Category: Data Quality

Hoover’s Concierge Services for SMBs

The Data Health Scan Report is one of the Optimized Customer Data Services.
The Data Health Scan Report is one of the Optimized Customer Data Services.

Hoover’s began rolling out a set of concierge services for small businesses after being merged into the Emerging Business unit last summer.  Dun & Bradstreet CEO Bob Carrigan has positioned the new services as important to turning around the Hoover’s product line which has been struggling of late.  In December, Carrigan noted that the new services have had “good success.”

The Concierge services target SMBs with revenue up to $250 million.  Concierge services target the standard Hoover’s B2B verticals including technology companies, business services, financial services, and manufacturing.

Dun & Bradstreet Product Leader (S&MS Solutions) Michael Umbach noted that the company has long received requests for assistance with customer marketing.  Dun & Bradstreet is meeting this need by combining their expertise with their data and solutions to help customers implement targeted list building, marketing file enrichment, and marketing campaigns.  Furthermore, Dun & Bradstreet Credibility Corp, which was acquired last May, has had success offering concierge services to SMBs for credit products.

The services enable customers to perform targeted marketing without requiring them to add marketing headcount.  According to their website, “Our experts can serve as an extension of your marketing team, helping you find—and reach—your best customers.”  The underlying services utilize the Hoover’s, Optimizer, and NetProspex WorkBench (aka Optimizer for Contacts) platforms for fulfilling customer needs.  Dun & Bradstreet has also partnered with an undisclosed email platform to support email services.

Hoover’s is providing three related services for SMBs:

  • Targeted List Building – Identifies prospects similar to a client’s best customers.
  • Effective Email Marketing – Delivers email services including messaging, design, email coding, blasting, and testing.  Dun & Bradstreet also supports email verification, analytics, and unsubscribe / bounce management.  Landing site hosting is provided via an undisclosed partner.
  • Optimized Customer Data – Supports data cleansing, standardization, and data enrichment for customer company and contact files.

As a promotional tool, Hoover’s is offering a free data quality check which assesses email deliverability, phone connectivity, and duplicate records.  This service would be provided by their NetProspex Workbench platform.

Many of the Concierge clients are signing up for a full year of service allowing them to run multiple campaigns, target different audiences, and maintain and enrich their marketing data files.

The acquisition of NetProspex has also directly benefited Hoover’s users.  Following the acquisition, the NetProspex contact file was merged into the Hoover’s contact set.  Furthermore, the professional contacts file was processed through NetProspex CleneProspex validation process.  Umbach noted that the improved contact data quality has raised customer satisfaction around contacts.

Avention Launches DataVision for Marketers

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Late last month, Avention announced availability of OneSource DataVision, a hosted marketing platform which integrates internal and external customer intelligence.  By matching Avention company and contact data against customer and prospect files, Avention improves the accuracy and firmographic fill rates of marketing databases.  The result is improved customer segmentation and targeting based upon enriched data from Avention’s Global Live database of companies and contacts.

DataVision also provides analytics and visualization tools for marketers.  “As a result, you will be able to identify and leverage key customer and prospect segments to make more informed decisions, identify cross-sell opportunities, key industries, verticals and much more,” states Avention.

“In a world where gaining new customers has become more complex and competitive, and customers engage with vendors later in their buying processes, marketing and sales teams need to align their data more than ever. OneSource DataVision is a powerful – yet easy-to-use – tool that helps marketers understand their current customer bases in detail and identify the most relevant target companies and segments,” stated Lauren Bakewell, SVP of product for Avention. “Initial customers have seen positive business impacts and results from their use of OneSource DataVision.”

DataVision provides a centralized marketing view of customer data which may be housed across multiple platforms including CRMs, Marketing Automation Platforms, and order entry systems.

“Companies need accurate, deep data to gain the marketing intelligence needed for better targeting and advancing customer relationships.  The ripple effects of greater marketing intelligence within the enterprise are improved sales cycles, cost of lead and sale and revenue generation,”  blogged Jennifer Nash.  “OneSource DataVision helps marketers increase the value of existing customer and prospect data by centralizing, analyzing and visualizing multiple data sources.”

DataVision includes a gap analysis tool which assesses the total addressable market in order to identify underserved markets and growth potential.  After enriching and segmenting the data, DataVision users can prospect for similar companies.

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DataVision Look-Alikes segmented by state.

 

As DataVision provides ongoing cloud based data cleansing and standardization, It is likely to be competing against similar offerings from ReachForce, Zoominfo, and D&B NetProspex.  The service is also likely to butt up against predictive analytics companies such as Lattice Engines and Infer.  While Avention offers a set of predictive tools (e.g. Business Signals and Ideal Profiles), they do not appear to be fully integrated into the initial release.

