Imagine knowing the buying habits, anticipating the behaviour of your client or being able to visualize future scenarios in advance to reduce costs by making an accurate planning of the necessary resources. Well, achieving all this is possible thanks to a predictive analysis strategy that aims to anticipate future risks and detect opportunities.
Table of content
• Sectors where predictive analytics is most used
• Advantages of predictive analytics
• Contribution of predictive analytics to companies:
• Predictive Analytics and Customer Experience: Levels at Which It Operates
In reality, the ability to predict has always been present throughout our history thanks to reflection and analysis of past events. Today, we can be much more precise with the help of technology and especially through the use of data as we are able to structure and analyse information, extracting knowledge from it.
Departments such as marketing, sales, financial and purchasing are some of the areas that benefit the most from this technology in companies, which uses the application of statistical analysis techniques,
analytical queries and automatic learning algorithms to data sets to create predictive models capable of extracting the probability of a particular event occurring.
Sectors where predictive analytics is most used
already helps many sectors. The most active are the industry as it helps to strengthen business decisions having a perspective of the future situation, maximize efforts and rationalize budgets, that's the goal.
Banking, automotive, health, insurance companies, retail and even energy are areas that are already extracting value from the ability to predict behaviour. We have an example in Amazon, which has been betting on predictive analytics for some time to build a recommendation system that suggests products related to the tastes of visitors to its platform or the same happens with Netflix. A reality that is achieved by analysing historical data.
Advantages of predictive analytics
The ability to anticipate customer demand is one of the greatest benefits of predictive analytics, but there is more:
• Avoid the risk of customer leakage
• Identify the probability of purchase
• Cost reduction
• More personalized campaigns
• Capture potential customers
• Process reinvention
Predictive analytics identifies the probability of purchase
, something fundamental for the B2B and B2C sectors. It reduces costs, it can predict the performance of each campaign depending on the channel, it even allows you to identify customer segments, carry out more personalized campaigns or identify, for example, which new products should be recommended to your customers.
Contribution of predictive analytics to companies:
• Real vision of everything that is produced
• Allows a high degree of accuracy
• Minimize risks
• Improve operational efficiency
• Help plans for the future
• Discover trends
The most important thing is that predictive analytics, in addition to helping decision-making or better defining the business strategy, serves to anticipate trends and strengthen the position of your company in the future.
Predictive Analytics and Customer Experience: Levels at Which It Operates
Countless levels of customer experience benefit from predictive analytics. Let's see some of them:
• Customer knowledge
The collection of data from our clients (such as purchased products, contracted services, seasonality of commercial transactions, among other possible ones) and its subsequent predictive analysis allows us to have the ability to anticipate future demands in the short or medium term.
Knowing the interests of users or customers allows detecting patterns, trends and correlations, segmenting them according to common characteristics and thus improving the message so that it is effective.
• Decision making
Predictive analytics provides tools that allow you to analyse a large volume of data, which favours decision-making in a company. This procedure would be impossible to carry out manually, it would even be unreliable, because the “human error” factor would enter the field.
Thus, the type of analysis proposed by data analytics
is not limited to designing possible scenarios, but also aims to detect which decisions are more appropriate to take according to the moment and the circumstances.
Predictive analysis is not limited to designing possible scenarios, but also aims to detect which decisions are more appropriate according to the moment and the circumstances.
So predictive intelligence plays a crucial role in crafting effective strategies to achieve customer loyalty, for example.
• Prediction of Customer Behaviour
On the other hand, the ability to predict the behaviour of different customer profiles, and thereby determine which products will be most consumed or demanded, optimizes sales, and return on investment.
Knowing the behaviour of the buyer allows to anticipate their needs and thus, offer more personified attention. Using predictive analytics, it is easier to detect patterns of purchasing behaviour and, consequently, to identify dissatisfied customers.
• Analysis of the Customer's Customer Journey
It is key to constantly monitor every interaction that the company has with the customer during their shopping experience. Do not neglect that predictions may vary with each piece of information provided throughout the customer journey.
When in an agent / client interaction, for example, it is detected that the client manifests a sensitive preference to be considered, predictive analytics tools allow it to be included, immediately, in a new analysis, in real time and adjusted to the needs of the client.
• Knowledge of Customer Opinion
By means of predictive analysis tools it is possible to consider each opinion and comment made by the client and advance on the needs and demands that it manifests.
Predictive analytics provides a comprehensive understanding of customer preferences and wishes. In this sense, it "indicates" what decisions should be taken to satisfy them, meeting their expectations and demands.
• Improved Productivity and Performance
This type of data analysis makes it easy to locate new business opportunities such as cross-selling, for example. In addition, predictive models are used to forecast inventory and manage resources.
Considering trends in the business sector, the use of predictive analysis to understand the future and predict customer behavior is essential to be competitive in a changing and increasingly demanding market.
Using predictive analytics to understand the future and understand customer behavior allows you to anticipate customer needs and at the same time avoid unnecessary investments or risks resulting from bad decisions. This improves profitability and, by extension, the customer experience.