We look beyond the hype of AI and Machine Learning to translate it into quantifiable and customized solutions to data-intensive problems. WMK’s AI and Machine Learning Services are tailored to help companies create applications to uncover hidden data value, monetize data, provide consumer intelligence and seamless consumer journey solutions, improve operating efficiency and develop innovative solutions to disrupt established workflows and business processes. We make Data Sciences “real” to drive growth and profitability for the customers we serve.
Sale Force Optimization
Predictive lead scoring and website tracking immediately identifies HOT inbound leads and passes them to the sales team. We summarize the lead's true interest based on website engagement. Real-time inbound lead data enrichment gives intelligence around the lead's authority, location, company and facilitates lead engagement by providing links to social media and contact details such as phone number and website. WMK makes it impossible for sales reps to miss a good lead, as we bring all data to them through Slack, browser or email notifications and we push ALL data to Salesforce, Pipedrive, or any other system you want to. You could also automatically add them to a sales drip in sales outbound solutions such as Reply.io, Salesloft, Outreach.io or else.
With WMK’s tools, you can trigger real-time alerts to the right person in your company when a lead arrives. You can dynamically assign them to sales reps and then automatically create them in your CRM - depending on your own automation rules. Combined with point based lead scoring, real-time custom alerts and hundreds of options to build work flows with Zapier, you have all you need to never miss a great lead again or annoy sales with poor leads.
Using Data to Leverage Customer Experience on Banking
The implementation of Data Science in banking is changing the face of the banking industry rapidly. That will help them to understand the customers for increasing customer loyalty by providing more efficient operational efficiency. Various methods of data analysis like data fusion and integration, Machine Learning, Natural Language Processing, signal processing, etc. can be used for this purpose.