Analytics & Marketing should go together like ham and eggs, but for many marketers the combination doesn’t result in sumptuous, customer-attracting omelets. Instead, the omelet often ends up inedible. Rather than analysis providing insight, analysis frequently yields simply frustration.
Time and again, we see analysis-induced aggravation at our clients. Looking deeper, we realized that the issue was not the quality of their analysts (many were true lovers of data science). Instead, we discovered four distinct causes for these analysis frustrations. Addressing these four factors will improve the value of analytics to both your marketing team, and your whole organization.
- Asking the Wrong Questions – this is the most frequently observed factor driving marketer frustration. Questions that are too broad, or are focused on “nice to know” data can easily fill binders while driving marketers to distraction. Examples of overly broad questions are “What is my market share?” or “Are prices in my markets going up or down?” Both questions are lagging indicators – they show the impact of a host of other factors driving those trends. Just asked or tracked by themselves add little value. Examples of “nice to know” data are “micro” trends, such as a particular size of a particular color of a particular shirt in a particular market in a particular week. Having that information at your fingertips may be satisfying for a store manager, but has no value to marketing as a whole.
Instead, pose questions that are highly actionable, like “Which of my best customers haven’t come into my store (or on my web site) in the past two months,” or “Which customers bought a product last year that is due to wear out by this year?” Specific actions emerge from questions like these, don’t they?
- Data issues – frequently marketers ask questions that are more complicated to solve then they realize, or they don’t have the necessary data to answer those questions. Often, with some understanding of data availability and quality, a request can be tuned to make analysis faster with more accuracy, usefulness and less stress for everyone involved. For example, if your database does not include email conversions (purchases that come from a customer clicking through to your website and making a purchase) – you can analyze purchases made within 3 days of receiving an email and roughly estimate the impact of the email on revenue. Exact? Of course not. But fast and directional. You can only help to set the rules for email attribution if you are aware of the inherent limitations of your data.
- Actionability – before requesting analyses, marketers must ask themselves, “What am I going to do with this information?” For example, detailed analysis of social media data is only valuable if you are going to change the content and interaction strategies as a result. If you are only going to change the strategies once per year, weekly tracking of social media likes, shares, retweets, etc. are not valuable. One of the key rules of successful marketing analytics is not to spend time tracking anything more frequently than you are going to make changes.
- Lack of “measurement” discipline – the other type of analysis that is very useful is measurement, which is frequently forgotten. By measurement, we mean determining customer and prospect behavior change, usually compared to control groups – the deep dive into the impact your actions caused. Measurement is forgotten because this work is not exciting – often high-level program results are already known and management has little appetite for deep-dives. However, this is where the real learning happens. Every program works for some customer segment, for some offers and at a specific time. The learnings from that sort of analysis can significantly improve the next year’s marketing efforts – which makes measurement so critical.
Wasting analytics has two effects – it wastes resources and squanders opportunities to improve results. By exercising discipline in the questions marketers ask and diving deeper to understand the data, marketers can establish themselves as the champions of customer data and the drivers of incremental revenue.
We’re in the analytics age of marketing. Make sure your analysis makes you a better, and less frustrated marketer.