{"id":8365,"date":"2024-08-07T14:08:08","date_gmt":"2024-08-07T14:08:08","guid":{"rendered":"https:\/\/tup.kxe.temporary.site\/mastering-data-driven-decision-making-in-the-strategy-phase\/"},"modified":"2024-08-07T14:08:08","modified_gmt":"2024-08-07T14:08:08","slug":"mastering-data-driven-decision-making-in-the-strategy-phase","status":"publish","type":"post","link":"https:\/\/okdesign.ca\/fr\/mastering-data-driven-decision-making-in-the-strategy-phase\/","title":{"rendered":"Mastering data-driven decision-making in the strategy phase"},"content":{"rendered":"


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The ability to have the tools and information to make data-driven decisions is no longer a \u201cnice to have\u201d but a necessity. Teams relying on gut instincts and anecdotal evidence for planning are falling behind more sophisticated brands using predictive analytics and data insights.\u00a0<\/p>\n

Data-driven marketing<\/a> is the right approach for running campaigns. However, the challenge lies in changing current behaviors and decision-making processes to incorporate better information. This article is the first in a three-part series aimed at helping marketers transition to data-driven decision-making by overcoming common barriers.<\/p>\n

We begin with the initial strategy phase, which forms the foundation for successful marketing initiatives. Avoiding anecdotal evidence and other pitfalls during this phase sets the stage for a campaign\u2019s success. Addressing these challenges early on leads to more effective and impactful marketing campaigns.<\/p>\n

Where data-driven decision-making goes off the rails<\/h2>\n

Using great data to make strategic choices seems straightforward and obvious, right? But why do many organizations hit stumbling blocks when they try to take this approach? Let\u2019s explore three common ways the best intentions sometimes quickly go off-course.<\/p>\n

Relying on anecdotal evidence<\/h3>\n

Marketing leaders and experts gain their positions for a reason. They have had great successes and experiences that taught them a lot of what they know. In many cases, what they rely on to help make strategic decisions.<\/p>\n

While this sounds like a good thing, there is a danger here. Decisions based on personal experiences or isolated examples \u2014 anecdotal evidence or use cases \u2014 often have an unintended effect. After all, no two situations are exactly the same, and the lessons learned from one situation may not apply to the current situation.\u00a0<\/p>\n

For instance, a marketer might advocate for a specific social media strategy because it worked well for a colleague\u2019s retail startup, disregarding the significant differences in target audience and market conditions to their enterprise brand.<\/p>\n

Through trial and error, great strategists and leaders learned how to apply anecdotal lessons to a situation and when they need to rely on data to determine the best approach. Quite often, even if there is a relevant component of anecdotal evidence, it should be tempered with data directly applicable to the situation.<\/p>\n

Is your organization trapped relying on expert advice that doesn\u2019t utilize the available data? Implement robust data collection and analysis processes to steer clear of anecdotal evidence. Start with comprehensive market research to understand your audience\u2019s behaviors, preferences and needs.\u00a0<\/p>\n

Gather quantitative and qualitative data using tools such as surveys, focus groups and analytics platforms. Ensure decisions are backed by data relevant to your current market conditions and objectives to create more targeted and effective marketing plans.<\/p>\n

Dig deeper: The data analytics hierarchy: Where generative AI fits in<\/a><\/em><\/strong><\/p>\n

Lack of clear objectives and KPIs<\/h3>\n

It is difficult to use data to effectively drive decisions when you don\u2019t know where those decisions need to point you toward. Vague goals and no measurable targets can ruin any marketing strategy, making it nearly impossible to be data-driven.\u00a0<\/p>\n

For example, a lack of clear objectives manifests as a general aim to \u201cincrease brand awareness\u201d without specifying what that looks like or how it will be measured. Without clarifying goals and key performance indicators (KPIs), it\u2019s challenging to gauge success or make informed adjustments. Teams might find themselves working hard but not making tangible progress toward meaningful outcomes.<\/p>\n

There is also an important distinction between using data after<\/strong> an objective is established versus using data to help you set your targets. For instance, setting a target such as \u201cincrease website traffic by 20% in the next quarter\u201d or \u201cgenerate 500 new social media leads from the upcoming campaign\u201d are good starts. But what does the data tell you about the best sources for traffic or leads? If an increase in web traffic will not likely lead to revenue growth or if social media tends to give you your least valuable customers, all the data available won\u2019t make them good goals.<\/p>\n

Instead, determine the high-level problem you want to solve and then use data to drive how you solve the problem. For instance, if your goal is to drive 500 new leads through a new marketing campaign, look at the channels that have driven your customers with the highest loyalty and lifetime value. If that channel is email, for example, then instead of creating a social media campaign, which might waste resources to create, focus on email instead.\u00a0<\/p>\n

Data silos and lack of data access<\/h3>\n

All of the above advice is predicated on having good access to data and that the data you have access to has a high degree of integrity. This brings us to the third important stumbling block to making good data-driven decisions in the strategy phase of marketing.\u00a0<\/p>\n

Data silos, poor integration and lack of access to critical data make decision-making anywhere from difficult to downright impossible. Among the many causes of this are: <\/p>\n