Do you feel as though everybody is talking about predictive analytics in marketing and this puts you behind because your understanding isn’t as good? If so, you’re probably overthinking. In all likelihood, your competitors are in the same position (they might even read this guide for help!). However, the latest statistics suggest that the predictive market is growing by around 23% per year, so it won’t be long before you are behind the competition. In conclusion, you need to act NOW!
What’s Predictive Analytics?
What is the likelihood of X happening? This is essentially what predictive analytics aims to calculate through a combination of statistical techniques and historical data. If you’re wondering, these techniques include predictive modeling, machine learning, and data mining. Don’t worry, we know there’s probably a confused look on your face as you think of all the other terms you’ve recently seen such as descriptive analytics, prescriptive analytics, and others.
With descriptive analytics, the aim is to identify what happened. Meanwhile, prescriptive analytics highlights potential future strategies based on both what has happened (descriptive analytics) and what is likely to happen next (predictive analytics).
Let’s look at a basic example of predictive analytics that most people reading this article have encountered, credit scores. When applying for a mortgage or another type of loan, banks will consider your credit history as well as use predictive analytics to identify your financial responsibility and likelihood to meet all payments. If the results aren’t good, you won’t get the loan. If the algorithms suggest otherwise, you’ll receive the loan because the bank believes in their systems.
Although this is an old system, you’ll find newer examples in Amazon recommendations and Netflix suggestions. Don’t they always seem to know what you want to buy and watch? Well, they do this through predictive analytics.
Examples in Marketing
When discussing predictive analytics in marketing, it’s important not to fall into the trap of thinking that it exists in only a couple of forms. In reality, you can deploy predictive analytics in a variety of ways:
Customer Acquisition
Using identification modeling, some businesses use historical customer data to find new prospects with an interest in the business (or products). To start, you create a profile around existing customers and find those with similarities. If all goes well, you can base the model on your most loyal customers and find others willing to remain loyal to the brand.
Those who advertise on Facebook will recognize this system because it’s the foundation behind lookalike audiences. Ultimately, you can upload a custom audience and wait as Facebook finds similar people for your marketing efforts.
Segmentation
Should you segment your audience? Yes. Should you segment by interests, demographics, behavior, or another variable? It depends. To find a tailored answer for your business, plug the information into various cluster models. Soon enough, you’ll identify patterns and have more impetus for your marketing decisions.
Rather than guessing and potentially wasting time and money, identity these patterns and segment your customers in the right way.
Content/Product Recommendations
This is the example of Amazon and Netflix, and it’s a winner in 2021. Just think of the number of customers that Netflix has kept over the years because of their content recommendations not only in the app but through email campaigns. Customers look for something new to watch, they receive suggestions from Netflix based on their viewing history and find something new to binge on. When done correctly, this stops the customer from seeking streaming alternatives.
The technology behind these features is called collaborative filtering, and it uses past behavior to suggest potential products, content, and services.
Lead Scoring
Next, we want to focus on predictive scoring and propensity modeling because they rank leads in order of likelihood to convert. Therefore, you’re no longer picking names at random and missing out on people who were seconds away from converting. After scoring all prospective leads, you’ll know who the sales team should contact first.
In fact, you don’t even need to make the next step a manual one because you can rank leads before then sending automated marketing messages relevant to the lead’s position in the funnel.
Personalization
Finally, personalization is the way forward for marketers and predictive analytics provides it in bundles. Rather than just using the customer’s name in emails - this is no longer enough for personalization since the niche has progressed so rapidly - trigger content recommendations, send content that resonates, and provide the best customer experience possible.
Predictive Analytics - Step-by-Step
This all sounds good in theory, but how does the process work in reality? The good news is that you don’t need to know about intricate machine learning algorithms because this is left to the experts. However, here are some basic steps that marketers can follow.
Identify the Question
What are you trying to answer? What’s your biggest question? If you don’t have a clear vision when starting with predictive analytics, you’ll find that you waste most of your time because you don’t generate anything useful. You might want to learn about the best types of content for a specific segment, the value of your leads, or something else entirely.
Collect Data
If you want to answer this question, data will lead the way. Therefore, think about the data that will help to answer it. As an example, let’s say that you want to value specific leads. In this case, one of the best sources of information is historical data on qualified leads. What’s more, you might also want to gather demographic and firmographic data.
For those trying to locate leads that will convert within a certain period, you’ll also need a list of leads that haven’t yet converted.
Analyze Data
With all data collected, it’s time to start analyzing. How long does it take for leads to convert? How does this change from one channel to another? Does your industry and business size play a role in the conversion process? In truth, you can answer thousands of questions from this data, so prioritize.
Test and Build a Predictive Model
After the previous step, you will have reached several conclusions. So, it’s time to put these conclusions to the test. If you’ve determined that it takes longer for small businesses to encourage people through the funnel compared to larger businesses, test this hypothesis in the real world.
While you’ll prove some conclusions correct, you’ll render others invalid. Therefore, you’re now ready to build a predictive model based on your learnings. Thankfully, there are plenty of engineers who know what they’re doing, and you’ll also find lots of tools like Trapica that will save lots of hassle in this area (more on this in the next section).
At this stage, you have a predictive model in your business. Did you think that this day would ever come? Although it’s scary, it’s time to put this model to good use. While predictive analytics won’t inherently make decisions, it will provide the insights that you need to make the decisions yourself.
Modify and Expand
Lastly, remember that it doesn’t take much before your model needs changing or updating. Let’s not forget, nobody at the beginning of 2020 predicted a global pandemic (even the very best predictive analytics models can’t foresee external events!). Consequently, be willing to continually improve your models and create new ones when others are outdated.
Tips to Get Started
In this final section, we want to go over some of the best tips for those starting with predictive analytics. Firstly, you can instantly cut your task in half by teaming up with a clever market intelligence tool. We promised we would come back to Trapica, and now is the time. This market intelligence tool considers various forms of data to provide insights into your audience, market, brand, and competitors. All in all, valuable insights should help to make strong marketing decisions.
Especially for small businesses, you don’t need to worry about data, models, and tasks that only advanced engineers should handle. Tools like Trapica are accessible, simple to use, and ultimately effective.
Also, we recommend educating your staff and getting everybody on board with the new system. If everyone buys into predictive analytics, their motivation and dedication will bring the strategy to life. During the early stages, create an environment whereby it’s acceptable to make mistakes. Since everybody is in the same boat, encourage a positive atmosphere and you’ll eventually get it right.
If you don’t want to use predictive analytics right now, be aware that this does mean a game of catch-up later. More and more businesses are adopting the technology, and this is something with the potential to infiltrate every corner of the globe. Predictive analytics help businesses to spend their money wisely, utilize resources, learn about their audience, and boost conversions, sales, revenue, and profit.
However, there’s a big difference between dabbling in predictive analytics and getting it right. Why not start with a reliable tool like Trapica?