⚡Why aren't my ideal customer's criteria the same as my usual ones?

When Sypher machine learning algorithms analyze your sales and marketing data (demographic, firmographic, and behavioral), they may come up with different ideal customer criteria than you originally expected.

Here are some reasons why this happens:

1. Cognitive biases: Human decisions are often influenced by cognitive biases, such as stereotypes or past experiences. Algorithms, on the other hand, analyze data objectively and can identify customer segments that you might not have considered.

2. Complex data analysis: Sypher algorithms can analyze a large amount of complex data and identify correlations and patterns that the human eye cannot see. For example, they might find that certain demographic or behavioral attributes, which you didn't consider important, are actually key indicators of success.

3. Evolving data: Customer behaviors are constantly changing. Sypher algorithms can detect these changes more quickly and adjust the ideal customer criteria accordingly. What was true a few months ago might no longer be true today.

4. Multifactorial interactions: Sypher algorithms can analyze how different factors interact with each other. For instance, a certain combination of demographic and behavioral characteristics might be a strong indicator of sales potential, even if each characteristic alone is not.

5. Revealing new opportunities: By analyzing data in depth, algorithms can discover market segments or niches that you hadn't considered. These segments can present untapped growth opportunities.

In summary, the criteria for your ideal customer might differ from your initial expectations because Sypher algorithms analyze data objectively, in detail, and in real-time, revealing insights and opportunities that you might not have anticipated.