Preface:

Every business aims to get ahead of the curve. This can be achieved with a balance of precise human decisions and strategized used of machine-based insights. As the decision-making ability of a human improves with time, a machine’s efficiency to provide insights improves with the use of proper analytical tools and relevant algorithms.

Analytics is a broad topic that includes Descriptive Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis under its umbrella. While all of this can seem a lot to take in at once, with a step-by-step approach towards the implementation of these tools, organizations can put these technologies to work. To infuse these business analytics tools within an organization’s workflow, the pre-requisites would be –

  1. Analyzing areas where there is room for improvement.
  2. Goal-oriented transformational strategy preparation.
  3. Application-specific data collection and extraction.

First, let’s gain an insight into what Predictive Analytics does:

In a business, foresight is everything. If you can predict what is going to happen, you are way ahead of your competitors already. Predictive analytics helps you with just that.

Predictive Analytics uses Mathematics, Statistics, and Machine Learning Models on the data that has been recorded in the past, to foresee events that can happen in the future by recognizing patterns in data.

With Predictive Analytics in hand, key factors that led to specific outcomes in the past can be identified. This helps organizations in making data-driven decisions that result in more revenue, a loyal customer base, and lesser risks. Predictive Analytics encompasses a variety of techniques including Machine Learning (ML), Artificial Intelligence (AI), and Intelligent Process Automation (IPA).

To read more about how Predictive Analytics actually functions, you can read this Synopsis of Predictive Analytics Tools.

How and Where Does Using Predictive Analytics Help?

The holistic approach of these technologies makes them useful to more than just the marketing department of a business. So, here are 6 ways in which Predictive Analytics proves to be fruitful:

  • Optimizing outbound sales with automated lead segmentation: Advertising can be costly when the target audience is not properly channelized. Predictive Analytics helps you find the targeted prospect that matches your description of an ideal customer profile. Adding it to your CRM process and targeting only the ones with a higher conversion rate, proves to be cost-efficient, profitable, and helpful in customer retention.
  • Improving productivity by allocating the right employees to the right jobs at the right time: Accurate labour forecasts can tell the number of hours your staff will have to work in the future. With this information in hand, organizations can recruit new staff and prepare existing staff for a productive future work environment.
  • Reducing maintenance costs by anticipating faults before they occur: By continuous monitoring, Predictive Analytics can anticipate faults like system shut down beforehand and thus help avoid bottlenecks.
  • Mitigating risks by detecting fraud: Fraud examiners can leverage Predictive models to determine future events, where there are chances of fraud, by taking sets of variables involved in past fraud events as reference data.
  • Unlocking new business models by anticipating future needs: By addressing untapped information about customer needs and demand, new business models can be discussed. These businesses that would be set up in accordance with the customer’s needs, would definitely have a higher scope of success than traditional businesses.

 According to Entrepreneur Magazine, those who use predictive analytics report levels of forecast accuracy to be as high as 82% on a deal-by-deal basis.

While all the above statements sound impressive, is there proof of effective implementation of Predictive analytics? Here are some satisfying stats that point in that direction:

  1. Predictive Analytics models helped Tipalti, a supplier payments automation provider, by look-alike account modelling to achieve a 20% higher conversion rate. The process included the identification of new customers that resembled the anatomy of its previous most profitable customers.
  2. Five9, an intelligent cloud contact centre, achieved a 250% increase in outbound sales using lead segmentation. These segmentations can be based on customer sophistication (based on their product or industry acumen), behaviour (working on customer’s feedback), or tiering (on the basis of customer lifetime value).
  3. Bloomreach, a commerce-specific digital experience platform provider, utilized predictive Modelling offered by Leadspace (CDP), in conjunction with Artificial Intelligence to discover ‘net-new’ leads. The predictive model used by Leadspace named 78% of the new leads included in the target account list used by Bloomreach to expand their reach.

According to IDC forecast, global spending on cognitive and artificial intelligence (AI) systems is forecast to reach $77.6 billion in 2022.

Conclusion:

Analysis in business events using predictions isn’t new. Statisticians in organizations have always used decision trees to help businesses use data for their profits – by identifying patterns and key points. But what’s new is the huge amount of data that is being processed, the kind of precise analysis that can be made using advanced ML and deep learning algorithms, and the variety of avenues that businesses can now reach.

Predictive Analytics can only prove to be profitable to an organization, if an organization as a collection of individual departments, commits themselves to change their strategies and aligning them with the ones prescribed by these technologies. With this in mind, businesses can easily leverage these tools to stay ahead of the curve.