A predictive freight analytics tech company 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 evidenced by reviewing their won/lost opportunities and 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 the supply chain tech company needed an account-based GTM that lands and expands high-value accounts!
They needed to fix the complete account experience (including the interactions all teams have and the experiences they deliver across the pipeline and customer life-cycle) rather than just take a more targeted demand-gen approach with technology. Before you can fix the experience, though, the supply chain tech company needed to identify the accounts that would see them as a must-have vs. a nice-to-have.
Before investing in our ABM readiness program, the supply chain tech company was defining its ICP by industry and freight spend. They weren’t segmenting their existing customers to uncover their “best fit” accounts and identify the characteristics of high-value accounts that use their platform regularly (making it a must-have) vs. monthly, quarterly, or yearly (making it a nice-to-have). They weren’t looking into how and why the predictive freight analytics platform was being integrated into the daily programs of the most frequent users and how they were utilizing the platform differently from the others. They didn’t look into supply chain maturity.
The supply chain tech company targeted accounts with a level 1 or 2 maturity that would automatically see them as a “nice to have” as all of the teams were 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 the supply chain tech company’s predictive freight analytics platform, which should touch the entire supply chain, including distribution, finance, sales/marketing, and operations.
The supply chain tech company didn’t uncover the strategic priorities for those on the top 25 global supply chains to see the role they can play within the strategic organization.
For example:
* Schneider Electric was building on its STRIVE program (sustainable, trusted, resilient, intelligent, and efficient) to drive transformational change.
* Cisco prioritized 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 are 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 was 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 were building a resilience dashboard and establishing a control tower to track and trace cold chain products in real time.
* PepsiCo wanted to capture real-time and predictive data to anticipate and resolve problems ahead of time.
* Pfizer wanted to accelerate their go-to-market while reducing their cost to serve.
* Microsoft was focused on generating actionable insights about the end-to-end supply chain, enabling fast-turn decision making and embedding resiliency in operations.
* Lenovo was evolving their supply chain intelligence framework and leveraging 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, wanted to expand its supply chain network, accelerate order fulfillment, and reduce shipping costs.
Failing to identify key strategic priorities also impacted the effectiveness of the company’s content and conversations.
While completing our ABM readiness review, our team reviewed hundreds of pieces of content, including articles, white papers, 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.
- The supply chain tech company lost a high-value deal to Uber Freight because the rate analytics the prospect was getting was “good enough.”
- There are many E2Open customers who are not the supply chain tech company’s customers, as E2Open’s free rate analytics are “good enough.”
- The supply chain tech company’s win/loss data shows that they repeatedly lose 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 to use it, or highlight 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 that 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 that 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.
The supply chain tech company wasn’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, our client 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 needed to drive demand with high-value accounts, capture demand across the organization, and accelerate that demand into revenue.
Marketing content is leading sales to “nice to have conversations” with VPs and the C-suite.
When reviewing data from sales and RevOps, we saw that most engagement occurred 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 our client can play in the bigger picture. Even the transportation managers saw our client’s predictive freight analytics platform as a nice-to-have, as reports from Clozd show
- Toyota was only going to use the 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. The supply chain tech company 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 predictive freight analytics platform into a transportation analyst silo and not thinking about the impact it can have across the organization.
Besides making the supply chain tech company’s predictive freight analytics platform a “nice to have” vs. a “must-have”, the conversations made it harder for DAT to overcome pricing structure objections.
The company charges by the customer’s rate spend rather than by seat. Their licensing model proved to be a hard sell for companies like Toyota, which was not charged based on “total spend in an area” for other data avenues. KPMG chose our client’s competitor due to the pricing model.
By showing the role our client’s platform 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 our client becomes a must-have.
Because sales and marketing were having the wrong conversation… so did customer success
As sales and marketing were not embedding their predictive freight rate analytics platform across the organization, neither was 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 the platform 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 churned clients, we found that, in most cases, they were using our clients’ platform on a monthly, quarterly, or yearly basis. Infrequent use makes you a nice-to-have vs. a must-have.
