important decisions based on gut feel? Are you sure that deal will

close this quarter and was it optimally priced? Are your sales resources allocated properly to drive

growth?

In my experience, when sales organizations make major decisions and plans based on gut feelings,

there are costly consequences. One company missed its fourth-quarter forecast by a significant amount

and had to reset all quotas for the next year, delaying quota distribution by several weeks. Another sales

team relied too heavily on experience and judgment to make pricing decisions for large deals and left

millions of dollars on the table. One company failed to leverage its data on relative productivity of sales

reps across geographies and inefficiently allocated scarce sales resources to the right growth

opportunities.

In a world where data is everywhere, too many companies fail to take advantage of the power of data

and analytics to fuel sales performance improvement.

Why does this happen in so many companies? Historically, sales has been labeled an art. Selling

revolves around people, and with that comes emotion, beliefs, opinion, and the careful management of

relationships with customers, partners, and others within the sales organization. Accessing data, and

figuring out what to do with it, has been a difficult endeavor. However, with more modern business

intelligence platforms emerging, and easier access to sales performance data, the application of

science to selling has become a key differentiator in managing the sales organization. Companies that

embrace data and analytics as the foundation for sales planning and performance management will

achieve breakthrough improvement in sales productivity.

What could this look like? I’ve seen several use cases where advanced analytics have been applied to

sales:

• Improved pricing and discounting: Many sales reps and leaders are time-constrained and haven’t

been trained to effectively apply data analytics to pricing decisions. Oftentimes, it’s faster and easier to

just offer the same pricing to customers or use the floor of the discount matrix to speed up the customer

buying process. Typically, sales reps don’t know that other reps in their organization have achieved

higher prices for the same deals with similar customers. This is an area where data and analytics can

yield several points of margin improvement. With an advanced analytics platform mining all historical

sales data, sales leaders can see where they are pursuing suboptimal pricing and challenge sales

teams to reconsider their deal structures.

• Better forecasting accuracy: Sales leader judgment can be an important ingredient in forecasting

deals, but human judgment often fares far worse than analytical models in assessing the likely outcome

of deals and sales teams. The data does not lie. By leveraging data points on opportunities (e.g., the

customer’s historic buying behavior, sales rep performance, product type, and sales stage), a predictive

model can actually deliver a more accurate forecast than traditional “roll-up” processes can. Using these

types of analytical models can also save countless hours in management meetings trying to analyze

human judgment and adding “manager overrides” to lower-level forecasts.

One Brand's Success in the "Science" of Selling LinkedIn applied data and analytics to empower its sales teams with insights that drove success. Before, the company stored close to a petabyte or more of sales data using internal databases, Google Analytics, Salesforce.com, and third-party tools. One analyst serviced daily sales requests from over 500 salespeople, creating a reporting queue of up to six months, which left team members questioning their performance and the status of customer relationships.

The business analytics team adopted a new BI platform to centralize customer data and used dashboards to track performance and predict churn. To support even deeper analysis, they also leveraged predictive models in the BI platform to forecast churn— empowering sales to increase customer success within at-risk accounts. This has created a more proactive sales cycle and increased revenue. Michael Li, Senior Director of Business Analytics, said, “We decided to focus on how to scale the BI solution that we built and really provide the scalability and empower our sales team to get what they need in time. It became a one-stop shop for sales people to get what they need in a very self-service way.”

Today, 90 percent of LinkedIn’s sales force accesses the BI solution weekly. By tracking overall sales performance and digging deeper to understand the customer experience, sales now identifies when customers increase

• Reduced customer churn: Armed with a comprehensive profile of customer behaviors (e.g., support

incidents, attendance at training classes, and website engagement), sales reps and/or customer

success managers can more accurately identify at-risk customers and take preventative actions to

prevent churn. Marketing outreach can also be tailored to target at-risk customers and increase overall

engagement.

Establishing data and analytics as a foundation within the sales organization isn’t easy. Getting there

requires leadership to invest time and resources into acquiring the right data, systems, and people to

build these new capabilities. In my experience, it’s vital to build the right Sales Operations function with

the charter and resources necessary to prepare and analyze data, synthesize the analysis into effective

action plans, and drive change management across sales. These leading Sales Operations teams bring

deep insight into performance improvement opportunities and become trusted advisors to leaders

throughout the company. All of this is built on a solid foundation of data, from governance to preparation

to analytics and reporting.

Enabling the “Science of Selling”

Building a new capability to harness the power of

analytics in sales begins with clean, prepared, and

well-managed data. This data must come from a

widely-adopted CRM system and should be

analyzed by a robust, modern analytics platform.

And as mentioned above, the right analytically-

minded Sales Operations staff need to be in place

to understand the data, glean insights from

analysis, and recommend effective actions for sales

leaders to take to improve performance.

1) Start with clean data

Enterprises already know the pain of disparate data

sources, siloed departments, and legacy software—

a broken infrastructure that hinders performance,

growth, and development. Scaling advanced

analytics enterprise-wide means having consistent

definitions and sales practices. This also requires

product usage and can proactively connect around opportunities to increase overall spend and avoid account churn.

Setting up the right processes, systems, and people to acquire, prepare, and analyze key sales data will enable better decision-making for any sales organization. By putting data at the center of your approach to sales planning and performance management, you will also be able to realize a breakthrough in growth and productivity.

activating staff who will ask the right questions of

data, perform analytics, and discern what must

happen next.

2) Enable sellers with the right solutions

Globally, how is your CRM system being used? Is

your account and opportunity hierarchy defined and

structured the same way across teams—does

“closed won” mean the same thing to your

commercial sales team as it does to your enterprise team, to your teams in the UK and Australia, for

example? Setting definitions and hierarchies within your CRM is a best practice that leads to cleaner

data. Embrace an analytics platform with the capability to connect to your CRM and other data sources,

which has intuitive data-prep tools and optimizes advanced analytics to provide a single source of truth.

Then you can take advantage of modern business intelligence capabilities and scale quickly.

3) Hire inquisitive, driven, tech-savvy talent

Beyond standardization of data analytic definitions and processes, you need the right talent in place to

set your organization up for success. Your sales operations staff are trusted advisors to the business

and should have a seat at the table to support sales planning and resource optimization. For a true ROI

in Sales Operations, they should not be relegated to back-office reporting but instead should have the

organizational support and technology resources to apply advanced analytics. The right people,

empowered with the right analytics platform, and backed by the right data, drives sale performance

improvement.

To leverage data analytics to prosper as a modern sales organization and bring more science to your

selling, visit the Tableau Sales Analytics Solutions page. This one-stop resource for all things data and

sales, will support new and better possibilities for your sales operations.

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