Today, I spoke with Frannie Danzinger, Head of Market Development for 6sense, a Predictive intelligence martech vendor.
Josh: In the Martech Maturity Model I wrote about last summer, I placed Predictive tools at Stage 6 – the very end of the 24-36 month growth cycle for firms. Do you agree with this? Where do you see Predictive being the most useful?
I agree and disagree here. This is a nice, linear model. And in many ways, Predictive is in the same place that MAPs were six years ago – still being tested and adopted. If a firm is sophisticated in their use of martech, then yes, Predictive has certain benefits. And if a firm is not at Stage 6, and they already bought Predictive, then their mindset is more advanced and ready to use Predictive.
Where I disagree with this model is that the people involved do not always need the technology assumed by the model. As times evolve, marketers are increasingly ready to open the floodgates of predictive scoring without a full martech stack. At Enterprise and Mid-market firms, where most have a CRM system and likely have a MAP (marketing automation platform), marketers’ primary focus is on net-new acquisitions. Our product 2sense focuses on net-new with web analytics and limited internal data. This gives us the ability to drive ABM strategies without input from all of the other martech datasets.
Thus, it is entirely possible to use Predictive much earlier in the martech lifecycle than you suggest.
[Josh – I’ve been hearing this from predictive vendors, so I’d love to hear from customers – is this your experience too?]
Josh: How does 6sense think about ABM+Predictive? What’s different about ABM+predictive vs. statistically correlated lead scoring?
For leading B2B organizations of all sizes, Account Based Marketing (ABM) has become the dominant strategy to ensure high-value target accounts are being marketed to in a way that is highly personalized and specific. As ABM adoption has soared, with more than 70% of B2B companies focused on implementing ABM, the selection process of identifying target accounts has become the most critical component of effective strategies. The introduction of predictive intelligence has allowed organizations to build lists not simply on gut instinct and static look-alike demographics, but on buyer activity that demonstrates a business need and propensity to purchase.
ABM strategies are built on a company’s ability to create target account lists based on the business needs and challenges they are uniquely positioned to address. Having access to buyer activity through predictive intelligence allows marketers to identify accounts with greater precision and confidence. Now, enterprise and mid-market customers have the ability to create targeted account lists on continuously monitored intent data, enabling them to track the needs and trends driving the evolution of their customers and iterate accordingly
Josh: Which firms are most ready for predictive tools? Which are not?
Forward thinking firms will always be more ready to embrace predictive tools. Are the marketers ready to be uncomfortable with new technology as well as challenge assumptions real data brings? Companies that are more open to data-driven marketing and believe in decision-making powered by data will better reap the advantages predictive offers because of how they respond to the results. Companies with data-driven cultures are more receptive to applying and integrating predictive cross-functionally and are quicker to transition quantitative results into more efficient and effective real-life practices.
The most sophisticated and informed companies are increasingly using analytics to explain and predict performance and facilitate their decision making. As organizations place more of a focus on analytics, sales and marketing can align and concentrate on the data to improve business processes and overall team performance. Businesses that optimize analytics and predictive to the fullest will continuously rely on the numbers to help drive change, seek new opportunities and identify the best ways to improve efficiency and results.
In terms of the minimum volume of data required to build predictive models, they’re not just volume-based, but instead rely on the quality of the data signals. The ideal is two years of historical data.
Josh: If you had budget for just 3 tools, which would you purchase to run ABM?
- A DMP: (e.g.: Oracle DMP (BlueKai))
- ABM Tool: (e.g: Madison Logic, Terminus, Kwanzoo)
- A DSP: (e.g.: Media Math, Turn)