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Build vs. Buy: 3 Problems with Building In-House Analytics for Marketing

Lucas Stewart | 10.11.2017

The build vs. buy debate has been a part of the analytics conversation –from start ups to Fortune 100 companies– for decades. In the realm of marketing intelligence and analytics, building internal tools comes with a few caveats.

The idea of a standalone, pre-built solution that will always work for marketing data (or even for a few months) is itself becoming outmoded. Beyond the investments of time and capital required to build an internal tool, the most unanticipated constraint is the requirement of an intimate familiarity with marketing data and programs among developers.

This expertise comes into play from data modelling to visualization, and is most critical when ingesting new tools into marketing reporting workflows. The marketing expertise problem is the most unanticipated, but others may arise. Here are 3 problems with building a full or partial in-house marketing analytics solution:

1. Marketing data requires marketing expertise to grasp

Even if developers learn the lexicon of marketing acronyms, understanding the nuances of marketing metrics –and how they need to connect– requires a marketer’s knowledge.

For example, the “keyword” dimension needs to be consistent across Paid Social, SEO, internal search, Paid Search, Display Advertising… the list goes on, and may extend. What if a marketing organization is just getting “keyword” in place as a UTM parameter, and has recently added this as a Salesforce custom field? The interdependency of marketing systems, and the evolving nature of marketing data, may not be immediately apparent to a developer coming from the product team. (And understandably so.)

2. Marketing data is expanding– and that brings changes

The number of marketing technology (martech) companies has exploded from about 150 data-producing tools in 2011, to over 5,000 in 2017. This expansion means that marketers can expect to have to incorporate new tools almost constantly.

Each new tool can herald a sit-down problem for your developers– after all, there is no global standard in data storage types or programming languages used by marketing technology software. At the bare minimum, a new tool (or even feature of an existing tool, like Hubspot) means an additional API that needs to be accessed, integrated, and harmonized to previous marketing data.

An in-house solution used to benefit from having the flexibility of using your own developers. But flexibility can quickly turn into a full-time job when it comes to integrated the influx of marketing data the industry is experiencing.

3. You’re relying on precious resources and teams, not software

Resources are limited. Will an analytics project be the next big growth provider for your organization? It’s important to take into account exactly what you’re building and the specific resources required. For example, it’s not likely your developers will build a database, but rather license a SQL-type, commercially-available cloud solution. For visualizations, your developers can use a library like D3, or you can license a BI tool– which you will also need to maintain.

Regardless of what libraries are used and software is licensed, putting internal tools together will build tribal knowledge around the tool. The most devastating problem here is if a key developer leaves your company. This can leave you with a non-working tool at the first sign of change in your data, post-exit– and a headache to try and fix.

If you’re considering building in-house for all or part of your marketing analytics solution, keep in mind the team or individual that builds your tool will need to:

  • Keep up the costs and updates required in the software and libraries licensed and used
  • Remain devoted to QA for months or years
  • Be available for project-like updates of undetermined length on a daily or weekly basis
  • Have marketing expertise

In some departments and industries, building an in-house analytics application can make sense. But in the evolving world of marketing data, it’s important to understand the risks and requirements that accompany an marketing analytics solution built in-house.


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