You’re struggling to make sense of your marketing data. How can you ensure it’s accurate?

Cómo saber que tu estrategia de #marketing está dando resultados? En base a mi experiencia, funciona tener el objetivo claro y tener dos campos de acciones claros: 1- Un campo y estrategia a corto plazo. 2- Un campo y estrategia a largo plazo. ?Por qué a corto? Porque el marketing que no vende no es un buen marketing. El marketing que no realiza AB testing y que no es rápido al ajustar su estrategia, no es buen marketing. Por eso las acciones de #Growth SON CLAVES, por que te permiten saber si estás por el camino correcto. Este campo y estrategia enriquece el campo de largo plazo, te permite medir. 2- A largo plazo visibilizas a donde quieres llegar en 1,2 y 3 a?os. y ambos deben hacer match con el objetivo global.

This all seems like a no-brainer, but nonetheless, nearly every company I've ever worked or consulted for had a lack of documentation and/or consistency surrounding data and touchpoints to one degree or another! Without everyone speaking the same language, and reports and dashboards comparing apples to apples, the whole model is essentially useless. You get out what you put in, and so it's very important to get this right at the outset, or, if you're diving into existing systems, make sure you go back to this step and get everything clarified before attempting to make any improvements.

Consider the historical trends so that we can understand the trend so far and atleast give a fair projection with a factor of uncertainty

En marketing digital, la precisión de los datos se logra con transparencia desde la fuente. - Identificar meticulosamente cada fuente, desde métricas en LinkedIn hasta interacciones en redes sociales, es esencial. - Establecer estándares sólidos, como cimientos, define cómo se recopilan y procesan datos de análisis web, correos y CRM. Este marco evita confusiones, asegurando una base firme para estrategias de marketing con métricas precisas. En el dinámico entorno digital, esta hoja de ruta es crucial para decisiones informadas y mejora continua.

For B2B, the stone cold truth is that your marketing data has holes in it. CRM is great, but are you capturing: Why did we win? Who did we compete against? What other technology is part of the customers’ stacks? The blind spot here is that these answers are known by some people, but not in the CRM. eStaff level execs have a feel for this. For everyone else in an organization, it’s usually buried in emails (unstructured) or known to different people scattered in the organization (fragmented). This type of info is essential for accurate lead scoring, up-sell, cross-sell, and customer references. There's no time like the present to start recording this info in your CRM. It's not easy, but the competitive advantage is overwhelmingly great.

Le choix des outils de recollecte de données in site est aussi stratégique. Notamment pour vérifier les données des champs des formulaires : nombre de chiffres dans le champ numéro de téléphone, message d’erreur si l’adresse n’est pas reconnue ou déjà connue. Faire en sorte que la qualité des données fournies par le client soit facilité par l’UX et des outils de validation pendant la récolte fait gagner énormément en temps et en efficacité.

Data quality is essential for any successful marketing campaign. Data audits are a more comprehensive review of your data quality. They typically involve looking at your data from multiple angles, including data sources, data collection methods, and data usage. Here are a few tips for data quality checks and audits: (i) Define your data quality standards. What does good data look like for your organization? Once you know what you're looking for, you can start to identify and correct errors. (ii)Automate data quality checks. This will help you identify and correct errors more quickly and efficiently. (iii)Regularly audit your data. This will help you ensure that your marketing data quality is maintained over time

One thing we find helpful is to install all the tools Tag Manager etc. ourselves in order to have quality data. Once we have access to the data we check the tools data with the actual data (invoices issued/sales) to keep data precise. We make adjustments if errors and inconsistencies occur in our data and the actual data.

Regularly conduct data quality checks and audits to identify and rectify inaccuracies. Utilize tools and processes to monitor data integrity, completeness, and consistency. Establish protocols for resolving issues promptly.

Regularly check and clean your data to spot and fix errors. Set up routine audits to ensure data quality and accuracy. Train your team on these processes to maintain high data standards.

Assign clear roles and responsibilities for managing and protecting your data. Create data governance policies to control how data is used and shared. Ensure accountability at all levels to maintain data integrity and security.

Implement a robust data governance framework that includes defining roles and responsibilities for data ownership. Assign ownership of specific datasets to individuals who are responsible for maintaining the accuracy and reliability of that data.

After identifying and documenting all data sources and doing quality checks, the next (third) step is to establish data governance and ownership. This is where the marketing Operations (MOPs) team comes into the picture. They are well-equipped and trained to ensure data governance is maintained at the highest levels.

Using a robust, resilient data framework with clear roles and responsibilities is essential to reap maximum ROI from your efforts.

