But all of this begs the question, “Which AI claims are hype and which are reality?”
In order to know what a new technology like AI can bring to the table, it’s important to fully understand the problems you’re trying to solve. When it comes to the current state of AI solutions for marketing and sales, today’s reality is less futuristic robots or automating every single marketing workflow, and more about how data can answer one important baseline go-to-market question: who to sell and/or market to. There’s a wealth of intelligence that predictive analytics and machine learning bring to the task of answering these questions – and that’s the crux of where AI is delivering value today.
Sales performance management
Forward-thinking enterprise sales teams are saving tons of time by simply using predictive solutions to improve the way they filter and prioritise inbound leads. Companies with the “champagne” problem of an overwhelming volume of incoming prospects are using predictive analytics and AI to automatically research and qualify leads who looks like their company’s ideal customer. For example, Shoretel’s market development team found that predictive scores could tell them not just which prospects are the best fit, but also which ones are showing current buying behaviour. As a result, the telco leader’s sales reps can instantly understand who their best prospects are and determine where their time should be spent — insight which has resulted in 8X greater conversions. Now it takes just 12 calls to uncover one marketing-qualified lead (MQL) vs. the 100 calls it took before the company adopted predictive analytics.
A related use case we’re seeing quite often is lead routing. Rather than traditional approaches of distributing new leads based on geographical regions or a simple round robin without much consideration to actual lead quality, companies are starting to leverage AI to route leads more intelligently. Businesses use predictive scores to decide which leads should be put into marketing nurture tracks until they show more potential, which should be sent to sales development reps for further research, and which should be aggressively followed up on by account executives right away.
Another real-world use case for predictive analytics is business expansion. As more teams adopt account-based sales and marketing strategies to move upmarket, a common question is “how do we find customers who will spend more with us?” But with outbound prospecting, it’s tough to focus on the right target accounts vs. trying to boil the ocean. That’s yet another problem that predictive models are solving by using a vast array of data sources to identifywhitespace opportunities and pinpoint new markets to go after. Machine learning even predicts the value of an account (i.e. its anticipated deal size, revenue impact and other metrics) before the team engages, so they have confidence they’re spending time in the right places.
This brings us to the opportunity for today’s marketing teams. Any marketing strategy – whether focused on inbound demand gen or account-based outbound tactics – requires insight into who the business is marketing to. Predictive analytics helps marketers optimise conversions for funnel efficiency by finding the best ways to move leads down the funnel and turn them into prospects, opportunities and eventually closed/won deals.
One type of predictive model that’s gaining traction is behaviour scoring, which tells marketers whether a prospect acts like a customer. These models look at engagement patterns in marketing automation and web analytics systems to determine when leads and accounts are showing buying intent and getting close to a likely conversion threshold. This valuable marketing intelligence helps companies deliver just the right message at just the right time, and optimise campaigns to reach their highest-potential-value prospects. Smart teams at fast-growing startups like New Relic are going after prospects that are not only the right fit, but also demonstrating the highest level of buying propensity. This technique also helps marketers shift their focus away from quantity metrics (“how many leads can we deliver”) and towards a quality mindset (“how many good leads can we deliver”).
In addition, predictive analytics is an effective way to find blind spots where go-to-market teams might be missing opportunities. Sadly, “nurture” has all too often become a code word for “neglect” in many organisations. AI has turned this idea on its head by automatically scanning older prospects sitting in nurture databases, and picking out the ones that have lit up again and started to engage. By using these insights to ensure conversion optimisation at the bottom of the funnel, many businesses are turning 1% conversion rates into 2%, which can have a massive impact on revenue.
Finally, AI delivers immediate feedback on the quality of marketing campaigns. Rather than waiting for sales cycles to play out before measuring campaign impact, marketers can now easily calculate key performance metrics in real-time. For example, predictive analytics can quantify the cost of each good lead, average lead quality, pipeline-to-spend ratio, etc. By using these KPIs to look past vanity metrics and identify top performing campaigns and content, marketers are gaining priceless insights into which programs attract the highest quality leads, drive larger deals, and accelerate deal velocity.
As each of these examples illustrates, there’s plenty of value to be gained from adopting the predictive analytics solutions that are in practice at real businesses today – even as the hype of tomorrow’s science fiction use cases continues to swirl. Rather than try to get an elusive grasp on wide-reaching AI claims that are yet to be realised, marketers should dip their toes in the water with proven solutions like predictive scoring. Innovative companies are already achieving tangible business impact by adding this layer of intelligence to their operations and reimagining automation as we know it.