In a simple scenario, predictive marketing experts (usually data scientists, data analysts, or some specialized analysis companies) collect business-related data from multiple sources and analyze it together with the company’s marketing data and customer data.
Predicting the future is no longer a cinema thing for marketers. Through predictive data analysis strategies, companies can understand the market and consumer trends. From the data collected companies will have an understanding of their consumer profile and market trends, this will help them to focus on the efforts on the needs and wants of the consumer.
In addition to knowing what the disadvantages are to the competition. In this way, companies can create specific and effective actions to strengthen their relationship with their customer. If part of their competitors does not invest in campaigns in a digital environment, for example, it may be a good alternative to overcome them. The same occurs if he does not have quality in person attendance. With predictive analytics companies can keep an eye on all this and always stay ahead of the competition. For example, marketers can research on that by learning from different services, and use the predictive analysis and better understand how it works.
Control stock, meet market demands
When applying predictive analysis in the company, there is an external and internal benefit at the same time, as pointed out above. The internal benefit is the possibility to control the entry and exit of products based on the market’s behaviour according to its predictive analysis. Consumer behaviour and developing special offers for a given period are part of the external benefits. It can prepare the company in advance to better meet market demands. With this type of data, companies have the chance to negotiate with suppliers even before there is growth in the demand for a certain product, guaranteeing advantages for the “home” and the consumer. It would be a great alternative to keep the stock with an ideal quantity of products and, thus, not lose potential customers to the competition.
Predictive analytics applications in business
In the business world, predictive analytics can be applied in different ways. When applying predictive models, it is possible to understand the real needs of your client, make decisions based on data, predict behaviours, identify trends and opportunities and anticipate crises.
Predictive analytics applied to marketing will help your company to identify your customers’ profile and consumption patterns. You can create much more effective marketing campaigns with this information. If you have a customer base, for example, you can identify purchasing patterns associated with each customer’s profile. With this, you can offer specific products to customers with greater purchasing potential. Or you can identify seasonality in the consumption of your products, and define the campaign calendar according to this seasonality. Countless applications will make your marketing strategies even more effective.
How do you know the right time to approach a lead? How to identify the ideal customer? How do you know which product attributes to highlight to delight that prospect? How to know the right time to apply for a discount? How do I set my sales team’s goals? Predictive analytics applied to sales will answer all of these questions and many more.
Decreasing churn is a challenge for many companies. What if you could predict when your customer is about to cancel your product or service? Predictive analysis can identify patterns that precede the churn moment, for example, less product use, inactivity, complaints in customer service channels, etc. This way, you get to know which customers are about to cancel your product and can take specific retention actions to avoid cancellation.
From the predictive analysis, it is possible to identify which products or which aspects of the product are displeasing your customers. From a predictive model, you can identify that those who consume a certain product are more likely to give a negative score in the satisfaction survey. Or that those who arrive at a certain page on your site are more likely to leave your e-commerce before closing the purchase. With this information, you can make improvements on this specific page of your website or on the product that is not meeting expectations.
Those who work with large stocks know the desperation that shelves are too full – or too empty. The predictive analysis identifies product turnover, which is the most and least consumed, the time a product is in stock and how these variations occur over time.
How to start using predictive analytics
If this all seems too advanced for your business, stay tuned. Predictive analysis is part of the digital transformation process that will reach all companies, and it is necessary to prepare. The era where marketing and technology grow together has come. The digital wave has caused tremendous changes in the marketing industry. Data and technology have penetrated all areas of marketing. Enterprises are not only “meeting” user needs, but also “forecasting” and “creating” demand. Today, data has become the undisputed focus of marketing activities. For every marketing activity, data plays an important role. Every marketing decision is inseparable from data support, but until a few years ago, data analysis only relied on some analysis tools on the market. Later, with the explosion of data science, we ushered in new concept-predictive marketing, which allowed us not only to see the past data but also to predict the future.
The Definitive Guide to Predictive Marketing
Digitalization has covered a lot of marketing work, which is nothing new. And now it determines how we position the brand and what new tools or products we ultimately use. Globally, 1.9 trillion people are expected to shop online. Take the United States as an example, 79% of people have done online shopping, while in 2002 this number was only 22%. What’s more exciting is that the intelligence of digital marketing can better manage potential customers and increase sales opportunities. With several rapid developments in technology, marketing has become increasingly complex. Some emerging companies operate entirely online, so digital marketing (such as product and service advertisements on the Internet and electronic devices) has become a key point for companies to go beyond traditional marketing in this process.
Just as digital marketing has become the core focus of the company, market data has also become a key factor in the success of the marketing process. Needless to say, for every marketing activity, data is crucial from start to finish. We obtain data from multiple marketing channels and use this data to make wise marketing decisions, such as how to target advertising and determine marketing budgets. To improve marketing decisions, in marketing, we have introduced more complex methods to better utilize and analyze data, and ultimately obtain better results, and “predictive marketing” is one of these methods.
Predictive analysis and predictive marketing
Although these two terms can be used interchangeably, predictive marketing is more predictive of marketing business and has a broader meaning. Predictive analysis uses predictive models to provide insights into the future, while predictive marketing uses predictive technology to test corporate marketing strategies and provide insights to make better marketing decisions in a continuous (or iterative) process.
In a simple scenario, predictive marketing experts (usually data scientists, data analysts, or some specialized analysis companies) collect business-related data from multiple sources and analyze it together with the company’s marketing data and customer data. Armed with this information, data scientists can apply predictive models suitable for the business and predict the likelihood of their marketing success relatively accurately.
Predictive analytics, therefore, has a bright future ahead of it. This discipline, which seems very scientific at first glance, is however not only a matter of complex and expensive software. The process must be initiated in all companies. We need to train data experts more, but that’s not all. Today, all employees must be made aware of the importance of collecting information and at least analyzing it. Employees can also learn to research and use their skills by learning, it will benefit them. By demonstrating a spirit of synthesis, analysis, and common sense, it is possible to predict many customer behaviours. Meet the demands of its customers, adapt products and services to their real needs. This is the recipe for a lasting relationship in the future.