ABM Readiness Program Shows Opportunities for DAT Freight to Increase Deal Sizes and Win More Gartner 25 Supply Chain Accounts

DAT Freight and Analytics was assessing ABM tech like Demandbase, but these platforms would have only scaled bad “transactional” sales and marketing processes that are leading to their solution being seen as “nice to have” vs. “must-have.” This was an issue that led to lower value deals, as clearly seen by reviewing their won/lost opportunities and their current pipeline.

In assessing the deal sizes of their won/lost opportunities, we saw:

  • 104 won/lost deals that were greater than $10K but less than or equal to $25K
  • 56 won/lost deals that were less than or equal to $50K
  • 33 won/lost deals that were less than or equal to $75K
  • 8 won/lost deals that were less than or equal to $100K
  • 4 deals that were greater than $100K

In looking at the customer base, we saw:

  • 30 existing customers that have an ARR of greater than or equal to $100K
  • 39 existing customers that have an ARR of greater than or equal to $75K
  • 73 existing customers that have an ARR greater than or equal to $50K
  • 185 existing customers that have an ARR greater than or equal to $20K
  • 132 existing accounts that have an ARR greater than or equal to $10K

This shows that DAT needs a complete account-based GTM where they are bringing in high-value accounts that will see DAT as a must-have and then accelerate those accounts to revenue!


They need to fix the complete account experience (including the interactions that all teams are having and the experiences they deliver across the pipeline and customer life-cycle) vs. just take a more targeted demand gen approach with technology. Before you can fix the experience, though, DAT needs to identify the accounts that would see them as a must-have vs. a nice-to-have.

Before investing in our ABM readiness program, DAT was defining their ICP by industry and freight spend. They weren’t segmenting their existing customers to uncover their “best fit” and identify characteristics of those high-value accounts that were using their platform on a regular basis (making it a must have) vs. monthly, quarterly, or yearly basis (making it a nice to have.) They weren’t looking into how and why the DAT platform was being integrated into the daily programs of the most frequent users and how they were utilizing the platform differently than the others. They didn’t look into supply chain maturity.

If DAT targets accounts with a level 1 or 2 maturity, they will automatically be seen as a nice to have because all of the teams are siloed. They would not be able to look beyond the “transportation management role” and “cost savings.” They would not realize the full value and returns from their investment in DAT which should touch the complete supply chain, distribution, finance, sales/marketing and operations organization.

DAT wasn’t looking into the characteristics of those on Gartner’s list of top 25 supply chain organizations even though 13 companies on the list are DAT clients.

They weren’t uncovering the strategic priorities for those on the top 25 global supply chains to see the role they can play within the strategic organization that would make them a must-have.


For example:

• Schneider Electric is building on its STRIVE program (sustainable, trusted, resilient, intelligent and efficiency) to drive transformational change.

• Cisco is prioritizing capability development that closes critical operational gaps. They are leveraging scenario planning, building out playbooks that are designed to protect the business in an increasingly volatile environment due to semiconductor shortages, and focused on multi-year forecasting, planning at a component level, supplier qualification, risk-buy management and other key initiatives that drive revenue, income, and margin growth.

• Johnson & Johnson is looking to advance its ability to make real time and predictive decisions based on real time and predictive data across their end-to-end supply chain. They are building a resilience dashboard and establishing a control tower to track and trace cold chain products in real time.

• PepsiCo wants to capture real-time and predictive data to anticipate and resolve problems ahead of time.

• Pfizer wants to accelerate their go-to-market while reducing their cost to serve.

• Microsoft is focused on generating actionable insights about the end-to-end supply chain, enabling fast-turn decision making and embedding resiliency in operations.

• Lenovo continues to evolve their supply chain intelligence framework and leverages AI to provide analytics and optimization solutions across 10 different areas including demand forecasting, operations, risk sensing, and packaging optimization.

• Sam’s Club, a Walmart subsidiary, wants to expand its supply chain network, accelerate order fulfillment, and reduce shipping costs.

Failing to identify key strategic priorities also impacted the effectiveness of DAT’s content and their email, social and live conversations.


While completing our ABM readiness review, our team looked at hundreds of pieces of content, including articles, whitepapers, case studies, podcasts, and webinars. While the content provided thought leadership, it did not provide commercial insights that help GTM teams teach for differentiation against their competitors and the status quo.

• DAT recently lost a high-value deal to Uber Freight because the rate analytics the prospect was getting was “good enough”

• There are lots of E2Open customers that are not DAT customers as well as the rate analytics are “good enough.”

