Written
by Rebecca White @Rebecca_White94
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Between
developing a scalable sales process, collecting data, and helping your team
sell, it can be hard to properly track your sales team's performance.
However,
bad data and reporting could result in fewer sales, a decrease in customer
satisfaction, and poor decisions. In fact, your sales team could be wasting
time chasing poor leads due to bad data.
According
to Gartner, organizations
believe poor data quality to be responsible for an average of $15 million in
losses per year.
So,
how can you avoid this?
Below,
you'll learn how to avoid bad data and collect accurate data on your sales
team's performance.
What is Bad Data?
Bad
data is data that is inaccurate or inconsistent. For sales teams, bad data
fails to give you an idea of how your sales reps are performing. Data can be
incorrect for a multitude of reasons, including missing data, poor sources,
human error, dated information, and duplicate data.
Our data shows that 27%
of salespeople spend over an hour a day on data entry work instead of selling,
meaning critical time is lost to administrative work and your data is more
likely to succumb to human error.
According
to Dan Tyre, a sales director at HubSpot, "The key is to find ‘Goldilocks
data' — that is data that is not too simplistic, not too complex, but that
shows enough pertinent information with a big enough sample size so that you
understand trends. Sometimes this is easy to identify and fix because
salespeople fall into the same potholes all the time and it's obvious.
Sometimes it's harder because it's not a consistent trait or issue."
Below
are a few signs that your data could be inaccurate:
·
Accounting for seasonality:
There are several factors that can impact your data, including holidays or vacation quota relief
months. If you don't factor that in, your data can be skewed.
·
Expecting similarities:
Each year is different, so expecting similarities year over year can be a
mistake. Be careful about comparing current data to past data.
·
Not having one source of truth:
If there are multiple places that data is tracked, that can create confusion.
Without having a single source of truth, it will be hard to analyze your data.
·
Human error: If
your teams have to manually enter data or create data visualizations, human
error can be expected.
·
Lack of resources: Higher-ups
may not want to spend money on reporting software,
however collecting your data shouldn't be a manual process. Sales expert David Fisher says,
"It's common to analyze incomplete or small data sets and then jump to the
(usually wrong) conclusion. For example, if you are analyzing call data that
only comes from one representative or from one week, you don't have enough
information. Make sure your sample size is big-enough and encompasses all the
data sets you need."
·
Time management:
If you spend more time fixing problems and get stuck in the day-to-day tasks,
it can be hard to find time to analyze and use your data to improve your sales
team performance.
Eric
Quanstrom, CMO at CIENCE says, "The
biggest key to avoiding bad data is the age-old saying, ‘Garbage-in,
garbage-out.' Having proper structure in place is imperative for data input
and collection. Quality assurance of some sort matters. The ultimate goal here
in a sales organization is trust, and the dictionary definition of trust is
reliance on the truth of someone or something. Reliance on inaccurate data will
produce a vicious cycle of unintended consequences — not the least of which
will be poor decision-making based on flawed data."
To
avoid bad data in your reporting, Tyre also recommends using professional
technology (like HubSpot's Sales Hub),
benchmarking, throwing out outliers (the best and the worst), and looking at
industry benchmarks. Importantly, don't rely solely on data — listen to live
calls and recordings, and provide continuous training for
your team.
Bad Data Visualizations
One
of the first steps to analyzing your data is to create data visualizations or a
sales dashboard. This
dashboard could track metrics such as revenue, conversion rates, opportunities
and prospects, sales qualified leads, average
sales cycle, deals closed, and more. Below are a few examples of what not
to do for your data visualizations.
Bad sales cycle data
In
this example, the data compares the sales cycle of three different sales reps.
While this isn't inherently incorrect, you should never compare a ramping sales
rep to an experienced one. Tyre says, "Don't expect all reps to ramp at
the same rate — salespeople who have different learning styles should get it
with a little bit more time."
If
you want to compare sales cycles, you should ensure that you're comparing reps
with a similar amount of experience who are working on similar deals.
Bad sales seasonality data
In
this chart, which is tracking sales progress versus goal, seasonality or
month-to-month holidays and changes are not accounted for. Data visualizations
should be making equal comparisons, not comparing apples to oranges.
When
you create a sales progress versus goal chart, be sure to account for
seasonality and take the information with a grain of salt.
Bad sales data visualization
This
data visualization not only features poor data, but the data is
incomprehensible. Choosing the right chart and understanding how to put
together a visualization is important when analyzing data. This example also
uses dated technology. With more advanced resources like Tableau, this data could
be presented in a way that it's useful.
In
order to ensure your data is accurate and reliable, conduct data cleansing and data
audits. It might also be worthwhile to look at software and a CRM that can track data
for you automatically.
Want
to learn more about cleaning your data? Check out our blog, "Six Ways
to Keep Your Data Clean."
Originally published Oct
1, 2019 7:30:00 AM, updated October 01 2019]
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