Predictive analytics makes forecasts and predictions about likely outcomes in the future. It involves a wide variety of statistical methods ranging from predictive modeling, machine learning, and data mining that critically analyze past and current facts to make forecasts about future events. Business processes rely on deploying AI techniques to gather actual data and take calculated risks, instead of making random assumptions.
If you know how to make effective use of large data sets, and play and mold them to observe trends and patterns to generate valuable insights, predictive analytics comes in handy in understanding humongous amounts of data. While it helps organizations to draw conclusions to significantly work towards the growth of management, it also causes inconvenience in managing data and deploying data mining techniques.
Nonetheless, businesses nowadays harness the power of analytics and gain countless advantages to optimize productivity but there are certain limitations of predictive analytics that need to be addressed. There are plenty of predictive analytics examples manifesting AI’s significance in the contemporary business world.
Let’s dig deep to take a closer look at the advantages and disadvantages of predictive analytics to know if it’s worth capitalizing on.
Advantages of Predictive Analytics
We cannot overlook predictive analytics significance in recognizing abnormal patterns to avoid criminal behavior. The rise in cybersecurity assists in uncovering cyber vulnerabilities and threats. Companies are peculiar about keeping their business safe from fraud and notorious threats, it helps them ensure a better procedure for smooth operational management.
Optimizing Marketing Campaigns
Market success is dependent on how well they know their customers and in order to get thorough know-how about shoppers out there, one really needs to observe customer responses and buying behavior. Strategies are redefined based on customer likes and dislikes. Agencies use predictive analytics to categorize existing and potential customers. The secret lies in delivering the right message at the right time which ensures customer retention.
Smart Decision Making
Companies invest time and effort in making smart decisions that can change the course of business in all aspects. The art of decision-making encompasses all factors, one of which is analysis drawn from Ai-driven techniques. Machine learning models solely rely on data sets and they predict the outcome based on data fed into it. With advanced insights, firms make informed decisions for better growth and development. Businesses are harnessing the potential to acquire customer touchpoints and successfully utilize them for profitability.
Drive operational excellence and boost your company’s growth rate with predictive analytics. Organizations are empowered by Ai-based solutions for charting a future map and revolutionizing uncertainty into a functioning procedure with high profitable prospects. Change is the only constant and how well a business keeps pace with changing demands and preferences of the customer base is the real game-changer. Actionable insights are provided by analytics for operational benefits.
Disadvantages of Predictive Analytics
Data collection, storage, and maintenance draw a lot of expense. Initial implementation of predictive analytics is expensive in terms of hiring specialists who can manage data. Purchasing dedicated software and tools that can acquire specific types of data for data analysis is expensive. A huge investment is required to kick start your venture in setting up AI-driven solutions.
Lacks Data Security
A large amount of data is generated every day and it is pretty clear that companies make use of real-time huge data sets for analyzing consumer behavior and smart decision making. But, securing critical information from hackers is a crucial challenge in storing data at a safe place. Large enterprises encounter challenges in minimizing access controls and installing protection setups. They have to keep checks on data updation to ensure that verified users are making changes. Organizations need to evaluate a data security framework to safeguard individuals’ personal credentials.
User Privacy Violation
How do brands notify their customers through SMS text messages about seasonal sales or discounted offers? Marketing techniques revolve around customer data utilization for reaching the target audience at the right time. User personal data is a gold mine for marketers to sell their products and services. Private information is used to understand customer behavior and identify similar buyer personas.
Data sets are largely gathered from surveys, emails, data-entry forms and they are filled in by users, who usually don’t tend to put much effort into sharing accurate information with the researcher. When data is collected from different firms, data format will differ in terms of attributes, fields, and structures. Data cleaning is done for skewed data that causes inconsistency and compromises compatibility among data fields. We need to feed accurate data into our model so major preprocessing is done to make it analysis-ready.
In a Nutshell
In the current rapid technological landscape, understanding the data is immensely significant. Data acuity coupled with deploying AI solutions promotes cross-sell opportunities, assists businesses to attract, retain and grow profitable customers. Whether you are an owner of a large-scale business or running a small, medium enterprise, a dire need to understand customer intent, what they want to purchase, their likes/dislikes, and the factors that motivate them to buy from a particular store. One can gauge and predict the future buying behavior of consumers through predictive analytics.
Virtual and augmented reality (VR and AR) applications that we use today rely heavily on predictive analytics. You may not even realize this, but you’ve already interacted with predictive analytics when using virtual assistants such as Siri, Cortana, Google Now, and Alexa. These assistants present results (called suggestions) based on the information you’ve provided in the past. Those suggestions are personalized to ensure the delivery of meaningful results in a short period of time.
In the world of customer service, chatbots have become one of the go-to strategies for dealing with customer inquiries. Instead of sorting through a never-ending list of calls, chatbots allow us to deal with individual customers as they reach out to us. The algorithms that run these virtual assistants are learning from each interaction and start to predict what a customer’s response would be. As a result, we’re able to further support the customer without hiring a lot of support staff.
Let’s take the example of Alibaba, an e-commerce giant that has developed a recommendation system that uses real-time data to predict consumer wants. E-commerce Brain is Alibaba’s predictive analytics system, which has provided recommendations or has enabled e-tailers to offer personalized recommendations on their websites. TOTAL GMV of gross merchandise value topped USD155 million within 20 seconds of the opening of Alibaba Group’s annual 11.11 Global Shopping Festival, it crossed USD1 billion in 2 minutes, and the day ended with over USD 30 billion in gross merchandise sales value.
With the emergence of this software, prediction modeling has become increasingly important.
These days, consumers want more than just a discount and a great product. They want an experience. And that’s where the algorithm comes in. Well, it’s actually more than just an algorithm — it’s a system of artificial intelligence powered by big data and lots of machine learning algorithms.
The art is all about how companies create, capture, use, and instrument data to envision upcoming steps/actions for actionable insights. Predictive analytics is slated to revolutionize and unify consumer experience which is dynamically impacting our work, leisure, and overall lifestyle.
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