We’re all familiar with the big data explosion; more data is being created and technologies are simplifying and reducing the cost of data storage. Marketing organizations are left between two extremes — on one hand, there is an inordinate amount of data that, ostensibly, can be used to drive the business — and on the other hand, teams are spending more time organizing data, rather than completing value-add analytic work.
The risk of becoming a data hoarder, hanging onto every bit of data as if it’s that favorite old sweater in the back of your closet, is greater with the rapid evolution of technology. Surveys show that marketers collect large amounts of data that are not fully used. For example, Forrester estimates that 60-73% of all data is not fully leveraged for analytics.
It’s been revealed that that data science teams are spending most of their time on mundane data collection activities. A 2017 CrowdFlower data scientist report showed that 72% of data scientists time spent collecting, cleaning, organizing, building and modeling data. These folks are spending the bulk of their time on tasks they most dislike. Access to quality data was listed as the number one roadblock to success for data scientists.
To make the most out of your marketing data assets, you need a coherent strategy for capturing, organizing, and analyzing it. The best approach is to keep it simple, like my recommendation resembling the old who, what, why, where adage.
WHY: Why do you need the data?
The “WHY” defines the purpose for which you need data and is driven by your organization’s goals and objectives. Start with overall marketing objectives like: Increase profit margins, customer lifetime value, brand engagement, customer acquisition, customer retention, lead generation or increase revenue. For example, for a customer retention objective you need to understand the customers who leave to uncover and address the issue. Some of the questions supporting this objective could be: Which customers have left in the past 6 months? How did they engage with the brand (i.e., purchase behavior)? And, what customer service issues did they raise.?
The “WHY” serves as a data filter in that it guides the capture and storage of data, providing clarity on what data you truly need versus what’s available.
WHAT: What data do you need?
The “WHAT” identifies what data you need to answer the business questions. Creating this moves you from a “more is better” approach or “capture everything” approach to an informed decision approach to define what data is needed in your marketing data environment.
Take these questions and decide what data is needed: “Which customers have left in the last 6 months?” requires customer data with the date acquired or first purchase, purchase history, behavior data (i.e., web browsing, abandoned cart, etc.), promotion/contact history, etc. Begin with the high-level data categories and drill down into dimensions, specific attributes, and even metadata.
WHERE: Where do you get and store the data?
The “WHERE” has two components; where do you get your data and where do you store your data? The first step generates the list of data sources that provide the needed data. Data sources can come from a variety of places. Most of your data will probably come from internal systems where you find customer data, sales/purchase data, customer service data, customer behavior data (i.e., online interactions), promotion data and more. Third parties can provide demographic and firmographic data giving insight into preferences, income ranges, etc. Here you want to have a continuous process to evaluate data usage and value, especially sources for which you are paying.
The second step, data storage, focuses on where the data will be stored. This involves deciding on the architectural structure best suited to support your data and how you will be using it. There are several ways in which to store data; data lake, relational databases or data platforms.
HOW: How do you get and store the data?
The “HOW” defines how the data is collected, transferred, processed, and stored within your data environment. This is defining how you get the needed data from the sources, how the data is processed and then stored within your data environment.
Some sources may have well defined API’s to make data access more straightforward, while other internal legacy systems may require special coding of data extracts to pull data out.
You will also need to define how you need to process the data to make it ready to answer your business questions. For example, do you have a customer segmentation strategy that you want to apply to your customer records? This type of work will be completed on the raw data, assigning segment values to your customer data and updating as new data is made available.
The “HOW” also includes data privacy, security, and archival. As the regulatory environment evolves, for example GDPR, compliance with the appropriate regulations is important. Ensure that your team is up to date with the ever-changing rules and incorporate any required actions into your data management strategy.
WHO: Who manages and accesses the data?
The “WHO” defines the roles and responsibilities of the data management team and who accesses the data. The latter revolves around data security; ensuring that you define and manage those needing access to the data as well as identifying permissions (i.e., read, write, what you can see). The former lays out the roles required to manage the data environment, the skills and competencies required for each role and the responsibilities of the team members. Clearly articulate the roles and responsibilities of the data management team (those who are on the front lines of executing the data strategy) to ensure the team operates well and efficiently.
Assigning executive level ownership of your Marketing Data Strategy is an important step to ensure consistency and adoption across the multiple Marketing divisions. Ensuring this team sits within the Marketing business and is therefore close to the processes, use cases and KPI’s will reduce friction and ensure that the strategy is agile and aligned to Marketing objectives.
Conclusion
A marketing data strategy is essential to keeping your data environment healthy and one that ensures business value — transforming data into meaningful decisions and actions. It is the tool by which your team is actively managing the data environment and thoughtfully making decisions. Building out a marketing data strategy and incorporating it into your overall data management process is well worth the investment. Ensuring there is a senior level leader and/or consulting support in place to help setup the strategy is important. With the right level of investment, a well thought out Marketing Data Strategy will drive significant financial gains (and reduce risk).
