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How to Use AI for Effective Demand Generation Outreach
In today's rapidly changing B2B marketing landscape, leveraging AI is becoming a strategic necessity rather than a luxury. As businesses strive to navigate economic uncertainty and achieve more with limited resources, many companies have naturally been exploring the possibilities of using AI in their demand-generation efforts.
So, how can you incorporate AI into your demand generation campaigns?
AI can enhance your demand generation efforts in numerous ways, from content creation to advanced data analytics, right from top of the purchase funnel (TOFU) to bottom of the purchase funnel (BOFU). The crucial step is to identify the areas and objectives you want to optimize with AI, as these decisions will vary across companies and brands.
Here are three ways you can leverage AI to quickly launch demand generation initiatives and ensure their success.
AI for content and copywriting
Once you have identified all the demand generation channels, the next step is to explore using AI to write content and generate images for all those channels. You can create content pieces, email scripts, organic social posts, paid ad copy, presentation slides, podcast scripts, etc., with the help of popular AI writing assistants or bots.
Here are some popular AI writing assistants you should check out.
And if AI can create content, why not use it to generate images on top of it? Here are some popular AI image generator tools.
As long as your prompts are clear and specific, AI can come up with a first draft of content or various image selections for you. That said, you need to know how to tweak the output to ensure it’s relevant and relatable to your audiences. And as far as images and graphics go, they need to be on-brand.
AI can give us many writing and design options, but at the end of the day, you need to be able to have a tight grip on quality control and that's only possible with a human in the loop.
AI for specific tasks of demand generation workflows
In addition to writing and designing, another option is to evaluate the existing workflows of each demand generation channel to determine what tasks can be delegated to an AI bot. One example is a lead routing process. A BDR (business development rep or outside sales rep) needs to manually route leads to different salespeople based on leads’ geographical locations. Can this process be automated and allocated to AI to take over based on the pre-established selection criteria?
If a paid ad's call-to-action is to contact sales, can an AI bot take over the initial engagements by sending customized or personalized emails? Can AI suggest subject line recommendations based on past email campaigns’ subject line open rate to improve the performance of current and future drip emails?
There are many ways to automate the demand generation processes and workflows, so you need to document your workflows at each step, and then work with various stakeholders to determine where you should inject AI’s help and support.
AI-powered chatbots for lead scoring and nurturing
In demand generation, the challenge is not just finding leads, but finding the right leads. In many companies, lead scoring is subjectively and manually established by marketers. With the help of AI models, you can gather data by correlating customers' and prospects’ engagements on your website with the customer data and deal closure rate information in your CRM.
With AI analysis, you may be able to optimize your lead scoring systems. Furthermore, you can integrate your ideal customer profile’s (ICP) demographic, firmographic and behavioral information to identify patterns that hint at a future “lookalike” ICP’s likelihood to convert as well as project closure possibilities.
AI models can process vast amounts of data quickly, uncovering correlations and trends that human analysis might miss. AI correlates engagement metrics like webinar attendance and content downloads to determine the prospect’s next behavior. That will ultimately help marketers to personalize their nurture campaigns.
Lead scoring and nurturing both enhance the prospect’s probability of conversion, especially in the long purchase cycle. Therefore, predictive lead scoring empowers businesses to prioritize leads by likelihood of conversion, ensuring that marketing efforts are focused on those prospects that are most likely to convert. It also ensures optimal resource allocation.
You can continue to refine AI’s lead scoring models over time by evaluating their performance and making adjustments, thus improving conversion rates and increasing ROI.
AI chatbots can also utilize natural language processing to interpret user intent, allowing for personalized conversations to guide potential customers through the buyer journey. By collecting data from these interactions, chatbots continuously improve their ability to qualify leads, passing the most promising ones on to sales teams for follow-up. This ensures marketing resources are focused on high-quality leads and improves efficiency by reducing the time spent on less valuable prospects.
Stay focused on the purpose of your demand generation
While optimizing workflows and processes with AI, please bear in mind why you do demand generation to start with. It’s all about building relationships with prospects. AI can assist in writing emails, but it's essential to retain a personal touch. AI can automate engagement stages, but be mindful of how much decision-making you delegate to it. AI can route leads based on strict lead scoring, but be ready to make exceptions when necessary.
Demand generation still relies on your judgment, marketing expertise and product knowledge. You need to make tough decisions, AI can’t take those decisions for you.
AI still has its limitations
AI tools like chatbots and content generation models have limitations, including hallucinations, biases and outdated information. Hallucinations occur when AI generates incorrect or fabricated data due to insufficient context or inaccurate training data. This can damage brand credibility and mislead consumers. Like humans, AI can be biased based on the datasets it was trained on. Outdated information is another concern, especially in rapidly evolving fields where AI may struggle to keep up with recent changes.
To mitigate the above risks, human oversight is essential. Marketers should scrutinize AI-generated content to ensure it aligns with brand guidelines and is factually accurate. Additionally, data privacy risks can arise when sensitive information is inadvertently fed into AI systems. Companies should establish clear protocols on what data can be used with AI and consult legal teams to safeguard data privacy and compliance. Combining human oversight with AI allows marketers to leverage the technology efficiently while maintaining brand integrity.
In summary…
As AI advances, it will continue to play a pivotal role in B2B marketing and demand generation. By combining AI with human expertise, businesses can stay ahead in the competitive marketing landscape. Just remember to stay vigilant to ensure you’re using this technology to your advantage, rather than to your detriment.
