5 Steps to Overcoming Data Complexity Issues

November 11, 2019

A recent study found that one third of marketers cite data complexity as the biggest challenge they face – bigger than creating an engaging customer journey or demonstrating the ROI of marketing’s efforts. Data and its rabbit-like ability to multiply has become the elephant inside a room that’s walls are closing in. The good news is you can stop the walls from closing in — even as your data continues to replicate. Below, we’ve created a proven path for eliminating problems produced by data complexity.

1. Take Inventory of All Data Sources.

Most marketers use around two dozen martech tools to get their jobs done. A major motive for the purchase of any martech is to simplify processes. However, the rate and volume at which all of these tools create data has inadvertently added to the data complexity issues marketers face.

The first step you will want to take is to make a list of every tool inside the marketing department that is producing data. All of the tools in your martech stack should be on this list regardless of how often they are used. If there are other departments that use or generate marketing data, such as sales, customer service, IT, etc. add their data sources to your list. Don’t leave any tools off because these tools form the foundation from which you view the big picture.

2. List the Types of Data Each Tool Is Gathering.

According to author and technologist, John Spacey, there are nine basic types of marketing data that are useful for improving everything from customer experiences (CX) to sales and brand strategies. For each of the data sources in your inventory list, you will want to note the type of data each is gathering. This may take a bit of time, but it’s a worthwhile exercise as it enables you to get an informed view of what information you have, what information you need, and what information is being needlessly duplicated.

Marketing Data Types List

DATA TYPE	EXAMPLES
1. Customer	Contact information like title, company, address, phone, etc.
2. Market Research	Target market data such as current needs and preferences
3. Competitive Intelligence	Competitor intel on products, services, pricing, etc.
4. Sales	Leads, quotes, proposals, closed deals, time-to-close, etc.
5. Transactions	Records on purchases, returns, size of deal and pricing
6. Customer Interactions	Site visits, support, questions, requests, complaints, etc.
7. Customer Feedback	Ratings and rankings of site, content, customer journey and CX
8. Customer Preferences	Data such as preferred communication channels, frequency of communication, product interests, etc.
9. Marketing Metrics	Customer acquisition cost, customer lifetime value, churn rate, etc.

3. Connect All Data Sources

Integrating your tools is one of the most important steps you will take in the pursuit to eliminate data complexity. Fortunately, it’s also fairly easy these days. Cloud-based integration systems, like Sureshot’s Connect, enable marketers to connect everything in their stack. Naturally, there are a myriad of benefits to having all your data sources connected to a central platform. First, it enhances your ability to manage data by empowering you to eliminate most of the manual processes your team is forced to perform in order to make everything in the martech stack work together. Second, integration enhances data orchestration by enabling you to control how data sources collect and share data with each other. Third, it enables you to exercise more control over your martech stack by creating integrations that perform the tasks that you need them to.

4. Set Ongoing Data Quality Standards

The quality of marketing decisions you make rests solely on the quality of marketing data you gather. Once all of your data sources have been accounted for, categorized and connected, it’s time to initiate ongoing data quality standards. Data visibility tools, like Sureshot Command, greatly simplify the implementation of company-wide data standards by enabling you to see and score the quality of data your information sources are receiving and sharing. More importantly, it takes the hassle out of ensuring data remains clean and ready for use by automating several processes, including:

Verification – Ensures data collected from users is correct and does not contain input errors
Standardization – Ensures all data shares a common format and appears uniform across sources
Enrichment – Merges third party data with company data to form a more complete picture of customers, or other desirable datasets
Validation – Ensures data being used is correct, complete and conforms with company data standardization rule

5. Determine Critical Data Metrics

When you perform a data inventory, you will likely discover your tools collect a lot of information that is not immediately useful for your purposes. This is not to say that it won’t someday prove useful, which is why it’s nice to have a tool, like Command, that enables you to track the history of data gathered by integrated tools. However, what you want to do is focus on the metrics that are most critical to your business goals. In our recent ebook, A Guide to Optimizing Marketing Operations, we created a handy table to help you hone in on the metrics that matter most. It’s reposted below so that you can sidestep the deluge of TMI that comes with data complexity, route the data you need to the tools you use, and align your metrics with your marketing strategies.

Marketing Ops KPI Quick Reference Table

Insights Sought	KPI	Definition and Tips
Brand Awareness	Net New Users	Visits from a browser with a cookie containing a unique user ID. Tip: Use a cross-device User-ID on your website to reduce the number of return users counted as new.Organize by source to see which channels produce more traffic.
Customer Journey	Marketing Qualified Leads (MQLs)	Prospects who have requested information, i.e. eBook; demo, etc. Tip: Score leads based on fit and interest to determine the level of nurturing needed to move them down the pipeline.
Sales Funnel Activity	Sales Qualified Leads (SQL)	Prospects vetted by marketing and sales with a high intent to purchase. Tip: These leads are the most time-sensitive. Research has shown a direct correlation between speedy follow-ups by sales and an increase in number of deals closed.
Lead Quality	Lead to Opportunity (AKA MQL to SQL)	The percentage of MQLs that get converted to SQLs. Tip: This metric is also useful for gauging the efficiency of sales development reps.
Sales Forecasting	Sales Cycle Length	Time from first contact with prospect to a closed deal, averaged across all closed deals. Tip: Growth-driven companies focus on shortening the cycle in order to accelerate revenue growth.
Sales Cycle	Pipeline Velocity V = # of Opportunities x Percent of wins x Deal Amount/Days in Sales Cycle	Speed at which highly qualified opportunities move through the sales pipeline and close on a deal. Tip: Monitor velocity throughout the stages of the sales cycle to identify bottlenecks and areas for improvement.
Channel Value	Cost Per Lead (CPL) CPL = Spend/# of Leads	Dollar amount spent per channel to acquire leads. Tip: This metric should be used in conjunction with lead quality to determine the ROI of various marketing channels.
Profit Margins	Customer Acquisition Cost CAC = Sales and marketing expenses/# of customers acquired	The cost of extracting money from a customer. Tip: Long-term investments that require significant time before producing results may skew the accuracy of this calculation.
Customer Acquisition Costs	Lifetime Value of Customers (LTV)	Amount of money that can be extracted from a customer over a period of time. Tip: LTVs are important for accurately assessing benchmarks for good or unacceptable customer acquisition costs.
Marketing Value	Return on Investment (ROI)	Determines the true profit marketing campaigns generate for an organization. Tip: There are multiple formulas for measuring ROI including: 1. Gross Profit – Marketing Investment/ Marketing Investment 2. CLV – Marketing Investment/ Marketing Investment 3. Profit – Marketing Investment – Overhead Allocation – Incremental Expenses/ Marketing Investment