Every year, new advancements within the technology space have assisted businesses by creating new opportunities for customer outreach. Over the last couple years, the B2B space has noticed real potential in incorporating Artificial Intelligence in Marketing, however, there is still a significant lag in adoption. Even though marketers have tested the waters when it comes to machine learning algorithms, there is still major ground to be covered when it comes to predictive analytics, personalization, statistical analysis as well as lead generation. Given its potential, Artificial Intelligence in B2B sales and marketing is here to transform the way people interact with brands, information and services.
A good handful of enterprise giants dread the idea of automating their marketing functions with the use of AI, however, by measuring the effect of AI in the customer service industry, one can affirm that understanding customer nuance and deriving insights from relevant customer data will not be entirely manual or managed by humans alone.
Such real-time statistics highlight how customers are open to making their online presence an indivisible part of their lives. This is also a vital indicator of how marketers urgently need to shift their focus on developing more powerful pre-sales strategies to leverage the potential opportunities offered by modern methods of B2B marketing.
However, all online marketing campaigns and efforts revolve around how much business value is drawn from the data related to their everyday customer interactions and engagements. Certain factors involved in the data management process make or break the final outcome. So how do you go about manipulating data that offers insights into your customer journey?
Challenges in harvesting precious data
To address every minute requirement of customers and reach maximum acquisition within the B2B marketing space, enterprises should concentrate their efforts towards learning their customers. Be it end users or corporate clients, each individual leaves behind a plethora of information through their online clicks and search, live campaigns, chat or e-mail communication, website visits and purchases. When it comes to insights in the form of customer mindset, demographics and their behavior from heaps of data — enterprises need to consider incorporating Artificial Intelligence in Marketing and Sales strategies.
Lack of proper skillset being a major challenge, businesses often miss out on insight as data collected is disposed or mismanaged or considered redundant — resulting in poor pre-sales marketing strategy. This is why, when it comes to harvesting and processing customer interaction data, the presence of Artificial Intelligence in sales and marketing would offer unparalleled insight resulting in significant ROI.
Customer’s ethos, impulse and buying pattern
There really is no better place for businesses to invest in Artificial Intelligence solutions than customer service and engagement.
Whether it is prediction or personalization, marketers will be able to touch all domains of brand marketing through 360-degree navigation of customers’ habits, tendencies, impulses, and buying patterns. To give you a quick overview, Artificial Intelligence in Marketing can help in the following –
· Predict potential customers
· Discriminate between buyers and visitors
· Identify special trends and choices
· Personalize various online campaigns
· Improved lead generation
· Smart decision making
· Increased efficiency
· Drive more sales and revenue
Reports on consumer research also suggest that 80% of marketing executives believe that Artificial Intelligence in B2B marketing will revolutionize the field completely in the next five years.
Artificial Intelligence in B2B Marketing leads to empowered customers
Machine learning + intelligence + digital marketing = empowered customers.
The adoption of artificial intelligence in B2B marketing will not only help businesses, but it will also touch customers by empowering them by giving them more than they can expect. This is where marketers can reap insights from their software and transform it into smart purchase decisions for customers.
With predictive analytics blending with natural language processing, it becomes easier to predict customer’s future choices and shopping behavior.
We are already seeing the rise of AI-assisted message prompts where customers receive relevant suggestions and purchase offers in the B2C space.
Real-time machine learning use cases
· Chatbots and Voice Assistants: Chatbots and digital voice assistants are quintessential examples of conversational computing combined with powerful AI to drive seamless user experience using transient data like Google, Amazon and Facebook.
· User Engagement: Making a predictive analytics model derived with the help of active machine learning will help merchants run their commerce more efficiently by proactively sensing customer pulse and boosting retention.
· Natural Language Processing: Machine learning can be further expanded with NLP to enhance digital advertising & data organization as well as build far more accurate predictive models that work on most relevant keywords as done by QuanticMind.
Artifical Intelligence in Marketing = more relevance and control
Before the Internet became an everyday part of our lives, real-time advertising was a cul-de-sac. Traditional one-way means of advertising and customer service ruled the market, generating no sufficient response. Prior to the widespread adoption of the Internet, B2B sales and marketing suffered from the absence of interactive dialogue. It was hard for potential customers to identify the right solutions given that there were no social channels to share brand experience in words.
Cut to the scene today — things are poles apart. Customers can not only control purchase journey but identify and select their favorites in no time. Online media is now fluid, fast and provides uninterrupted but more importantly, relevant services for customers to avail.
Real-time data analysis and forecasting
Online marketing moguls often parrot the term “real time” while describing the performance of pre-sales efforts or customer service. But, the arrival of machine learning in the face of intelligent marketing has made it quite possible. Artificial Intelligence in Marketing has successfully broken all the barriers that stopped businesses from reaching their prospects. All it takes for a machine is to process the online data created by their behavioral pattern to produce relevant, customer-specific solutions along with forecasting future buying trends based on past purchase patterns.
Marketing content gets persuasive and influential
To interact with the target audience, company’s marketers take it upon themselves to use gathered insight to design email campaigns and compose creative ads. The content writers have to make precise guesswork about what customers can and will relate to. However, with the integration of Natural Language Generation, content curation can be automated based on customer preferences and demographics.
Developing relevant content pieces for your target audiences in order to move them through different stages of the marketing funnel will far more streamlined with the incorporation of AI in Marketing.
Algorithms can be ran to collect and collate data of your customers/audience pertaining to what they like to read, their current challenges and concerns with regards to your business or service offerings etc. Post acquiring data that is highly personalized to this extent, marketers can then curate and create content that is relevant and answers their questions either through outreach systems like emails or social media or by incorporating intelligent chatbots that can directly converse with their potential customers.
All in all, it is safe to say that a lot is happening and set to happen in the world of B2B marketing with respect to AI. Acknowledging the fact that Artificial Intelligence has powerful potential to shape sales and marketing is imperative. All the practical use cases suggest that Artificial Intelligence and Machine Learning can help manage the wild flow of data for businesses to create real-time predictive models and effectively engage with customers while simultaneously gaining competitive advantage.
Optimized decision making, shorter sales cycle through ‘predictive’ buying and personalized outreach are some compelling outcomes to result in a WIN/WIN scenario for both — enterprises and its customers. Enterprises should collaborate with the right technology partner in order to assist them in the transition of adopting AI in their marketing strategies.