9 Use Cases HyperGrowth Partners Use AI - pt. II
AI Use Cases to Drive Growth Across Outbound and Performance Marketing
In part II of our exploration into how AI is rethinking the boundaries of what’s possible in growth, we delve deeper into how HyperGrowth Partners leverages it across outbound and paid marketing. As businesses continue to grapple with the rapidly evolving AI landscape, integrating these technologies when executing your growth motions becomes increasingly crucial.
Guillaume Cabane on AI-Assisted Outbound & Targeting
One of the most impactful areas where AI is making its mark is in outbound and targeting. The tooling for these use cases has evolved significantly, and it’s becoming all the rage on our LinkedIn feeds!
1. Automate lead list building and scoring with LLM-assisted intent signals
SaaS such as Waterfall, Clay, and Common Room, are rapidly emerging to streamline the aggregation, cleansing, and enrichment of data before it's used in outbound campaigns. These tools leverage Large Language Models (LLMs) to scrape data from multiple sources and enable the use of AI with custom datasets to inform critical strategic aspects like TAM definition and lead scoring.
For example, with Clay, you can scrape LinkedIn or Google Maps to define your TAM, according to targeting criteria like Industry, company size, type, and location. Clay will give you a potential TAM figure straight off the bat, helping you define and prioritize your go-to-market strategy.
On the other hand, Waterfall helps you enrich your lead lists from multiple data providers at once without you needing to sign up for long-term, individual contracts to each data provider like Apollo, Lusha, or Zoominfo, allowing you to reduce your CPL and overall CAC.
These tools are becoming more and more critical to engage the right customers and improve conversion rates, especially in the current market landscape where capital efficiency is key.
2. Scale personalized outreach and growth ops with AI
Clay — together with Cargo and Unify — play also a crucial role in lead scoring, routing, and outreach personalization your outreach with the help of the data gathered from across multiple sources.
Clay, for instance, is invaluable for — including LinkedIn, Google Maps, and CRM systems, or by adding additional data vendors. By using an “Excel-on-steroids”-like UI, GTM teams can easily enrich these lists with additional data points such as LinkedIn engagements, Technographics, or competitors' actions.
Once you’ve built these highly relevant and segmented customer lists, Clay helps you use ChatGPT to create personalized copy snippets that you can add directly to your email template in Unify, Smartleads, Instantly, Apollo, or AmpleMarket, and drive reply and demo conversion rates.
Cargo helps GTM teams automate pipeline operations like adding custom Salesforce fields, pushing to CRM stages, or scoring leads with signals from across various sources. Whereas this might not be relevant for early-stage growth ops, this automation is particularly beneficial for scaling from 1 to 10 in growth and late-stage orgs (eg. Series B-E), where data collection on Ideal Customer Profiles (ICP), lead scoring, pipeline, and intent signals is readily available.
Players like Unify also show how players are increasingly verticalizing, closing the gap between accessing prospecting contacts, enriching them with signals, orchestrating them, and activating them by sending personalized emails at scale.
Need help to devise your best AI-powered outbound strategy? Our team has decades of combined experience as well as close relationships with all the AI startups mentioned in this post! Let us scale your lead generation with AI.
3. Increase sales efficiency
Switching gears from outbound and growth ops to sales, that’s when the role of AI in enhancing sales efficiency — namely the SDR team’s ability to book demos — cannot be overstated.
At Ramp, SDRs are increasingly using AI tools like ChatGPT to craft personalized email drafts by identifying common traits and patterns among prospects, such as shared educational backgrounds or industry experience. This personalized approach has led to a 3x increase in response rates compared to fully automated outbound strategies, which adds credit to the fact that AI-powered teams are still better than standalone AI.
Another example of AI empowering human performance lies in audio-based solutions like Orum. Orum is revolutionizing salesfloors that reach customers via phone, by rethinking the dialing process from the ground up. Teams using Orum can use AI to boost sales efficiency up to 6x, scaling the number of daily customer conversations. With Orum, your team could:
Automate the dialing process, by calling your entire CRM without teams having to touch a dialpad.
