Artificial Intelligence (AI) is dominating the headlines, yet it’s not always clear to enterprise leaders how AI can contribute to business performance. Put simply, AI is the capability of a machine to imitate intelligent human behavior. The techniques vary and include: machine learning, deep learning, predictive analytics, natural language processing, and many more.
AI alone won’t solve the issues that B2B company leaders face on a regular basis, it’s how the application solves relevant business problems that makes the difference. In my conversations with customers, I regularly hear from business leaders who are up against a challenging and fast-changing environment. Slow-growth economies, declining customer loyalty, overloaded sales teams, eCommerce and new competition have made hitting quarterly revenue and profit targets increasingly difficult.
In my conversations with customers, I ask them a few what-if scenarios. What if:
- You could know all customers as well as you know your top accounts?
- Every sales person performed like your very best rep?
- Every seller could effectively sell the entire product portfolio?
- You knew the full economic potential of every customer relationship?
Invariably, B2B leaders light up. They are craving the insight that helps them maximize the value, and full potential, of every customer relationship. However, that’s easier said than done.
They’ve tried CRM tools; they’re great for structuring sales processes and providing pipeline visibility, but they don’t generate customer insight or monitor transactional impacts. And CRM data is only as good as its inputs.
Business Intelligence (BI) tools are great to view historical data, but they don’t predict and prescribe actions. In addition, sales people generally don’t have time to dig through BI reports to find the insights they need.
Transaction data is often used to analyze historical customer behavior, and many pricing applications use historical transactions to provide a suggested price. Historical data is important, but alone it can’t predict price sensitivity in advance of customer discussions. On top of that, pricing-focused applications don’t deliver product recommendations or customer churn insights.
Alone, none can help where B2B companies need it most – delivering intelligence designed to maximize every existing customer relationship. Focusing on existing customers is critical, according to Bain and Company, increasing customer retention rates by five percent can increase profits by 25 to 95 percent.
A reliable, actionable and predictable application using AI for B2B.
The only way to truly maximize the lifetime value of each of your customer relationships is to apply AI to a much broader set of data that includes: existing CRM data, transaction data, competitive data, web data, market data and more. It’s combining these varied data sets and applying sophisticated mathematical algorithms to them that can increase the business value by orders of magnitude.
This approach puts prescriptive, actionable guidance directly into the hands of your sales team, highlighting opportunities and threats to sales reps. They’ll be alerted when customers begin purchasing from competitors, given product recommendations to grow purchases within customer accounts, and recommended prices that are aligned to both the market and customers’ expectations.
An AI-enriched platform delivers a unique blend of insight and action to:
- Continuously evaluate every customer and turn those insights into action plans
- Quantify customer potential and health of the account, and easily view those KPIs
- Identify millions of profit and revenue opportunities
- Enable data-driven collaboration between reps and managers
- Empower executives to implement revenue-generating campaigns that effectively focus sales activities performance
The kicker? This isn’t theoretical. This AI-powered solution has been used by industrial enterprises for more than 15 years. And its applications extend to other high-volume industries such as high-tech manufacturing and distribution companies.
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About the Author
Pete brings 20 years of product strategy experience, helping Fortune 500 companies harness Big Data to improve business performance. As Senior Vice President of Products and Science, Pete is responsible for leading Zilliant’s R&D efforts and defining the product lifecycle and requirements. Prior to Zilliant, Pete served as Vice President of Product Marketing at Yclip. Prior to Yclip, Pete managed KD1’s highly scalable data mining and decision support applications used by Walgreens, Lowe’s Home Improvement and Pepsi/Frito Lay. He also worked at Kelly Information Systems where he oversaw product development and implementation of their multi-terabyte data warehouse analytics product line. In addition, Pete has worked in consulting and product development roles with Category Management, Inc. and Procter & Gamble. He earned his B.B.A. in Quantitative Analysis and Information Systems from the University of Cincinnati.Follow on Twitter More Content by Pete Eppele