Two new studies on B2B data and marketing highlight both challenges and opportunities in putting the workhorse of modern-day marketing — data — to profitable use. They are worth reading.
eMarketer’s B2B analyst Jillian Ryan is lead author on “B2B Marketing Data — Capturing and Managing Data for Actionable Insights.” EverString with Heinz Marketing has published “The State of AI in B2B Marketing” with a focused look at artificial intelligence, machine learning and predictive modeling tools in this emerging area.
Both studies say a lot about promise, pragmatism and skepticism when it comes to collecting, integrating, analyzing and using first-, second- and third-party B2B data. The eMarketer study is also particularly useful, as it is culled from dozens of interviewees, plus a scan of multiple published reports in the field.
Credit: “B2B Marketing Data — Capturing and Managing Data for Actionable Insights,” p. 2. by eMarketer, 2018. Used with Permission.
Consider these current marketplace observations, reported by eMarketer (with additional attribution where other studies are cited):
- Skills Gap — We Need Scientists. The top obstacles to data-driven marketing success include (Adweek Brandshare and Dun & Bradstreet, 2017):
(1) lack of data expertise (42%);
(2, tie) the reliability of third-party data sources (38%);
(2, tie) accuracy of audience data (also, 38%);
(4) integration of marketing and sales platforms (37%); and
(5) lack of appropriate audience data (35%). - ‘Difficulty’ Does Not Necessarily Refer to Accuracy — but how easily it is to integrate, state, analyze and apply available data. The most “difficult” sources of marketing data include:
(1) channel partner, value-added reseller data, etc. (46%);
(2, tie) social networks and public data (45%); and
(2, tie) internal sales and customer service data (Ascend2, in partnership with ReachForce, 2018). - The Most Productive Data Sources are internal — but not exclusively so. The most “effective” sources are:
(1) internal marketing programs (53%);
(2) internal sales and customer service data (52%); and
(3) third-party information vendors (Ascend2, in partnership with ReachForce, 2018). - Data Quality Woes Persist: “Only 26% of B2B marketers worldwide polled by Ascend2 and Reachforce said data quality and accuracy was ‘extremely good.’ Roughly half said it was ‘somewhat good’ and 21% said ‘somewhat poor.’ ”
- Even Dirty In-House. A LiveRamp B2B expert estimates that as much as 40% of internal CRM [customer relationship management] data for B2B marketers is “dirty” — inaccurate by being outdated, entered incorrectly or other inaccuracies.
- Data Conflicts Happen (Sometimes Often). When appending data from third-party sources, and a conflict arises between existing values and appended values, respondents (Openprise, 2017):
(1) Maintain the existing field value in the CRM solution, 38%;
(2) Keep both the existing field value and the new data from the data provider, and reconcile “later” (27 %);
(3) Replace existing CRM data field with the newly acquired data from the data provider; or
(4) Keep both fields and never reconcile (11%) - Merging Is Complicated. Fifty-five percent of U.S. B2B professionals “said the inability to merge data from disparate sources in a timely manner was the biggest challenge of leveraging data to achieve go-to-market goals at their company (Harvard Business Review Analytics Services).
- Reliable vs. Agile — Make the Choice: “B2Bs frequently make compromises working within the confines of the current technology limitations. At T-Mobile, [Gavin] Warrener [director, B2B demand generation and integrated marketing, T-Mobile for Business] said his data warehouse is ‘fantastic and scales amazingly,’ but the tradeoff – which he believes is a standard most B2Bs encounter — is that it takes a very long time to integrate the data. ‘We are compromising between data reliability and agility. We cannot have both,’ he said.”
Now that social data, behavioral data and mobile data — structured and unstructured — are entering the mix, B2B organizations struggle to integrate, analyze and score all these data, in conjunction with more traditional CRM and sales data. Some are building tech stacks internally, some outsourcing to third-party providers, and some are considering adding AI and machine learning tools to the mix.
Is AI Moving Beyond ‘Shiny New Object’?
In its report, EverString/Heinz Marketing finds confidence in AI is still building — with nearly a third of survey respondents uncertain in explaining the nuances between artificial intelligence, machine learning and predictive modeling. Fully half are not yet using AI in their marketing strategy. Yet interest is high, with seven in 10 exploring AI use in personalization. More than 63% see AI useful in identifying trends in data, while 58% cite customization applications for AI, and 55% for processing of data.
AI success also is contingent on quality of available data — 70.4% are “somewhat confident” in their achievement of marketing objectives, based on current data access, while 21.4% are “very confident.” Confidence here, however, is increasing — with nearly 90% “somewhat confident” or “very confident” in both their marketing tech and marketing strategy investments.
While it may be tempting to substitute AI where B2B data skills are lacking, AI tools are best handled by data scientists themselves. They are the ones who can recognize “garbage-in, garbage-out” most readily and can verify and validate any patterns discovered and uncovered through AI applications.
Ad tech, martech, AI and other marketing and predictive tool advances are all great outcomes. The biggest prize, however, is strategy that is driven by well-managed, reliable and accessible prospect, customer and account data. As B2B marketers invest in tech — they must also invest in quality.