Mastering Customer Data Management

Managing and integrating customer data into existing workflows is a top priority for many businesses. In fact, 86% of marketers from medium and large companies [1] believe that first-party data is the most important part of their media strategy.

Given this industry consensus, both IT and marketing teams within organizations are facing growing pressure to capitalize on the vast volumes of data gathered around customers in their marketing, sales, and social channels. 

But how can teams improve their ability to collect, analyze, and manage customer data at scale? Let’s look at some steps that your small to midsize business (SMB) can take to improve your customer data management, allowing you to leverage those insights to pull ahead of your competition.

At the core of a robust customer data management plan is a data governance strategy. Data governance refers to the processes around the security, integrity, availability, and usability of data within an organization. Developing a strategy will require leveraging policies around data usage and other robust internal data standards that you may have put into place. 

The phrase “data governance” is often used around concerns about following data protection and sovereignty regulations. However, good data governance is also crucial in allowing teams to safely and efficiently use data at scale in their operations.  This is because good data governance also involves standardizing customer data you have on hand. Data standardization can be crucial, as it allows your team members to easily look at both individual data points or trends, and spend less time formatting or wading through “junk” data.

Data governance, formatting, and standardization mean more effective decision-making by your team. But it also allows you to better explain to decision-makers every piece of data being collected, what that data is used for, and who in the organization is using it. So along with providing better real-time insights, data governance also gives teams the tools to use data to help better inform the decisions of your stakeholders, whether they be executives, investors, or customers. 

A sophisticated or extensive data collection strategy doesn't mean much if it doesn't result in insights to help you make decisions. And ultimately, collecting too much data can be just as bad as collecting too little.

It can cause bloat and lag in the data platform you use to handle data, making it harder for marketing and data scientist teams to quickly and effectively discover productive insights.

Whenever you're reviewing or planning your data collection strategy, it's always vital to ask the following:

• Who needs this data?

• What can the data do for us?

• Would we notice if we stopped collecting this data?

If you can't find answers to all of the above regarding any particular data point you're looking to collect, it's worth reviewing with all relevant team members to see if there's something you've missed. And if you still can't find an answer, you should save yourself the time and risk and not collect that data point in the first place.

If different departments across an organization are collecting data separately and not sharing it, then you have a data silo. Usually, this results from different business functions such as product, development, support, and sales teams growing to work with different resources and datasets in isolation from the rest of the organization. 

Data silos are a recipe for immense confusion and frustration. If data isn't shared across departments, it's easy to miss vital and straightforward opportunities for collaboration and problem-solving. Not to mention that silos also hinder the customer experience, as it often leads to the customer being asked for the same information multiple times and having to repeat themselves. 

Here are a few examples of beneficial collaboration that can be undermined through data siloing:

• If social media teams silo away data gathered regarding customer interactions and sentiment on social platforms, then product or customer service teams won't be able to leverage those insights to improve product or service delivery.

• If finance silos away data regarding costs or expenses that can help calculate unit economics, then the sales team may be unable to correctly determine their anchor points for price negotiations with customers.

• If web teams silo away data on click-through rates or cart abandonment, then the development and UX teams may not know how and where to improve the customer experience to increase sales volume.

In general, teams need to break down data silos by adopting a common data collection and storage platform across an organization. This can be somewhat difficult, as it can require breaking down legacy processes and systems that many teams have grown comfortable with, but it’s essential to fully realizing the potential offered by customer data.