Introduction To Predictive Analytics
What if someone told you it is possible to determine (among others)
- which of your business’ customers are close to leaving your company behind,
- which products (you wouldn’t expect) are going to sell in the upcoming periods,
- whether any fraudulent activity is happening within your operations, which if detected helps gain the trust of your customers?
The more knowledge and certainty you acquire about your company’s drivers, the more power you have to take actions on time. Instead of looking back later on asking “But how could I have known?”, why not take actions on time? Our aim with this post is to show how you can reduce uncertainty from now on!
The key lies within predictive analytics. It is a powerful tool for enhancing smart decision making with the main goal of reducing uncertainty. With predictive analytics data scientists aim to find informative attributes that contribute to the estimation of unknown values.
In everyday usage predicting means forecasting something that is to happen in the future. However, in data science prediction doesn’t apply necessarily only for the future. It more stands for estimating something that is unknown. This unknown can be in the future, the present and even the past.
For the sake of this post we will present 6 scenarios when predictive analytics can help overcome problems.
1. Customer Retention
Nowadays customers have close to endless options to acquire and meet any need they might have. Losing customers to competitors and acquiring new ones carries much higher costs than retaining the current ones. Predictive analytics can help finding early signs of customer dissatisfaction. It allows decision-makers to intervene on time and take necessary actions, such as retention offers before it becomes too late and customers leave.
2. Demand Prediction
Some other popular uses of predictive analytics is predicting future sales or identifying unexpected demand in certain periods. These help prepare your business on time and also help save unnecessary costs like a full inventory of expired products that failed to get sold.
3. Identify Most Profitable Customers
Predictive analytics can help identify the customers that contribute the most to the company’s income. This allows your business to re-evaluate the distribution of resources.
4. Evaluate Marketing Campaigns
Predictive analytics can also help to find out whether previous marketing campaigns or any other business development expenditures can actually be considered successful, or the numbers are just deceiving and have different drivers.
5. Enhance Marketing Campgains
With knowledge revealed by predictive analytics you can avoid marketing campaigns aimed at the wrong customer segments. This contributes to saving great amount of costs.
6. Revenue Estimation
Finally, with predictive analytics your business can estimate the expected revenue generated by recently acquired customers.
Data Scientists’ Perspective
Now let’s dive into how applying predictive analytics really looks like. Data scientists start by gathering, examining all relevant historical records, such as sales records, customer records, marketing materials. The next step is cleaning and pre-processing the acquired data so statistical analysis and machine learning techniques can be applied on it. These techniques are used to find patterns, informative variables, correlations. Finally, the predictive models can be built. These newly built models then can be applied to freshly incoming data and can generate predictions.
The developed models in most cases are tested on previous events. They are always evaluated for certainty and accuracy. In order to achieve high accuracy of the results, domain experts of the companies must work alongside the external data scientists.
In conclusion, predictive analytics can be a very strong weapon in the hands of your business if created and used in an adequate manner. It allows you to discover not-so-obvious patterns, activities that can have a great positive impact on the outcome of your business’ performance and can help you understand the main drivers of your company.
The application of predictive analytics presented here were all about the customers! However, these are just some of many examples and possible functions. Interested in exploiting the opportunities predictive analytics might have to offer your company? Or do you have any questions regarding our post? Contact us for a free consultation or a discussion via LinkedIn or our website.