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As a marketing analyst, you’re expected to know the ins, outs and metrics of your company’s marketing efforts. The data is abundant but every month, marketing analysts across the globe find themselves getting stressed. Know the feeling?
You’ve only got a few days until that big meeting to discuss the ROI on marketing, but the data is everywhere. Your SVP of Marketing wants to review marketing performance and forecasts and is going to ask the usual questions: “Which channels perform best in terms of conversion rate? Where should I put the marketing dollars next month? How can we cut the PPC spend but increase the conversion rate? And why are there some many clicks for so few conversions?”
You know these questions are coming, but for some reason you’re not prepared to answer them. The data you’ve been working on isn’t good enough to give a clear and accurate picture of what’s going on in marketing.
Stress levels are rising and you don’t know how you’re going to answer the questions because you’ve been dealing with multiple issues regarding the data itself.
Issues such as:
- Data spread across too many sources to efficiently create one dataset
- No easy way to present the data to senior management in a way they will understand
While marketing analytics is a challenge for many companies, it is essential to sustaining and growing the business. If you’re trying to get more accurate information on performance, you need a good, solid dataset to work with. And you don’t have time to sit around while a junior analyst cuts and pastes data from multiple into Excel and creates pivots. Time is money, my friend, and you can be sure the SVP of Marketing is keeping an eye on how long it takes you to get the numbers out.
Improving marketing analytics in your organization is – or should be – a key strategic priority. However, many companies are not set up to efficiently and effectively handle multiple sources of data. This is a real problem because it causes delays. And those delays can, and often do, equal missed opportunities.
To address this delay, many organizations are turning to tools like DataMatch Enterprise and Tableau to help them prepare, blend, and visualize data faster. They’re also adopting some best practices to improve their analytics efforts, and you can do the same:
Speed up your data preparation
Cut down the time spent in data cleansing, matching and merging and eliminate manual processes by using automation as much as possible. Most of your data sources will be the same from month to month. Once you have the right ‘formula’ to get your data in a usable format, you can simply repeat that every month using an automated workflow.
Also, remember that connecting directly to on-premise and cloud data sources will eliminate time spent on exporting and importing data from multiple sources. While it may only be a few minutes per data source, those minutes soon add up.
Transform your data
Decide which data you need to do a thorough analysis and transform it to a format you can work with. If you have currency stored as a text field with the currency symbol included, transform it to an integer to make number crunching easier. Once you have all your transformations worked out, add them to your workflow so it gets done automatically, then combine everything to a single dataset, if possible.
Work with your colleagues more effectively in order to maintain one single version of the “truth”
By sharing your data models, workflows, and visualizations with your colleagues, you can ensure that no matter who prepares reports or uses the data, everyone will be singing from the same hymn sheet.
These three key practices can help you reduce the amount of time to get to the insights hidden in your data. That, in turn, will speed up the time in which you can share those insights with everyone, and help to improve the ROI on the marketing bucks. Both of which should keep your SVP of Marketing happy.