To know your audience and predict their future behavior has become invaluable for video services today.
Why is predicting audience behavior important?
Knowing whether users are ready to make a purchase is essential for any marketer. This prediction is possible thanks to what we call machine learning. It lets us know at what stage of the journey users is by unifying customer data across all touchpoints to power your marketing ecosystem.
Today it is essential to understand predictive models in marketing, why they are useful and the importance of applying them to get qualified customers and increase sales. Predictive models can be used to forecast sports results and television audiences, technological advances, business profits, and strategic marketing.
By combining the use of Artificial Intelligence with traditional marketing, we can create predictive models that are essential in the development of more successful strategies.
Predictive strategies to succeed with consumers
Predictive strategies focus actions based on consumer profiles and business trends and help make them more accurate, and reliable and achieve better results.
They allow us to obtain valuable information that helps to proactively determine actions, anticipate situations, correct behaviors, and offer a personalized experience based on the likes and dislikes of each user to show them content that matches their profile and improves the user experience.
Three uses of predictive models
Depending on the needs of a company, predictive models can be used in any one of three ways:
- Predictive analysis: anticipate the needs of the client
A more effective digital marketing plan can be implemented if the data is previously analyzed to determine the individual customer’s behavior and base the strategy on customer-centricity.
In this way, we will know what they want, when they want it, and how. It will be possible to offer individual customers just what they need because a purchase attraction will be generated based on an analysis of their behavior and it will be easier to reach and convert them.
- 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? This can reveal the tastes of a specific demographic sector, and trends and allow you to maximize the opportunities for new sales.
- Build customer loyalty: use this data to personalize the service
Based on the modeling created with the smart recommendations, actions that personalize the user experience can be devised. Personalized messages are launched based on the consumer segment of the consumer to which the last user belongs.
Customers expect you to get to know them and address them in a personalized manner. Doing so correctly helps generate a relationship of trust, and exceed customer expectations with the consequent possibility of conversion.
Reasons to implement predictive your audience behavior in marketing
Thanks to audience data, it is easier to identify the best marketing campaigns, since it allows us to really understand our audiences, refine our tone and personalize our strategies. Adapting to the demand and needs of the user or client, having a relationship with them, and understanding their expectations will increase their feedback to the company.
The sales cycle is perfected
A satisfied customer is more likely to become a repeat buyer. Anticipating customer needs will give you an advantage over your competitors, because it allows you to reduce and speed up the sales process by knowing what to recommend to each customer, and when.
Remember that attracting a new customer is five times more expensive than retaining one, so investing in data analysis tools is extremely beneficial. At JUMP Data-Driven Video, a business data management platform designed specifically for video service players, we have a tool where we adapt the user experience by connecting AI to your video service, achieving personalized recommendations for each user. Find out how JUMP Data-Driven Video can personalize audiences in video player services.