First a data hub—then marketing automation

Share this article:
Martha Bush, SIGMA Marketing Group
Martha Bush, SIGMA Marketing Group

Marketers know that successful customer engagement involves leveraging customer data, turning it into intelligence, and then making smart marketing use of those insights. Messaging and campaign tools will continue to evolve, and their claims of creating marketing nirvana get more and more strident. But few of these tools take on the hardest, least exciting tasks of getting the data right. Often, data quality is handled by a completely different set of tools.

At the core of successful customer engagement is a solid customer database—Forrester calls this concept a Customer Intelligence Hub—that creates a single source of customer knowledge. It may be blasphemy to say this, but it's unlikely that a comprehensive, flexible intelligence hub can be created inside most marketing automation tools without a fair bit of agony.

It takes hard work to bring multi-sourced customer and prospect data together in a usable way. Presentations are filled with talk of “pipes that are open to receive structured and unstructured data” can be misleading if they oversimplify what can be an arduous task. Just because you have a garden hose, doesn't mean you have a garden.

Think of a Customer Intelligence Hub as an engine that can link, clean, and refine customer data collected both online and offline from multiple operational systems. The hub must be flexible enough to regularly accept new data elements and sources. It should offer the business intelligence chops to create a robust portrait of your customer base, while giving you access to reporting and dashboards that can flow up your organization. Some marketing teams have more sophisticated analytical users who will be happy with the ability to easily pull data out of the hub into tools like SAS or SPSS to perform more customized deep data dives, or create robust predictive modeling. An intelligence hub helps marketers drive smarter marketing decisions and spend their resources more wisely.

any companies struggle with implementing marketing automation tools because their IT teams need significant help in pulling data out of internal systems. Often there is a skill gap in trying to make sense of customer data for effective integration to a marketing automation tool. The promised “database inside” most marketing automation tools will work only if the hard work of data consolidation, cleansing, business rule creation, and automation of data extracts can be accomplished before data reaches the tool. This work is often beyond the capability of the internal IT or marketing team. Building a customer intelligence hub between the internal systems and the marketing tool solves many of these challenges: integration, cleansing, and delivering the kind of multichannel insights marketers need.

The proper sequence of evolution to marketing automation is to build a customer intelligence hub first. Follow this by developing customer insights that can drive more and more sophisticated customer engagement strategies. Once the success and investment return is established for more data-driven marketing, then chose and build out your marketing automation platform—fed by smart, clean data from your intelligence hub.

Martha Bush is SVP of Strategy & Solutions at SIGMA Marketing Group. Follow Martha on Twitter or connect with her on LinkedIn.

Share this article:
close

Next Article in Database Marketing

Sign up to our newsletters

Follow us on Twitter @dmnews

Latest Jobs:

Featured Listings

More in Database Marketing

What's H-appending? DiscoverOrg Taps Marketo's Webhooks

What's H-appending? DiscoverOrg Taps Marketo's Webhooks

Cloud-based marketing automation behemoth Marketo joins forces with marketing intelligence company DiscoverOrg to improve its data collection capabilities.

A Toast to Marketing Attribution

A Toast to Marketing Attribution

Vino accessories and storage company Wine Enthusiast indentifies top and underperforming affiliates using algorithmic marketing attribution.

Q&A: When (and How) to Bust Down the Data Door

Q&A: When (and How) to Bust Down the ...

Some people run into issues with trying to build the perfect solution when often an 80% solution will do, says MailChimp's chief data scientist.