There’s a lot of hype and a sense of urgency surrounding predictive marketing.
Why?
Because things are moving very fast, and the brands that were able to get away with making educated guesses as to where best to direct their marketing budgets are realising that they must now make informed decisions to remain competitive.
This is exactly where SLINKY steps in.
The good news is that predictive marketing is not an unattainable technology for many brands, even without breaking the bank and without requiring a large staff of IT professionals.
All you need is a quality digital marketing partner that understands how to apply the principles of predictive marketing to your business goals.
In addition to being easy to implement, predictive marketing is a powerful tool for transforming how a business goes about planning, timing, launching and adjusting marketing campaigns.
This article provides an overview of how predictive marketing works, why it has become such a key component in creating digital strategies today and how a digital marketing agency like ours can assist clients in making smart decisions, rather than getting lost in dashboards.
What Does Predictive Marketing Really Mean Without Using Tech-Speak?
Many people who hear the word “predictive” think of algorithms and computers that control every aspect of the marketing process while the marketer sits at home sipping coffee, watching the numbers move.
The truth is that predictive marketing is far less dramatic and much more practical.
At its core, predictive marketing means being able to predict what your customers are likely to do next. Not exactly. Not perfectly. But… better than guessing.
Predictive marketing uses the following elements to determine what customers may do next:
- Past behavior
- Demographic patterns
- Signals of interest
- Preferences of media channels
- Timing data
- Real-time engagement
Predictive marketing is like seeing someone glance at the dessert menu before they even finish their main.
You don’t know for sure, but you have a pretty good idea that they’ll be ordering something sweet. And in the world of business, those “good chances” are worth a lot of money.
A digital marketing agency uses various tools, analytics platforms, audience models, and campaign data to help businesses predict what might happen and then take action on that prediction before a competitor does.
This prevents issues, including wasting money on ad spend, running a campaign at the wrong time, and creating content that is received poorly.
Why Is Predictive Marketing Such A Big Deal Today?
Customer behaviour has become harder to detect with the naked eye.
Consumers now use multiple devices, switch between different channels, compare more options than they did previously, and expect brands to recognise the subtlest of signals.
Predictive marketing is a way to bridge those gaps.
Here are four reasons why predictive marketing is important now more than ever:
1. Attention Spans Are Collapsing
Consumers are skimming more and scrolling past ads in a matter of seconds. If you fail to put the right message in front of a potential buyer at the right time, you’ll lose them.
A digital marketing agency can identify the micro-patterns when consumers are actually paying attention to messages and then adjust campaigns so that impressions are not wasted.
2. The Competition For Consumer Attention Has Never Been More Fierce
Brands aren’t only competing against other brands. They’re competing against endless notifications, streaming platforms, auto-playing videos everywhere and the cacophony of the online world. Predictive marketing cuts through that chaos.
3. Data Privacy Changes Have Disrupted Digital Advertising
With less third-party data available, marketers must find new and smarter ways to understand the data they do have access to. Predictive models fill in the gaps by identifying behavioural patterns without requiring invasive tracking methods.
4. Customers Expect Brands To Understand Them
Consumers are tired of seeing irrelevant ads and emails that seem outdated. Predictive marketing enables brands to create personalised experiences for their customers without crossing the line into creepy territory.
How Predictive Marketing Works in Actual Campaigns
While it sounds fantastic, the question is: How do businesses actually use predictive marketing to make their campaigns smarter, faster and more effective?
Here are some examples:
Predicting What Buyers Will Buy Before They Know It
Most customers don’t suddenly decide to purchase a product. Instead, there is a pattern of behaviour leading up to the final purchase. It can be a long one, a short one, but a pattern definitely exists.
By analysing repeated behaviours such as:
- pages visited
- products hovered over
- blog topics viewed
- abandoned shopping carts
- time spent on specific services
- content engagement
Your marketing team can forecast what customers are likely to do.
Once the trends are identified, campaigns can be made much smarter:
- show ads that relate to the future interests of the customer
- provide content that they’re likely to read
- send emails that are relevant to the customer’s recent behaviour
- offer services when they reach peak relevance
To the customer, the experience should feel natural, as if the brand were paying attention to them.
Timing Your Campaigns At The Right Time
Timing is not half of the battle in digital marketing; timing IS the battle.
Predictive marketing identifies:
- days when conversions typically occur
- times of day when engagement typically occurs
- seasonal or microseasonal spikes in interest
- when interest begins to wane
- how long the decision process usually takes
For example, suppose a digital marketing agency notices that a customer engages with service-related content at night but converts the next morning, that small piece of knowledge can flip the way campaigns are launched.
Rather than throwing the budget at random, campaigns get delivered when the customer is most receptive, not when the marketing department simply hits publish.
