ChannelEyes has launched Day2Leads, a new way for organizations to drive repeat business. Day2Leads is a cloud-based SaaS platform that applies predictive analytics for installed base sales. Using cutting-edge data science and machine learning, Day2Leads will find untapped leads for the next purchase an existing customer is likely to make.
Over the last eight years, ChannelEyes has helped large and mid-sized companies that “sell everything from software to furniture,” said Dave Geoghegan, CEO of ChannelEyes. “A recurring theme with our clients has been ‘how can we generate more leads?’ We developed Day2Leads because the answer was already in their data.”
“Existing customers are an untapped source of high quality opportunities,” Geoghegan said. “B2B Sales and Marketing teams typically focus on new logos and don’t spend enough time maximizing revenue from existing customers. Using predictive analytics on sales transaction data we can predict the next items your customer is likely to buy. It’s similar to how Amazon recommends or suggests products to buy while you are shopping.”
Day2leads identifies high-potential opportunities for upsell, cross-sell, new products and consumables from historical sales transaction data. It loads sales transactions from an ERP or Accounting System and returns a pipeline of high-potential leads.
“Using your existing workflow, customer lifecycle teams can feed these leads into their CRM for follow-up or leverage selected leads to make email marketing campaigns more effective,”Geoghegan continued. “For organizations with a Reseller Channel, distribute the leads and have partners do the follow-up. The result is a more efficient sales process and a faster path to predictable revenue for installed-base sales.”
The Day2Leads SaaS Platform is powerful enough to handle large numbers of transactions and complex products, the company says. No matter what color chair a customer purchased or how many pages per minute the printer supports, Day2Leads analyzes buying patterns and predictive indications to identify the next item a customer is likely to buy, when they will most likely make their purchase and how much they will spend.