• DAT’s win/loss data shows that they repeatedly lost to Freightwaves, whose costs are 2X-4X lower, and provide rate analytics that are “good enough.”

The content didn’t reframe the prospect’s thoughts on rate benchmarking analytics, show new ways of using it and the role it can play in key priorities across the supply chain organization. They didn’t show the impact that predictive rate analytics at a component level (something others cannot deliver) can have on:

• Pricing strategies to protect future margin growth

• Demand planning, distribution, and merchandising so you can reallocate products based on balancing demand data with predictive future “industry” and “component” freight rate trends.

• Supply procurement and operations as they can leverage demand forecasting with future component rate analytics to determine inventory levels for a stronger P&L based on inbound logistics costs.

• Sales and marketing as they can plan ahead for promotions at the right time based on demand planning and predictive freight rate benchmarking for minimal impact on the margins.

• Operational scenario planning to help build out playbooks to protect future profitability.

They aren’t showing how they can help each team across the supply chain, finance, sales, marketing, and operations make decisions based on real-time and predictive rate benchmarking data at the industry level and at the component level. As a result, DAT wasn’t getting the right accounts into the pipeline. Because they focused on transportation managers and their pain points, most of the deals coming into the pipeline through marketing were low ACV deals. They weren’t focused on the content they needed to drive demand with high-value accounts, capture demand across the organization, and then accelerate that demand to revenue.

Marketing content is leading sales to “nice to have conversations” with VPs and the C-suite.


When reviewing the data from sales and RevOps, we saw that most of the engagement was at the transportation manager level, where the content was geared. When sales spoke to a VP of Supply Chain or the Chief Supply Chain Officer, they were consistently directed to speak a few levels down to the transportation manager or director. Sales was not armed with the account intelligence they needed on target accounts to uncover areas of prioritization, what the companies were already investing in, and where gaps still lie that will affect the outcomes that companies want. They weren’t armed with the right content to show how predictive freight rate benchmarking at the industry and component level should have on the complete organization where it becomes a must-have, embedded solution.

As a result, sales focused on cost savings and the size of their data which VPs and the C-suite saw as nice to have as they did not see the role that DAT can play in the bigger picture. Even the transportation managers saw DAT as a nice to have, as reports from Clozd show:

• Toyota was only going to use DAT data to understand the value of long-term agreements between them and carriers, and they felt that they would not see the value based on the high price tag. DAT was only responding to the predefined need (use case) vs. building a vision for how their platform can be used.

• Matthews felt that they would not use the platform enough throughout the year to justify the investment.

• Dole thought the cost of the product was high to the extent it could equate to an analyst’s salary, and they thought there could be more cost-effective solutions. They were putting the DAT platform into a transportation analyst silo and not thinking about the impact it can have across the organization.

Besides making DAT a “nice to have” vs. a “must-have”, the conversations made it harder for DAT to overcome pricing structure objections.

DAT charges by rate spend vs. by seat. Their licensing model proved to be a hard sell for companies like Toyota was not charged based on “total spend in an area” for other avenues of data, KPMG chose DAT’s competitor due to the pricing model and others.

By showing the role DAT can play in the larger organization and embedding their platform, they can make the argument that they license the product based on freight spend because when they charge by “seat”, the platform is often siloed, and seats are given to the transportation managers. As a result, companies aren’t seeing the full value in terms of resiliency, volatility protection, margin growth, and future revenue and profitability growth. They do not see the role that rate benchmarking analysis can play in network optimization, where companies can get closer to their customers and at the same time protect their P&L. By having a subscription model based on spend, it promotes collaboration across the organization where DAT becomes a must-have.

Because sales and marketing were enclosing the wrong conversation… so did customer success


As sales and marketing were not embedding the DAT platform across the organization, neither did the customer success team. Focus, time, and resources were spent on the accounts that yelled the loudest, so they were doing customer support vs. building key relationships and continuing to evolve how DAT is used. And, when budgets became tighter, and CFOs and supply chain teams reviewed their spending, they got rid of the nice-to-have technology. When reviewing the churned clients, we found that, in most cases, they were using DAT monthly, quarterly, or yearly basis. Infrequent use makes you a nice-to-have vs. must-have.

So, you see that DAT did not yet need ABM tech – they needed a focus on building the right processes for a complete account-based GTM motion.


They need to put in the right processes where they bring in high-value accounts that will see DAT as a must-have and then accelerate those accounts to revenue at a higher ACV. This is what Personal ABM will be working on with DAT in 2024 in partnership with Winning by Design before they execute an ABM program and think about technology like Demandbase.

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