Kickstart Your Social Media Lead Generation with Machine Learning Part 2: Best Practices

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This is the second part of a special two-part post from the Jugnoo algorithm and language scientists, looking at the complexity of natural language and lead gen opportunities in social media. Yesterday we looked at Machine Learning – today we look at how to put the science and technology into practice.

Machine learning greatly compliments the business practice to establish and maintain one-to-one customer relationships and help businesses grow.

It can be evolved with adaptive learning, where user feedback can loop back to the knowledge base for enhanced learning and improved accuracy in predictability.

With a cloud-based solution, everyone – from the solo entrepreneur to small and medium businesses – can benefit from the machine learning technology without incurring millions spent in the infrastructure and data required.

To start your social lead generation, here are some ways you can begin to benefit now, based on your goals.

Case 1: Sales leads and opportunities for your brand

Actively listening to the social web using machine learning can identify sales leads and opportunities; be aware of urgent customer issues; and keep a tight control on your brand reputation.

Some of the ways you can do this include:

Set up social listening with topics for your business:

Track tweets, Facebook posts and blogs based on your brand, specific product and service you want to monitor.

If your business is local and targets specific demographics, make sure you qualify your monitoring with these criteria. If you don’t know the regions that have most of buzz for your product, set up and review your core analytics, collect the data to uncover these specific factors, and add them to your search criteria.

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Monitor and track leads and customer services:

Analyze the conversations. Not just an individual social update, but complete conversation threads in order to identify the instigator and closer of the conversations on a topic, and any outcome of the conversations.

Machine learning can be applied to analyze these conversations and chatter, and identify potential sales leads or customer  service requests. In addition, the sentiment of the conversation can be incorporated to enhance the predictability of the intent.


Intent to buy, use or acquire your brand products and services can be uncovered as potential leads and scored (on a 1-10 basis, for example) with confidence, based on the sentiment or mood of the user.

Once a prospect is identified by analyzing the prospect profile information and social footprint,the business can reach out for a friendly introduction – or provide a promotion – to nurture the relationship, and guide the process of converting a prospect to a customer.

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Co-sell and upsell opportunities:

A product comment can lead to the opportunity to co-sell or upsell companion products and services. For example, a purchase of a laptop with a happy mood can lead an opportunity for a business to present the new client a value-add service warranty, or replacement batter supply.

Customer services:

Complaints about your products and services tend to have negative sentiment and you need to reach out to the customer to resolve the issues as soon as possible, initially in public then perhaps more deeply in a private conversation.

Reputational issues:

Conversations with strong negative sentiment and spreading fast with high velocity can be flagged for actions through public relations, or direct communications with the consumer and an in-house crisis communications team.


Case 2: Risk to your competitor is opportunity for your business

Businesses can follow the competition, and use opportunities to convert unhappy customers of your competitors to (potentially) become customers of your own.

Using a top down approach, machine learning can analyze wide range of conversations and buzz, uncover the implicit intent as the opportunity for you.

Setup the social listening with topics for your competitors:

For known competitors, track conversations and reviews about them. If you are not sure about your specific competitors, enter the industry terms, set up the topic profiles for your industry and begin to uncover these top topics for your own insight.

A Presidential example:

I entered “presidential election” into our algorithm, and received recommended terms that are related to “presidential election”, including “Barack Obama” and “Democratic National Convention”.

By utilizing the concept tagging technology, I can now track all these related topics to monitor relevant content.

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Case 3: Co-sell or upsell from listening to your industry

Monitoring conversations about your industry, or the consumer behavior patterns within it, can identify new sales opportunities you may have otherwise missed.

For example, tracking “travel between US and Canada” can uncover the needs of “roaming” and “battery”, prompting mobile operators to provide business travelers discounted roaming packages combined with extra mobile battery, or enterprising vendors offering special data phones just for that trip.

Now it’s up to you

The above examples can help you kickstart the social lead generation process, and really begin to benefit from all the potential social media and machine learning has to offer.

And because you know your business and industry the best, complimenting machine learning with your current business know-how and existing operations will allow you grow your business through the social media.

Which is what really matters for any business owner.

Ready to use our tools to kickstart your social media business success? Sign up here!


  1. SalesPortal says:

    “Risk to your competitor is opportunity for your business”

    Just remember that the opposite holds true too! You want to protect the relationships you have with your customers as much as possible. One misstep can send them running into the arms of your competition.


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