Have you ever felt limited in the analysis and segmentation of your customers because you felt you didn't have all the information you needed to understand them? And have you noticed that your marketing campaigns were underperforming due to poor targeting or an incomplete view of your prospects' needs?
This was also the case at Viva Technology. In this article, we explain what was put in place to remedy this situation and we share what we learned along the way.
What if the problem began with... your data?
Data is always more complex than it seems. The challenge of a successful data strategy lies in the quality, relevance and completeness of the customer data on which it is based. In other words, if the data you collect is incomplete or of poor quality, you risk basing your decision-making on erroneous analyses... decisions that can ultimately be fatal to your business.
This observation deserves to be analyzed and investigated, particularly if you find yourself in one (or more) of the following situations:
Your customer data is stored in different databases, tools or platforms, or collected by different teams, which prevents you from centralizing it and cross-referencing it.
Your data is structured in silos that do not communicate with each other, depriving you of a complete view of your customers' needs and journeys.
The use of multiple solutions (CRM, ERP, etc.) forces you to manually export data from each platform.
The scattering of your data prevents you from consulting the data history and collecting enough context to effectively qualify your data.
Your various solutions are not synchronized, which forces you to update your data on several platforms, with an increased risk of errors and data obsolescence.
These problems that undermine the relevance of your analyses are all too common, but they are not inescapable. Want to know how to fix them? Discover how to reconcile your data and maximize the impact of your marketing campaigns and customer journeys with this five-step process.
Boosting the performance of your marketing campaigns through better knowledge of your customers
The key to a good data strategy lies in its ability to reflect the full complexity of your customers' needs, identity and behavior.
Step 1: Analysis of the existing situation
For starters, the first stage of this process consists of analyzing your existing ecosystem to determine where your data is stored, how it is analyzed and if you are encountering difficulties in obtaining a 360° view of your customers and prospects.
So, in the case where you have, for example, a CRM equipped with a marketing automation solution to personalize your campaigns, it is essential to list what the tool allows you to do and what you are still struggling to achieve (for example: refining your segmentation using a scoring system that cross-references activity indicators on your website, behavioral criteria and responsiveness to emails, etc.).
Step 2: Implement a data repository
Once you have identified the limitations of your existing ecosystem, focus on implementing a data repository (Customer Data Platform) to centralize, reconcile and synchronize your data. Such a repository allows you to harmonize your data within a single model, regardless of their origin (ERP, communication channels, emails, PDF files, etc.).
Step 3: Segmentation
The centralized collection of unified data allows you to better segment your audiences and to refine your analysis of their needs. You thus discover the behaviors and motivations of your prospects in all their complexity, and you can build much more personalized customer journeys.
Step 4: Identification of relevant Use Cases
Today's tools allow us to analyze everything in all directions... Which doesn’t mean it's in your interest to do so! On the contrary, it is recommended that you identify specific use cases, which will allow you to solve recurring problems.
Step 5: KPIs and performance monitoring
Finally, it is in your best interest to list the KPIs (key performance indicators) to be monitored for each use case to assess the relevance of the strategy deployed. This will enable you to quickly communicate the benefits of this implementation and provide your teams with coherent and comprehensive insights into your audiences.
VivaTech did it!
This is the approach that VivaTech has taken in recent months, combining Salesforce Marketing Cloud with the Data Cloud data platform.
According to Caroline Roullet, Chief Marketing Officer of VivaTech, this has been a gradual process: “We were aware from the outset that we were collecting a lot of data as a result of our events business, but we didn't have a tool to analyze it properly.” This is why the organization decided to rely on Salesforce Marketing Cloud to structure its data and improve consistency, while complying with GDPR policy and conducting its first audience analyses.
Marketing Cloud is a software and service platform for the automation of marketing campaigns developed by Salesforce. In particular, it enables the connection of data from various sources and channels in order to obtain a unified view of each customer – and, right from the start, this solution was able to meet VivaTech's expectations in terms of data collection and processing.
From Marketing Cloud to Data Cloud
In 2024, VivaTech decided to integrate Salesforce's Data Cloud - a platform that allows the unification of company data within a single data model - into its marketing tools.
At the time, VivaTech was struggling to obtain a 360° view of its customers. Data collected from disparate sources failed to provide the company with a more cross-functional overview of the visitors attending VivaTech’s annual event.
So the company had some ambitious goals: to carry out a more in-depth segmentation of targets; to obtain a more complete overview of customer behavior; and to develop a cross-functional vision of the individual.
“In our sector of activity,” Caroline Roullet explains, “a visitor can also be a speaker, an exhibitor and/or a company representative. We used to work in silos because each category of prospect has a very specific behavior, but today we can see synergies between these different roles and we understand the value of developing a more cross-functional view of the data within VivaTech.”
Getting support from an expert integrator
This six-month project began with a scoping phase, essential for identifying use cases and validating the technical choices made, then continued with the deployment of the solution and a testing phase. VivaTech was supported in its implementation by Cloudity, an integrator specializing in the Salesforce ecosystem.
“Cloudity played a key role in this analysis phase,” says Caroline Roullet, “by advising us on the use cases to be favored and on the technical implementation of the solution. They gave us a lot of advice on the purpose of this integration, the associated objectives and the scalability of the solution, recommending that we opt for Data Cloud. We followed their advice, because we have great confidence in Cloudity's expertise in the Salesforce ecosystem."
Bonus - 4 tips to help with your data issues
The implementation of a centralized data platform can address many key concerns for organizations, but the process is not simple, and it requires a significant amount of preparation. To bring you up to speed, here are four tips from our experience:
Identify where the value of the data lies for your company. According to Caroline Roullet, one of the main challenges of data reconciliation projects lies in data governance. This is why it is important to avoid collecting just anything and everything, and to limit yourself to essential data, in order to have indicators on the quality and source of the data.
Work by use case. The risk with these solutions is that you can get “sucked in” by all the possibilities offered by the tool and lose sight of your objectives. It is therefore essential to prioritize in order to avoid unnecessary costs or complexity. Working by use case allows for an immediate return on investment and also helps teams to gradually take ownership of the tool, which guarantees a better adoption rate.
Don't neglect documentation. It is necessary to have access to the history of data structuring within the company to avoid repeating past mistakes or to avoid new ones due to lack of context.
Surround yourself with the right partners. Caroline Roullet considers this to be a decisive factor in the success of any integration project: “You need to call on experts in the solution, who are agile, flexible and willing to immerse themselves in your business.”