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How to implement a Data strategy that supports customer knowledge ?

How to implement a Data strategy that supports customer knowledge ?

This is explained by an obvious problem: The volume of data generated by consumers is growing, so much so that it is now impossible for humans to process this data. The number of data sources has also increased tenfold with the spread of smartphones and connected objects.

Since the release of Big Data and AI, it is now possible for marketers to process and exploit this volume of data on an ongoing basis. We propose to discover in this article, 7 fundamental steps to improve customer knowledge through the use of data and AI.

1. Collect data on all points of contact

Without data, it is difficult to have a fully customer-oriented view… The first step in setting up a data strategy is to collect a maximum of data on all the points of contact which you can have with your customers. You will need to make sure to connect to all potential sources of data whether online or offline and store this data in a structured database.

2. Organize and structure

The second step is to organize, homogenize and structure your data to obtain a consolidated and unified view of each of your customers. By setting up a single client repository, you will be able to attach all the data it generates to each customer of your database whatever their sources. This organization of data will make its operation possible and effective.

3. Transform and clean

The transformation and cleansing of the database is a crucial step in the process of data processing. Indeed, the higher the level of data quality management (DQM), the more efficient the analysis of the data made by AI. If we associate the predictive models with the “engine” that is responsible for finding correlations among consumer behaviors, the data can be compared to the “fuel” that allows the engine to run. The more the datasets are “clean” and correct, the more accurate the recommendations made by AI.

4. Value and enrich

Data collection and processing are useful only if they are exploitable and exploited. Once the data are cleansed, it is particularly interesting to value them through the use of different predictive models, which can be spread over 3 different worlds:

Client DNA :

The set of predictive models used to segment, qualify or score clients. They use notions such as customer value, personas, product affinity or promotion awareness.

Product DNA : 

The set of predictive models used to categorize or qualify product references. These models include product features such as recruitment capacity, loyalty, cross-selling, and so on.

Event DNA :

The set of predictive models based on the probability that a client performs an action. For example, they are used to predict the likelihood that a prospect will subscribe to a newsletter or that a customer makes a second purchase.

5. Explore and exploit

The exploration and exploitation stage of the database is undoubtedly the most complex exercise in setting up a data-driven strategy. This step aims to identify the best moments to communicate with prospects and customers. The detailed analysis of the data, carried out by the various predictive models, will enable you to extract key insights, which will then be used to create precise audiences.

These specific audiences will be used when setting up acquisition or loyalty campaigns. These refined audiences can improve the performance of your marketing campaigns by making them more “intelligent”.

6. Activate

By targeting precise audiences, your communications will be adapted to the profile of each consumer. It’s this real-time individualization of communication to customers on all points of contact that will allow you to create a consistent customer experience while improving the performance of your campaigns.

From a technological point of view, it is preferable that the activation of your campaigns face a single interface, connected to all the dissemination tools, without the intervention of the IT teams. This automation of the transmission of audiences is now possible thanks to the synchronization of a large number of connectors with different technological solutions such as email routers, retargeting tools, on-site customization etc.

7. Mesure

Last important element: the measurement and control of the results of your campaigns. This measurement of the performance of your campaigns must be systematic and must be done through the establishment of control groups. These groups consist of individuals excluded from campaign targeting, allowing you to quickly and easily evaluate campaign uplift.

To make it easier to read performance and to be able to quickly evaluate the ROI of each campaign, it is particularly useful to use dashboards, on which the previously defined KPIs appear.

These reports must be easily exportable in order to be transmitted to the different departments of the company. The key lessons from the evaluation of the campaigns favor the daily decision making for all the departments dependent on customer knowledge.

Want to accelerate your data strategy and access real customer knowledge? Do not hesitate to contact us. We will be happy to explain how our platform can help you reach your goal.