Know the Future Requests of your Clients By Predictive Models in Marketing

Know the Future Requests of your Clients By Predictive Models in Marketing

Are you one of those who still analyzes results once they happen instead of predicting them? Predictive models in marketing put at your disposal a new way of creating campaigns and strategies that are not only based on past data but also future ones.

Not that we can see now or the future. But almost. A predictive model is based on a system that uses data, statistics, and machine learning techniques so that we can know consumer behaviour in advance.

How? Based on our historical data, the probability of specific future results occurring before they are achieved is identified. High-value machine learning is capable of learning about itself.

Today it is essential to understand predictive models in marketing, why they are helpful, and the importance of applying them to get qualified customers and increase sales.

What are predictive models in marketing, and what are they used for?

Predictive models can be used to forecast sports results and television audiences, technological advances, business profits, or -in this case- strategic marketing.

The case of the American supermarket chain Target, which became famous for predicting a teenage pregnancy, is well known. The brand knew how to use the data and process and analyze it most appropriately.

Thus, combining Artificial Intelligence with traditional marketing, the result is these predictive models that allow the creation of more accurate strategies.

Predictive strategies to hit consumers

Predictive strategies focus on actions based on consumer profiles and business trends, making them more accurate and reliable, and getting better results.

In this way, valuable information is available that helps us determine actions proactively, anticipate situations, correct behaviours and offer a personalized shopping experience based on the tastes and lifestyle of each user to show products accordingly.

Three uses of predictive models in marketing

Depending on the needs of a company, we could point out these three aspects:

1. Predictive analysis: Anticipate the needs of the client.

A more effective digital marketing plan is carried out if the data is previously analyzed to determine a client’s behavior and base the strategy on customer-centricity.

In this way, what he wants, when, and how will be revealed. It will be possible to offer the customer what he needs because it will generate a purchase attraction based on his behavior analysis. It will be easier to reach him to convert. It has caused the client to identify his needs with those created.

2. Identify customers: obtain customer identification data.

Why predict what just one customer will do if you can identify the relationships between multiple customers and group them? In this way, it will be possible to know the tastes of a specific demographic sector, know its trends, and maximize new sales opportunities.

Once again, the client will have been reached, in this case, a group of them, with a greater probability of converting.

3. Build customer loyalty: use this data to personalize the service.

Based on the modeling created with the intelligent recommendations, actions personalize the user experience. This is when should launch customized messages and launch personalized notifications based on the consumer segment to which the last user belongs.

Because customers expect us to get to know them and address them in a personalized way, this generates a relationship of trust, exceeding customer expectations with the consequent possibility of conversion.

Reasons to implement predictive models in marketing

Then you should be aware of the benefits of including this model in your sales strategy:

Increase engagement:

It is easier to recognize the best campaigns thanks to predictive marketing data since we can know our audiences, refine the tone, and personalize the strategies. Adapting to the user’s demands or client’s needs, being related to him and his expectations, will increase his feedback to the company.

The sales cycle is perfected:

If a customer is satisfied, it is easier to buy again. Anticipating customers’ needs will give you an advantage over your competitors since you manage to reduce and speed up the sales process by knowing what to recommend, sell, and when to each customer.

Also, we must remember that getting a new customer is five times more expensive than retaining one, so investing in data analysis tools will be beneficial.

Improve lead scoring:

Each lead or client has a value based on their engagement with the brand. This tool measures the degree of interest of each one, knowing where they are. Therefore, the best message can be offered to the customer, through the most appropriate channel and at the time of most excellent receptivity.

All in one:

Effort and money are profitable since customers’ information is integrated into a tool, from how they behave on social networks, your e-commerce, the web, or other interaction channels.

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