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The old adage, “nobody counts the number of ads you run; they just remember the impression you make” is truer now than it has ever been. Even though marketers run far more ads today as compared to before, many still struggle to reach their respective markets. As a result, they burn through their marketing budgets without achieving significant returns.
Studies show that over 70% of consumers dislike having ads on their phones, and only 7% clicked an ad because they found it compelling or provocative. (Lyfe Marketing)
The problem arises because of two things: (1) thousands of brands are gunning for limited customer attention, and (2) marketers are delivering generic content to generic audiences.
The first cannot (and should not) be helped, for the advantages far outweigh the disadvantages. Because of the Internet, companies of all shapes and sizes are able compete on a global scale. This results in more choices for the customer, and a level playing field for businesses all over the world.
The second is where marketers can and should definitely act. Rather than blast out generic ads to an undefined audience (which is a very poor way to spend a marketing budget), marketers should aim to understand their customers on a deeper level.
Getting great returns from marketing is no longer a function of accumulating ad real estate or capturing the most eyeballs; it’s about putting the right message in front of the right market at the right moment. You can do this by applying basic analytics to everyday marketing activities.
This article will walk you through 7 marketing analytics use cases to get the most bang out of your budget.
According to Tech Target, customer segmentation is “the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.”
Customer segmentation can be thought of as a magnifying glass that allows marketers see unique groups within their broader audience.
In the UK, skincare brand NIVEA Sun was able to divide their market based on demographic and attitudinal characteristics. This allowed the brand to grow its portfolio to over 40 products, increase its market share by an average of 6.4% per year, and expand into new markets such as men’s facial care, aftershave, and deodorants.
Customer segmentation is a marketing analytics use case that can answer important questions you may have about your market such as:
Once you can identify groups within your larger audience base, you can easily set up campaigns that speak to each of them—then you can truly say that you know your market.
Market Basket Analysis
If customer segmentation is about finding people who share similar traits, market basket analysis is about finding products that share similar traits.
The underlying theory behind market basket analysis is this: if you buy a certain set of items, you are more (or less) likely to buy another set of items.
For example, people who buy pasta sauce might be more likely to buy canned goods and seasoning. A grocery that knows this might see an uplift in sales by placing the pasta section beside the canned goods and seasoning aisles—or, better yet, by creating a promotional bundle that features all those products.
Market basket analysis was first applied in supermarkets and groceries to predict which items were likely to go together in a single cart or basket (hence the phrase “market basket”). Today, its applications extend to all forms of retail, including consumer goods, fashion, and others.
One of the most well-known applications of market basket analysis is in the fast food industry. Restaurants like McDonald’s and Burger King know by experience that burger buyers are highly likely to order fries and soda on the side, which is why they upsell nearly every burger purchase. In fact, we’ve already gotten to a point where the upsell has become the default.
This simple bundling practice has made fast food chains billions—and it’s doubly impressive because they make far better margins on the upsell than on the initial purchase!
Marketing Mix Modeling
One of the questions marketers face all the time is: “What is the best way to allocate my budget?” or “What is the optimal mix of marketing channels if I only have X amount of money to spend?”
Marketing mix modeling is a marketing analytics technique that predicts the ROI of each marketing channel, and suggests the best combination of marketing channels given a certain budget. It helps marketers know where and where not to spend, and has other applications such as predicting sales, tracking campaign effectiveness, and creating data-driven budget plans.
Any marketer that can apply marketing mix modeling will have a breeze with budget approval.
These graphs are too upward sloping. They must be true.
Propensity to Purchase Modeling
As the name implies, propensity to purchase modeling (also called lead scoring or likelihood-to-buy modeling) is method used to determine the likelihood of a customer or customer group buying a product, or performing any predefined action.
Most models under this use case classify prospects on a spectrum of “highly likely” to “highly unlikely”—the more similar a prospect is to previous buyers, the higher the likelihood of a purchase (or action), and vice versa.
Propensity to purchase modeling is especially helpful for calibrating prospects. In practice, you might decide to focus your ads on customers who are most likely to purchase, while deprioritizing those who are less likely to purchase. This can potentially save you tens of thousands in ad expenses, increase your conversion rates, and boost ROI.
Time-to-Next Purchase Modeling
Time-to-next purchase modeling draws its inspiration from survival analysis or time-to-event analysis. However, instead of solving traditional problems like mortality, morbidity, and half-life, it is used in marketing analytics to forecast the time to a purchase or conversion.
This could be the time for a driver to change car tires, for an organic restaurant to restock on fresh produce, or for a beauty blogger to buy new makeup.
Using time-to-next purchase modeling, you can anticipate your customer’s needs ahead of time and plan accordingly.
Let’s say you work for an optical shop and know that it takes roughly two months until contact lenses need to be replaced. A month and a half after a customer purchases a set of lenses from your store, you can send them an email reminder to restock and offer them free delivery while you’re at it.
According to Listrak, replenishment emails have the highest conversion rate (28.59%) of all post-purchase emails. That’s easy returns for very little effort.
In our social media-driven world, it pays to know exactly how people feel about your brand: Are they happy with your product? Is your service enjoyable? Are your sales reps leaving a positive impression on your customers?
Sentiment analysis (also called opinion mining) is defined as “the process of determining the emotional tone behind a series of words” (Brand Watch). It uses a method called natural language processing (NLP) to help machines uncover meanings behind certain words or groups of words. Unlike the above use cases that measure probability and magnitude, sentiment analysis attempts to measure human emotion (e.g. happy, sad, angry, excited, etc.) by looking at unstructured data in the form of social media posts, reviews, blogs, and the like. Popular applications of this use case include: social listening, intent analysis, and contextual search.
Good marketing is about listening as much as it is about communicating. Using sentiment analysis, you can get better and faster feedback so that you can meet your market where it needs you.
As we mentioned earlier, getting good returns from marketing isn’t a matter of running the most ads or having the widest reach; it’s about putting the right message in front of the right market at the right moment.
Apply these 7 marketing analytics use cases and you’ll be well on your way to doing just that.
We’ve only just scratched the surface of marketing analytics. In our recent Marketing Analytics Masterclass, we tackled different marketing analytics use cases and how to apply them for business.
Anton is a freelance copywriter and analytics consultant at Cirrolytix. When he’s not staring at data, he can be found telling dad jokes and chasing after his two-year old daughter. He still can’t figure out how to automate potty training.
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