Why Traditional Sales Strategies Are Failing and How AI Can Fix It

If you’re in manufacturing or complex B2B sales, you’ve probably felt like you’re fighting an uphill battle. Long sales cycles, complex product configurations, and ever-evolving customer demands – it’s a tough gig. 

And if you’re still relying on traditional sales strategies, well, you’re likely feeling the pain more than most.

Think about it: endless cold calls, sifting through outdated industry directories, and hoping your perfectly crafted pitch lands at the right time. 

Sound familiar? These tactics, once the backbone of B2B sales, are increasingly ineffective in today’s fast-paced, data-driven world.

Let’s dive in!

The pain points: Where traditional sales fall short

Manufacturers face unique challenges that traditional sales strategies simply aren’t equipped to handle. 

Let’s break down some of the most common pain points.

Pain point 1: Long and complex sales cycles

Complex B2B sales like in Manufacturing, often involve intricate product specifications, lengthy approval processes, and multiple decision-makers. This translates to extended sales cycles, draining resources, and delaying revenue.

Traditional methods struggle to keep pace with the constant need for information sharing and relationship building over these prolonged periods.

Pain point 2: Inefficiencies and wasted time

Sales reps spend countless hours on manual tasks like lead research, data entry, and report generation. This time could be better spent building relationships and closing deals.

Traditional methods lack the automation and efficiency needed to streamline these processes.

Pain point 3: Outdated prospecting methods

Relying on cold calls and generic emails often results in low response rates and wasted effort.

Today’s buyers expect personalized, relevant interactions, something traditional methods struggle to deliver.

Pain point 4: Data silos and lack of insights

Information is often scattered across different systems, making it difficult to get a complete picture of customer needs and preferences.

This lack of data-driven insights hinders effective decision-making and personalized outreach.

Why do outdated prospecting methods lead to lost revenue?

Imagine trying to navigate a complex manufacturing deal with a map from the 1980s. 

You’d be lost, right? 

That’s what relying on outdated prospecting methods feels like today.

Missed opportunities

Traditional methods often fail to identify high-potential leads, resulting in missed opportunities and lost revenue.

According to LinkedIn’s State of Sales report, sales professionals who leverage social selling are 45% more likely to exceed their sales quotas. This shows that the old methods are no longer sufficient. 

Increased costs

Inefficient prospecting methods increase the cost of acquiring new customers. Time spent on unproductive activities translates to wasted resources.

Decreased customer engagement

Generic outreach fails to resonate with today’s sophisticated buyers, leading to decreased engagement and lost trust.

A study by Forrester found that B2B buyers complete on average 90% of their buying journey before contacting a sales representative. This means that if you are using only cold calls, your customer has already made up their mind. 

AI to the rescue: A real-life example

Let’s look at a real-life scenario:

A mid-sized manufacturing company, Precision Components Inc., was struggling with long sales cycles and low lead conversion rates. Their sales team was spending countless hours on manual lead research and cold calling, with minimal results.

They decided to implement an AI-powered lead generation tool. This tool analyzed vast datasets, including industry trends, company profiles, and online behavior, to identify high-potential leads. It also automated personalized email outreach, tailoring messages to each prospect’s specific needs.

The results were remarkable:

• Lead conversion rates increased by 40%.
• Sales cycles were shortened by 25%.
• Sales reps were able to focus on building relationships and closing deals, rather than on manual tasks.

According to a study by Harvard Business Review, AI can lead to a more than 50% increase in qualified leads. 

A tale of two sales processes: Traditional vs. AI-driven

To really drive home the difference, let’s compare two sales processes:

Traditional sales process

• Lead Generation: Manual research, cold calling, attending industry events.
• Lead Qualification: Based on gut feeling and limited information.
• Outreach: Generic emails and phone calls.
• Sales Cycle: Long and unpredictable, with numerous delays.
• Data Analysis: Time-consuming and often incomplete.

AI-Driven sales process

• Lead Generation: AI-powered tools identify high-potential leads based on data analysis.
• Lead Qualification: AI scores leads based on behavior and demographics.
• Outreach: Personalized emails and targeted content based on customer needs.
• Sales Cycle: Streamlined and efficient, with AI predicting potential roadblocks.
• Data Analysis: Real-time insights and actionable recommendations.