Detect voicemails vs. human voices, increasing call coverage and SDR efficiency dramatically.
Productize objection handling from calls, feeding back into CRM and pipeline generation, and product roadmap, ultimately distributing more insights across the organization.
Integrate Orum with CRM and sales intelligence tools like Gong also helps make strategic decisions around rep performance visibility, coaching, and hiring.
Save many hours on logistics that can be allocated to prospecting, customizing emails, and collaborating with Marketing and Product to refine the GTM.
On another note, what doesn’t work yet is fully AI-based SDR voice agents like air.ai — we’ve tried it, and the AI is not there yet.
Rex Gelb on AI-Assisted Paid Marketing
Paid marketing is another domain where AI is driving significant advancements.
1. AI targeting and bid optimization is finally thriving
For example, Google Ads Performance Max utilizes LLMs to optimize bids across channels like Search, YouTube, and Display, enhancing spending efficiency and targeting precision. The evolution of LLM-driven bidding optimization — which now ingests billions of data points every minute — is a testament to the advancements in AI technology, which now allow for more sophisticated and effective campaign management than was possible with earlier iterations of the tool.
Before LLMs, teams used to segment ad campaigns by various customer attributes like gender, interests, geolocation, product, etc. With LLM, targeting is getting so much better at showing the right creative to the right customer segment that you’re better off setting the budget at a campaign level using features like Advantage+ on Meta instead of manual ad-set-level targeting.
Many could ask — what’s the minimum conversion data needed to leverage these AI capabilities effectively? According to Rex:
For Meta, the best practice is around 50 optimization events within a 7-day period.
For Google, you should have a minimum of 15 conversions over a 30 day period.
If your budget can support generating such conversion volume, then you’re all set. Teams should approach minimum conversion volumes as a curve rather than a step function; the more data the better.
Furthermore, the incorporation of intent signals into broad match queries by LLMs has increased relevance and conversion rates by up to 40% in comparison to traditional exact or phrase match types. For example, if you hire a celebrity called “Naomi Campbell” to promote your product, your product will never show up for board match queries like “Naomi Campbell”. However, the latest LLM improvements to broad match terms will scout for relevant intent signals and show your product ad with Naomi Campbell when those signals apply.
To Rex’s surprise, the AI promise made by these tools was not delivered just 6 months ago, while it is very much exceeding expectations now. This shows how fast the AI space moves, and how much incremental improvements compound. That’s why teams experimenting with AI tools must incorporate frequent re-testing to re-validate their potential. Ultimately, teams that won’t are the ones who’ll miss out!
2. AI Chat Ads are Coming
Innovations in ad placement continue to evolve, with Bing testing new AI chat placements that allow advertisers to show ads in Bing Chat results. As AI chat is eating into traditional search behavior at a fast rate, ad platforms are racing to roll out these new experiences.
As advertisers, the competitive advantage is on a first come first served basis, but it gives you the ability to streamline the discovery and comparison process greatly, simplifying complex purchases like B2B SaaS into a more natural and linear process that is done into one, chat UI. These interfaces drive accessibility and potential for PLG motions, where the buyer can self-educate and self-select the best software to buy based on their situation.
3. AI-powered creative creation and optimization
Additionally, both Meta and Google Ads enhance creative outputs with features such as automatic video creation, cropping, and cross-placement optimizations. This can be super useful for small and/or busy teams that don't have or have limited creative resources in-house.
“Major AI innovations in performance are happening at an ad network level, because these platforms own user attention, the data sets that inform LLMs, and the funding to significantly improve the machine learning process. By fall 2024, AI-powered advertising will be wild.”
Closing Thoughts
In conclusion, integrating AI into growth strategies not only enhances operational efficiencies but also fosters innovation and competitive advantage.
By understanding and applying these advanced AI use cases, teams can effectively harness the power of AI to scale growth and reduce overheads, ensuring they remain competitive in an increasingly AI-powered market.
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