AI solves the #1 sales time suck, makes crappy content, and detects fraud
Because wasting time, diluting your brand, and bleeding margin aren’t options.
Thanks for reading AlphaEngage issue #101. Read past issues.
Inside: AI lead scoring to fix sales inefficiency, content quality risks, and accounting fraud detection.
SALES
Conquer the #1 Time-Suck
If your business is like most, your sales team spends way too much time chasing dead-end leads. Way. Too. Much. Time.
The good news?
We can fix this time-suck by integrating AI-driven lead scoring into your sales team’s workflow. By leveraging machine learning algorithms and extensive sales data, AI can predict which leads are most likely to convert, allowing your sales team to focus their efforts where they matter most.
Old-school...
Manual and time-consuming: Lead scoring was a manual process that consumed a significant amount of time and resources.
Subjective decision-making: Sales and marketing teams often relied on gut instincts and subjective criteria, resulting in inconsistencies and inaccuracies in lead prioritization.
Difficult to scale: The manual nature of traditional lead scoring made it challenging to scale and adapt to changing market conditions.
Missed opportunities: Inefficient lead-scoring processes often resulted in missed opportunities and wasted resources.
New-school...
Increased efficiency: AI streamlines the lead qualification process, saving your team countless hours of manual work.
Improved accuracy: Machine learning models can identify patterns and insights that humans might miss, resulting in more precise lead scores.
Higher conversion rates: By prioritizing the most promising leads, your sales team can close more deals and boost revenue.
Implementing an AI lead scoring strategy isn’t all that hard. Seriously. Here are the steps:
1. Define your ideal customer profile (ICP) and key scoring criteria
2. Clean your data (CRM, marketing automation, web analytics, etc.)
3. Choose a solution that aligns with goals and the existing tech stack
4. Train the AI model on historical lead data
5. Integrate into the workflow and prioritize high-scoring leads
6. Refine the AI model constantly based on feedback and new data
And here are some of the more popular AI lead-scoring platforms to pass along to your CRM admins to research further:
MadKudu is a predictive lead scoring and customer lifecycle management platform that integrates with popular CRM and marketing automation tools, providing real-time lead scores and insights.
Infer combines internal customer data with external signals to create accurate lead scores and provide valuable insights into a lead's fit and engagement.
6sense is an account-based marketing and predictive intelligence platform that analyzes intent data, engagement signals, and firmographic information to identify and prioritize high-value accounts and leads.
Versium leverages a vast database of consumer and business data, applying machine learning algorithms to identify the leads most likely to convert.
Salesforce Einstein is part of the Salesforce CRM ecosystem, providing predictive scores directly within the Salesforce interface.
Leadberry identifies high-quality leads based on their website behavior and engagement by tracking visitor actions and applying machine learning algorithms.
forwrd accurately predicts which leads are sales-ready, based on historical data and AI algorithms - no technical skills required.
DiGGrowth.AI is an AI-powered lead scoring and nurturing platform that analyzes customer data and behavior to provide actionable insights and personalized engagement strategies.
Akkio is a no-code AI platform that enables businesses to quickly build and deploy custom lead-scoring models without requiring extensive technical expertise.
HubSpot's Predictive Lead Scoring is a built-in feature of HubSpot's CRM that utilizes machine learning to assign scores to leads based on their likelihood of conversion, enabling sales teams to prioritize their outreach efforts effectively.
Bottom line...
Implementing an AI lead scoring strategy can be a game-changer for your sales prospecting efforts and help your org to conquer the time suck by focusing on the leads that matter most.
Optimize resources, close more deals, grow the biz. Easy peasy!🤑
AI IN ACTION
Optimizing Energy
Problem: Lineage Logistics faced high energy costs and a negative environmental impact due to the energy-intensive nature of its temperature-controlled warehousing operations. Managing energy efficiently while ensuring optimal warehouse conditions was a significant challenge.
AI Solution: To address this, Lineage implemented an AI-driven method called "flywheeling." This approach utilizes machine learning to optimize cooling times by analyzing data, including weather and energy demand. By super-cooling warehouses during low-cost, off-peak hours, Lineage efficiently reduced its energy use during high-demand periods.
Results: The "flywheeling" technique resulted in a significant reduction in energy consumption, thereby lowering operational costs and minimizing environmental impact. This innovative AI solution improved Lineage Logistics' energy efficiency and was recognized by the U.S. Department of Energy for its effectiveness.
