Benefits and Challenges of using Customer Data for marketing.



          Today, the stakes of customer knowledge are particularly heavy for all customer relationship management professionals. The customization of every aspect of the relationship, and an ever more fluid and efficient follow-up are part of the requirements of a new generation of customers appreciating efficiency and immediacy in all things.
Customer data can be divided in 4 categories : 

Descriptive data : This includes demographic data like gender, age, geography and income. It also includes self-described attitudes and preferences toward products categories and technology

Behavioral data : These are the general patterns customers exhibit when using your products and services. It includes making purchases, registering, browsing, and using difference devices.

interactive data This includes the clicks, navigation paths and browsing activities found on websites and software.  The classic usability test typically focuses on this level of granularity by simulating real interactions. You can use real time data from A/B testing, Google Analytics and lab based or unmoderated testing to collect data for this grouping.

Attitudinal data : Preference data, opinions, desirability, branding and sentiments are usually captured in surveys, focus groups and usability tests. 


The benefits of customer data for marketing

Analytical tools developed as part of Big Data enable us to implement predictive solutions, monitor trends in real time and better anticipate potential risks related to the business and customer relationship.

 This allows retrieved data to be stored and analyzed by business managers to improve their knowledge of customers and the context in which they operate. Taking Big Data in consideration can bring many benefits to businesses and help improve their competitiveness, notably through:
  • A better knowledge of its customers
The collection of data from prospects allows a more detailed analysis of the company’s customers. Marketers are then able to define typical portraits (personae) in real time and as close to reality as possible.
  • More effective marketing campaigns
Because the audience is better defined, commercial actions will be more targeted and therefore more effective. Lead nurturing strategies become more effective with content that really matches the expectations of different targets.
  • A personalized user experience
Internet users are now sensitive to content that truly meets their expectations. Thanks to Big Data, it is possible to meet these customization requirements by relying on the data collected through the various digital platforms and therefore to offer "tailor-made" content based on the profile.
  • Better loyalty
Data analysis tools make it easier for the company to “segment” its client portfolio. On one side: those who are “hot” (and therefore ready to make conversions). On the other hand: those who send signals of withdrawal (and who can then be the subject of a re-engagement campaign with a view to loyalty).
  • The prediction of trends
As the name suggests, predictive analysis models developed as part of Big Data allow companies to anticipate customer's needs. By offering an adapted offer, the company demonstrates that it is in control of its customers' problems.

Data managing challenges


 However, managing customer data bring somes important challenges in terms of integration, organization and privacy issues : 

  • Data Quality
Define clear procedures for qualification within the organisation. The objective is to ensure the accuracy, consistency and traceability of the data in order to meet the requirements of business users

  • Securing the data
Data management must ensure the confidentiality and security of customers' data within the organization’s IT infrastructures.

  • Compliance with legal obligations
Companies have been widely aware of the GDPR, the General Regulation on the Protection of Personal Data. It promotes ethical use and traceability of data within companies.
  • The creation of a reference data warehouse
Data management at large encompasses the management of reference data and the creation of a single repository.








References :
-Jeff Sauro 2014, 4 types of customer analytic data to collect, (https://measuringu.com/customer-analytics/)
-Gartner. 2020. Why privacy and personalisation go hand in hand for driving business growth - CMO Australia. [ONLINE] Available at: https://www.cmo.com.au/article/663048/gartner-why-privacy-personalisation-go-hand-hand-driving-business-growth/. [Accessed 17 February 2020].
-Martin, N. (2018) How To Overcome The Challenges of Data-Driven Digital Marketing, Forbes. Available at: https://www.forbes.com/sites/nicolemartin1/2018/12/18/how-to-overcome-the-challenges-of-data-driven-digital-marketing/ (Accessed: 18 February 2020).
-SmartInsights (2019) ‘How to reach the full potential of your Customer Data Platform with product data’. Available at https://www.smartinsights.com/archive/digital-marketing-platforms/big-data-digital-marketing-platforms/ (Accessed: 18 February 2020)

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