Reactive vs. Predictive Analyitcs: Where does the best intelligence lie?
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Reactive vs. Predictive Analytics:

Where does the best intelligence lie?

If you wonder where the opportunity to sell advanced analytics might lie given data analytics has been ‘around’ for a long time, we believe it lies in the powerful difference between reactive and predictive capabilities.

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The real commercial advantage your customers crave relies on them being able to stay ahead of their rivals at all times. Or if they are in the public sector, they’ll want to stay ahead of citizens’ opinions and service needs and better predict budgets in order to make wiser spending decisions, amongst other things.

Reactive or predictive analytics: does it really matter?

Yes. Reactive analytics uses historical data to give an accurate answer to the question ‘what happened and why?’, then finding resolutions. So it’s great for things like reviewing marketing programmes, looking at public health campaigns, analysing the success of enterprise processes, for example.

Here comes the ‘But’

However, if your customer is looking for a BI solution, you might be doing them a disservice if you don’t probe their business needs to understand their strategic intentions. Why? Because predictive analytics combines a number of very sophisticated techniques to find relationships between seemingly unrelated events. This will give your customers a much richer picture of their environments, helping them to model forward-looking actions and predict their outcomes, speeding up sound decision-making.

Use case aplenty

Predicting buyer behaviour: combining lots of different types of information, ranging from historical checkout data to social media opinions, helps online retailers create bespoke, highly personal online customer experiences, recommending offers and products based on previous purchasing activity or intentions.

De-risking decision-making: from countering terrorism to forecasting agricultural demand, predictive analytics can, by using the broadest, richest combination of real-time datasets to model scenarios and outcomes, make evidence-based decisions that significantly mitigate risk. Something that almost every organisation can benefit from.

Adapt enterprise workflows: organisations can model various workflow scenarios to predict their outcomes in terms of efficiency, individual productivity, quality of output and profitability, for instance, and select the optimal approach.

Combatting fraud: banks and credit card companies, as well as local authority organisations transacting benefits, increasingly use predictive analysis to detect fraud.

Price setting: for the travel industry, where seats on a particular journey can only be sold once, unlike products that can go on multiple promotions, using predictive analytics allows them to create and amend ticket prices based on travel trends, future weather conditions, world events, economic and other factors.

As the big data era continues, your customers will be increasingly thirsty to gain an advantage from the hidden intelligence. The key to you staying ahead of the sales curve will be to predict new and inventive use cases to take to market first.


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