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Blog / 7 Challenges of Your Data-Driven Marketing Strategy

7 Challenges of Your Data-Driven Marketing Strategy

Working in data marketing, you probably know that there is no such thing as a dull moment. Something’s always happening. It’s dynamic, it’s hectic, and you love it. Yet, every now and again, having things change around you all the time can become somewhat overwhelming. After all, managing and executing a data-driven marketing strategy is no piece of cake. Knowing that feeling all too well ourselves, we’ve decided to focus on some of the challenges that come with the job description.


1. The challenge of asking the right questions

The need to ask the right questions may seem like an obvious one. It probably is, too. After all, how are you ever going to drive your business forward if you don’t know which questions - and their answers - will help you solve actual problems? Clearly, it’s great to know how many visitors you had in a day, or how long they stayed on your page. But will that get you any closer to fulfilling your key performance indicators (KPIs)? Not really. So think about your marketing goals and the questions you want to have answered. Do you want to convert more visitors into paying customers? If so, then maybe you need to look at the channels that generate the most conversions and focus on them. Remember: Even the best data won’t matter if you don’t know what purpose to use it for.


2. The challenge of finding high-quality data

There is no question that, as a business, you generate a ton of data every day. Yet, that in itself is no reason to believe that your data is good enough to yield actionable insights. First and foremost, high-quality data needs to be consistent and up-to-date. Timeliness is one of the main characteristics of marketing data, as it enables businesses to respond to customer needs as soon as they emerge. High-quality data is also as complete as possible. Any missing information, in fact, would only get you to make decisions based on educated guesses instead of facts.


3. The challenge of breaking down silos

The thing with marketing data is not only that it’s big, it’s also fragmented. It’s messy, and finding your way to what you need exactly isn’t always easy. Because your data flows in from a number of different channels (just think about your social media presence) and is accumulated by a number of different teams (marketing, sales, finance?), it is often scattered and messy. As a result, you get multiple data sets – often referred to as silos. Silos are a marketer’s nightmare as they provide no single overview of all available output and make it difficult to detect anomalies in the data. The key to solving this is by breaking those silos down and having all output up to date and in one place.


4. The challenge of normalizing your data

Once you have your hands on all relevant data, it’s time to focus on cleaning and harmonizing it. Because it is often pulled from various sources, marketing data comes in different formats and they all need to be unified if you are to generate actionable insights from your analysis. In other words, it is about creating a consistent target data schema. There is no right or wrong way to do that, but since marketing is becoming increasingly specialized, professionals are also required to master ever more specialized techniques and approaches to managing and analyzing their data. Our tip: a next-generation marketing analytics tool can do the job for you - in near-real time, too.


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5. The challenge of interpreting your data

The key to interpreting your data well and deriving actionable insights from it is to find and know what’s relevant. This is where marketing analytics really kicks into gear. Unlike tools that focus on individual channels, integrated marketing analytics takes into account your activity across all those channels, and over time. It is your key to making informed and quick decisions when it matters the most. It can also help you find patterns. It can help you understand which of your marketing activities resonate with your customers, and focus on them - both in terms of time and resources spent. It is about determining the ROI of your marketing efforts. Remember the part about asking the right questions? Marketing analytics can help you find the right answers. 


6. The challenge of working with real-time data

Real-time marketing data can be challenging to work with from both a collection and analysis point of view, but it’s the latter that often causes the most difficulties. While many companies readily gather data in near-real time, only few of them are able to turn it quickly into insights. According to a recent report from the CMO Council, only 7% of all polled said that they “are always able to deliver real-time, data-driven experiences across all customer touchpoints and across both digital and physical engagements.” The majority are able to perform partial real-time data analysis, while more than 40% are struggling or only starting to do so.


7. The challenge of bringing it all together

At the end of the day, the real key to a successful marketing strategy is data integration. While the rise of marketing technologies has certainly made it easier for businesses to collect, harmonize and analyze data, this data is often still accumulated in silos. According to the CMO Council report, only 3% of all respondents said their systems are fully integrated, with data flowing seamlessly between channels and teams. More than half have either very few connections between systems or are only now developing their own integrated tools. Another 25% report little to no integration at all, relying heavily on spreadsheets to do the job. In a time when big data is everywhere, and it tends to become bigger by the day, working with integrated marketing analytics is crucial. Many businesses may still be lagging behind, yet they do see the potential in turning fragmented and messy data into smart, actionable insights. What challenges do you face when it comes to your data-driven marketing strategy? Feel free to share your thoughts in the comments below.


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