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With the aim of continuously improving customer relations, database management and analysis are crucial to a company's development.
A recent McKinsey study showed that companies that carry out in-depth analyses of their customer database have an ROI of more than 115% and revenues of more than 93% !
Many companies collect massive amounts of data, but don't know how to exploit it properly afterwards. As a result, they gain no insights into their customers and are unable to create or maintain personalized relationships with them.
Focus on the sinews of war: analysing the customer database.
An analysis of a customer database consists of producing an overview of the information collected and stored in the database. The aim is to gain a better understanding of customers and prospects in general.
A customer database can be analysed using various data analysis tools. In addition to their data management role, a CRM software increasingly offers analysis tools to make the data readable for companies.
With a complete and efficient analysis, a company can adapt its strategy and implement concrete actions such as marketing campaigns, fine-tune its global strategy or improve its product / service to enhance the customer experience.
Before launching into a tidal wave of data, it is important to prepare the analysis. First of all, all the data needs to be correctly indexed within the customer database. Centralising data is crucial if you are to analyse complete information.
So, how do you group your customer data? In most cases, it is the sales staff who hold the information. It is therefore essential to make them aware of the need to collect and enter data into the database.
This is why companies are generally strongly advised to use a database management tools such as CRM. A CRM database makes it possible to centralise information and make it easily accessible to all the company's teams (marketing, sales, management, after-sales, etc.).
In order to carry out an efficient analysis and implement relevant actions, it is essential to ensure that all the data is up-to-date. This means sorting through your database.
The aim is to concentrate on relevant information. For example, the information of a customer who has not interacted with the company for 2 years can be deleted. On the other hand, information about a loyal customer who has made regular purchases is particularly interesting in order to better understand your relationship.
To save time and automate this stage, you can use CRM software. This tool can, for example, automatically update your customer database.
The customer database can be analysed using several different models. Segmentation remains the most common model, as it is easy to set up and provides an overview of your customer database at a glance.
The aim here is to categorise prospects and customers into different groups. It is generally advisable to classify them into 3 to 10 groups maximum. Often carried out by data analysts, this so-called exploratory analysis enables key trends in the customer database to be identified.
Once you've analysed the data, the next step is to adapt your strategy and identify the actions you need to take. To be effective, these actions must be based on your customer data! The overall aim of the action plan is to improve the customer experience and ultimately boost the company's overall performance.
Take, for example, a group made up mainly of young working people who make frequent purchases. It may then be appropriate to run a personalised marketing campaign on social networks, adapted for a pre-defined platform, to offer the latest product on sale.
You can take the analysis even further! Once you have defined your action plan, you can carry out a predictive analysis: in other words, an analysis that aims to anticipate future customer behaviour. This enables the action plan to be refined accordingly, making it more effective.
Once a precise action plan has been put in place, it's time to map out the customer journey. By analysing the customer database, we have gained a better understanding of customer behaviour. The customer journey therefore needs to be rethought accordingly in order to optimise the chances of making a sale.
To begin with, we need to retrace the customer's current buying path. What stages do they go through before buying a product? These stages are specific to each company, but certain typical stages recur regularly: research, discovery, exploration, choice, purchase and recommendation.
By studying the behaviour of prospective customers, it is possible to improve the customer's buying journey in order to optimise sales. For example, if customers are having difficulty finding a product at the search stage, it may be worth reviewing the different types of contact points used to bring customers to the product page.
In conclusion, for a successful analysis of a customer database, it is strongly recommended to use CRM software, an effective data management tool, to centralise and prepare the data for analysis.
With up-to-date data, it makes sense to use a segmentation method to visualise customer trends. This makes it easier for companies to adapt their strategy. Business strategy is then materialised by the implementation of action plans such as marketing and sales campaigns, or by improving the customer buying journey. With this new strategy, a company can hope to improve its customer relations and thus achieve better performance.
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