In today’s digital age, customers have become critical and demanding with easy access to choices and information. In competitive industries where companies have little product differentiation from one to another, marketers and sales professionals must seize every marginal advantage to differentiate themselves from competitors and to win deals. The marginal advantage of personalized marketing and engagement has emerged as a critical competitive edge.  We become obsessive in collecting customer data for unique customer insights. Nevertheless, most of us fail to take full advantage of consumer data for better customer engagement.  Why is that?

The execution of personalized engagement is dependent on a comprehensive understanding of all customer data. This is a tall order to achieve when 83% of business faces challenges in customer data management.  In other words, the first step towards personalized marketing is to establish an effective process to organize and integrate customer data that accurately reflect their thoughts and expectation.

Challenges for Customer Data Management

Consumers today engage with businesses on the internet from different devices via different channels, leading to the following top 3 issues:

Data Quality:

Unstructured data that require a person to interpret and assign a qualitative value are often subject to bias and inconsistency, resulting in poor data quality.

Example: SDRs documenting similar client phone conversations with different formats, qualifiers, and assessments.

Data Silos:

Different business departments generate customer related data for different purposes and store them in different locations.  They often lack a common customer identifier to combine the data and eliminate duplication.

Example: Without the visibility on the record of post-sale customer support, a sales rep was not aware of a customer complaint from a recurring customer of a large account.

Data Accuracy:

Data collected from different channels are often in an incompatible format that lacks well-defined governing rules to interpret them accurately for meaningful application.

Example: Without a systematic logic to synthesize data from 1) website viewing behavior, 2) e-mail exchanges, and 3) a customer’s LinkedIn profile into a quantitative value, SDRs would not know how to prioritize resources for customers with high potentials.

It’s not difficult to imagine how a fragmented or flawed customer data set hinders the SDRs from proper and timely customer engagement in the nurturing funnel, resulting loss in productivity and deal opportunities.  With more customer interactions and transactions moving online each day, businesses without strategies or plans to adapt will gradually drift into irrelevance.

Developing a Customer Data Management Strategy

A data management strategy is your plan and/or process on how to handle your data to meet your unique business needs.  A proper strategy takes the commitment across the organization, including the approval from executives, support from IT, and a culture of keeping the data accurate and consistent in all functional roles.  However, even if you have little control or influence on others, you can still implement such strategy within your immediate group to improve performance for internal goals and agenda.

Why do you need a customer data management strategy?

You need a scalable process to synthesize data from different locations for more complete and accurate customer insights.

What customer data do you need to manage?

Think about your customer segments and sales nurture flow; what kind of information do you need to move your customers forward in the nurturing funnel?

How do you turn your customer data into meaningful information?

Through the steps of:

  1. Data Collection
  2. Data Integration
  3. Data Interpretation

Data to Insights: A 3-Step Process

As we mentioned earlier, an effective data management strategy requires a collaborative effort from all stakeholders.  Even in a large organization where data scientists implement most of the data management initiatives, marketers and SDRs need to understand the process to control data inputs and interpret data outputs.

To start, you need first to determine how to best store your data.  Depending on your business and your channels for customer engagement, you likely use different technology solutions that collect and store your data in different locations.  Choose the master database> (e.g. CRM, Marketing Automation software, Sales Lead Engagement, etc.) that can best allow you to apply the customer insights derived from this process.

1. Customer Data Collection

Consider your customer segments and your nurture flow to determine what data to collect.  Depending on the availability of applications and resources to collect customer data, you need to work on obtaining data that enables you to make informed decisions and to improve customer engagement.  There are two different categorizes of customer data:

  • Structured Data

Structured data refers to data that can be easily organized in a spreadsheet and analyzed through a data-mining tool with limited modifications. Example: e-mail address, web visits, last login time, location, gender, # of interactions, etc.

  • Unstructured Data

Unstructured data refers to data that doesn’t conform neatly into a spreadsheet and needs to be prepared prior to data analytics. Example: e-mail content, images, video, phone conversation, pdf files, etc.

Unstructured data need to be converted into a structured format before it can be used for analysis – a challenging task when it comes with different varieties for different purposes and may or may not be significant.  With limited resources, you should focus only on data with significant value (e.g., top 20%) and collect it in a standardize format to minimize effort in data transformation.

2. Customer Data Integration

To integrate our data from the various channels, we need to first transform our data into formats that are compatible with our master database.  How do you transform data from one data model (data structural relationship) to another?

  1. Data Mapping – We first establish the governing rule that map the relationship of data across data systems.
  2. Data Transformation – Based on the data mapping specification, we convert the data from one format to another.  This can be done with software applications, customize algorithms, spreadsheets, etc.
  3. Data Integration – Ideally in real-time, load the transformed data from various sources into the master database.

3. Customer Data Interpretation

The goal for customer data management is to have a complete view on the customer’s buying journey and to obtain unique customer insights for better customer engagement.  The customer information should be readily available for SDRs to access in a meaningful and easy to understand format.

The following highlights a few suggestions:

  • Consider putting all your customer’s historical activities across channels in a chronological format to help SDRs identify the stage of the customer lifecycle.
  • Highlight key customer info and use statistics (e.g., regression analysis, clustering, etc.) to profile your customer data for unique customer insights.
  • Transform unstructured data into quantifiable value to help SDRs easily evaluate the customer sentiment and expectation.
  • Map customers into the likely customer segments for targeted customer engagement.

How to Manage Your Data as a Startup

Startup and SMB often have limited resources and don’t have the budget to hire data scientists or implement expensive data technologies.  Fortunately, there are plenty of applications nowadays that offer free or relatively cheap entry level packages to collect and manage your data.  For example, you can use Zapier to integrate and transform data from various applications to collect, transform, and integrate data into an intuitively simple customer view with insights to help you improve your customer engagement.

Final Note

The process of collecting, integrating, and interpreting data is often depended on logic or estimation that may not hold true after testing in the real environment.  Therefore, it is critical to constantly evaluate 1) how and what to collect from each channel, 2) the logic and statistics used to map and transform the data, and 3) the accuracy of the customer insights derived from data.

The skills needed for data management are often highly technical, and the implementation can be challenging for non-technical teams.  Spreadsheets and free applications may be a good starting point for startup or small teams, but not be sufficient as the business grow.  As your business scales with more data complexity, you could consider implementing technologies that enable you to take full advantage of your customer data.