A 5-Step Approach to Managing Your Manufacturing Marketing Data
Good data is the driving force behind successful marketing. Data can be analyzed to determine what your customers are looking for, what will drive them to purchase, and to establish a best prospect profile. According to a report by GlobalSpec, the primary marketing goals for manufacturers are customer acquisition (43%) and lead generation (29%), with 54% planning to increase marketing spend.
However, many firms in the manufacturing industry are overwhelmed with the sheer volume of data they face on a daily basis. And in a challenging economy, getting this data under control is imperative to sustain a competitive advantage.
As data tends to accumulate from many sources within the organization, it develops specifically for each individual department’s needs. Formats vary, discrepancies develop, and a multiple of application systems are created to support the growing data.
The larger the company, the stronger the risk is for communication breakdown arising from distorted information. Silos of data within an environment such as this are usually not cross-referenced. The result is what is commonly referred to as multiple versions of the truth.
Establishing consistent data management methodologies across an organization is not a new concept, but one that can be extremely challenging. So how do you get to that single version of the truth, where everyone can depend on the same view of clean data?
Here is a look at an integrated 5-step approach to better data management. By incorporating a data quality and integration solution that follows this methodology, you can ensure your data is fit to be shared across the enterprise for any number of new operational and marketing opportunities.
5-Step Approach to Data Management
- 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 be a combination of various 1st party data (tradeshows, customer loyalty programs, billing systems) and 3rd party data supplied by regulatory agencies such as the Department of Transportation and other public agencies.
- Data Cleansing
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.
- Data Standardization
Each data type must have the same kind of content and format. Consistent formats need to be identified for data elements such as equipment numbers, phone, 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 data for a data source, particularly customer data. However, this data can become dirty and deteriorate over time, or the data may have originally been incorrectly entered. Performing matching to identify and correct these errors discovers intelligent links among customer records to merge duplicate records.
- Survivorship
By following this 5-step approach, manufacturers 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.
Addressing data chaos to achieve a single version of the truth ultimately leads to a better understanding of end-users and channel relationships. Armed with clear insights and a comprehensive 360-degree customer view, manufacturers are able to proactively identify, prioritize and address issues, improve organizational alignment, enhance resource utilization, and open a whole new world of marketing opportunities.
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