Back in 2017, the Economist titled one of their articles, “The world’s most valuable resource is no longer oil, but data”. This has been a fact ever since.
As humans, we create over 2.5 quintillion bytes(2.5*1018 bytes) of data every day. Merely storing this data won’t be of any value; certainly not more than OIL. There must be some business applications of these data, and there are.
Here is where business intelligence(BI) comes in.
Business Intelligence is a combined application of all the data. In BI, all the collected data is analysed, sorted, categorised, filtered, and presented in the form of useful, actionable information which businesses can use to make informed decisions.
BI is performed with the use of several tools and techniques. One such method used by businesses to predict is Predictive Analysis.
Predictive analysis is an advanced analysis technique used by businesses to make probable predictions. To do so, Analysts use technologies such as Machine Learning, Deep Learning, Data Mining, Statistics, Modeling, etc.
If this is implemented in BI, it can help organisations significantly.
Here’s how any business can leverage predictive analysis for their Business Intelligence:
Leveraging Predictive Analysis in business intelligence:
Though it doesn’t accurately predict the future, Predictive analysis can be hugely beneficial in followings:
Segmentation means categorising people based on different demographic, Geographic, Psychographic, and Behaviour.
Understanding based on segmentations can provide businesses a more definite way to handle & lure other firms & people into buying their products/service.
For e.g., Using predictive analysis in BI, Banks can understand from past data that a 21 y/o person, who just got out of college and joined an organisation, might be interested in a credit card scheme because he/she wants to buy a new iPhone.
In short, segmentation(with predictive analysis) can increase the chances of profits for organisations by showing them an apparent future if specific techniques are implemented on individual segments of people.
There are certain tools & techniques colleges, and even online universities like the James Cook University, let their students implement in their Data Science courses for segmentation.
The above example of banks was one of sales & Marketing. Predictive analysis can be used in BI for several Marketing purposes.
In marketing, individual channels create different impressions & ROI. Past data can help businesses finalize their budgeting on each channel.
For e.g., If Facebook ads create more impressions and a better ROI than Twitter, then better give Facebook a larger budget & remaining to Twitter.
Moreover, predictive analysis can also help predict the audiences’ likes & dislikes along with individual platforms algorithms.
For e.g., Past data can conclude things like ‘the videos got a better reach on Facebook as compared to images’, People interact more with specific videos, etc. All these data, when implemented using predictive analysis in BI, will lead to the perfect mix of platform, people, and content for further Marketing efforts.
Overall, data from previous marketing efforts, mapped with individual segments, can help make future marketing decisions.
3. Risk management:
Several types of risks can be eliminated before time by implementing Predictive analysis in BI.
For e.g., Banks & e-commerce sites can predict the probability of fraud & possibilities of losses before handing over a product/service to certain individuals.
Another example can be the use of BI in supply-chain management. Businesses can use future weather details along with the past effect of specific weather to minimize over-stocking or under-stocking.
In short, predictive analysis implemented in BI can help organizations manage risks better.
4. Customer assistance:
Based on past data, predictive analysis can help give a better customer experience.
Netflix, YouTube, etc. predict the kind of content an individual would like and show it on their recommendations page. This helps them stick the consumers on their platforms.
Predictive analysis implemented in Business intelligence also helps decrease Churn rate.
As the cost of acquiring a new client or customer is much higher than retaining an existing customer, Churn prevention becomes essential to run a successful business. Based on data like emails, calls, etc. made by the client before ending their account can help predict & improve businesses’ chances of retention. Sprint is a prime example of using AI-based predictive analysis to decrease its churn rate.
Long story short, predictive analysis helps organisations to give their customers a better experience and retains them.
5. Improving operations:
One of the benefits of using Predictive analysis in business is to operate better.
As mentioned above, companies can fill up their stocks based on previous entries. Hotels & Flights operators can change their prices depending on the supply & demand data.
Investors of the market can direct their cash flow in a proper & effective manner derived from historical data.
In short, there are several industries which can function efficiently by implementing predictive analysis in their BI.
6. Economics Forecasting:
From budgeting to spend, every decision is critical in an organisation and is highly dependent on forecasting. Implementing predictive analysis helps one do that better.
Patterns can be found by examining the previous data like the seasons, demographics of the people, external factors like elections, etc. Other efforts like Marketing efforts, new products/services introductions, etc. can also factor in.
All this information lets organisations forecast their economics, which helps them be prepared.
In short, predictive analysis has a significant role to play in business intelligence of any organisation.
Future predictions can help organisations be better. There’s no doubt about that. Therefore to wrap up this article, here’s a word for two crucial parties.
If an organisation hasn’t implemented predictive analysis in its Business intelligence, please do. As discussed above, it helps to segment & serve, implement effective marketing campaigns, decrease churn rate, handle risks beforehand, manage operations, and much more.
If any individual is deciding his/her future and wants to join the IT part of the world, Data analysis is in high demand. In data analysis, one will get to implement predictive analytics for BI. This can make them earn more than AU$100,000 in salary. Also, individuals who are looking for a leap in career and want to analyse & predict data, online universities can be of help.