Digital.ai: Turning Lost Deals into Game‑Changing
Insights

“Time‑to‑Protection” is Critical – Speed Wins Deals

 In enterprise security, deployment speed and ease of integration proved to be decisive. Customers repeatedly cited slow or difficult implementation as a reason for choosing competitors. For example, an IBM‑related prospect chose a rival AppSec vendor mainly because that vendor delivered a working proof‑of‑concept in under a week, with a “fully protected app in a day,” while Digital.ai’s solution was perceived as requiring much more effort. As one candid prospect admitted: “If AppSec was as plug‑and‑play and easy to implement as Appdome, I probably would have picked AppSec.” Speed of value delivery (“time‑ to‑protection”) was often the number‑one factor in decision‑making – faster, simpler solutions stole the show when Digital.ai’s implementation felt too slow or resource‑intensive.

Pricing & Packaging

Misaligned with Customer Size: Pricing came up in almost every interview, not necessarily because Digital.ai’s product wasn’t worth the cost, but because the one‑ size‑fits‑all pricing didn’t scale down for smaller customers. Several startup and mid‑sized clients said the six‑figure annual license was impossible for them to justify given their usage levels. One former customer (a small tech firm) noted they were paying ~$150K per year while only using a fraction of the features – they even described the cost as “ludicrous” once their mobile user base shrank. In another case, a prospect loved Digital.ai’s technology but simply needed a lower‑ cost, usage‑based tier until they grew larger. Compounding the issue, Digital.ai’s packaging was complex – one customer realized they had paid “$30‑40k for unused modules” due to bundle complexity. The takeaway was clear: Digital.ai was leaving money on the table by not offering flexible pricing/packages for lower‑volume use cases. In response, the study recommended exploring tiered “startup” pricing and more granular packaging to align cost with value delivered.

Customer Support & Onboarding Gaps

The quality of post‑sale support and onboarding was a make‑or‑break factor for many customers – and Digital.ai’s execution here was inconsistent. In one churn story, a customer struggled with a major iOS integration bug for over a year; despite many interactions with support, the issue was never fully resolved, and the client ultimately left for a competitor. Another lost prospect described how their evaluation stalled when their Digital.ai account manager departed and was not replaced for months. In stark contrast, competitors won loyalty by providing high‑touch, responsive support – one prospect was impressed that a rival’s founder and engineers became directly involved to ensure a rapid proof‑of‑concept. As one interviewee noted, support quality swings deals. If Digital.ai’s support had been more hands‑on and proactive during trials and early deployment, some customers believed the outcome might have been different. This insight highlighted the need for stronger customer success engagement, especially during critical periods like proof‑of‑concept and onboarding. Ensuring continuity (no gaps if a rep leaves) and faster expert assistance for technical issues were identified as key improvement areas.

Messaging & Value Communication Issues

DeltaGen uncovered a disconnect in how Digital.ai’s value was perceived versus what was being messaged. Several interviewees admitted they did not fully grasp Digital.ai’s value proposition or product category during their buying journey. For instance, one prospect intuitively associated Digital.ai’s AppSec product with the emerging concept of “Application Security Posture Management (ASPM)” – but noted “we never heard Digital.ai claim that label.” In other words, potential buyers were mapping the product to a category on their own, and Digital.ai wasn’t actively positioning it in terms familiar to the market. Another common theme was the absence of a clear, ROI‑based story for executives: one lost deal (PagoNxt) “couldn’t take a concrete number upstairs” to justify choosing Digital.ai. The product was highly technical, but the business value wasn’t being distilled into simple metrics or narratives that a CIO or CFO could appreciate. Additionally, Digital.ai’s rebranding and broad platform sometimes caused confusion – a few customers weren’t sure how AppSec fit into the bigger picture or what to call it, which hampered internal buy‑in. The findings pointed to an urgent need to clarify Digital.ai’s messaging (e.g., explicitly claim the ASPM positioning if it resonates) and to arm the sales team with sharper ROI calculators and case examples. In short, Digital.ai had to tell its story better, framing its superior security technology in terms of tangible outcomes and using language that matches what target buyers are seeking.

Competitive Intelligence

The interviews also shed light on exactly what competitors were doing right. Digital.ai learned that rivals like Zimperium, Promon, GuardSquare and Appdome were often winning deals not on core technology (in fact, some interviewees acknowledged Digital.ai’s technical superiority in security) but on experience factors: quicker deployments, easier “no‑ code” integrations, more attentive support, and sometimes slightly lower list prices to ease budget concerns. In one case, a customer who left Digital.ai actually returned after a cheaper alternative exposed them to security risks – proving that Digital.ai’s product was stronger, but the customer had to experience the competitor’s weakness to appreciate it. This competitive insight is golden: it suggested Digital.ai should market not just the presence of its advanced security features, but also the absence of compromise – i.e., the hidden costs of those “easier” but less robust alternatives. Armed with real quotes and examples from this study, Digital.ai’s product marketers and sales team could now vividly illustrate how they stack up against specific competitors on the factors that matter most to buyers.

