We all understand the importance of data. Since the inception of Big Data in the 90s through its breakthrough—via the power leveraged from Cloud Computing—to being the latest zeitgeist in the early 2010s and the growth of analytical tools such as Google Analytics and Omniture, marketing teams are more than ever aware of the need to have broad, deep data pools to help shape and inform their tactical and strategic marketing programs.
I can’t remember the last time an email, website, or web application did not have data at its core. It’s crucial. It’s every organization’s lifeblood.
And yet…we continue to see that when dealing with customer data, organizations do not value or see the importance of maintaining their databases— ensuring they know the origin of each customer record, ensuring that contact details for key contacts are kept up-to-date and, subsequently, the socio-demographic profiling and historical interactions with the brand are contemporaneous and meaningful.
There are a number of reasons for this:
- It can look overwhelming —you look at thousands, sometimes hundreds of thousands (if not millions) of customer data records, when opening your database and it just looks huge. It’s too much to take in. We are simply not capable of dealing with that amount of data in its raw form.
- There are sometimes so many customer data repositories circulating around organizations that getting the real picture of our customers is difficult. Even organizations with enterprise-scale CRM systems such as SFDC can, and do, still have multiple spreadsheets doing the rounds internally which are updated rather than the core system. We kid ourselves that we are becoming more digitally savvy and used to multiple desktop and SaaS platforms/applications, but the reality is people still feel more comfortable working from a spreadsheet. You can use colors, you can do calculations, you can pivot, you can have lovely charts…all at your own control. No IT/development dependency. No requirement to learn something new. That lovely customer data is there right in front of you and you control it. What could be better?
- It requires cultural and financial investment to keep your CRM data valid. In straitened times, that investment can seem of little value in delivering against KPIs or OKRs both at an organizational, departmental, or personal level.
- Many organizations operate in strongly isolated sub-departments or divisions as a hierarchical approach (sometimes for legal reasons, but sometimes not!) Having a holistic view of the entire organization’s customer base of data becomes someone else’s responsibility, not the individual teams/departments.
All of this is not aberrant or unique to any particular organization. It’s prevalent in most. However, the problem that a lack of clean customer data has is that any marketing program based off uncleansed data is, at the point of inception, bound to experience failure to a greater or lesser degree. Organizations simply cannot connect with and personalize marketing campaigns if they don’t know who their customers are, what their interests are, and what their purchasing behavior is.
More seriously than that are the legal implications. Data protection laws are being strengthened to a greater or lesser degree globally. If organizations cannot prove the source of their customer data and that each customer has agreed implicitly to certain types of marketing communications, they run the risk of falling foul of data protection regulators.
So what can be done?
1. Acknowledgement
Firstly, acknowledge there is an organizational problem. Much like any other issue in life, until and unless there is an acceptance that there is an issue with customer data, then the root cause of any issue cannot be identified and its impact mitigated.
2. Investment
As mentioned above, this is not just a financial investment, but also a cultural one. There is no point in running various Extract/Transform/Load (ETL) processes from various databases to create an organization-wide CRM database, or running data cleaning programs (either internally or outsourcing to a 3rd party data specialist vendor) as a one-off exercise. The organization, from top down, must accept that the maintenance of customer data is an ongoing priority that needs and deserves time and money invested in it.
3. Acceptance
Further acceptance! It’s impractical and nearly impossible to ensure that all customer data is correct at any given point in time. The ongoing maintenance of customer data shouldn’t drain valuable financial and time resources from delivering organizational growth. A practical approach to annual or bi-annual cleaning/update routines should be sufficient. That requires acceptance of a margin of error — it isn’t going to be right all the time, every time.
From these, organizations can have a greater degree of trust in their customer data. User profiles can be based off real and accurate data. Marketing programs can have a greater degree of personalization (ergo, resonance) when based off consistent, clean, contemporary data. We all know personalization is the holy grail for successful marketing— give customers what they want, when they want it, the maxim goes. If organizations don’t know who their customers are and how they behave, marketing programs based off of old/unclean data are compromised from the start. The natural follow-up is to try and create propensity models to assess and forecast purchasing propensity…again, it’s a wild dream, at best, to try this when customer data is so essentially flawed.
