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.
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.
Solution: 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 integration initiative.
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.
Solution: 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.
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.
Solution: 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 of integration.
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.
Solution: 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 name).
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.
Solution: 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 sources.
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.
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 data sources.
Solution: 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 analysis.
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 analysis.
Solution: 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.