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.
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.
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.
https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/starting-the-analytics-journey?cid=other-eml-alt-mip-mck-oth-1804&hlkid=506a39fa904147c7958bf4ef628f52e0&hctky=1627601&hdpid=970cbecf-3159-4378-b500-67cb89d81174
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