Data-savvy marketers have access to numerous valuable insights about their customers that can monitor their performance closely and identify data-driven potential for improvement. Marketing departments that don't engage in data-driven analytics run the risk of being overtaken by the competition and failing to meet consumer expectations.
Marketing analytics is all about finding patterns in data, from analyzing individual platforms and campaigns to evaluating how multiple campaigns interact with each other. Insights of this kind give companies a deeper understanding of how their customers are behaving and what trends are developing. In this way, companies can ensure that their marketing activities achieve maximum efficiency and impact. Some of the biggest benefits of marketing analytics include the following:
Holistic view of marketing data: a reliable marketing analytics program identifies all tools and platforms used in a company to centralize data for analysis in the next step. The data from paid marketing activities, for example, provide information about where the budget is used most effectively. If an image on Facebook generates a higher click-through rate than an ad on Twitter, the marketing team can take Facebook's winning image to Twitter and see if it performs better there as well. Over time, marketers can add more elements to the marketing mix and get a full picture of what's working and what's not.
Identifying potential for optimization: marketing departments only benefit from their data if they use it effectively to evaluate current performance. A/B tests are an important part of marketing analysis, allowing to compare different measures immediately. For example, two different ads, both in terms of design and the text used, are placed on the same topic to see which content is better received by the target group. A/B testing is about getting comfortable with the end-to-end process: setting up a hypothesis and the test parameters (channels, KPIs, duration), executing the plan and feeding these insights into the next marketing activity.
Measuring brand awareness: large-scale advertising campaigns have the potential to increase brand recognition, but with marketing analytics, companies can delve into the analysis of brand awareness. By comparing important key figures from social channels (followers, engagement rate, etc.) with those of the competition, companies can roughly estimate their brand awareness to begin with. Other data points can then be included in the awareness analysis, such as implementing social listening tools to capture relevant online chats, search volume and direct traffic data, surveys of target customers, Google Alerts, and data from Google Search Console.
Better segmentation of existing customers: profiling and segmentation allow companies to group similar prospects based on the criteria most important to them; consequently, allowing them to develop targeted strategies tailored to each segment's preferences. For example, a sports shop could target customers geographically by showing, for instance, surfboard ads to customers located on the coast and ads for skis to customers in mountainous areas.
Relevant information for each phase of the customer journey: the customer journey usually includes multiple touchpoints across a variety of channels. Marketing analytics provides the insight that most customers use social media for brand awareness, search engines for making purchasing decisions and the website for purchases. With this knowledge, the right information can be made available for each phase of the customer journey. For example, less detailed but more promotional content on social media pages and answers to the most frequently asked questions about the product on the website. In general, Google Analytics is a good first step: marketing teams can set goals here, like clicking the ad, booking a demo, etc., and then identify the sources that led customers to achieve that goal. Data from Google Analytics helps improve the customer journey by identifying stumbling blocks. For instance, many interested parties click on "Book a demo", but only a small percentage fills out the corresponding form, but it is important to find out why that is so.
Attribution modeling shows most important touchpoints: the analysis of the customer journey shows the individual steps of the customer before the purchase, but it is not clear which of these steps brought the highest ROI. This is where attribution modeling comes into play, identifying which touchpoints were decisive in the customer journey. Marketers can then spend their budget on activities that have proven successful. With the knowledge gained from attribution modelling, marketing strategies can be adjusted, the ROI of campaigns improved, and expenses saved. Certainly, it is important that the attribution model fits the business model perfectly. Most models start with the so-called "last touch", in other words with the last touchpoint before the purchase or conversion. However, there are many other touchpoints to consider, including time decay, first touch, and more.
Insight into acquisition costs: when all different marketing strategies have been determined, and when it is clear which channels and strategies are most effective, it is time to pull all this information together and determine the cost of a single customer acquisition. Fortunately, since the budget spent on a campaign is simply divided by the number of customers acquired, that’s easy to calculate. A marketing analytics program tracks the performance of all cross-channel campaigns and can therefore easily identify which strategies, products, and demographics have the lowest cost per acquisition (CPA). Hence, it is worth investing more in such tools to increase the marketing ROI.
By Daniela La Marca