Marketing Intelligence – Data Sources and AI in Marketing

Strategic marketing is powered by data-driven decisions, and data-driven decisions cannot be made without marketing intelligence.

All the knowledge about the happenings in the market or the data related to the marketing environment can only be provided through marketing intelligence.

Marketing intelligence includes a wide range of information like the changing market trends, competitors pricing strategy, change in the customers taste and preferences, new products launched in the market, promotion strategy of the competitor, etc.

There are multiple ways in which a marketing manager can collect marketing intelligence. They can collect this data by reading books, newspapers and trade publications.

They can talk to customers, distributors, suppliers and other company managers. A company manager can also monitor online social media. Although there are numerous ways of collecting this data, marketing intelligence must be gathered ethically and legally.

Categories of Marketing Intelligence

1. Competitor intelligence

Competitor intelligence is the process of learning and collecting more data about your competitors. To look at the strengths and weaknesses of the competition one must consider conducting a SWOT analysis.

2. Product intelligence

After analysing and understanding your competitors, one must concentrate on their own product or service. The objective here is to learn about the performance and the quality offered by a product or services.

If the product in question is a tangible item, it’s important to analyse the manufacturing process. All of this information should improve the user experience and helps produce the ultimate product.

3. Market Understanding

Only when we look at the various markets where a product is available, can we understand how the product is performing. Queries like whether we can expand the product to other markets and whether there are the markets that could benefit from the product or service help in growing the business.

Eventually, this information helps us show up in such areas by understanding where the audience is.

4. Customer understanding

The life-cycle of a product can be increased by understanding the key aspect, your customer. It is important that we know our audience and why they buy from us

Understanding the customer will help the marketing team come up with innovative and targeted campaigns.

Sources of Data

  • Marketing Intelligence – Internet
  • Distributor or Sales Agent Feedback Sites
  • Customer reviews and Expert Opinions
  • Customer Complain Sites
  • Public blogs

AI & Marketing Intelligence

With the increasing availability of information related to consumers, their interactions and buying habits; there is a great need for maintenance of data management.      It is a dire need to keep up with the changing consumer behaviour and social trends.

The AI component in marketing intelligence helps in getting up close and personal with the customer. AI or artificial intelligence aids marketers to understand the buyer better. This better understanding of the buyer’s persona helps in creating more relevant content which in return makes the customers experience more personal.

As long as marketers use the data keeping in mind the companies goals, artificial intelligence will forever be a major asset to the future of marketing.

Example: Netflix

Netflix is the worlds leading Internet television network with over 160 million members in about 190 countries. Its members consume hundreds of millions of hours of content per day as they can enjoy original series, documentaries and feature films.

During the first half, the company added 25.9 million members, more than double the additions for the first half of 2019. In order to optimise the service end-to-end and to continuously improve the experience for its members, Netflix invests heavily in machine learning.

A number of areas including our personalisation algorithms, content valuation and streaming optimisation are overlooked by the companies research applications. Netflix recommends types of shows or movies that their subscribers love to binge on.

It not only recommends movies or shows to individuals based on their preferences but also make sure that no single recommendation experience is the same for two individuals. So, even if the same movie is recommended to two or more subscribers, they are recommended differently.

Netflix uses big data analytics to create various types of user personas on the basis of their previous watches and then offers personalised recommendations in the form of changing thumbnails. If you’re an action freak, Netflix will show you some of the best action scenes of the movie.

Example: Netflix – Recommendation Algorithms

Recommendations are the core of the Netflix product and its ultimate experience. It provides members with personalized suggestions to reduce the amount of time and avoid frustration.

The company goes a step ahead of validating ideas on historical data to understand how people actually respond to changes in their recommendation system. They do so by running online A/B tests and measuring long-term satisfaction metrics.

This experiment also provides them with new perceptions to additionally improve research and their product. This has positively led them to move beyond rating prediction and into personalized ranking,  image selection, and much more.