Ascend Analytics is a 20-year-old energy analytics firm with strong brand recognition and proprietary data models, but severely underdeveloped organic search presence. The core strategic opportunity is to convert those institutional assets — historical data, customer stories, proprietary forecasting models — into authoritative content that ranks in Google's top 3 and gets cited by AI-powered search tools (ChatGPT, Perplexity, Google AI Overviews).
This article captures the positioning strategy proposed during an outbound BD call with Erin Vasseur (Ascend BD) on behalf of Asymmetric. See also: [1] and [2].
Despite strong inbound demand through brand channels, Ascend's organic search footprint is critically underdeveloped relative to competitors:
| Metric | Ascend | Wood Mackenzie |
|---|---|---|
| Keywords ranked | ~1,700 | ~23,000 |
| Monthly organic visits | ~1,135 | — |
| Branded traffic share | 62% | — |
| Organic traffic share (industry) | <5% | — |
Key problems:
The SEO landscape is undergoing a structural shift driven by AI-powered search. This changes the positioning target:
The practical implication: if Ascend is not in the top 3 for a query, it will not appear in AI-generated answers. Given that AI search is increasingly the first stop for research-stage buyers, invisibility at that layer means Ascend is absent from the earliest and most formative stage of the buyer journey.
The traffic distribution is also polarizing. What was once a bell curve — a large middle tier of moderate performers — is collapsing into a spike. Top performers are gaining traffic; everyone else is losing it. There is almost no middle ground left. This makes early action disproportionately valuable.
See [2] for a deeper treatment of how LLMs select cited sources.
Ascend's apparent weaknesses in digital presence are offset by assets that are genuinely difficult for competitors to replicate:
The positioning thesis: these assets, properly packaged as authoritative long-form content, are exactly what AI systems are trained to cite. Thin, generic content is being devalued. Original, data-backed, expert-authored content is being elevated. Ascend has the raw material; the gap is execution.
Three pricing structures were discussed; the hybrid model was recommended as the best fit given Ascend's conservative posture toward new spend:
| Model | Structure | Risk Profile |
|---|---|---|
| Retainer only | Fixed monthly fee | Risk on client |
| Success fee only | % of revenue above baseline | Risk on agency |
| Hybrid (recommended) | Smaller retainer + success fee on originated leads | Shared risk |
Indicative pricing for Ascend:
- Retainer: $6,000–$10,000/month (scope-dependent)
- Success fee: percentage of revenue or flat fee per converted lead, applied only to leads originated by Asymmetric work
Exclusivity note: Asymmetric works with one client per industry vertical. Engagement with Ascend would preclude working with Wood Mackenzie, Fluence, Arania, Ventix, or S&P in the same space.
See [1] for full contact log and pipeline status.
Proprietary data is an underutilized AI-era SEO asset. Companies with original research, longitudinal datasets, or first-party customer outcomes are better positioned to rank in AI-cited results than competitors relying on synthesized or generic content. The strategic move is to convert internal knowledge into structured, authoritative, publicly-indexed content before competitors do the same.
This pattern applies beyond Ascend. Any B2B client with deep domain expertise but weak organic presence is a candidate for this positioning approach. See [3] for the generalized framework.