Analyst David Raab noted that DataVision’s hosted data model is likely to result in fresher data “since any query to DataVision will return the latest information available to Avention.”  He also complimented DataVision’s visualization tools and the platform’s ability “to compare those distributions with the entire Avention universe of known firms.”

A significant trend over the past two years has been the blurring of the lines between sales and marketing with sales intelligence vendors addressing marketing requirements (e.g. DataVision, Zoominfo,  InsideView for Marketing) and marketing functions migrating down the pipeline to sales reps (e.g. SalesLoft Cadence, Salesforce IQ).  Historically, OneSource shied away from building marketing tools in order to focus on the sales and research functions.  While they long offered match & enrichment services, this offering was managed by their Professional Services team as either a custom project or CRM enrichment.  DataVision is their first product designed specifically for the marketing team.  Of course, improved data quality at the top of the funnel provides benefits to sales reps in the form of improved lead quality, enriched leads, and properly routed opportunities.

Data Science and Competitive Advantage

GlassDoor Tech Salaries

Social media job site Glassdoor recently published its second annual ranking of the top jobs in America and, of the top twenty-five jobs, ten were in technology.  The top ranked position was data scientist which jumped from ninth last year.  Other high ranked positions were Solutions Architect (#3), Mobile Developer (#5), and Product Manager (#8).  Glassdoor bases their rankings on three variables: the number of job openings, salary, and career opportunities rating.

The Median Base Salary for a data scientist is $116,840.  Other tech base salaries can be seen in the above graphic.

When Network World interviewed data scientists about their position, they noted the pleasure of discovery as a key benefit.  A common complaint amongst data scientists was the headache involved with data preparation.  “At times, munging [parsing] through data can get tedious,” said data scientist Jeff Baumes at Kitware. “The worst times are when I realize the quality, quantity, or other aspect of the data simply prevents me from gaining the level of insight that I hoped to gain from the data.”

The McKinsey Global Institute found there is a growing shortage of analytics talent in the United States.  By 2018, they projected a shortfall of 140,000 to 180,000 professionals with analytical expertise.  They also projected a deficit of 1.5 million analytics trained managers and analysts.

Data scientist talent acquisition and retention are a significant problem for organizations, particularly amongst firms looking to initially establish data science capabilities.  In an article in the MIT Sloan Management Review, Ransbotham, Kiron and Kirk Prentice found that 55% of analytically challenged firms had a problem recruiting and retaining analytical talent while firms described as innovators had much less difficulty.  Only 29% of innovators reported difficulty recruiting with 24% reporting difficulty retaining.  Innovators also are much more confident that they have the appropriate skill levels in house.  While 74% of Innovators believe they have hired the appropriate analytics talent, only 17% of the analytically challenged felt the same.

One advantage of partnering with sales predictive analytics companies such as Lattice Engines or Leadspace is the ability to bypass hiring of in-house data scientists and instead work with their resources and tools.  While it is still important to understand the results and train staff in data interpretation, much of the complexity is removed.

Furthermore, the strategic advantage accruing to analytics capabilities is declining as more firms develop such capabilities.  In 2012, 67% of surveyed respondents believed analytics capabilities conveyed a strategic advantage.  By 2014, the percentage had dropped to 61%.  The authors posited two reasons for the decline: an increase in the number of firms investing in analytics and a difficulty in converting analytical insights into business action.  Half the respondents noted difficulty in translating insight to action.

“Technology is no longer the main barrier to creating business value from data: The bigger barrier is a shortage of appropriate skills,” said Ransbotham et al.  “Companies with appropriate analytical skills are far more likely to say that analytics is creating a competitive advantage in their organization than are other organizations.”

Owler: Jim Fowler on Crowdsourcing Content

Owler Profile of Lyft

Jim Fowler, who founded three crowdsourcing startups (Jigsaw which was acquired by Salesforce.com and renamed Data.com,  InfoArmy, and ), was asked how crowdsourcing has changed over the past decade.  His observation was broader than crowdsourcing and applied to any tech company looking to gain mindshare:

I think they change in the same way that we all have. We all are just overloaded with information.  Getting people’s time and getting them to pay attention is much more difficult now than it was back in the beginning of Jigsaw for sure. Getting journalists and analysts to talk and write about you is different because there’s so much going on. In fact a lot of the big publications don’t even exist or don’t write about it anymore.

It’s become much more flat, if you will. More players in it, so that’s interesting, but I just think the biggest thing is just people … There’s so much stuff flying around out there now that really making sure you have a crisp clear message so that they understand the value is even more important than it ever was and that’s just been the big change. People are more sophisticated, they’re more … They know how to use data and I see that trend continuing.

