Data is a powerful resource that can be found everywhere. No matter what your profession or job title, you undoubtedly come face to face with some form of data on a daily basis. However, the value of data is not in its volume, but rather the actionable and meaningful insights it provides.
While many marketers understand the potential data holds, they often don’t know where to begin. According to Experian’s 2016 Digital Marketer report, using data to better understand customers’ needs, wants, and attitudes is a top priority and challenge for marketers. Consumers should be at the center of a brand’s strategy. And it all starts with planning. Careful data planning can help marketers transform the wealth of data into a meaningful strategy that they can implement to make better business decisions and ultimately improve interactions with consumers. The following are five steps that will help you improve your data planning.
Step 1: List all decisions based on opinions versus data
Start a list of decisions you are making based on opinions rather than data. This list will vary greatly based on job responsibilities and field. Brainstorm the list of decisions with your team and be prepared to continue adding to it for at least several weeks as other things come to mind. No decision is too small or mundane to be included on the list. Keep in mind that this should be an extended capture of the decisions that you make to perform your duties within your role, not just a 30-minute brainstorm. One of the most practical ways to complete this task is to go through a week or more of your normal routine. As you do, stay aware of the decisions you make and record them as they come up. Here are a few examples:
– Did my last display advertising campaign drive a positive ROI?
– What are my optimal work hours?
– Where should we recruit more sales professionals?
– Which products should be showcased at events in October by marketing?
Step 2: Rank decisions based on the value to your organization
Identify a dollar amount for each of the decisions on your list. This will help you prioritize the decisions from step one in terms of value to you and the business. How you come up with this will vary greatly depending on the situation you are analyzing. However, you should always remember to factor in labor hours as a cost. Your time and your employees’ time are often the most expensive factor. In addition, depending on the particular problem you are trying to solve, you may want to include opportunity costs as well. If we look at one of the example decisions cited above, “Did my last display advertising campaign drive a positive ROI?”, we would look at:
– Media costs (Ex: $50,000)
– Creative development costs (Ex: $3,000)
– Cost of employee time (Ex: $1,300)
– Opportunity cost of closed sales (Ex: $100,000)
If we estimated the cost for each of these components, we come up with a total value of $154,300. When we assign a value to the decision of “What are my optimal work hours to increase performance?” we would estimate the value of one additional hour per day of productivity, which equals a value of $15,600. This easily shows which decision should be a higher priority in a less objective way. The important thing to remember in this step is that you cannot be too caught up on the exact numbers. Instead, use rough estimates, find your priorities, and continue down the steps.
Step 3: List each piece of data required to analyze each problem and identify insurmountable data gaps
Determine the top three to five most valuable decisions to the business based on your dollar ranking and map out the basic data points needed to inform analysis. Next to each field, attempt to identify a source of the data. Hopefully the data is easily accessible, ideally from your own team. Most likely, you will need to coordinate with other teams, if not other companies, to access the required data. Some of the data needed could be production costs, original transaction details, previous customer purchasing behavior, and cost to maintain a program.
A good way to determine the data points needed is to have a brainstorm with a group and have everyone throw out ideas about things that would help solve the problem or answer the questions. During this step, you will uncover data points that are important to making your decision but may not be easily accessible.
Step 4: Identify the gaps in data that need to be filled
Using your review of the data fields required, identify the data gaps that need to be filled. You will need to determine if these gaps are too significant to continue, or if they can be filled by seeking out a vendor relationship or making an educated guess to continue the analysis. When looking at our display advertising campaign, it may be easy to get ahold of the transaction data for before, during, and after a campaign, but what if a control group was not defined? Some missing elements will hinder the confidence in your overall output, and if you already know that obtaining these elements will poke holes in the results of the analysis, better to stop now. Without a path to get an audience to use as point of comparison, it will make solving this big data problem difficult. You will need to find ways to collect this information or start your next media execution with a better plan for better data collection.
Step 5: Build your 2017 data plan
Hopefully your list started with more than 20 business decisions that could benefit from a data-driven approach, and you have reduced that list to three to five based on business impact and data gaps you were unable to fill in. Now it’s time to build out the plan framework using a method that has worked well for you and your team in the past. Make sure to identify:
– The data sources and key contacts needed to acquire the data
– The date range for the required data
– The exact fields required (over communication is best—assumptions can lead to delays and pulling data multiple times)
– The data gaps and information required to fill them (outside vendors, approximate cost, etc.)
– Accountable team members for each step, including conducting the actual analysis
– A detailed timeline for collecting the data and conducting the analysis
One of the decisions from our original list was the decision of “Which events should we attend to showcase our products in October?” If this were the decision to be solved, here is a summarized plan for execution:
– First, I will review the list of possible events to attend.
– Then I will compare each event’s content with the buyer personas for the list of products. There may not be events that draw attendance of some buyer personas within October. As such, those events should be removed from consideration, and those buyer personas and related products should be removed as well.
– Then I will use the profitability of each remaining product to determine potential opportunity by each remaining event. Once I add the cost of each event, I can quickly see which products and event pairings will result in a positive ROI.
– Now that I have a list of product/event pairings, along with their ROI, I can quickly calculate the wisest way to spend my October event budget.
Before using data and analytics, I may have chosen an event to attend based on my gut, selected whichever one was the cheapest, or imagined which space would work best for my booth setup.
The beauty of data is its scientific nature. There’s no guesswork needed. But just because it is scientific in nature doesn’t mean you need to be a scientist to leverage its true value. Marketers who plan and prioritize using data are able to leverage it to its fullest capabilities—and can directly impact job performance, effectively manage resources, and positively affect the bottom line. Better data planning means maximizing your impact.
Lindsey Harju is a senior product manager for Experian’s Targeting division, where she is responsible for product development and strategy to help brand advertisers better target and measure the performance of their media. As an experienced digital marketing leader, Harju focuses on helping advertisers unlock the wealth of data available to them to improve digital marketing practices. She holds one patent for a targeting product focused on financial institutions, and has another patent pending for a measurement solution.
Prior to joining Experian in 2013, Harju worked in various roles at Yahoo!, where she focused on data-driven marketing, display advertising and leveraging the latest innovations in the digital marketing industry. She specialized in the pharmaceutical and financial industries, building products to meet their specific challenges in the market.
Harju holds a Bachelor of Science in marketing from University of Illinois at Chicago.
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