Tag: Data Strategy

Radius: Bad Data Is a “Rotten Ingredient”

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Stephanie Kong, Product Marketing Manager at Radius, recently compared dirty data to rotten food.  Working with either consumes more expertise and results in sub-par results:

Handing dirty data over to data scientists is tantamount to passing rotten ingredients to a chef and expecting that he/she transform the inputs into a gastronomical masterpiece. In both instances, the quality of the inputs impacts not only the quality of the outcome, it also impacts the experience and efficiency of the professional– how much time can be spent experimenting and applying the artistry for which the professional was hired versus overcoming hurdles to get to a sufficient baseline.

Bottom line: the quality and state of your internal data can impact– and even worse, impede– the ability of even the most talented data scientist to generate breakthrough ideas. Many turnkey data solutions can help you maintain data, even enhancing accuracy and comprehensiveness, in addition to extracting insights. It’s not simply a means of “killing two birds with one stone”; accurate and complete data is a critical first step. In other words– and without being too macabre– good data is the essential and necessary “first kill.”

Marketers are becoming more strategic in their approach to data as they realize the limitations and costs of poor data.  Predictive Analytics systems are only as good as your underlying data.  Bad data is simply noise (or as Kong would call it, “rotten ingredients”) that obscures the underlying signal.  Without accurate data, how can you expect your predictive systems to give you anything more than random nonsense?

Likewise, the shift to Account Based Marketing requires strong firmographics for identifying the companies you wish to target.  Furthermore, strong linkage is necessary for targeting subsidiaries and branches.  Whether you are extending an MSA or looking to establish a beachhead, you need a holistic view of the organization across industries, regions, and job functions.  You also need an accurate set of contacts spanning all functions, levels, and locations.

When evaluating B2B content vendors offering predictive or DaaS solutions, ask about their

  • Data Processes: Data sourcing, update cycles, verification and validation, feedback processes
  • Hygiene Services: Do they offer email, phone, and address verification, field standardization, deduplication
  • Matching Capabilities: Is it a direct match or probabilistic match based upon multiple fields? Are fields standardized prior to matching? Is the focus on company or contact matching?
  • Connectors / Integrations: CRM, MAP, DaaS cloud, API, etc.
  • Ongoing Data Refreshes: Frequency, Cost, Level of Automation
  • Contact Coverage: Emails, direct dials, functions, levels, bios,
  • Company Data: Scope, depth, firmographic fill rates, identifiers, linkage, etc.
  • Other Data: Intent data, technology platforms, business signals, etc.

Data quality is a strategic asset so your content and technology partners need to be thoroughly vetted.  It is important to understand the strengths and weaknesses of each offering during both the vendor selection and implementation stages.  Otherwise, you may only partially address your “rotten ingredients” problem.

Photo: Wikimedia Commons

From Data Science to Data Strategy

InsideView CEO Umberto Milletti offered three marketing themes for 2016.  The first two, Sales and Marketing Alignment and Data Driven Messaging and Targeting, have been well discussed over the past few years.  It has long been clear that sales and marketing need to work together and that data should be driving the marketing function.  The new idea for 2016 is the elevation of the data scientist into a strategic position in the company.  According to Milletti:

If 2015 was the year of the data scientist, then 2016 will be the year of the data strategist.

We’re in an explosion of sales and marketing technology, and every system relies on data. The more data you have, the more important your ability to update and sync that data becomes. Companies are consolidating systems and that is driving the need to implement a strategy for customer data that resides in multiple places. Otherwise, you get silos of customer information.

Good data strategy considers the flow of information, the accuracy of the data, and the consistency of the data. To do that well requires someone focused full-time on a company’s strategy for their data.

This is why the title “Chief Data Officer” seems to be more popular with search frequency trebling over the past three years on Google Trends:

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Google Trend for the search term “Chief Data Officer”

Data quality, an element of broader data strategy, is becoming increasingly important.  While the statement “garbage in garbage out” goes back decades, marketers long allowed their databases to go stale.  Many marketing databases are rife with out of date contacts, incomplete or inaccurate firmographics, and undeliverable addresses.  With predictive analytics and big data, the ability of these systems to provide insights is dependent upon the underlying data quality.  Data quality is also required for tying together historical data silos which have employed different standardization rules and identifiers.  Pulling together all these elements requires an enterprise owner of data strategy.

If your company isn’t ready for a broad data strategy, you should at least consider implementing data quality practices in your CRM and Marketing Automation platforms.  Several vendors including ReachForce and NetProspex are developing ongoing data quality solutions that synchronize data across multiple platforms.  These systems verify and standardize global address, validate emails and phones, manage duplicates, and enrich platforms with company firmographics.

Another important feature is web form verification which matches prospect records against their database and performs real-time validation of entered fields.  Not only is data validated at time of entry, but the number of required input fields can be reduced, resulting in a lower web form abandonment rate and higher ROI for your digital marketing investments.

NetProspex Workbench also offers Dun & Bradstreet linkage, D-U-N-S Numbers, emails, direct dials, and tech platform variables (products and vendors).

Although InsideView doesn’t offer lead verification tools (e.g. phone, address, email), it supports match and enrichment for a broader set of CRM and marketing automation platforms.

Keep in mind that data quality not only benefits your marketing though better targeting, segmentation, and lead scoring, but it also provides value to your sales function.  By infusing leads with broad firmographics and linkage, you are more likely to be passing actionable leads to your sales team and routing them to the correct sales reps.  Furthermore, when leads are mapped to sales intelligence platforms, reps can quickly qualify them and begin planning account messaging.

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The NetProspex Workbench Data HealthScan report provides a free PDF detailing pre and post enrichment field population rates, data error rates, and segmentation reports.

InsideView, NetProspex, and ReachForce are all cloud based solutions with low barriers to adoption.  They also include data health analyses, segmentation reports, and integrated prospecting as part of their feature set.  So even if you cannot implement a global data strategy across your enterprise, sales and marketing can begin by focusing on a solution which improves the quality of their leads, contacts, and accounts.