In the Mad Men era of marketing, appealing to the customer’s emotional drivers was the standard – the “go-to” play.
Appeals to the heart won the wallet. Take a more recent example, from a category with a very emotional appeal: the Budweiser Clydesdale horse campaign.
For some people, their feelings about those horses and what they say about the brand transcends more logical price or taste comparisons.
While categories with basic emotional appeals may always exist, today’s customer is more sophisticated and the marketing necessarily more complex.
Simultaneously, there is great pressure on chief marketing officers to deliver organic growth for their companies. The playbook must have more pages!
One of those new pages is predictive analytics, which empowers marketers by providing insights into customer behavior and how certain strategic decisions can increase sales.
Those who can best predict the customer and act on those insights will ultimately take market share from their less attuned competition.
In fact, 83% of marketers say they now use predictive metrics to build competitive experiences and make smart product decisions.
Predictive Analytics X’s & O’s
Predictive analysis of data allows you to play out different “what if” scenarios so you can develop campaigns that achieve optimal growth.
Getting more specific, here are four predictive analytics plays CMOs might consider, depending on their business:
1. Sentiment analysis
A sentiment analysis identifies and categorizes the opinions expressed in a section of text to determine whether the writer’s attitude is positive, negative, or neutral.
If customers post product reviews on their blogs or discuss your services on social media, a sentiment analysis will dissect their text for clues about their satisfaction levels.
Scoring the relative sentiment expressed in mediums from social media commentary to call center transcripts, and comparing peaks and valleys and their drivers over time is powerful.
It essentially transforms the idea of “voice of the customer” from a concept to a real tool to sharpen campaigns, products, and customer service operations.
2. Hedonic analysis
Hedonism means the pursuit of self-indulgence, so it follows that a hedonic analysis studies consumers’ preferred features.
Instead of guessing what people want, you identify which options are most attractive to them. If you’re an automotive manufacturer, your next model could include dozens of feature combinations.
Understanding which combinations will achieve the highest value in the marketplace is useful for optimizing product offerings to best match customer desires and, ultimately, demand.
3. Credit analysis
Credit rating agencies originated in the early 1900s, making credit the oldest form of predictive analysis and a basic tenet of modern businesses.
Whenever you offer financing, you’re asking, “Does the customer have both the good faith and the ability to pay?”
If you can tweak your approval formulas to qualify more candidates without triggering higher default rates, you’ve mastered organic growth creation. Someone figured that out already, right?
The difference today is the availability of abundant supplemental behavioral data that, it turns out, have a lot to do with understanding whether the customer will be a faithful creditor.
Social media patterns, cellphone ownership, and usage are just a few ways credit can be predicted more reliably, which is very useful in markets that don’t have traditional credit-prediction methods like FICO scores.
4. Churn propensity analysis
Another behavior prediction measurement, churn propensity analysis, anticipates how likely customers are to cancel their annuity contracts.
This metric is perfect for subscription-based businesses as well as credit card providers, media brands, cellphone carriers, and direct response companies.
If you know which customers will leave and when, then you can court them to remain with you by employing preventive marketing measures.
Altered service options and tailored discounts help you avoid losing their business long-term.
One rule of marketing that hasn’t changed is that it’s less expensive to retain a customer than to acquire a new one.
Use with caution
Be careful with the possibilities enabled with the new playbook.
Predictive analytics enable you to engineer “wins” such as mastering dynamic pricing, increasing profits, and retaining more customers, but those victories aren’t achieved by numbers alone.
The most successful CMOs marry in-depth metrics with original thinking to deliver standout campaigns.
“Data analytics — big data — is not a substitute for innovation. It’s not a substitute for creative thinking and leadership,” said Sandeep Sacheti, executive vice president for customer information management and operational excellence at Wolters Kluwer.
CMOs who can tap into both the logical and creative will achieve greater professional success today and in the future.
Anthony Scriffignano, senior vice president and chief data scientists at Dun & Bradstreet, echoed this idea: “The environment is busy and chaotic. Marketers are under a lot of stress and pressure to deliver the growth,” he said.
This new data era moves away from the creative and the gray to the very specific, the black and white, the binary. They’re not going to deliver that growth with data-driven analytics unless they take the careful time and process to do it right.
Know your data strategy
With some success, perhaps in the form of a pilot project, you’ll want to get much deeper.
Consider your overall scheme if you and your company are going to be real data players.
1. Get intimate with your data
Verse yourself in your analytics and look for gaps in the numbers.
It’s worth identifying your current analytics state and your ideal. What would the perfect setup look like, and which metrics do you need to get there?
But be discerning about which numbers you emphasize. You may not need every metric available to you, and your vision for how to use your data should evolve as your company grows.
Blind pursuit of an ideal state that doesn’t match your organization is wasteful, so evaluate which information moves you toward your goals.
As you determine the most valuable threads, you’ll have to make spending decisions about how to gather, validate, integrate, and analyze the right data.
2. Think scientifically
You don’t need to be a data scientist yourself to use analytics effectively, but you do need to hire some.
Point-and-click technology is not an honest replacement for the hard work data scientists perform to root out causal relationships, and it creates a false sense of control over the numbers.
Your competitors are already catching on to the importance of data scientists and going after top talent via direct hires or consultancies.
You can’t afford to fall behind in this area, and data science is not a DIY game. Recruit the best data team you can find by offering candidates big, bold projects and a culture that values their work.
3. Know what matters to you
Do you know your five most impactful marketing data metrics? I don’t mean in concept; I’m talking about hard numbers. Have you tested your data, assessed its weaknesses, and identified statistically proven causal relationships?
For instance, you might find that if you drop service wait times by 10%, you can increase prices by 5%.
The data might indicate that changing a single term of your offering like the subscription cancellation policy and its accessibility could reduce customer churn by 10%.
Make a plan to discover the undeniable facts of your business performance. As a leader or marketer, you must deal in proven, impactful metrics, or what I call the lowest-hanging analytical fruit.
Predictive data and the future of marketing are intertwined. “Data analytics is going to be standard, expected practice in every business function and business model,” Sacheti said.
Just like HR is a function that exists everywhere in every company and finance exists everywhere in a company, big data scientist is going to be a profession that’ll be expected in every company.