MARKETING
With Great Power Comes Great Responsibility.🦾
All of these new AI tools that have been popping up recently are pretty cool. Marketing folks love ‘em—especially large language models (LLMs), like ChatGPT. And when used in concert with core marketing principles, they can indeed make people more productive.
But at what cost?
One concern is that marketing teams are producing excessive amounts of low-quality, AI-generated content. This so-called “content” is being used for search engine optimization (SEO) purposes, product copy, ad copy, landing page copy, and other similar applications.
This crappy content can lead to a dilution of a brand's unique voice and a loss of authenticity, which can ultimately harm its reputation. Oh, and search engines like Google are working around the clock to crack down on low-quality, AI-generated content using their own AI algorithms [insert irony here], making it even more important to prioritize quality over quantity.
Another area to monitor is the use of AI-generated imagery in marketing. While AI has made remarkable strides in creating stunning artwork, it still has plenty of limitations when it comes to replacing traditional photography. Woah... is that an extra arm back there?!?
Consider taking a holistic approach to AI research, testing, and adoption, so that your organization can harness AI’s full potential while mitigating the risks associated with siloed sandboxes.
CUSTOMER SERVICE
Helping Customer Service Teams Embrace AI
As chatbots and other customer-centric AI tools are introduced into the organization, it's natural for customer-facing employees to worry about job security.
Here are a few ways to nip those fears in the bud:
Be transparent: Openly explain how AI will work alongside human team members, not replace them. Keep the team informed about the company's AI plans and address any concerns they may have.
Offer training: Invest in programs to help representatives build the skills they need to work effectively with AI, handle more complex customer issues, and become better problem-solvers.
Emphasize the human touch: Remind your team that they bring empathy, context, and a personal touch that AI can't match. These human qualities are key to building strong customer relationships.
Celebrate wins: Give a shout-out to team members who use AI to create better customer experiences or who go the extra mile. Recognizing their successes can boost morale and ease fears about AI.
Encourage innovation: Urge your team to view AI as an opportunity for innovation. Welcome their ideas for using AI to improve processes, collaborate, and wow customers.
By addressing fears head-on, providing support, and emphasizing the enduring value of human interaction (sappy but true!), leaders can help customer service teams navigate the integration of AI with confidence while continuing to deliver exceptional customer experiences.
ACCOUNTING
The Unfortunate Need for Automated Fraud Detection
Fraudsters suck. And detecting fraud can be difficult. It can also lead to substantial financial losses and damage a company's reputation.
Fear not...
AI is proving to be a powerful ally in the fight against fraud. AI-powered fraud detection systems can analyze vast amounts of data in real-time, identifying suspicious patterns and anomalies that may indicate fraudulent behavior. They utilize machine learning algorithms to learn from historical data and continually enhance their detection capabilities.
For example, AI can detect duplicate invoices, identify fake vendors, flag suspicious transactions, spot anomalous payment patterns, verify invoice details against other documents, analyze behavior patterns, monitor for unauthorized changes to vendor data, prevent phishing attempts, and enhance matching processes.
Deploying these systems typically requires collaboration among the CFO, CIO/CTO, IT department, accounting manager, and internal audit team to ensure the technology aligns with financial objectives, integrates seamlessly with existing systems, and effectively mitigates fraud risk.
Here are a few trusted AI-powered tools available to help in the fight against fraud:
AppZen: This AI-powered platform can audit 100% of invoices and expenses in real-time, identifying potential red flags and saving teams valuable time.
Oversight: This standalone AI-powered platform analyzes an organization's accounts payable transactions and spend data, using machine learning to identify patterns, anomalies, and potential risks across various financial processes.
Medius utilizes AI and machine learning to automate the accounts payable (AP) process, detect fraudulent invoices, and prevent duplicate payments.
MasterCard In Control: This solution utilizes AI to monitor and analyze company spending patterns, enabling the detection of suspicious activity before it escalates.
Highradius: While focused on accounts receivable, this AI-powered solution also helps detect and prevent fraud by analyzing customer payment patterns and identifying suspicious activities.
This is a big topic for a newsletter, so we’ll get into more detail in future issues. For now, direct your teams to start researching this stuff if it’s not already in place. The lighter side of AI will be required to fight the darker side of AI, which makes this stuff necessary in the first place.