Getting Technical with Data-Driven Marketing
The volume of available data as we browse the web or monitor things like POS transactions has absolutely exploded in recent years. Marketers who want to get smarter about the decisions they make can make good use of this data by developing a workflow that consults data before making big decisions.
Many of the technical processes behind such analyses are quite complex, but the essential goals boil down to three simple tasks:
1. Visualize and Attack
C-level decision makers should prioritize efficient data visualization to start along the path to action. Configuring a business dashboard or building visualization right into your database can allow for rapid snapshots that keep you out of the dark and answer questions immediately.
For example, data visualization can help companies avoid knee-jerk reactions to a complaint. A hypothetical customer with a high-profile social media presence may make a public complaint about wilted lettuce in their salad at a hotel lobby cafe. Some may react by completely overhauling the way vegetables for the hotel chain are bought, stored and served.
However, this may be an isolated incident, requiring a personal fix rather than a systemic one. Data visualization will tell you the difference, allowing companies “to respond to individuals relatively quickly if there’s a big problem and to avoid getting sucked into making a big decision based on a short timeframe,” says Data Informed.
Using a real world example, insurer Cigna offers visualized data sets to healthcare providers to help them “manage patients in an actionable way by identifying gaps in care and interventions,” says senior director for collaborative care Lynn Garbee. These reports can improve patient outcomes, as well as satisfaction, while reducing risks associated with making big changes.
2. Use the Scientific Method to Get Granular with Predictions
Hypothesize, test, refine — this is the routine of a data-focused marketer, and it allows them to become smarter over time.
With increasing volumes and categories of data, the scientific process of testing hypotheses allows companies to move beyond mere reporting. Making data-backed guesses about how changes in the business or marketing process could lead to positive effects is “turning organizations into little crystal balls,” in the words of Timothy King of Solutions Review.
While this type of predictive analytics has become common, many organizations do not dive deep enough. For example, an analysis of CPA (cost per action) may reveal that a brand’s retargeting campaigns are costing $200 per action. Since that is far too high for their goals, they may try to find ways to optimize their retargeting budget across the board.
However, looking at the big picture and calling the campaign a failure could miss out on opportunities. Marketing Land suggests to “apply any learnings and changes at the very base level” instead in order to make the best use of campaign performance data. Analyzing every site or even ad placement on a case-by-case basis and seeing if an increase in budget for the top performers can lead to a lower CPA overall is better than trying to make corrections in broad strokes, they advise.
3. Preventing Silos Through Interoperability
Data interoperability is a top priority for huge brands like Visa, which weighs marketing technology investments on how well the system plays with others. “The winners are the ones who have that open cloud perspective,” says Visa’s Chris Curtin. He looks for assurances like ‘Our software is engineered to work with your existing infrastructures’ before making the leap to try new technology.
Other brands should follow suit by ensuring that any technology they invest in can graft within their workflow while enabling insights at every level of the organization. Otherwise, knowledge gaps can become wider between departments while, simultaneously, important data sits unused.
Making Data Actionable Means Getting Smarter by Getting Specific
Organizations that consult data before making big decisions, apply observations and hypothesis-testing at the granular level, and prioritize systems that can readily share data between software and users will benefit the most from their data-driven marketing programs. Those who do not risk fumbling about, making decisions in the dark despite all the data that surrounds them, asking, “Need a light?” Don’t get caught in the dark. With the help of the digital marketing experts at EverSpark, you can make sure your campaigns are optimized for maximum efficiency. Visit our services page or contact us today to learn more.