Quels sont les défis les plus courants dans l’utilisation de l’analyse marketing pour analyser le comportement des clients ?

Regardless of what you do, the main problem is that people are not consistent in what they say with what they do. In my book "The Trump Nations" I point out how the states with the most "religious" people are the ones with the most divorces, child abuse, child deaths by maltreatment, etc. One way to get around this is to ask what they do rather than what they think/believe. But even her the problem of "social desirability" (SD) persists. You can try to sculpt your questions in such a manner the SD is removed or minimized. This is difficult but not impossible. Imbed some intra-rater reliability questions to insure you got it right.

Customer behaviors vary. When you think you understand how to convert, retain, and delight them, you realize you’re still in the dark. One of today's greatest opportunities is gathering customer insights and buying patterns. But many systems collect this data without establishing a meaningful connection to the customer. Data remains unscrubbed, inaccurate, or disorganized leading to ineffective marketing campaigns. To improve data quality, I think it’s important to baseline/rationalize data. Data should tell a story, highlight opportunities, and answer key questions. If not, it’s just a perspective and not the complete story. An unbiased view can shape a business for the better, but a biased one can potentially fold a company forever.

Marketing analytics is a powerful tool that can greatly contribute to the growth and success of an organization. By harnessing the power of data and insights, businesses can make informed decisions and drive impactful marketing strategies. However, like any other tool, marketing analytics comes with its own set of challenges. Addressing and overcoming these challenges is essential to fully utilize the potential of marketing analytics for organizational growth. One of the most common challenges in using marketing analytics is the availability and quality of data. Organizations often struggle to collect accurate and relevant data from various sources, which hinders their ability to gain meaningful insights.

I would call out an additional challenge that needs to be tackled - I.e. the constant evolution of customer behavior. Customers are interacting with brands in new and innovative ways, which means that marketing teams need to be able to collect and analyze new types of data. This can be a challenge, especially for smaller businesses with limited resources

Common challenges in using marketing analytics for customer behavior analysis include data quality, privacy concerns, data integration complexities, interpreting insights, real-time analysis, talent gaps, technology costs, impact measurement, and benchmarking. Addressing these challenges requires data management, skilled staff, ethical practices, and adaptable strategies.

The most persistent challenges in B2B are dealing with disparate data sources. Companies gather information from various channels, platforms, and tools, which lead to a fragmented data. Marketing efforts often span multiple platforms such as CRM, MAP, and ad platforms-Google Ads, LinkedIn, and Facebook. Each platform generates its own set of data and metrics. Without a unified approach, marketers struggle to measure campaign performance accurately and allocate budgets effectively. These siloed systems can result in inconsistencies and gaps in data, making it difficult to gain a comprehensive view of customer interactions.

It's important to realize that group data can disguise the true trends and sometimes even produce incorrect information. When some in the group are very high and some are very low, the group data will show average, when in fact it is bipolar. This happens often. the best way to protect against this is to look at individual profiles and get a sense of what the collective individuals are doing, and then if the trend really is characteristic of the individuals, you can accept the data, Yes. it's a lot more work this way. Want to shorten the process? Compare the median and the average. They should be about the same. Big differences point to the disparity. Small differences will mean you can assume the group trend is the individual trend.

Start from a hypothesis when working through data. Large amounts of data are as much a bane as they are a boon. If we don’t know what we are looking for or solving for, we might become the needle in the proverbial haystack, lost without achieving any end state.

Shifting from "product over time" to "customer over time" is crucial for a customer-centric approach. Holistic Understanding: It captures all customer actions, not just isolated transactions. Customers as Assets: Recognizing customers as the key long-term assets emphasizes their sustained importance and potential. Lifetime Value: Focus shifts to nurturing prolonged relationships, not just one-off sales. Strategy Adaptation: Companies can proactively adjust to changing customer needs. Customer Value Management: By understanding a customer's lifetime interactions, businesses can optimize interactions and offers, enhancing overall customer value. This mindset enables true customer-centricity and sustainable growth.