3 Data-Driven Deficiencies that AI Solves for Marketing Teams
Data-driven marketing has been the norm in the industry for a long time now. The typical process involves companies gathering firmographic, demographic, and engagement data and placing their prospects on a lead-scoring scale.
Numbers on the scale indicate how far along the buying journey the prospect is, and sales strategies are tailored accordingly. While a data-driven approach is better than relying purely on intuition and guesswork, there are several drawbacks to it.
Research shows that the average SMB generates anywhere from 100 to 500 leads per month. Keeping pace with these many leads is a tough task if metrics have to be constantly reviewed. As consumer behaviour changes over time, metrics have to be updated, and this delays the sales process.
The solution to this problem is adopting AI-driven marketing. Here are 3 ways in which AI plugs the gaps in a data-driven model.
Better decision making
One of the problems with the lead scoring model is that faulty assumptions can wreck the entire sales process. A prospect at the top of the funnel might engage with bottom-of-the-funnel content without navigating through other funnel stages.
Relying solely on content engagement might lead you to score this prospect as an SQL. However, your sales team is likely to discover that this prospect is nowhere near ready to buy. This creates a missed opportunity.
An AI assistant adds a layer of lead validation by using engagement data backed with real-time conversation. For instance, the prospect in the example above could be qualified to determine whether they’re willing to be contacted by a sales associate. The prospect’s answers can be correlated to prior actions, and thus the AI assistant can improve your lead scoring model. Over time, you’ll identify gaps in your system and prospect journeys you previously weren’t aware of.
AI can also help you identify a prospect that is displaying high engagement. However, they maybe stuck at the top of your funnel due to underscoring. As your lead scoring model evolves and becomes more accurate, the relationship between marketing and sales improves. This leads to an even better model in the long run.
The point of big data collection is to understand your prospects better. Analytics dashboards help you mine and make sense of your data. However, analytics powered by ad-hoc or parameterised reports rely on human judgment and cannot always react to real-time data.
AI assistants trained on your historic data sets can monitor real-time data and develop models that predict future behaviour. When your sales reps are in conversation with a lead, an AI system can give them relevant data-driven conclusions or raw data, depending on the rep’s choice.
As a result, interactions are more personalised, and this increases the rate with which deals close. Predictive analytics also help you identify new prospect trends. This can help you get ahead of the curve, preparing your sales and marketing teams with the additional information they need.
For instance, an AI system might notice that certain clusters of content on your website aren’t receiving high engagement anymore. This is in contrast to other historical data. It might highlight a need to improve your product features. The existence of a better product in your competitor’s offerings, or a shift that your industry is experiencing.
Business moves at a fast pace these days. An AI assistant can help you identify new trends quickly and react to them before your competitors do.
Complete engagement at all times
Businesses generate a lot of leads and a manual qualification process will result in many leads slipping through the cracks. Your marketing and sales teams might be overwhelmed trying to keep up with all of the data collected.
Your employees need some time to log off and step away from work to recharge. This may be another reason why leads may not be effectively followed up. It’s impossible to run a 24/7 operation without incurring significant costs.
In contrast, an AI assistant is always on and doesn’t need any additional investment to be present all the time. With your assistant working around the clock, it doesn’t matter when your lead lands on your web assets. They’ll be met with a human-like conversation that engages them and moves them into your funnel.
You can even create workflows that qualify leads and engage them while your employees are off the clock. For instance, you could point leads to useful content and engage them through custom newsletter campaigns. When your sales and marketing employees engage with your lead, they’ll have the pleasure of interacting with someone warm.
These AI workflows can also identify and filter out leads quickly, thus saving your marketing teams valuable time.
Integrate, not overhaul
Integrating AI within your data-driven marketing strategy is the best way to realise the full potential of your data. According to Exceed.ai, companies that utilise AI in their marketing and sales workflows uniformly report a 35% increase in productivity. 25% increase in SQLs, and 5% increase in sales rep quota attainment.
It’s time to power your marketing data to the next level, and AI is the right solution to streamline and optimise your lead nurturing and qualification workflows!
Exceed.ai is a Conversational Marketing and Sales Automation platform enabling sales teams to scale and sell more. Using AI, machine learning and conversational bot technology Exceed generates more qualified opportunities. Unlike traditional marketing and sales platforms that rely on forms, one-way email campaigns and manual follow-ups, Exceed.ai automates many of the customer interactions currently performed manually – like a virtual assistant to your sales and marketing teams.