Data is foundational for today’s marketing. Here’s why, along with best practices for extracting the most value from it.
By Akshat Biyani, Contributor to MarTech
Data is the lifeblood of digital marketing. With so many B2C and B2B purchase journeys passing through digital touchpoints, there have never been greater opportunities to collect customer data at an enormous scale. Data creates opportunities to optimize and personalize customer journeys, driving conversions and reducing churn. But the data needs to be good, clean, warehoused and managed, and above all, available for activation in a timely manner. Accessing and managing high-quality data is the biggest challenge facing marketing organizations today.
Marketing is no longer about publishing generic advertisements in newspapers, hoping the right people will see them. Instead, with data-driven marketing, you can use tangible data such as cost per click (CPC), cost per lead (CPL), customer acquisition cost (CAC), return on investment (ROI), and more to track your campaigns in real time. Additionally, you can connect marketing campaigns with website traffic and other metrics to understand how different strategies and channels affect customer behavior. While collating and analyzing marketing data takes effort, doing so eliminates the guesswork in your marketing campaigns.
In short, data gives you direction. It allows you to improve the efficiency and impact of your marketing campaigns. Both external trends and the nature of your customers inform what marketing channels to use. Data helps marketers understand which channel maximizes their ability to reach their intended target audience, producing high-quality content accordingly and reallocating spending toward channels that yield more conversions. Data-driven marketing is, therefore, the trump card of your marketing efforts.
What is data in marketing?
There is a slight difference between marketing data and data in marketing. The former is a narrower category that specifically refers to data about your marketing strategy and consumer-level data needed to develop campaigns. The latter is a more extensive term that refers to any data that may help your general marketing efforts. This can include customer, financial and operational data — even macroeconomic data. How creatively you leverage data to aid your marketing efforts depends on your marketing team, their methodology, and your tools.
The ultimate goal of data-driven marketing is to collect, analyze, predict and optimize marketing performance to increase return on investment. With everything else held constant, using data increases the return on each dollar you invest in your marketing efforts. Data-driven marketing also helps improve customer communication and engagement while pointing out opportunities for innovation.
About 90% of marketing leaders expressed that marketing functions need to be “more adaptive to shifts in customer needs,” according to a recent Gartner survey. However, most struggle to achieve that desired adaptability. Data-driven marketing empowers marketers to react to changing customer needs more quickly and strategically. This is precisely why 90% of marketing leaders cite “martech, data, and analytics, CX and loyalty as top priorities.” And companies that effectively use data analytics to drive marketing and sales are “1.5 times more likely to achieve above-average growth rates” than their competitors, according to a McKinsey report.
Why leverage data?
The most straightforward answer to this question is to increase profits. All marketing efforts aim to increase sales and create value for the business. Leveraging data improves accuracy in targeting customers, which helps you achieve these aims more quickly and cost-effectively.
The purpose of any data-driven activity is to quantify and measure parameters and variables. First, start with data collection. Once you have collected data, it needs to be cleaned according to its relevance to your project or campaign and then stored appropriately. Data can be categorized based on its type (customer, financial, operational, etc.) or how it’s collected (zero-party, first-party and third-party data).
The first step is to use this collected data for descriptive and analytics purposes. This allows you to quantify and measure various metrics to get a broad overview of your operating context. Typically, metrics are of two types: brand-oriented metrics and revenue-oriented metrics.
Brand-oriented metrics measure success related to awareness, relevance, and differentiation when it comes to your brand. It includes parameters such as website traffic, social engagement, branded search volume, and impressions. Essentially, this helps you measure your marketing efforts’ current success and standing.
Revenue-oriented metrics, also known as conversion metrics, measure to what extent your target audience is converting into actual customers. Specific examples include sales metrics, customer acquisition costs, customer lifetime value, and other demographic data. This high-quality data is leveraged to generate insights and optimize marketing efforts.
The data you collect and measure needs the right mix of tech tools and human intervention to generate implementable insights. There are various ways in which you can analyze your data, depending on your methodology.
Analyzing your marketing data, such as important campaign KPIs, keeps you informed of your campaign’s performance. Benchmarking these metrics to your competition and past performance will allow you to tweak your current marketing efforts to generate the highest return on investment.
Advancements in tech, especially artificial intelligence, have given marketers tools capable of rigorous data analytics. They enable marketers to use predictive analytics to better forecast changing customer behavior, which informs the usage of marketing channels.
The next step in data-driven marketing is ensuring your analysis is understood and implemented to optimize your marketing efforts. The feedback and insights generated through data analytics help marketers better understand the changing external environment and tweak their campaigns accordingly for maximal impact.
Predictive insights from your data analysis can help you devise effective marketing strategies. You can further use prescriptive models based on data from various sources to inform your marketing efforts. For example, what customer segment should you target? Which channel is the most effective in improving the reach of your intended audience? What kind of content are your customers most likely to react to?
You can also draw causal inferences between various variables and events. For example, how much have profits increased since you launched the new marketing campaign? Is there a relationship between ad spend and overall profit? Even if these inferences aren’t exactly causal, understanding any strong correlations can help you optimize campaigns to reach their true potential.
Best practices in data-driven marketing
While data can unlock multiple insights for marketers, it is essential to ensure you’re following best practices to get maximum information from your data. This includes everything from collecting relevant data to performing the right analytics.
Collect new and better data. For any data-driven activity, the most critical factor is the data itself. Regardless of how rigorous your methodology or innovative your tech is, inputting old, irrelevant, and unstructured data will always generate inaccurate results. Additionally, your methodology is critical to any kind of analysis — issues such as low sample sizes and biased samples can skew your results, leading to ineffective campaigns.
Business leaders should take a “wide-angle approach” to data collection by gathering not only behavioral trends and location-based insights but also third-party analytics on their business, customers, and competitors to complement their in-house customer data.” Gathering data from various sources adds to the richness of your analysis, offering you a holistic view of your consumers.
Leverage AI models. A volatile environment driven by ever-evolving consumer preferences has made it critical for marketers to respond quickly. Developing and testing hypotheses in rapid succession and updating data accordingly is key. An agile operating model combined with tech that learns at scale can help marketers implement such quick responses. Artificial intelligence models can speedily process large amounts of data, pick up on changing consumer preferences that cause volatility, and quickly assess what does and does not work in a challenging environment. In addition, the more data fed into these models, the better their capability of self-enhancement through machine learning.
AI models can also guide marketers’ decisions on which market segments are ripe for conversion and will most readily yield customers, including at what times and through which channels. This information allows marketers to develop precisely targeted campaigns.
Article originally appeared on MarTech.
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