Predictive Analysis Marketing: How to Use Data to Create More Effective Marketing Strategies

The competition among businesses is fierce, especially in the SaaS landscape of today.

People have more options than ever before that are just a click away. They want reliable and efficient results and they want them quickly. As informed as users are, they have increasing expectations from your business. And if you fall behind these expectations, you may risk losing them as customers.

Predictive analysis marketing is one effective way to build a customer experience that fulfills your customers’ expectations.

Using consumer data that can help you predict the behaviors of your customers gives you a deeper understanding of your customers’ wants and needs, and will push you ahead of your competitors.

If you want to learn more about predictive analysis marketing that will enable you to understand customer behaviors and trends, predict future shifts, and plan your campaigns accordingly, keep reading.

What is Predictive Analysis Marketing?

Predictive analysis is a form of analysis that uses current and past data with a combination of statistical techniques like data mining, predictive modeling, and machine learning to predict marketing trends and possible scenarios.

Incorporating machine learning into predictive analysis now allows companies to analyze massive amounts of data at a great speed. It boosts the efficiency of predictive models, and allows you to try and see into the future in a sense.

Why is Predictive Analysis Marketing Important?

Marketers have been leveraging data to understand and improve marketing effectiveness for a long time. Over the years their efforts have gotten more and more advanced.

Customers have more choices today, they are not confined only to products/services that are in their city or country anymore. They can order and sign up for what they want, when they want it. This results in more and more competition over the years.

Predictive analysis allows you to understand your customers, predict future shifts, and plan your marketing campaigns accordingly. The insights from predictive analysis better equip you to determine the best strategy for your customers, and, therefore, your business.

How is Predictive Analysis Done?

So, you know what predictive analysis is and why it’s important. 

But how can you conduct it? 

Below, I have listed 6 steps to help you get started.

1. Define the result you want to achieve

Predictive analysis allows you to visualize possible outcomes. Clearly defining your objective will help you optimally conduct your analysis to give you the results you need.

Some examples of questions to which predictive analysis can provide answers are:

• Which of your customers are more likely to stay loyal without any incentives?
• Which product/service will be in more demand by the end of the year?
• Which areas of your production/development might prove more costly than before in the next quarter?

You may see that your existing data might not be enough to answer all your questions. In this case, you need to work on collecting relevant data.

2. Collect relevant data

Analysis is all about data. Therefore, collecting the right kind of data that can answer your questions is essential.

With the help of tools like Hockeystack, you can collect all your data in just one place and create dashboards that let you see patterns in user behavior to make accurate predictions about future actions. You can also integrate your CRM to Hockeystack and manage all your data from a single tool.

3. Clean and analyze your data

Once you have the data, invest some time into cleaning your data up.

There is a computing term, “Garbage in, garbage out”, which basically means that no matter how advanced your predictive analysis model is, it is limited by the quality of data you put in it.

Your predictions will be inaccurate if the data you rely on for analysis is poor. You can cleanse your data to ensure that the results you get are not only accurate but also actionable. Now that you collected, organized, and cleaned your data, it’s time to perform analysis by asking questions like:

• Does the average number of days to convert vary between different areas?
• Do variables like company size or industry correlate with the number of days to convert?
• How do processes like localization affect the likelihood of conversion between different cultures?

The number of questions you can ask here is basically limitless.

4. Build and test your hypotheses with statistical techniques

Once you are happy with your data, it’s time to test your hypotheses.

Just because you might logically assume something, it may not be necessarily true. Test all your hypotheses and go with your data.

You can use techniques such as A/B testing where you split your audience numbers to test a number of variations of an element and determine which one performs better. It lets you try out different designs and different models to figure out what your visitors would like. It lets you try out two different models at the same time.

5. Choose predictive analysis solutions or build your own model

You’ll require experience in data science or have to rely on the assistance of data scientists or someone with advanced analytics skills to build predictive models from scratch.

You can also outsource this work to a firm that provides predictive analysis services.

But if these solutions seem too costly to you, there are many software solutions that have predictive analysis tools.

Though these tools may not offer you the kind of benefits that a data scientist can bring in, they offer built-in predictive models and are easy to use. A predictive analytics software can be a good start for your business to start predictive analysis marketing.

6. Deploy the model

Now that you have a working predictive model, it’s time to start putting the results of your analysis into practice.

Remember that this analysis or any tool in the world will not make decisions for you. It is up to you to turn your data into insightful actions.

Examples and Use Cases of Predictive Analysis Marketing

Now that you know about the importance of predictive analysis, let’s take a look at some examples of how you can use predictive analysis in marketing.

1. Customer segmentation

If you don’t know how to segment your customers in different ways such as based on behavior, demographics, firmographics, interests, etc. predictive analytics will help you find out which works better for your business through experimentation and data analysis.

Experiment with different cluster models and you may stumble upon something you didn’t expect or find the best audience segmentation for your company.

2. New customer acquisition

You can use your customer data to create identification models. This essentially refers to identifying and targeting people that resemble your existing customer audience in some way.

3. Lead Scoring

A study in 2015 identified predictive lead scoring as one of the top three use cases of predictive marketing analysis. The process comes down to using past customer data to rank identified audiences according to their likelihood to convert.

Depending on your business model, you can use this data to send relevant marketing messages to or prioritize your sales efforts toward a certain kind of possible customer when they reach a threshold in your lead scoring model.

4. Content & advertisement recommendations

Basically, collaborative filtering is using past behavior like content consumption patterns to make recommendations.

For example, let’s say you found out that one particular feature of your product/service is really helping with getting conversions based on the data you got from your analysis on your customer journey. You might want to advertise this specific feature more and make it more prominent in your marketing campaigns.

And you can use a tool like Hockeystack to keep track of your online advertisements and see how many conversions and how much revenue they return in order to manage and optimize your online advertisement campaigns. 

5.  Personalizing customer experience

We all know and love “Hey {first name}” emails. But you can go far beyond that now.

Take a look at your segmentation, lead scoring, and content recommendations. You are now able to offer a personalized customer experience and increase the relevance of your marketing strategies and their return to you as revenue.

Using Predictive Analysis to Create More Effective Marketing Strategies

In this article I explained what predictive marketing analytics is, what you can do with it, and how the process works, here is what you should do next to create more effective marketing strategies.

Choose technologies based on what you need

You can work with your data team to figure out what your business’ technical requirements are, and work out the best solution.

For example, can start by choosing and collecting all your data in a tool like Hockeystack to view all your data in one place. This way your employees can utilize easy-to-access and organized data for your predictive models without wasting precious time cleansing and searching data.

Learn and adjust

After your first couple of attempts at predictive modeling, you will notice you are getting better each time if you take your findings and apply them to your future analysis for even better results. As you learn the limits and capabilities of predictive analysis marketing, your models and marketing campaigns, therefore, your results will improve.

While there are a lot of ways to approach marketing and a lot of strategies to follow, predictive analysis marketing is one of the most used and reliable strategies you can leverage.

You need to collect, organize, and analyze a large amount of data to make accurate predictions. Visualizing inter-departmental data with the help of a tool like Hockeystack can help you extract key insights from your data without spending hours on data collection and analysis. With the easy-to-use interface and automatic cookieless tracking, you don’t even need to rely worry about writing complex blocks of code to get the information you need. 

Now you know how to start your data collections, conduct your predictive analysis, create and experiment with models, and watch as your business grows and flourishes.