3 Advantages Machine Learning Systems Can Bring to Your Lead Generation Campaigns

Machine learning marketing processEvery marketer longs for the day when eighty percent of his marketing efforts would be propelled by a well oiled, intuitive, efficient and high ROI ‘machine’.

Although this is achievable. It is seldom the norm.

Because even in the most well thought out marketing plans, little fires will definitely break out from time to time.

Consequently, lead generation campaigns, direct copywriting projects, and client work end up taking more time, energy, motivation, and patience than we’d care to give. Even veteran marketers can attest to this.

So, for all it’s worth, you can take solace in the fact that; you are not alone.

According to the 2017 State of Inbound Report by HubSpot, 63% of marketers surveyed; identified traffic and lead generation as their number one headache! Click To Tweet

When you add mismatched personas. Missed deadlines from your freelance marketing writers. Cue content fails and sleepless nights. Then you’ve got a recipe for a nervous breakdown and an epic marketing fail.

Extreme? Maybe, but, you get the point, don’t you?

On the other hand, your consumer’s content palate is constantly evolving. Once upon a time, consumers favored more of blog posts.

But according to this report by HubSpot, 53% of consumers want to see more video content from you, while only 14% want to see more blog posts.

 

Create Content Your Leads Want More

  1. Identify the type of content your potential leads want to see more of.
  2. Create it.
  3. Get them to take notice. A.K.A Get it in front of their eyes.

Just three simple steps really. But, implementing it is a different kettle of coffee.

Customers are whimsical creatures. Moreover, they are never completely yours, to begin with.

Therefore as a marketer or business owner; part of your job description falls between:

I. Nurturing new leads.

II. Cajoling, pampering and sometimes ‘threatening’ them; albeit subtly. While consistently delivering great value to them. Fear of missing out (FOMO)? That’s a subtle threat right there.

 

How to Identify if Your Lead Acquisition Process is Ripe For Machine Learning Automation

First things first, who is a lead?

A lead is a person who: has indicated interest in your company’s product or service in some way, shape or form. (Source: HubSpot)

Computer machine learning system

Lead acquisition is, therefore, a systematic process of attracting and converting total strangers and/or prospects into leads.

As you already know, the process of identifying and generating Inbound leads are a fairly stable and ‘straightforward’ process.

Hence, it is fit for the application of Machine Learning systems.

And according to Tom Smith, Research Analyst and Business Strategist, DZone Inc., The only limitations to automating a process are:

  1. Is the process stable?
  2. Is it unlikely there will be unexpected changes that require adaptation?
  3. Do you have enough examples/scenarios to train an AI algorithm?

If you answered “yes” to the above questions, then your marketing process is a prime candidate for AI automation.

Agreeably, the marketing and lead generation methodologies of SaaS companies, e-commerce businesses, and several other organizations are quite stable processes. So, barring a few dynamic tweaks and changes, this process seldom undergoes any sudden upheavals.

What this means is, with increasingly intuitive data-gathering and analytic systems available for use across several of your customer’s touchpoint, hundreds; if not thousands of examples and scenarios are available for you to train an AI algorithm on.

Still with me so far? Good. Now we’ll take a deep dive into the benefits of using Machine Learning systems in your marketing campaigns.

In line with that, we’ll also itemize the steps to an AI-powered lead generation and marketing campaign.

All aboard?

 

Growing Adoption of Machine Learning Systems in User-Targeted Content

With the increasing application of AI’s ML system in technology, visual search, language processing, e-commerce, Healthcare, and marketing ecosystems.

Including its radical transformation of personalized recommender systems. User-targeted content personalization is at an all-time high, and consumers expect marketers to keep up with their evolving preference for tailored content and product recommendations.

We, therefore, believe every serious business, who isn’t in the process of applying ML systems in their campaigns. Should do so immediately, the benefits far outweigh the initial snags they’d encounter.

 

How to Use Machine Learning to Identify Pre-Qualified Leads for Your Business.

First, the benefits.

Seamless Lead Scoring

Machine Learning systems, can help you process high volumes of data better than any human. Click To Tweet And they can help you glean hitherto hidden insights, and suggest actionable ideas you can take to increase a customer’s engagement with your brand’s email messages.

Consequently, AI-powered lead scoring is a powerful approach that prioritizes your leads based on their intent, willingness to engage with your offers, and how close they are to become paid customers.

These insights are what transforms your marketing messages into compelling contextual content that leads cannot refuse.

Apart from identifying hidden insights in your historical data, and its ability to connect previously ‘unconnected’ aspects of your lead generation campaigns. ML’s lead scoring ability can also identify other users/customers who fit into similar personas or molds of your present paying customers.

Examples of powerful AI systems making waves in the lead scoring market are the ubiquitous Sales Force, Absolutdata and Inside Sales.

It is pertinent to note that data is the lifeblood of AI. Therefore without data, AI systems no matter how advanced cannot deliver meaningful experiences and practical insights for you to leverage on.

Thankfully marketers are exposed to an abundance of consumer data, and this is a huge advantage.

 

Lead Capture And Nurture

The ability to identify new customers, and also ratchet up your lead acquisition is one of the endearing aspects of an AI-powered marketing system.

Consequently, lead engagement tools like Conversica can help you contact, engage, qualify and follow-up with leads using natural, multi-channel two-way conversations right after they opt-in.

It goes beyond analyzing basic datasets like user demographics and location. To reveal understandable insights and patterns about general shopping activity, products purchased, customer lifestyle, search history and individual-specific data like shoe size et al. To generate highly targeted and ultra-specific products that similar customers, who fit your ideal customer profile, are interested in.

Finally, Machine Learning (ML) systems, can help you round off the edges of your customer persona, to create a well-rounded customer profile from the datasets you provide.

The software integrates Machine Learning (ML), Natural Language Processing (NLP), and Natural Language Generation (NLG) capabilities into a powerful lead engagement AI software.

This involves less guesswork. And your team is more proactive in predicting emerging patterns and less reactive to changing consumer trends.

 

AI-Based Lead Segmentation

All contacts on your database, cannot be sent the same messages. They have to be broken down into groups and sub-groups.

This is the essence of having a segmented list of leads.

Therefore, we can broadly define segmentation as dividing your existing and/or potential customers into groups or niches based on shared characteristics like location, sex or job title. Segmentation as we know it also involves getting customers separated in specific ways that are relevant to your marketing objectives and goals.

Consequently, it is the difference between delivering user-specific contextual marketing messages, and bland untargeted messages.

So, with the growing advancement of AI technology, just dividing customers into groups of leads sharing similar characteristics won’t cut it anymore. Neither is it profitable to your bottom line.

In order to recommend offers that are positioned at the intersection of a customer’s needs and your business’s marketing objectives. You should consider the context which prompted a lead to opt-in to receive your marketing messages in the first place.

 

Conclusion

In conclusion, machine learning systems can help you deliver meaningful and practical insights you can leverage on. It can also help you identify new customers, connect, engage and prioritize your leads and deliver compelling content to your audience.

In my next blog post, I’d post about the connection and advantages of artificial intelligence in customer satisfaction and loyalty.

If you enjoyed this post, I’d be grateful if you’d help spread it by emailing it to a friend or sharing it on Twitter and Facebook.

 

 

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