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Successful lead generation is imperative for any business to thrive within today’s competitive marketplace. There are several steps involved in lead generation, some requiring significant time and expenses, such as organizing and hosting tradeshows or marketing events. Within the digital landscape, lead generation and completed sales face many challenges, each requiring unique strategies to reach individual goals.
Artificial Intelligence (AI)-powered software tools and features implemented by skilled software developers effectively streamline marketing and business strategies and operations. At the same time, its versatility continues to facilitate unprecedented business transformations resulting in expanded markets and new revenue opportunities.
Lead generation comprises data collection that can be used to convert a prospect into a customer. Sales and marketing representatives who were once reliant on traditional methods like cold calling prospects have long ago replaced this method with digitally-based action plans.
Technology constantly evolves, and adjusting the industry approach is paramount for realizing the benefits of implementing new resources. Skilled software experts have created and implemented highly intuitive Customer Relationship Management (CRM) systems with detailed lead analytics, virtual agent chatbots, and intelligent data capture software. But what would the positive impacts be if a company adopted AI within its lead generation strategy?
AI can analyze large volumes of data while providing users with unique data-driven insights to recognize overlooked patterns. Such patterns efficiently identifying what client traits transform a lead into a conversion are invaluable not simply because they result in a sale, but they also enable personnel to reduce or even eliminate human resources spent on boilerplate tasks. Surveys and research data have yielded that sales cycles last longer, and 79% of leads do not convert. AI can redirect personnel’s attention to leads with greater chances of being converted, thus reducing efforts spent on tasks that do not produce positive results.
Successfully completing a sale reinforces a marketing team’s efforts and duplicating this outcome becomes an immediate goal. However, while no two customers have the exact same needs, AI expediently identifies data trends and automatically generates a database of similar attributes from the previous, successful sale. This enables marketing departments to utilize the detailed database to pursue leads that will generate higher sales probabilities.
Personalizing a campaign is paramount for the success of account-based marketing within B2B spheres. What signals should a marketing team look for? Utilizing AI can automatically manage a prospect database while alerting a marketing department to critical changes of a lead. Leadership and personnel changes within an existing company client account may lead to a reassessment of the client’s business needs. With an AI-based alert, a marketing team will immediately learn what prospect is experiencing leadership transformation and may warrant outreach.
Engaging in timely communication with lead prospects ensures that marketing efforts don’t ‘run cold.’ However, simply sending communication without careful considerations such as what medium, tone, frequency, and overall messaging quality will not result in achieving positive marketing goals. AI-based resources such as Natural Language Processing (NLP) can be implemented by skilled software developers to gain greater insight into an intended audience’s style and method of communication, understand customer behavior and preferences, and ultimately improve the communication relationship. NLP has the capability to further analyze a lead, or the customer’s ‘digital footprint’ by proficiently searching for and identifying keywords used in social media and browsing behavior, enabling marketing campaigns to tailor messaging to include language often used by a lead.
Machine Learning (ML), a subset of AI that ‘learns’ to recognize data patterns via algorithms to imitate human learning continues to be a cutting-edge and versatile technology. Its application within marketing and lead generation is used for predictive lead scoring, an intuitive resource that creates data-driven decisions to not only rank the value of lead prospects, but alert personnel to which leads can generate the most success and predict the time required to complete a sale. Additionally, ML carefully analyzes customer behavior such as spending habits, duration of purchases, and what product or service attributes the customer places significant value on.
Predictive lead scoring reduces efforts spent on estimating the value of a lead and how long it may take to close, enabling marketing personnel to redirect efforts to ML-ranked prospects that the system has determined to have the most value.
The easily trackable metrics of predictive lead scoring assists with creating and implementing campaigns that generate maximum return on investment. Predictive lead scoring models compile data to create nuanced customer profiles that include critical attributes such as age, location, job industry, company size, and even behaviors that lead to more engagement; is the customer more responsive to e-mail messages, phone calls, or Chatbots? These key insights are invaluable for sales and marketing departments because they enable access to real-time data tracking to not only produce new leads and customers, but also retain existing clients.
Relationship management is a key fundamental for successful businesses. Clients are selective of whom they engage with due to unlimited options that compete for their business, so successful adoption of available technology is crucial for sales and marketing teams to not only address evolving demands of clients but how to properly implement it to transform strategies to exceed sales goals and client expectations.
Personalizing relationships is critical, and having a carefully nuanced and targeted lead generation strategy is the backbone for successful sales and marketing campaigns. NLP eliminates archaic messaging that alerts and prompts a promising lead to purchase a product or service. Instead, it provides a new resource to create personalized and more refined brand messaging aligned with client needs while communicating within the same vernacular used by the client.
Artificial Intelligence has transformed how industries operate and undeniably changed how we utilize critical data sets and existing information and assets to move forward. Expert-level software developers can program more intuitive AI-based resources to carefully address and exceed individualized business goals and implement quality lead engagement strategies.
Precedence Research has forecasted that the global AI market will reach $1.591 trillion by 2030. With its proven versatility, we see skilled software developers playing a crucial role in this fast-growing market that continues proving its effectiveness, no matter the industry that implements it.
Chetu, Inc. does not affect the opinion of this article. Any mention of specific names for software, companies or individuals does not constitute an endorsement from either party unless otherwise specified. All case studies and blogs are written with the full cooperation, knowledge and participation of the individuals mentioned. This blog should not be construed as legal advice.
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