Helping you keep pace with the newest developments in digital marketing, here is a step-by-step guide to creating a predictive analytics model.
Digital marketing is the new and trendy topic that is painting the town red, and clever marketers have already jumped the bandwagon with heavy online traffic directed to their websites that have astounding conversion rates in the age of advanced telecommunications technology.
The world is your oyster with your smartphone and other technological gadgets so why not cash in on the advantageous side of the new technologies for doubling your business?
As the founder of the leading ad firm rightly puts, the ever-dynamic scene of modern-age marketing never seems to stop evolving, always coming up with newer avenues to be ventured and newer pathways to success to be explored.
Developing a predictive analytics model for digital marketing is one such comparatively new technique that has been delivering great results for quite some time, and has seen it become immensely popular among the marketers of the current age.
Here, we will see how to develop a successful predictive analytics model for digital marketing.
“Innovation needs to be part of your culture. Consumers are transforming faster than we are, and if we don’t catch up, we’re in trouble.”
-Ian Schafer (founder of Deep Focus, leading advertising agency)
What is predictive analytics?
Predictive analytics is a combination of statistical methods that employ the resources and tools available in the discipline to analyze the current and previous data to predict future events and occurrences through a systematic calculation and measurement process.
Forming the crucial bits in the predictive analysis mainframe, machine learning, data mining, and predictive modelling are some of the empirical methods that aid in the proper functioning of this system
For businesses, the predictive analytics model is used to review the data and transaction history for efficiently identifying as well as predicting risks and opportunities in the future. Identifying and analyzing the relationships between the factors present in data, the assessment of risks and opportunities for the future is made easy with the predictive analytics models in the business.
The model enables better decision-making for businesses as it can provide an empirical layout of the risks involved as well as future business opportunities by analyzing transaction history and previous data.
The main functions of the predictive analytics model is put to use for providing a predictive or probability score for each of the individuals such as employees, customers, patients, vehicles, product SKU, machines, components or any other unit within the organization to identify, determine, analyze, assess and evaluate the data for influencing organizational processes that concern mentioned individuals.
The organizational processes can range from credit risk assessment, healthcare, manufacturing, law enforcement, fraud detection, government operations, and marketing. The fields that see the maximum usage of the predictive analytics model are financial services, mobility, healthcare, telecommunications, actuarial sciences, travel, pharmaceuticals, insurance, retail, capacity planning, and marketing.
How to create a predictive analytics model for digital marketing?
“We must move from numbers keeping score to numbers that drive better actions.”
-David Walmsley (Chief Customer Officer, House of Fraser)
The question on everyone’s minds right now is how the whole framework of a predictive analytics model fits into the digital marketing sphere. Evidently, the technique brings a lot of new things to the table and hence its rising popularity among the marketers of the modern age.
However, before we get to the part of explaining the steps involved in creating a predictive analytics model for digital marketing, we must at first know about the technology as well as how big data has gradually led to digital marketing adopting the newer technologies and statistical methods with ease.
Big data, as we all know, is a large collection of data sets that are too voluminous to be managed through standard database management tools. The sheer volume, variety and velocity of the data contained as big data pose problems of identifying, storing, sharing, searching, analyzing and visualizing the data for productive use. Big data attuned to the needs of digital marketing can be found in weblogs, search indexes on the Internet, social networks, details of call records and the like.
As the predictive analytics model has made it possible for the marketing minds to analyze and interpret large data chunks such as big data, exploring the realm through a stable statistical framework in place has thus become a cakewalk for the big business conglomerates and companies.
Here is a simple guide on how to incorporate the predictive analytics model into your digital marketing strategies through clear and concise steps.
1) Defining your goal
Defining the goal is the most important step you need for creating a predictive analytics model for your digital marketing sphere that takes your business many leaps forward.
By defining a clear and achievable goal that is quickly implemented – such as setting a target to increase the sales of a certain category of product within a stipulated timeframe – is of the key essence for delineating how you wish you predictive analytics model to be.
Driven to serve the purpose laid out by the business goal, the predictive analytics model can thus be shaped to achieve the goal using data and analytical techniques that are relevant to the product chosen and the customer base for the certain category.
The business objectives, if clearly defined within the predictive analytics model, will be achievable with the help of cutting-edge analytical tools and resources designed specifically for the predictive analytics framework within the sphere of digital marketing.
2) Exploring and preparing data management
One of the most vital steps in developing a predictive analytics model lies in the exploring and preparation of data management suited for your business. Identifying the sources of information that will be used for your predictive analytics model in digital marketing is thus of great essence.
You can begin with the simple questions like what the sources of data that are to be used are, and what the different data formats you will be dealing with for the creating the model.
Furthermore, you also have to determine the kind of workload you will have while the data cleanup process after the analysis, the amount of past data that is going to be put to use, and the various methods of feeding data into the analytics engine to bring out an integrated analytical report based on the information provided.
3) Testing reliability
Testing the reliability is perhaps the easiest to do while incorporating a predictive analytics model. All you have to do is put a small analytics model to the test with just the essential tools and functions that let you predict the preliminary stages of customer traffic and behavior on the Internet.
If it is reliable, it will reap results that say so, and you will also be able to know about which loopholes you need to plug for implementing it for your digital marketing model in the large scale.
Once the matter of reliability is established, take a closer look to determine whether it fits into the model of project metrics and business goals so that the validity of the model for your business is also reviewed once more.
4) Deploying the model and incorporating modifications
The fourth step in the predictive analytics model for digital marketing comes from the deployment of the results obtained from the analyses and taking business decisions and shaping business policies accordingly.
The range of implementation varies between sharing valuable insights with the business users to automating business decisions and processes.
The fourth and very crucial step will also involve very active participation on your behalf in case you are a keen digital marketer as it allows you to identify the necessary changes in the model suited to your growing customer and business needs as per the data and predictions dictate.
Once the model is deployed, you will always have to be on the lookout for useful management and optimization of the performance through the marketing strategies as well as cutting out unnecessary activities and improving accuracy in the predictive design.
Using the latest tools and online resources for integrating a predictive analytics model within the digital marketing functions for your business, you can now aim higher and bring in prosperity and growth for your business.
Some of the predictive analytics tools that can be put to great use if yielded by the deft minds of clever digital marketers are Mixpanel, Marketing evolution, Kapost’s Content Scoring, and Lattice Engines.
With the steps explained in this blog and by using the tools mentioned earlier, you too can now form a useful and practical predictive analytics model that can be used for enhancing the effects of digital marketing for bringing in more business and generating more revenues.