B2B Marketing Technologists: This is How You Execute a Data Management Strategy

As the end of 2018 nears, we’re already in the midst of intensive planning for next year. All of us marketers are planning for 2019 with an objective to create innovative marketing strategies and power them with world class technologies, like artificial intelligence, predictive, real-time marketing, personalization, cross-channel orchestration and more. Rightly so. That should be our goal.

But before we mire ourselves in big plans, let’s first step back and evaluate the readiness of the most important, foundational piece of our current and future successes. It’s the 4-letter word that we use every day: “DATA”.

The importance of data for today’s marketer

Be it a marketing technologist, product manager, marketing operations professional, planner, content marketer, analytics or a sales professional – their roles and responsibilities center around data.

Data is the oxygen that keeps today’s organizations happy and healthy.

Here’s just one example of the many ways data influences the breadth and depth of marketing today. Specifically, this is how data affects the account-based marketing framework.

For every phase of this ABM framework, we need data to answer the questions:

  • How do we select and prioritize the right accounts?
  • Which contacts constitute the buying centers?
  • Which contacts are right now in the market to purchase?
  • What are the important buying signals from accounts and relevant contacts?
  • What types of content/messages resonate best with individuals within an account?
  • Which channels of engagements are most effective?
  • How are the different sales and marketing plays working to meet the marketing objectives?
  • What are the key measure of success?

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The impacts of bad data

According to a Kissmetrics statistic, businesses lose as much as 20% of revenue due to poor data quality. The reasons are obvious. Consider this, if your personas are differentiated by functions and levels and 40% of your data is incomplete or incorrect (e.g., test, abc, xyz, N/A, Junk, etc.) or inconsistent (e.g., Mgr, Manager, MGR, Mgr., Manger, etc.) or blank, then think about how this bad data hinders the effectiveness of:

  • Targeted lead generation
  • Re-targeting
  • Segmentation
  • Dynamic content
  • Personalization

How to create and execute an effective data management strategy

Your checklist should look something like this:

  • Identify all the data sources feeding data into your databases. They can be forms, Excel sheet (or another format of list loads), CRM, API-driven integrations (webinar, event management, other marketing engagement tools).

  • List your target personas.

  • Determine the minimum data points that you need to do basic marketing. These are the mandatory fields that you need at the entry of all your data sources. For example, for some companies, first name, last name, email address are sufficient to get started. For others, depending on their business model, they need more data points to create a basic profile.

  • Determine the data priority for each of these sources. These data priorities dictate the data-update priority; that is, which sources can overwrite data and which cannot. We do not want a list upload overwriting data on CRM contacts, as it’s of lower quality. Accordingly, you can configure your systems to manage these priorities.

data quality process table aditi

  • Identify the data points that you can standardize at the source. Picklists are a great tool to enable this. Country, prefix, gender, function or role are perfect examples of standardization.

  • This is a very important, yet often overlooked component. For each of the stages in the buyer’s journey, identify the channels that are going to feed data at every stage. Accordingly, you can identify opportunities to enrich your data set. The beauty of creating this framework is that the data enrichment is purely customer/prospect led. There is no guess work, no intuition. They’re giving us information at their own will. Data quality will be the highest.

datat sources fro aditi post

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  • Now, once we’ve identified a customer-/prospect-driven data management strategy, it’s time to plan data management tactics driven by technology and 3rd-party vendors. To make things a little less scary, it might be helpful to dissect data management into modules. Here’s one way to do it. Before we dive deep into each, it’s critical to do a database health checkup. Example of things to check here are contact field completeness, contact engagement, reachability, unreachability, total new contacts, duplicate.

Here are the 4 most common data management functions that marketers seek in a technology:

  1. Data cleansing/correction – Proper casing of core contact fields like first name, last name, title, company, salutation, addresses or formatting prefixes and suffixes or phone nos.

  2. Data normalization and standardization – Examples are:
    • Title to function and level mapping, using lookup tables or advanced Regex models
    • Identification of gender from first name, last name, country combination or deduction from the gender
    • Automatic salutation generation using gender and country logic
    • Extracting suffix and prefix from first names or last names
    • Standardizing functions or levels

  3. Data validation – Validating whether certain data points about the individual are correct, like email address, phone no, title, company, as well as identifying junk data from actual valid data.

  4. Data enrichment – Way to enrich a contact (social media handles, phone no., title, gender, preferences, etc.) and account profile (annual revenue, address, D&B info, account hierarchy)

Now, if we build an in-house solution or leverage external vendors to do our day-to-day data management functions, I can say with 99% confidence that it does not meet all our data quality needs from a global standpoint. Considering the diversity of languages, cultures, social protocols and etiquettes in the world, it’s difficult to find a solution that caters to every permutation combination of scenarios and rules when it comes to data management for any organization.

So, what do we do? Do we live with the x% of database that cannot be cleansed or enriched because there’s no technology to do that?

This is where the human factor to data management comes into play. If x% of data is getting cleansed through an automated program/workflow, then (100-x)% if accessible in a convenient program can be reviewed and acted on by people.

Let us consider a scenario. We might have last names like “McDonald” (2 upper case letters in the same word) or Lloyd-Atkinson (hyphen in one word). Similarly, there will be more such exceptions or anomalies from the usual patterns for different contact data points. The important thing here is to build logic to identify these exceptions and not process them through the usual process. Otherwise, our data quality will worsen if we have “McDonald” changed to “Mcdonald” or “Lloyd-Atkinson” changes to “Lloyd-atkinson”.

Instead, isolate these scenarios, append reasons for why they’ve been flagged for review (“first name – proper case” or “not in function/level lookup table” or “improper Suffix”) and make them available for review either through a landing page or a manual sheet or through another interface, whichever is more convenient, secure and cost effective for the organization.

I like the idea of a “data cleansing landing page” where the reviewers can see the flagged records with the relevant fields and review reason with no need to touch the data directly in the systems of record. If we want to make it more sophisticated, we can add filtering, sorting features on the page for the reviewers’ convenience like only filter records for a specific country or by a specific review reason. You can also enable reviewers to add comments or highlight/check contacts that they don’t want coming up for review again or to suppress them from going through the normal data program. Cool. Right! You get all the freedom to modify data without worrying about the backend processes to update the correct data in the database.

Coming back to where we started from. Data quality across different sources and different business models needs to be managed strategically yet practically. It should closely align with an organization’s marketing objectives (What data do we need to do effective marketing across all stages of funnel management). It’s equally important to identify metrics against which you will monitor your database health. But most importantly, plan how will this data improve your future marketing efforts.

Know your data, take care of it and use it wisely!

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