Practical tips for implementing AI in your complex B2B sales process

Tip #1: Start with a clear strategy

Define your goals and identify the specific areas where AI can make the biggest impact.

• Begin by outlining your key performance indicators (KPIs). What are you trying to achieve? Increased lead conversion? Shorter sales cycles? Improved customer retention?
• Conduct a thorough audit of your current sales process. Identify bottlenecks, inefficiencies, and areas where data is lacking.
• Prioritize your AI initiatives. Don’t try to implement everything at once. Focus on the areas that will yield the highest return on investment.
• Create a phased implementation plan. Define clear milestones and timelines.

Example: If the KPI is to increase qualified leads by 20% in 6 months, then the strategy should focus on AI lead generation and lead qualification tools.

Tip #2: Focus on data quality

AI is only as good as the data it’s fed. Ensure your data is clean, accurate, and up-to-date.

• Implement data governance policies to ensure data consistency and accuracy.
• Regularly cleanse and de-duplicate your data.
• Integrate data from various sources to create a unified view of your customers.
• Invest in data enrichment tools to fill in missing information.

Example: Regularly auditing the CRM data to remove old or incorrect information.

Tip #3: Choose the right tools

Research AI solutions that are tailored to the unique needs of manufacturing sales.

• Look for solutions that offer industry-specific features, such as product configuration tools, quote generation, and inventory management.
• Consider solutions that integrate with your existing CRM, ERP, and other sales tools.
• Evaluate the vendor’s reputation, customer support, and track record.
• Request demos and pilot programs to test the tools before making a purchase.

Example: Tools that provide 3D product visualization for complex product configurations.

Tip #4: Train your sales team 

Provide your team with the necessary training and support to effectively use AI tools.

• Develop comprehensive training programs that cover the features and benefits of the AI tools.
• Provide ongoing support and coaching to ensure your team is maximizing the value of the tools.
• Encourage your team to share best practices and tips.
• Address any concerns or resistance to change.

Example: Regular training sessions on how to use AI lead scoring systems.

Tip #5: Monitor and optimize

Continuously track your results and make adjustments as needed.

• Establish clear metrics to track the performance of your AI initiatives.
• Regularly review your data and identify areas for improvement.
• A/B test different AI strategies to determine what works best.
• Stay up-to-date on the latest AI trends and technologies.

Example: Tracking the conversion rate of leads generated by the AI system.

Tip #6: Integrate your CRM (or use a better one)

Make sure the AI tools work seamlessly with your existing CRM.

• Integration ensures smooth data flow and eliminates data silos.
• It allows for a unified view of customer interactions and data.
• It streamlines sales processes and improves efficiency.

Example: Making sure that the AI lead scoring information is visible inside the CRM.

Tip #7: Use AI for content personalization 

AI can help you deliver content that is relevant to each potential client.

• AI can analyze customer behavior and preferences to deliver personalized content recommendations.
• This can include product recommendations, case studies, and blog posts.
• Personalized content increases engagement and improves conversion rates.

Example: Delivering product information based on the prospect’s viewed pages on the company website.

Tip #8: Implement AI chatbots

Use chatbots to answer basic questions and qualify leads before they reach your sales team.

• Chatbots can provide 24/7 customer support and answer frequently asked questions.
• They can also qualify leads by asking relevant questions and gathering information.
• This frees up your sales team to focus on more complex tasks.

Example: A chatbot that answers basic questions about product specifications.

Tip #9: Use AI for sales forecasting

AI can analyse market trends and past data to give accurate sales forecasts.

• AI can take into account many more variables than a human can.
• This allows for better resource allocation and improved planning.
• It also allows the company to be more agile in changing markets.

Example: AI prediction of product sales, based on past years’ data, and current market trends.

By implementing these practical tips, manufacturing companies can harness the power of AI to transform their sales processes and achieve significant business results.

To wrap up

The future of manufacturing sales is here, and it’s powered by AI. Don’t let outdated strategies hold you back. Embrace the power of AI to streamline your sales processes, increase revenue, and build stronger customer relationships.

Don’t wait, take the first step today. Start by assessing your current sales process and identifying areas where AI can make a difference. 

So, what is the one thing you can do this week to start the process of AI implementation into your sales process?