Identifying When Customers Are Going To Drop Off
Surprisingly, customers do not always leave a brand abruptly. There are often warning signs, such as:
- decreased click-through rate on email
- reduced duration of website visits
- avoiding key pages
- decrease in social engagement
- increase in time between purchases
Predictive models can flag these signs early enough for a business to re-engage with a customer before they leave.
A digital marketing agency can develop re-engagement campaigns based on these signals and enable brands to maintain customer loyalty rather than constantly searching for new leads, which can be extremely costly.
Estimating How Well a Campaign Will Perform Before Launch
Perhaps the greatest advantage of predictive tools is the ability to estimate how well a campaign will perform even before spending a single dollar. This includes estimating:
- changes to cost per click (CPC)
- fluctuations in search volume
- likelihood of conversion
- behaviour regarding email opens
- interest decay
- content fatigue
- new opportunities emerging
This enables businesses to develop marketing strategies based on data rather than just hoping. After all, hope does not pay ad bills.
How a Digital Marketing Agency Can Make Predictive Marketing Work
Predictive marketing sounds amazing in theory, but most businesses lack the resources to collect and analyse data, develop predictive models, and test and implement new strategies as fast as required.
A digital marketing agency provides the missing link between a business’s desire for predictive marketing and actual implementation.
Below is how an agency can make predictive marketing actionable.
1. Collecting relevant data, not overwhelming amounts of data
There is a large difference between “data” and “usable data.” An agency can assist in eliminating unnecessary data points, determine which ones are worth tracking, and set up systems that don’t require constant manual review of data.
2. Turning raw numbers into actions
Data is only useful if it indicates what action to take next. The agency will convert the behaviour patterns, predictive indicators, and platform insights into actions to be taken by the marketing department.
Examples of these actions include:
- budget adjustments
- keyword selection
- content topic selection
- targeting changes
- creative refinement
- landing page improvement
In other words, the agency will convert numbers into strategy.
3. Developing adaptable campaigns as opposed to static campaigns
Traditionally, campaigns ran from start to finish with occasional tweaks. Meanwhile, predictive marketing creates campaigns that can adapt and change based on the customer’s response to a campaign at any given moment.
An example of this is to add budget to a trending area of opportunity or remove budget from a campaign that is declining rapidly.
4. Minimising guesswork that leads to wasted advertising spend
Guessing can be very expensive. Predictive insights allow businesses to assign budgets to the best opportunities available, versus a “let’s try it and see” approach.
5. Establishing long-term strategic planning through continued learning, not luck
The longer predictive models continue to run, the more they become intelligent.
An agency utilises this compounding knowledge to:
- improve lead scoring
- develop stronger content calendars
- finetune customer personas
- identify seasonal patterns
- stabilise conversion rates
It is marketing, just with fewer surprises.
Real-World Examples of Predictive Marketing
Let’s explore some basic, real-world examples (the type of examples that occur daily).
Example 1: A Service Brand Identifies When Customers Are “Warming Up”
Over the course of six months, a digital marketing agency may track customer behaviour and identify that customers typically visit three specific pages before submitting an inquiry form.
This insight allows the business to:
- promote those pages earlier
- create content about those pages
- retarget users who stalled in their journey
Now, the business sees increased conversions even without changing the product.
Example 2: Predictive Timing Enhances Ad Efficiency
A business thought that its audience was most active during business hours. However, predictive analysis reveals that the real engagement spike occurs later in the evening and early in the morning.
By allocating the budget to those windows, the cost per conversion decreases. In many cases, significantly.
Example 3: A Brand Recovers Customers Before Losing Them
Predictive dropout indicators identify a segment of subscribers going silent. A re-engagement sequence is created and sent just before the customer becomes unreachable.
Rather than losing the customer quietly, the business wins back a percentage of customers that were almost lost.
Why Predictive Marketing Allows Businesses to Compete More Fairly
Small to mid-sized businesses often feel like they cannot compete with larger corporations, particularly in digital. However, predictive marketing allows smaller businesses to level the playing field in several ways.
Smaller businesses actually have some advantages:
- they move faster
- they can adjust faster
- they have fewer layers of red tape
- their audiences are easier to understand
- they benefit more from small increases
A digital marketing agency assists in making decisions with the same level of intelligence that corporations use, minus the bloat.
Final Thoughts
Predictive marketing does not eliminate the need for strategy, creativity, and human decision-making. Rather, it provides businesses with a clearer foundation and reduced anxiety related to guessing.
Using predictive marketing is similar to turning on headlights while driving at night. While you can technically drive without headlights, … why would you?
With the correct digital marketing partner assisting in the process, businesses can identify opportunities earlier, avoid expensive mistakes, and execute campaigns that feel more intelligent and responsive.
If your business would like to utilise predictive marketing to guide campaigns, improve targeting, and increase confidence in decisions, a specialised digital marketing team can assist you in translating data into actionable results, and not just leaving it in a spreadsheet.