A LGPD exige que as empresas adotem medidas rigorosas para proteger os dados pessoais contra acesso n?o autorizado, vazamentos e breaches.?A governan?a de dados contribui para esse processo estabelecendo padr?es de seguran?a, criptografia e controle de acesso.

Educate the data users on what the data means, especially derived metrices. Ensure that they not only understand these but are also able to relate these to real life scenarios and where the metrices can be important. A lot of cases insights build overtime when we are able to assimilate the derived metrices across various sets/ analyses.

The fourth step in ensuring the marketing data makes sense to everyone and is utilized fully is to train and educate your data users. The users must be provided with the necessary training and knowledge to help them upgrade their skills.

One the biggest challenges we have at our marketing agency is to educate our clients on how we work, how data works and we track data to make sure they get their goal, which is mainly more sales/clients, ultimately, business. In the onboarding process we explain, present all tools we use (or they already use, e.g. Google Analytics). Most of the time they do not understand basic notions such as conversions, CPA, CTR, impressions. Once they do, we report on these data every month so they get a chance to understand the data themselves and draw conclusions.

Provide training and educational programs for your team members who handle marketing data. Ensure they understand the importance of data accuracy, and equip them with the skills to maintain and interpret the data correctly.

Provide regular training for your team on data handling and analysis. Keep everyone updated on the latest data tools and techniques. Encourage a culture of 'data literacy' and informed decision-making.

We find surveys efficient as we can show data to our clients about what customers think about them, about their brand about their services. We reflect on these data together and improve the marketing strategy to bring more attention to their business.

Regularly review and refine your data processes to enhance efficiency and accuracy. Identify areas of improvement in data collection, storage, and retrieval. Streamline processes to minimize the risk of errors.

Seek feedback and learn from data-driven decisions to refine processes. Stay adaptive to new technologies and market trends for ongoing improvement.

Data processes are living entities. By continuously reviewing and improving, you ensure your data remains accurate, relevant, and a powerful driver of success in an ever-evolving landscape. #data #datamanagement #datascience #businessintelligence #dataprocesses #datagovernance #continuousimprovement #linkedin #datadriven

Consider external validation and benchmarking services to cross-reference and validate your marketing data. This external validation can provide additional assurance and insights into the accuracy of your data.

Les données externes, notamment secteur ou concurrence apportent des insights précieux. A la fois pour identifier des pistes d’amélioration mais surtout pour prioriser les actions à mener pour gagner en compétitivité à court terme. Un taux de conversion extraordinaire bon par rapport au secteur doit déclencher une étude du site concurrent afin d’en ressortir les points forts susceptibles d’être ajouter au notre. Une note d’avis inférieure à la moyenne doit immédiatement déclencher une enquête clients.

There may be room for improvement within the organization, as the Pareto Principle indicates that 80% of time is allocated to data verification and formatting, leaving only 20% for decision-making. If this trend persists, it suggests a potential inefficiency that warrants system modification. In the contemporary landscape, where data serves as a pivotal asset, compromising on its handling is not advisable. Implementing changes to enhance the efficiency of data processing can significantly contribute to organizational success.

All of these steps require a significant amount of time, people, and resources to successfully execute. Here's where it pays off: without trusted, accurate data, the business team's first instinct is often to simply question the data and go with priors. And with the data-driven nature of bidding algorithms, going with your gut is an increasingly wasteful way to lose precious dollars. There isn't really a way around this investment, so it's best to do it right.

Start by understanding what really truly matters in your business. Then, armed with this understanding, get really clear about the data that's needed to provide insight into these most critical areas of your business. It's likely that there are gaps. You will often find yourself in a situation where you don't have the data at all or you'll have data that is not good enough to use with confidence to drive decision making. If you're lucky, you'll have some data in good enough shape to be used. In my experience, this happens very rarely.

ata documentation: Create comprehensive documentation for your marketing data, including metadata, definitions, and any transformations applied. This documentation helps ensure consistency in interpretation and usage. Regular data hygiene practices: Enforce regular data hygiene practices, such as deduplication, normalization, and validation checks. Implement automated processes where possible to maintain data cleanliness consistently. Collaboration and communication: Foster a culture of collaboration and communication within your team regarding data accuracy. Encourage team members to report discrepancies and share insights into potential data issues.

Involucrar a los departamentos que le competen : marketing, ventas, gerencia, inventarios, costumer service., etc Esto hara que los datos realmente destaquen en las areas que se necesitan, no compartir la informacion en el momento correcto con las partes correctas es peor que no tener la informacion, ya que se pierden las oportunidades. Inversión constante en data analisis e innovación, tener claro los objetivos para cada participante.