Why Your Business Needs Predictive Analytics
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Predictive analytics is a term commonly used in conjunction with «  Big Data,  » but without really understanding the meaning of the term, the concept can be pretty pointless. So, let’s find out what exactly predictive analytics is and why it can be beneficial for your digital marketing campaigns.

Predictive analytics is the use of data science for audience profiling. Generic audience profiling is about determining the specific characteristics of your target audience and creating specific personas to represent each type of person within your target audience. Predictive analytics is essentially the same process, but from a data perspective.

By looking at your past data, you can determine the main behavioral characteristics that determine the likelihood of a user converting or not. By extracting these behavioral characteristics, you can create a formula that will allow you to calculate the likelihood that a user of your website will convert. This will allow you to determine which users are worth chasing and which are not so that you can ensure that your marketing efforts are properly targeted and that your business is as profitable as possible.

Identify your predictors

The behavioral characteristics of driving in Predictive Analytics are called predictors. These could be all of the behavioral characteristics that determine the likelihood of a person converting, for example; length of visit, location, etc. These predictors are then used in a model (a formula) that calculates the profitability of a specific type of user. Predictors should be combined in a model and the more predictors you use, the more accurate your analysis will be.

For example, let’s say your data shows you that proximity plays a role in conversions; that is, the closer a user is to your premises, the more likely they are to convert. Also, the more time a user spends on your website, the more likely they are to convert. In this scenario, you would use the formula;

Location + duration of visit = probability of conversion

You would assign higher values ​​to locations closer to your premises, then focus your marketing efforts on the types of users with a higher likelihood of conversion.

The models

The practice of predictive analytics can be harnessed in three different ways (called models).

Descriptive model – Analyzes historical and current data to determine relationships and trends to identify actions to take moving forward. It is the most common model used in business.

Predictive model – Analyze past data to determine the likelihood of something happening. This model should be used when you have a specific desired outcome and want to calculate the likelihood of that outcome occurring.

Prescriptive model – Analyzes all the elements of the decision involving variables to predict the outcome of these decisions. This model examines all the possible outcomes of a decision and the likelihood of each occurring; allowing you to manipulate the situation to increase the likelihood of the desired outcome. This template is great for growing your business, as it can be used to understand what other potential products might be of interest to your users – so you can increase your product offering.

The model you choose will depend on the purpose of your analysis. However, one thing that is crucial in any case is that before a model is implemented, you must understand the goals and objectives that you are seeking to achieve from your analysis.

Your business and predictive analytics

By implementing predictive analytics, your business can gain a wealth of information that otherwise might have been missed. Obviously, how you deploy this method of analysis depends entirely on the goals and objectives you are trying to meet, but some key reasons for using Predictive Analytics are:

  • Identify user groups with the highest conversion probability so that you can create digital campaigns to target that specific audience.
  • Identify gaps in your existing product offering to grow your business in an informed and profitable way.
  • Determine what type of user is worth looking for. Just because you want your target audience to be a certain type of user doesn’t necessarily mean that it will be the most profitable for your business.
  • Identify the likelihood that future marketing campaigns will be successful to determine where or not they are worth the investment.

Warnings

WarningsA common misconception of predictive analytics is its ability to predict the future. While your analysis gives you a more accurate view of the future than just stabbing in the dark, it’s important to remember that analysis is based on probability, and therefore no analysis will ever be 100% accurate. . There are several reasons why this is not fail-safe:

  1. Historical data does not directly reflect things to come – other variables are always introduced that can influence the evolution of trends.
  2. While building your predictive analytics model there may be unknown variables eg external economic issues, weather conditions.
  3. Models can easily be manipulated to display favorable data – as with any analysis, it is possible to manipulate your model to display data that supports the argument you are trying to make. It is no accident that there is so much conflicting research!

Because of these three factors, revising your model should be an ongoing process. New variables must always be taken into account and introduced and tests must be carried out regularly.

Some examples

Let us give you some examples of using Predictive Analytics on a daily basis, so that you can fully understand the concept of use:

  • Amazon Product Recommendations – Amazon uses data from previous purchases and user behavioral characteristics to determine other products that may be of interest to them.
  • Facebook News Feed – Facebook uses behavioral characteristics to determine how likely you are to be interested and interact with a certain post in your News Feed. Those with a higher probability will be posted higher on your News Feed.
  • Email spam filtering – by analyzing your past behavior, email spam filtering uses data to determine how likely you are to be interested in a particular email and whether or not it should be marked as spam .

Hope this has given you a good understanding of predictive analytics and why it can be beneficial for your digital campaigns.

Naturally, you might be thinking that this is exactly what you need for your online business. Well, to get more information and help you implement predictive modeling for your business, please contact us today.

Image credits:

Blackboard predictive analytics by BigStock

Hazard warning sign by BigStock

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