We live in an amazing time for marketing and sales
teams. Every tiny element of the sales
cycle can now be tracked — from prospects performing research on a website, to
in-person sales meetings, to leads improving product preferences from email
nurturing campaigns. All of this data
can be used to fine tune digital marketing initiatives to find the best
prospects, convert prospects to leads at higher ratios, and aid sales teams in
knowing how to convert leads into customers.
Of course, analyzing every tiny element of the sales cycle
also requires work. For example, if your aim is to develop predictive models that
can increase results from marketing and sales efforts, you first need to
integrate data from disparate systems such as web analytics, CRM, email
marketing, and other platforms. For most
companies, this integration can be a challenging process. Below are a few of the largest obstacles that
you might run into when integrating these resources, along with some thoughts
on how best to overcome them.
1: No Consensus on Expected Value. Often times these data integration
projects start with one team trying to achieve superior results and simply
requesting data from other teams. It’s
important to start with a target that benefits all teams involved.
This should not be a difficult task as this type of analysis typically
demonstrates massive opportunities for everyone. Try developing several ROI modals that are
focused on the business objectives of each team, and then have an all-team(s) meeting
to share goals and gain consensus. Be
sure to revise and evolve the ROI modals until each team buys in to the
2: No Executive Sponsorship. Even if all teams agree on the benefits of
integrating data sources, there’s no guarantee that each team will put forth
the necessary resources and effort to share their data.
In the times when one team is not able to prioritize integration efforts,
it’s good to have a Plan B, such as an executive sponsor who can push the
priority of data driven decision in sales and marketing. The ROI modals will also help in getting the
attention and backing of the senior executives.
3: Not Starting from a Point of Integration. DJ Patil, the Chief Data
Scientist of the United States Office of Science and Tech said “If you're not
thinking about how to keep your data clean from the very beginning, you're
fucked.” We often find that disparate systems such as CRM platforms, email
marketing, and customer support platforms.
This may mean that different platforms have different
definitions/requirements of common fields such as company name and also have
different unique keys.
But don’t despair; normalizing this data
late is much better than not normalizing it at all. Putting off repairs to data can cause lost
opportunity and gain technical/analytics debt.
Utilizing data mining tools (i.e. Tableau, Qlik) will ease the process
4: Being Deterred by Messy Data. If your sales cycle involves offline or
manual elements (i.e. product demos, phone calls, personal emails, conferences,
or contract signatures) the odds are pretty good that you have messy data
somewhere. It’s not uncommon on
integration projects to find that email marketing campaigns have not correctly
tagged URLs for click tracking, sales team members have not consistently logged
client meetings, and marketing teams have not persistently tracked leads across
the sales cycle.
Don’t be deterred by messy data! The
longer you wait to clean up your data, the farther you are away from the
critical insights that drive marketing and sales success. Utilizing data mining tools we are able to
algorithmically start to large data sets with single commands. Also, we can often see “messy patterns” in
which we see consistent data omissions (i.e. in person meetings during sales
cycles) or consistent data shortages (i.e. only using a client’s first
5: No Data Extraction or Storage Tools. Platforms such as Salesforce, NetSuite,
Microsoft Dynamics, Google Analytics, Adobe Analytics, Marketo, Eloqua/Oracle,
and Exact Target are great at tracking data and reporting on their individual
data sets. But none of these tools are
great and integrating data from multiple sources.
Powerful insights require integrated data and integrated data requires
tools that can pull, manipulate, merge, and present data in new ways. Tools such as XPlenty, Tableau, Qlik, and
Power BI are focused on pulling data from multiple sources and enabling users
to create custom queries and reports to interpret the combined data
6: Infrequent (or non-existent) Data Audits. As we start to integrate the
data sources we invariably find that at least one data source (i.e. CRM) has
not undergone data audits to test the historic accuracy of the data. For example, we often find that large
prospects have multiple redundant records with different values (i.e. IBM,
I.B.M., International Business Machines) in the CRM. Most data sources require regular audits for
data clarity and accuracy. This makes
the integration more difficult as the data will typically require some level of
clean up while integration and analysis is happening. Similar to the other lessons, don’t let this
deter you. Starting a healthy data audit
process is always a good idea.
7: Overly Concerned with Trivial Points in Accuracy. It’s easy (and a bit healthy)
for naysayers to sit back and question the accuracy of analysis on combined
The truth is that manual data is only as accurate as the people entering
and configuring the data make it. It’s likely that analysis based on CRM/email
marketing will never be 100% accurate and all of the teams need to be accepting
as long as the data is close (i.e. 90%+). Team members should not be hindered
by “slight” inaccuracies in the data as long as they provide defendable
8: Thinking Integration is the End Game. Integrating the various data
sources to gain a superior knowledge of the sales cycles and find opportunities
is a great accomplishment. But the only
way to reap the significant benefits from the integration is to act on the
To be the best marketing and sales organizations possible, you’ll need to
analyze, optimize, update the data, and repeat.
Integration is the starting step to the continuous process of achieving
the most efficiency in the sales cycle possible.
Keeping these solutions in mind will help you get the most
from your data. To improve quality leads
and increase lead conversion, organizations of all sizes should have a clear
picture of how each step in the marketing or sales cycle supports the overall
customer lifecycle. Having a larger
picture of the entire process can help guide your data integration – across
platforms such as advertising data, web analytics, marketing automation tools,
and customer relationship management platforms. This may sound like a lot of
work, but of course the reward is also great. If your company can make
continuous advances to fully integrated data sources with applied analysis,
you’ll gain a critical edge over your competitors – and you’ll position your
company to be ready for the next generation of data tools.