If you have a business, you have data — but data by itself won’t let you optimize and improve your business. You need a data strategy if you want to turn data into value.
Data strategy refers to the tools, processes, and rules that define how to manage, analyze, and act upon business data. A data strategy helps you to make informed decisions based on your data. It also helps you keep your data safe and compliant.
The Importance Of Having A Data Strategy
Data is an indispensable tool for every business. But without a strategy, all that data can be too overwhelming to manage and interpret.
A data management plan will put you in a position to solve challenges such as inefficient processes, privacy concerns, or quality issues plaguing your ability to analyze important statistics about your customers and competitors alike.
A lack of clarity about current business needs and goals can be a huge problem, especially in large corporations where data is moved from one place to another or duplicated by multiple departments. With a data strategy, there’s no lack of clarity.
Components Of A Data Strategy
Although no two data strategies are identical, all successful data strategies include the following components.
Your data is your business, so it makes sense that you should establish clear goals and measurable objectives for how to manage this precious resource. For instance, one of the measures by which a company might gauge its success in implementing an effective strategy is cost reduction. However, you could consider many other equally important metrics, e.g., customer experience and security concerns such as cybersecurity or privacy regulations. The point is, don’t just set out on any old path because “it’ll do.” Establish meaningful parameters for your data strategy plan.
Collection Of Data
In any data journey, you need to start by capturing data before doing something with it. You should only capture what is required at each stage of a process. There are many ways you can unobtrusively obtain insightful knowledge about your consumers and their behaviors both online and offline. You could get customer email addresses or even understand customer behavior patterns to know how to serve customers best and stay compliant with relevant regulations.
The next step is finding out what you can learn from the collected data to create an effective data analysis plan. This means connecting dots and understanding how they relate to a wider objective for you to work out the next steps. You can use many powerful tools to do data analysis, for example, SAS or Power BI.
Once the data has been analyzed, it may be necessary to create a model of that information to explore potential levers on future performance. There are two types of models that are used when analyzing marketing: propensity and attribution.
Propensity models predict what customers might do based on past behavior, such as how likely they will respond to an email campaign or make another purchase; these can help drive personalization and targeting strategies for your company’s campaigns with more precision than other methods.
Attribution models allow marketers to see what each customer interaction contributed to the outcome of the overall sales. This provides an accurate and effective way of allocating marketing budgets, which ultimately drives growth through understanding how different customers interact with a product or service across their buying journey.
The goal of the analysis and model stages is to develop a plan that will take your business in the direction you want it to go. This means understanding what kind of marketing investments are most effective, developing strategies for retention, loyalty, acquisition, or even determining pricing. The process isn’t completed once, but rather it’s an ongoing journey where testing new approaches allows marketers to learn about customers’ attributes and motivations.
The modeling stage is a crucial step in the creation of an effective marketing strategy. In this stage, information gleaned from your website can be turned into clear and actionable insights for implementation that will result in better ROI.
Action And Optimization
Analysis and modeling provide the perfect platform for action. The attribution models should be used to change the mix of marketing activity, dialing channels up and down to get the best return on investment while ensuring that nothing is wasted on unyielding campaigns. Optimizing makes your campaigns yield even better results and customer experiences.
Creating An Effective Data Strategy In Marketing
If you’re looking to create a data strategy for your company, here are some key steps to get started:
- Define what information is most valuable and would be the best fit for your organization’s needs. For example, customer demographics may not be as important when you have a more specific goal.
- Determine what data sources are the most reliable.
- Set up a structure for how your organization will analyze and store this information.
- Establish goals that align with all of these steps to help you track progress.
- Build an actionable roadmap of where you are currently, where you want to be, and how long it will take to get there.
The bottom line is if you’re looking to keep up in today’s competitive market, it’s essential not to neglect having an effective data strategy.