Most marketers understand that poor data quality can be an absolute killer to the value of marketing campaigns.
Yet, many businesses choose to hold their breath and hope to be spared of data quality issues, despite the fact that a study from Gartner shows 40 percent all business initiatives fall short of their anticipated value, primarily as a result of poor data quality. That means that marketers like you must make a business case for the tools and talent needed to ensure data quality is high enough for your team to extract accurate and actionable insights from your inbound data streams.
Inevitably, building that business case can be quite the challenge, which is why the ReachForce team has put together this post to help marketers like you make a solid argument for how better data quality can positively affect your business.
First: What Exactly Do We Mean by “Quality” Data?
Admittedly, data quality can be a fairly subjective concept. The phrase “one man’s trash is another man’s treasure” comes to mind; the value one business extracts from a data set may be completely different than that of another. Nevertheless, there are some basic concepts surrounding data quality that prove fairly universal.
- Data quality means information is unified. Depending on the size of your team and marketing budget, you could have any number of different inbound data sources. If those different sources do not come together to create a single customer “profile,” you will find yourself working with disjointed, duplicative, or misinformed insights about your target audience. That is why a great data management platform sits at the foundation of your martech stack to ensure all data comes together into a single location.
- Data quality means data is scrubbed clean. Misinformation and duplication prevent many marketers from extracting quality insights from their data assets. Having the right tools in place to scrub inbound data of inaccurate or duplicative data ensures you are working with the highest quality information when shaping your campaigns.
- Data quality means data is enriched. A data enrichment platform allows you to gather further insights about your target audience without needing to ask for it. That means a better customer experience for them and more actionable insights for you.
Even with this clear understanding of what defines “data quality,” you will likely need a lot more information to put together a business case. So, consider how you can actually build a better case for enhancing your data quality.
Better Data Quality = More Revenue
In today’s data-driven marketing world, the quality of your data can often be the one thing that sets you apart from your competitors.
New tools, strategies, and approaches are constantly being developed in an attempt to give your business a creative edge in reaching your target audience. Beneath all those fun bells and whistles inevitably lies your data, the information you have about your customers. So, if you are working with poor quality data, even the coolest, most innovative new marketing tool will not be able to deliver the value you want to see.
You are likely wondering: "Great, but how do I prove that to my executive team to get the budget for more data quality measures?"
Step #1: Choose the Right KPIs to Prove ROI
The executive team wants to invest in activities guaranteed to do two things: boost revenue and improve the customer experience (the two are not mutually exclusive, of course). So, if you can put in place the right KPIs to demonstrate forward progress in either (or preferably both) fields, you will be setting yourself up for a successful business case. Here are the metrics on which you can focus:
Upsell: Within your existing customer base, quality data helps identify trends in customer behavior or firmographic insights that should trigger sales engagement. In other words, when you determine what makes one customer buy more, you can match that profile to similar customers and attempt to replicate the value proposition. If upsell opportunities increase, then the data quality measures are working.
Churn Reduction: Similarly, a demand generation marketer can take the same approach when it comes to reducing churn from existing customers. If quality data can illuminate customers with upsell potential, it can also denote customers with activity associated with product churn. As a result, putting a KPI around churn reduction is another great way to show the impact of data quality.
Lead Conversion: More leads in the pipeline mean more new customers. New customers mean more revenue. If the lead conversion rate increases, then your data quality tools are doing their job.
Step #2: Establish Your Baseline
Once you know what you plan to measure, the next step is to determine the baseline performance for each of those metrics as it stands today. Your current customer lifetime value, churn percentage, and lead conversion rate will be the numbers from which your data quality experiment begins.
Step #3: Choose the Core Tools Needed to Deliver Results
There are thousands of different martech solutions available and each of them promises to deliver some amazing value that no other tool can.
Start with the basics. Every business needs a quality data management platform to manage incoming streams of information. Once you have proven the initial value, you will have a lot more credibility to extend the reach of your data quality process to new, creative tools.
Step #4: Estimate the Costs vs. Value Delivered
Finally, put together an estimated cost analysis of the tools you will need and contrast them with the anticipated value delivered by the reduction in churn and increases in lead conversion and upsell (all of which should be fairly easy to calculate).
Once you have that information, you are ready to present the business case to your executive team.