Fowler also noted that Owler combines crowdsourcing and semantic mining with editors.  While machines can do much of the work around event aggregation and structured alerts for exec changes, M&A, and funding rounds, editors ensure that information is properly tagged and mapped.  While this editorial review of news introduces a short delay in information delivery, it reduces the number of false positives and passing mentions of companies.  Furthermore, it allows them to de-dupe the stories and accurately capture M&A and funding content.

Basically, it solves your signal to noise problem through the addition of a short editorial review step.

If you just used technology to try to do this, you would get a lot of noise in there because really it’s a lot harder than it looks to figure out that the article is actually about Apple. Apple gets mentioned in millions of articles. To know that it’s actually about Apple is … To just do it with technology is really hard. What technology can do is say, “We think this is an article about Apple and we think it’s an Apple acquisition and we think this is the company that they did and we think this is it,” but what you need to do is create a task that gets prioritized very highly that a human looks at really quick. Checks out all the data and goes, “Ah, that’s right. We’re good,” and then sends it on to the people.

Otherwise you get a lot of noise, what I’m getting at is that technology can get you way down the road, but you need humans to get you all the way down the road if you want high quality data.

It is this multi-process approach that is likely to be the future of data collection and aggregation.  Traditional methods of data collection via phone interviews or analyzing filings information are quite expensive while semantic mining can get tripped up on context (is this about company X? Is this a relevant story? Is this a discussion of current events? Is this an actual event, proposed event, or mere rumor?).  Likewise, crowdsourcing requires a very large audience to obtain the wisdom of the crowd and works best on easily defined fields such as address, phone, and email (i.e. Jigsaw contacts).  Crowdsourcing also works well at gauging sentiment.  For example, Owler captures sentiment around whether the CEO is doing a good job and the projected fate of private companies.  But crowdsourcing does a poor job around complex information such as industry code tagging or corporate linkage.  It is through complementary methods that vendors will drive qualify forward while keeping data costs in check.

Radius: Bad Data Is a “Rotten Ingredient”

640px-rotten_oranges

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.

Workbench-Deliverable-website-450x280
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.

GIGO: Did We Lose on Price Again?

Loss Reason

Steve Silver, a Research Assistant at Sirius Decisions, recently blogged about a client where the overwhelming reason for losing deals was price.  But the client had a differentiated service where price should not have been the primary factor.

Silver discovered the reasons for this anomaly:  The field was not used by any departments at the firm.  Without an owner, the path of least resistance was selected — the first choice in the picklist.  And in the case of the client, 90% of the losses were flagged as price-based.

Did we establish value?

Silver omitted a third reason, and one which is common amongst sales reps.  Price is an easy scapegoat for lost opportunities.  But if your service is well differentiated and you focus on your value proposition, price should not be the primary loss driver.  Yes, some deals will be lost because a competitor low balls the deal (a true price loss), or the prospect simply does not have the financial means to purchase your service (a poorly qualified prospect), but in most cases, losing on price is a failure on the part of sales reps.  If they thought about it more, they would realize that price is not an exogenous variable outside of their control.  That’s because price is tied to value.  Price is the critical variable if your value has not been established.

This isn’t to say that pricing could be wrong.  If your competitors are quickly moving up the value curve, your historical price may no longer be sustainable as you become less well differentiated.  With good data and analytics, you would capture this shift in the competitive marketplace and act accordingly (e.g. R&D to better differentiate your service, better product bundling, or reduced prices), but price should only dominate the loss reasons in a commodity business.

GIGO

So what else could be gleaned from this situation?  First, somebody needs to own data quality within the CRM.  If a field is viewed as busywork, your sales reps will populate it with junk data.

Garbage in, Garbage out.

Managers should also be pushing back on reps to better understand why deals were lost so that mistakes can be avoided in the future.  Does the sales rep need additional training or coaching?  Are additional sales tools needed for competitor handling or establishing value?  Are we poorly qualifying opportunities or failing to identify the key decision makers?

Yes, it is easier to move onto the next deal without taking the time to analyze deal losses; but a learning organization needs to understand its failure points.

Sales Operations

Sales Operations should be cross-checking fields.  If the loss reason is price or features, then a competitor had a better offering.  Was the primary competitor recorded in the CRM?  If the competitor is blank, then additional explanation should be required.  Did you really lose on price or features if you don’t know who the competitor was?

Or did you lose to no decision or the incumbent because there was insufficient value established to warrant funding the purchase or sustaining the switching costs?

If you don’t collect the data or you allow a field to be treated as busywork, it won’t be available for analysis.  I have had several instances where my clients did not record the loss reason or the competitors.  I have also had others where the fields were usually blank.  In short, the firms were operating in a competitive fog and not using their CRM for market monitoring.

In the end, it is important to not only gather win/loss information, but to use the data for sales training and coaching, marketing communications, sales enablement, and product development.  When information is valued by the organization, then sales reps are less likely to blithely skip fields or enter the first field in the required picklist.