Win‑Back Opportunities

Not all the feedback was negative. A pleasant surprise from the research was that some former customers still had positive regard for Digital.ai and were open to re‑ engaging under the right conditions. For example, one startup (Carveco) had left due to cost constraints, but the interview revealed they “would consider AppSec again” as their situation evolved and they were unhappy with the competitor’s product. In fact, the DeltaGen interviewer was able to immediately facilitate a reconnection: they suggested Carveco reach out to Digital.ai’s team again, and flagged this lead to John (Digital.ai’s sales head) during the project. This meant Digital.ai got a second chance at a churned customer directly as a result of the study. More broadly, by hearing customers out in a no‑pressure setting, Digital.ai actually improved its reputation in some eyes – multiple interviewees appreciated that the company cared enough to ask for feedback via a third party (a sentiment often echoed in such win‑loss interviews). This goodwill can translate into future opportunities, referrals, or at least a better brand image. The case proved that a transparent feedback process not only yields insights, but can also increase trust and respect for Digital.ai in the market.Each of these findings was backed by direct evidence – specific quotes, stories, and data points – giving Digital.ai the confidence to act on them. The insights were both humbling and empowering: humbling because they confronted some hard truths (e.g., “our support let a customer fail”; “our pricing is turning away startups”), but empowering because they pinpointed solvable problems.

Data‑Driven Clarity

Digital.ai’s leadership came away with a clear, prioritized list of root causes behind churn and lost sales – replacing gut feelings and internal debates with hard evidence. Instead of wondering if price was the culprit, they now knew how pricing was impacting different customer segments (e.g., startups vs. enterprises). Instead of speculating why a big prospect chose a competitor, they had the verbatim explanation (e.g., “fully protected app in a day” speed and “mobile security for dummies” simplicity won over the client). This clarity allowed the team to focus on fixing the right problems. As one outcome, Digital.ai quickly kicked off internal initiatives to address the top findings – for example, exploring a new “startup” pricing tier and usage‑based licensing options for AppSec, and allocating more engineering resources to simplify deployment and build more no‑code capabilities (so future customers could get value faster, matching the competitor playbook).

Executive Alignment & Urgency

Presenting these unbiased customer insights at the May 13 board meeting was a turning point. The DeltaGen study armed Digital.ai’s executives with a compelling narrative and concrete data that validated some of their strategic plans and spurred new ones. Board members and other stakeholders could see exactly why deals were lost and where the market was heading, which created a sense of urgency to invest in certain fixes. For instance, the insight that 50 % of lost prospects cited price but Digital.ai “would not lose because of a discount” sparked a discussion at the leadership level about overhauling pricing models and better communicating value, rather than simply relying on reactive discounts. Similarly, hearing how much “time‑to‑value” influenced decisions lent weight to R&D proposals for accelerating implementation. In short, the case study provided immediate credibility to proposals for product and go‑to‑market changes, since they were now backed by the voice of the customer.

Culture of Customer‑Centricity

The engagement had an interesting side effect internally – it helped instill a more customer‑centric mindset. Seeing candid feedback quotes circulated in internal emails and meetings was eye‑opening for teams that don’t normally speak to customers who churned. Digital.ai’s employees began rallying around the idea that to win, they must constantly listen to and learn from customers (even the ones they lost). John Roberts (Head of Sales) had set the tone early by emphasizing the importance of hearing the customer’s voice, and the successful outcome of this project reinforced that ethos. It’s notable that Digital.ai’s CPO, Nicholas, personally engaged throughout the process – from refining hypotheses to even troubleshooting his login to the DeltaGen insights platform – showing how invested the client leadership was. When the Chief Product Officer takes the time to dive into interview transcripts and then sends “glowing feedback” about the findings to the rest of the team, it sends a strong message company‑wide about the value of being data‑driven and receptive to feedback.

Quick Wins and ROI

On a very practical level, the project yielded quick wins. The identification of a willing win‑back candidate (Carveco) was one example – a churned customer ready to reconsider meant an opportunity to swiftly recapture revenue. Digital.ai’s sales team, armed with the context from the DeltaGen interview, reached out and was able to reopen that dialogue (a chance that might have been missed entirely without this analysis). Even beyond that one account, the list of follow‑up actions provided by DeltaGen (ranging from reviewing support tickets for unresolved issues, to refining marketing messaging, to re‑engaging certain lost prospects with targeted offers) forms a blueprint for improving retention and win rates in the coming quarters. By implementing these actions, Digital.ai expects to reduce future churn and improve win rate in new deals – measurable outcomes that will far outstrip the cost of the project. It’s not a stretch to say the insights could influence several million dollars of ARR (annual recurring revenue) when applied to pricing strategy and customer success improvements across their client base. Thus, the ROI on DeltaGen’s engagement was virtually immediate.

Cost‑Effective Insight Generation

It’s worth noting that DeltaGen delivered this impact at a fraction of the typical cost and time. Traditional win‑loss consultants or internal efforts might have taken 3–6 months and still not reached as many respondents. DeltaGen’s AI‑powered approach completed ~20 interviews and analysis in about one month, in time for key decision deadlines. The pay‑per‑insight model (≈ $699 per satisfied interview) meant Digital.ai paid only for quality results, which ended up being significantly more cost‑effective than previous methods the company had tried. Essentially, Digital.ai unlocked a scalable, repeatable process for customer feedback. Following this case, the company can periodically run similar programs (or even continuous “voice of customer” listening) through the DeltaGen platform to stay on top of why they win or lose business. This ensures they remain agile and tuned‑in to market shifts – a competitive advantage in the fast‑evolving DevSecOps space. In the words of one DeltaGen interview participant, hearing a company proactively seek candid feedback “makes me think more of the company – it’s nice they have this process in place.” That sentiment captures the broader impact for Digital.ai: beyond the immediate insights, the very act of engaging customers (even former ones) in honest dialogue strengthened Digital.ai’s reputation as a customer‑centric innovator.