marketing data strategy

Data is at the center of today’s marketing strategies –essential to driving the right connections with the right people and across the right channels. With the fluidity of data moving in and out of channel systems and consumers interacting with brands through any number of touchpoints, data is constantly changing. Marketers must be extra diligent to proactively manage customer and prospect data to maintain the integrity of such a valuable business asset.

Research firm Ascend2 recently conducted a survey examining the state of marketing data management.

Study findings included:

Measuring ROI to attribute sales resulting from the marketing data management investment is a top priority. Improving the quality and accessibility of marketing data are also top goals.

Marketing Data Strategy

Companies fail or thrive based in large part on the quality of their business decisions. Making more accurate decisions is the most valuable benefit of using marketing data for 54% of companies.

Database Marketing

Poor access to marketing data will limit its use. And if the marketing data is of poor quality, it will have limited usefulness. Combined, these are the most significant barriers to success.

Consumer Data Vendor

Tactically, the most effective use of marketing data is for campaign targeting. Getting the right message to the right person at the right time requires quality, segmented data.

Database Marketing

Here are three key strategies to successfully managing your marketing data as an asset:

One: Break Down the Siloes

Data siloes continue to be one of the biggest challenges that enterprises face when it comes to customer data. Data may be stored in various departments or different systems handle different sets of data, such as billing, shipping, and customer service records. These disparate systems of data prevent a unified customer view which impedes optimal marketing, sales, and operational performance.

How difficult is the challengeof siloed data? Many of the clients we work with have numerous systems of information. We integrated 7 systems of data for a regional furniture retailer and over 25 sources for a large manufacturer. And the larger the company, the more systems that must be created to handle growing volumes of information. As an example, the average hospital system maintains 100+ unique siloes of provider-related data.

Additional research by Teradata supports Ascend2’s finding:

  • Nearly 50% of marketers think data is their company’s most underutilized asset
  • 96% of marketers believe data siloes prevent a holistic view of campaigns
  • Less than 10% of marketers use their data in a systematic way
  • Only 18% of marketers have a single integrated view of customers

Two: Implement Ongoing Data Integration and Data Quality Measures

64% of companies outsource all or part of their marketing data management. In many cases, companies do not have all of the skills required or the rapidly evolving technology in-house.

data quality vendor

A data management provider will perform the following functions to ensure important customer details are integrated and remain current.

Data Identification:

An important first step is to identify all sources of data, fields of interest, format standards and definitions. Multiple sources of information may be used to contribute to the marketing database. These sources may include POS, e-commerce, customer loyalty programs, billing systems, and any other source of information that contains important customer details.

Data Cleansing & Standardization:

No data is perfect. Different and sometimes conflicting pieces of information can be found across multiple sources for the same contact or company. One-time feeds such as trade show data or prospect list purchases quickly age, and incoming data sources may lack critical data elements. The goal is to rely on the data being as accurate as possible. For example, ZIP codes can be corrected if city and state are correct, centuries can be inferred for dates, and area codes can be added where missing.

Each data type must also have the same kind of content and format. Consistent formats need to be identified for data elements such as equipment identifies, phone numbers, dates, etc. A data quality solution should contain built-in transformation routines that assist in this significant process according to your company’s requirements.

Cross Referencing:

Duplicate data is the top data quality problem for 30% of organizations. Cross referencing, or matching, is the checking of two or more units of data for common characteristics. The matching process removes data duplications and further improves data accuracy.

For example, names and addresses are often the identifying field for a data source, particularly customer data. However, this data can become inaccurate and deteriorate over time, or the data may have been incorrectly entered at the beginning. Performing matching to identify and correct these errors will discover intelligent links among customer profiles to merge duplicate records.

Data Enhancement:

Records are often missing important details. Data enhancement adds additional insight into contact details, demographics, lifestyle interests, and firmographics.

By following this 5-step approach, a company can achieve a single source of truth through consolidation of all cross referenced data and elimination of redundant information. Business rules should be applied to reconcile conflicting characteristics and maintain constant identifiers over time.

Three: Establish Business Processes to Ensure Data Quality is Continually Maintained

Business processes should also be established to ensure data manually entered into systems is of the highest quality possible. Many organizations experience data errors when information is manually entered, at a rate of 2% and 8%. Even one wrong number entered incorrectly can cause a payment to fail, a wrong part number to be shipped, or a host of other inefficiencies and lost opportunities.

marketing data quality

Data validation controls can be integrated into online forms, using rules to check the validity of data sets. For example, a web form may require a visitor to enter data in specified formats. Or an IRS form may utilize controls to check that positive numbers are being entered into fields. As prospects and customers engage with you online, perhaps filling out an online form, this information can quickly be tested, corrected, and entered into a marketing system through real-time web verification services. Training employees to be more aware of the importance of data quality is also a crucial step to achieving a company-wide awareness of maintaining high quality information.

Data is no longer a commodity that can be taken for granted, but the real value is only equal to the quality and accuracy of the data being used. Implementing a strategy to proactively manage this valuable asset will ensure you maintain a huge competitive advantage now and in the future.

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