The Impact of Quality Data on Marketing Success

Data is important to everything we do, as evidenced by a variety of cliches. The earliest of these was undoubtedly “data is the new oil.”

The idea was that data would revolutionize the way we produce wealth and spark new business models, much as oil changed how we make items, move people and commodities, and create billionaires.

“The new oil” cliché was swiftly followed by “garbage in, garbage out.” Data has the potential to be a game changer (but not for fossil fuel). But only when the data is correct and reliable. When you feed faulty data into a system, you end up with a mess. You need to feed your stack with high-quality data.  

But what does excellent quality data look like?

In the context of martech solutions such as CRMs, CDPs, and marketing automation platforms, data quality refers to:

• Accurate: Contact information, buying history, and preferences are correct and up to date.
• Complete: All essential fields include significant data, resulting in fewer missing entries.
• Consistent: Your data formats (dates, names, and so on) are standardized across all platforms.
• Clean: Identify and delete duplicate and unnecessary information.
• Timely: Data should be entered and updated quickly to reflect real-time interactions.

Doesn’t it sound great? Who doesn’t want quality data flowing through their martech systems, influencing sound choices and producing excellent results?

Now comes the difficult part. How do you get there?

A step-by-step strategy for improving existing data and ensuring future quality

Data scientists and data-driven marketing professionals go through a series of procedures to achieve this level of high-quality data.

Data assessment

The data evaluation provides you with the chance to analyze the data in the martech stack, as well as where and how it is used. The data assessment consists of two fundamental parts:

Data profiling: This is when you analyze the data in each platform to understand the vol.

Data mapping: Helps in determining how data moves between systems and pinpointing differences. 

Data cleansing and standardisation

During the data examination, it is not unusual to come across incomplete and inconsistent data. Inconsistent formatting, missing information, and mismatched fields are to be expected at this point.

Three techniques can help you clean and standardize your marketing data:

• De-duplication: Uses matching algorithms to detect and combine duplicate records.
• Standardization: It involves establishing standard formatting guidelines (e.g., date format, name titles) and automating data purification operations.
• Enrichment: Uses third-party data providers to fill in the gaps and acquire deeper consumer insights.

Data governance

It’s wonderful to assess, standardize, and clean data. However, like a teenager’s bedroom, your data will not remain in perfect condition for long once the cleaning is completed. If you don’t want to repeat the process, you should implement policies to encourage good, clean data habits.

Two strategies are useful in keeping your data clean as you move forward:

• Data Quality Policies: Create a company-wide policy that defines data ownership, access limits, and quality criteria.
• User Training: Educate CRM and marketing/sales teams on correct data input practices and the value of data quality.

Data monitoring and maintenance

Even with standards and training in place, you will need to examine your data periodically to guarantee it is of good quality. To ensure that the data in your martech stack is of high quality, you may implement a few checks:

• Schedule frequent audits: To identify and fix any emergent data quality concerns.
• Implement data quality KPIs: To track your progress, use important indicators such as data correctness and completeness. 

Data management tools and technologies

Of course, you can’t examine, clean, and enhance hundreds and thousands of records in your database by yourself, so you’ll need to enlist the help of the correct tools.

Among the tools you should consider:

• Data integration platforms (DIPs): These solutions automate data flow between systems, reducing human data entry mistakes and increasing consistency.
• Master Data Management (MDM) tools: MDM systems centralize customer data, resulting in a single source of truth that avoids duplication and inconsistency.
• Data quality management tools: These platforms provide features for data profiling, deduplication, cleaning, and standardization.
• Data Visualisation Tools: Visualising data quality indicators helps to detect patterns and areas for improvement. 

How does data quality affect marketing outcomes?

What we’ve discussed thus far will need a major investment of time and resources. You undoubtedly want to know, “Is it worth it?” That is an excellent question. And that is something your leadership will ask you.

To obtain the resources required to increase the quality of your data, you must first create a business case. You’ll also need to demonstrate the effectiveness of your efforts to keep the program running even when resources are limited.

Let’s speak about the outcomes.

Your data quality program should produce the following results: 

Improved campaign efficacy

The following modifications will increase the effectiveness of your marketing team’s campaigns:

• Improved targeting: With cleaner, more reliable data, the marketing team can segment consumers more accurately. This enables more focused advertising that better addresses individual client wants and problem areas.
• Improved personalization: Richer and more complete client profiles enable personalized messaging across channels (email, social media, etc.), resulting in increased engagement and conversion rates.
• Reduced campaign waste: Eliminating irrelevant or duplicate contacts allows efforts to target the intended audience, minimizing wasted spend and enhancing ROI.

Enhanced lead generation and qualifying

Your lead generation efforts should improve due to:

• Better lead scoring: Clean data enables the creation of reliable lead scoring models, which aid in identifying high-potential prospects for sales teams to focus on.
• Improved lead nurturing: Marketing may personalize nurture efforts to specific customer journeys and demographics, resulting in more quality leads entering the sales pipeline.

Better customer interactions and insights

Customer satisfaction is critical to corporate success. Existing clients are less expensive to recruit, provide upsell and cross-sell opportunities, and advocate for your brand.

Improved data quality will help your organization develop.

• Improved customer experiences: Accurate, personalized interactions across all touchpoints promote stronger customer connections and brand loyalty.
• Deeper consumer understanding: Clean data allows for better customer segmentation and analysis, giving significant insights that may be used to guide future marketing tactics and even new product development.

Improved efficiency and production

Quality data enables your marketing team to “work smarter, not harder.” More efficient use of the team’s time equals more work completed. Here are a few examples when this pays off:

• Reduced manual work: Data cleansing and standardization solutions relieve marketing teams of onerous data entry activities, giving them more time to focus on strategic objectives.
• Improved cooperation: The availability of consistent and accessible data across platforms facilitates communication and collaboration within the team, as well as between marketing and other departments.

Quality data is not a project, but a corporate lifestyle

If you’ve attempted to lose weight in the previous 30 years, you’ve probably heard healthcare experts talk less about diets and more about “lifestyle changes.” Diets have the disadvantage of eventually coming to an end. Lifestyle adjustments, on the other hand, are lasting strategies.

The same goes for data quality. You can’t spend a year assessing, cleaning, and monitoring data and expect the outcomes to endure that long. You’ll soon lose the benefits of excellent data if you stop working on it, just like you’ll gain weight if you consume soda again.

Data quality necessitates a broad commitment from across the organization, both in marketing and beyond. But the end product is worth the effort.

Source- Martech