Who should read this?
C-Level Executives – Why invest in marketing analytics?
Marketing Managers – How can our analytics team help with marketing?
Analytics Professionals – What marketing-related problems can I solve?
Analytics Job-Seekers – How can I improve my pitch during interviews?
The old saying, “nobody counts the number of ads you run; they just remember the impression you make” is truer now than it has ever been.
Although marketers run more ads today than ever before, many continue to struggle to reach their respective markets. As a result, the average marketer burns through their budget for minimal returns.
People hate ads. Specifically, people hate ads that suck—and the same can be said about any kind of marketing activity.
When a field is as saturated as digital marketing, you’re bound to have your digital space polluted with crappy content. There are plenty of people who claim to know what the market wants, but only a handful can walk the talk.
Rather than letting loose generic ads upon an undefined audience (which is a great way to waste cash btw), marketers should aim to understand their customers on a deeper level.
Making the right impression takes intelligent decision making, which in turn depends on having access to the kinds of facts and evidence that only a foothold in data analytics can provide.
This article will outline six use-cases that illustrate how working with data can yield greater returns. If you’re sick of feeding your budget to the black holes of guesswork and intuition, read on.
1. Customer Segmentation
Customer segmentation is the practice of splitting your customer base into groups that share common characteristics that matter when it comes to marketing.
Think of customer segmentation as a magnifying glass that allows marketers see unique groups within a broader audience. Segments might be based on factors like age, gender, interests, or spending habits—anything that helps you tap into the specific demands of your target market.
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:
- Which customers generate the highest lifetime value for my brand?
- What features does each customer group look for in my brand?
- Which customers provide the highest upside?
- Who should I target for a discount campaign?
Once you can identify groups within your larger audience base, you can easily set up campaigns that speak directly to each of them and create content that generates genuine interest instead of passive disgust.
Looking to learn customer segmentation?
Visit our Customer Segmentation 101 blog for some hands-on exercises—we also attached a downloadable dataset you can work with.
2. Market Basket Analysis
If customer segmentation is about finding people who share similar traits, market basket analysis is about finding patterns in what people buy.
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, let’s say people who buy underwear are more likely to buy socks during the same shopping trip. A department store that knows this might choose to place their sock aisle closer to other undergarments, or offer a bundle featuring both 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, “market basket”). Today, it’s applied to all forms of retail, including consumer goods, fashion, and others.
One of the best known applications of market basket analysis is in the fast food industry. Restaurants like McDonald’s and Burger King know 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’re at 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!
3. Marketing Mix Modeling
One of the biggest questions marketers face is, finding an optimal way to allocate their budget and effort across a slate of different marketing channels.
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 how best to spend, and can be used to predict sales, track campaign effectiveness, and create data-driven budget plans.
Any marketer that can apply marketing mix modeling will have a breeze with budget approval, effectively waving goodbye to awkward conversations about wasting company money.
4. Propensity-to-Purchase Modeling
As the name implies, propensity to purchase modeling (a.k.a. lead scoring or likelihood-to-buy modeling) is method used to determine the likelihood of a customer to buy a product or perform a 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’d be able to direct your ads to customers who are more likely to convert, while deprioritizing those who are less likely to make a purchase.
This can potentially save you tens of thousands in ad expenses, increase your conversion rates, and boost ROI. If you ask us, that’s a pretty good reason to go data-driven.
5. Sentiment Analysis
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 practice of extracting insights about the emotions within a series of words.
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 broadcasting ideas. Using sentiment analysis, you can get better and faster feedback so that you can meet your market where it needs you.
6. 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 it takes for a driver to change car tires, for an organic restaurant to restock on fresh produce, or for the average woman to shop for more 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 an easy return for very minimal investment.
As we said from the start, getting positive returns from your marketing activity isn’t a matter of running the most ads or having the widest reach; it’s about hitting the right notes to appeal to your target market.
These six use-cases illustrate how data-driven marketing does precisely that. By cross-checking the numbers, you can make reliable conclusions based on past encounters and experiences.
Intelligent guessing is no longer the intelligent approach to marketing. From here on out, it’s data or bust.
Over the next few months, we’ll be dedicating an article each for the six marketing analytics use-cases. These will include how-to’s on MS Excel and Google Analytics so that you can begin using data to drive real-life marketing strategies and campaigns.
Like us on Facebook, and you’ll be the first to know when a new article drops…or, if you’re after more than just snippets from our playbook, you can sign up for our upcoming Marketing Analytics Masterclass and leave with entire pages’ worth of strategies and techniques.