Big Data is has taken over digital marketing and the digital landscape as a whole. Currently:

• Over 2.5 quintillion bytes of data are created every single day
• By 2020, it’s estimated that 1.7 MB of data will be created every second for every person on earth

This means that the data available for planning, designing and implementing a marketing campaign is getting more and more in-depth. Analytics capabilities are expanding at a rapid rate and businesses that take advantage of them will not only get the most out of their campaigns now, but they are setting up their business for success well into the future.

Predictive analytics is one of these capabilities that can be implemented in your campaigns to generate significant results for your business. 

 

What is Predictive Analytics?

Predictive Analytics is a branch of advanced analytics that harnesses big data to predict future events or results. It integrates a number of techniques from other data fields such as data mining, statistics, modelling, machine learning and artificial intelligence to make predictions. 

Put simply, it predicts future opportunities and risks by analysing patterns in historical data that can be processed further for identifying future risks and opportunities.

 

What are the Steps in Predictive Analytics?

Step 1: Define Outcomes
Identify the business questions that you want your data to answer, such as ‘How many products, and which specific ones, does a repeat customer buy over a 12 month period?”

Step 2: Collect Data
Determine which data you need in order to answer the question, how you are going to collect it, and how to organize it.

Step 3: Analyse Data
Inspect the data that you currently have to determine useful information that you can use to form conclusions about your customers. Test your conclusions.

Step 4: Model the Data
Use the data you have collected to generate predictions about your customer’s future behaviour.

Step 5: Deploy Strategies
Create data-driven marketing strategies and implementation tactics using the data.

Step 6: Monitor Campaign
Monitor your data-driven campaigns and create reports to confirm how successful they have been.

 

What are Some Uses for Predictive Marketing Analytics?

Once you have identified the process and know that you have the capabilities in-house to implement it, predictive analytics can be applied to inform a number of marketing campaigns for your business. The following are some of the most common uses of predictive marketing analytics.

1. Predicting Customer Lifetime Value

The Customer Lifetime Value is how much the customer is worth to your business throughout the entire relationship. Predictive analytics can be used to look at the historical data of customers to identify trends that will predict the duration of the relationship as well as how much revenue it is likely to generate. This can help better inform your target customer acquisition costs and give you a more accurate expected ROI.

2. Targeted Distribution of Content

Predictive analytics can tell you which types of content work better for which audiences. It can allow you to customise the content so that it engages the recipient by identifying which type of content resonates with a specific audience, which channel to reach them on, and when to send it. When your audiences get high quality, relevant communications from your business they are more likely to see value in your products and services. For instance, are property buyers looking for consultations or white papers? How would they want to be contacted? Where do they hang out to effectively target your remarketing campaign?

3. Detailed Lead Scoring

Lead scoring is simply ranking leads (with a numerical value) based on how much they have interacted with your business. The idea behind this is that their score can help your business to identify where they are in the buying funnel. By assigning lead sores to prospects, sales and marketing divisions can collaborate more easily to make the buyer journey smoother. Once you have scored each lead based on their readiness to purchase, predictive analytics can let you know to target them based on what their future buying habits are likely to be.

4. Lead Segmentation & Nurturing

Lead nurturing is often necessary to move a prospect from awareness of your business and its products or services through to sale. Effective lead nurturing requires planning and strategising. Predictive analytics can help businesses group their leads by using demographic and behavioural data, and applying them to each audience segment. This allows businesses to create lead nurturing campaigns that are tailored specifically to move prospects further down the sales funnel.

Is predictive analytics something that is of interest to your business? Get in touch with the digital marketing specialists at Digital Squad to find out how you can apply it to accelerate your strategy.

 

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