Businesses today are shelling out loads of data in the form of customer information, social listening, app, cloud and product performance data. There is plenty of data everywhere and this has led to the birth of a practical form of analysis called the predictive analytics, which is a product of big data and business intelligence methods. Predictive analytics helps in obtaining all the information and concrete new insights to be ahead in the big data race.
Predictive analytics is used by organizations in several ways. Some of them are in the form of predictive marketing, applying machine learning, pattern algorithms, and data mining. These are applied to mainly streamline the business processes and read the underlying statistical patterns. Ideally, supercomputers are used, which comb through past behavioral patterns and previous businesses functions. Post this scanning these intelligent computers through various tools offers new information into how a company can really function the best way and achieve their goals.
Hence organizations are largely using predictive analytics to gain an advantage over their time and money invested and for the better functioning of the market.
The core of the Predictive analysis
Predictive analysis ideally uses regression analysis to forecast the connected values of multiple, correlated variables on the basis of a certain assumption that needs to be proved or disproved.
According to Allison Snow, a Senior analyst of B2B marketing at Forrester, predictive analytics is about understanding and decoding patterns in data to see the probability and outcomes. It is important to know that analytics is all about probabilities and never an absolute theory. Hence predictive analytics surpasses traditional methods and helps in determining data that is important and predictive of the result that we wish to foresee.
Not only is regression analysis used for predictive analytics but a good amount of data mining and machine learning is also infused. Data mining supports predictive analytics since it facilitates in examining huge data sets to discover patterns and decode new information. Using neural networks and deep learning algorithms, huge data sets that are unstructured are processed faster and accurately than conventional methods. This owes to the algorithms having the ability to learn and improve and run faster. IBM Watson, Google’s TensorFlow and CNTK from Microsoft are the best examples that provide machine language functionality for pattern analysis.
Predictive analysis and its presence
Predictive analytics is everywhere and following are the three categories of B2B marketing cases that clearly apply predictive marketing analytics.
- Predictive score
The usual entry point for B2B marketers to infuse predictive scoring and marketing enables the addition of a scientific, mathematical angle to a traditional way of sorting out that usually depends on speculation, experimentation, and iteration to obtain criteria and weightings. As described by Snow, this facilitates the sales and marketing domain to look for faster result-oriented accounts and devote fewer times on accounts that have lesser chances to change and also begin the process of cross-selling and up-selling.
- Discovering models
Identification of models is the type when accounts that portray the desired behavior which could be in the form of a product or service purchase, product or contract renewal mainly forms the platform for an identification model. This enables the marketing and sales vertical to look for valuable leads at a very early stage in the sales cycle, discover novice marketers and strategies. Not only this predictive analytics also helps to take into priority the older and accounts that currently exist and initiate power account-based marketing initiatives. This is done by taking the accounts into consideration that can perform well with the sales and marketing initiatives.
- Segmenting automatically
Traditionally, B2B marketers according to Snow have been able to partition only through attributes that are generic in nature and did so with manual strategies that customization was applied only to highly segregated and prioritized campaigns. But with the advent of newer technologies, attributes that were applied to feed predictive algorithms can now be joined to account records to support the segmentation that is automated and deeply designed. This greatly enables sales and marketing domains to carry out the outbound communications with appropriate messages, facilitate useful conversations between the leads and sales and inform the content and marketing strategies more effectively and smartly.
Predictive analysis in other fields
Apart from B2B marketing , predictive analytics is also used in several cloud-based software platforms across several industries. ‘Elevated careers’ is one website that has been using predictive analysis for recruitment and hiring. Though the platforms are still in their novice stage, the very thought of applying data to foresee a pool of job seekers and if they would be the ideal fit is actually reinventing the recruitment space into an entirely new dimension.
Zendesk a help desk provider have also integrated predictive analytics strategies by infusing its platform with analytics. Through this usage customer representatives can zero in problem areas catalyzed by a data-driven early warning system named satisfaction prediction. This ideally uses a machine learning algorithm to track satisfaction survey results and showing up variables inclusive of time to solve a raised a ticket , customer service response that has been dormant and precise ticket wording into a regression algorithm to measure a customer’s projected rating of satisfaction.
Predictive analysis undoubtedly has set its foot with the Internet of Things (IoT) too. Google is very much oriented with machine learning has been widely using machine learning algorithms in its data centers to run predictive maintenance on its server and running the Google Cloud Platform (GCP) public cloud infrastructure. The algorithms ideally use data values on weather, load and several other variables to accommodate data center cooling pumps and decrease power.
Predictive maintenance is catching up with predictive analytics and enterprise tech companies like SAP are offering service platforms on this basis by using the data collected from sensors and from linked IoT manufacturing devices. They enable in the prediction of a machine at a risk for mechanical problems or failure and hence gears up to take suitable actions. Aerospace apps are also into this race and Microsoft has partnered with Cortana to work on researching sensor data obtained from aircraft engines and its components.
Hence the list seems to be endless with predictive analytics as they have entered most of the industries. With the quest to map an artificial brain , the possibilities of pattern analysis is only fruitful and endless in growth.
Stay updated by checking out our resources page for some more interesting articles. If you are a novice or experienced professional and planning to make a career in the big data world, Edifyself is the right place for you.