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cleanFiles/bookmap_Exploring_the_Lead_to_Cash_Business_Proces_en-US_learning-sap-com_lsc-prod.zip (MMCP-DROPZONE) - 2024-11-08T16:15:26.000Z

Managing Leads in the Sample Business Case

<div id="content"><p tabindex="0">Implementing Lead to Opportunity at Mega Machines would bring several benefits:</p><ul style="list-style-type:disc"><li tabindex="0">With SAP Emarsys, Mega Machines can leverage powerful marketing automation tools to effectively qualify marketing leads. By utilizing Emarsys' advanced lead scoring and tracking capabilities, the company can identify and prioritize leads that are most likely to convert into opportunities, allowing them to focus their sales efforts on the most promising prospects.</li><li tabindex="0">SAP Sales Cloud provides Mega Machines with a comprehensive platform for managing the sales process.</li><li tabindex="0">The integration of Emarsys with Sales Cloud enables seamless handover of qualified leads to the sales team, ensuring that no valuable leads are lost in transition. The sales team can then utilize Sales Cloud's advanced CRM tools to further qualify leads, evaluate their readiness to purchase, and track their progress through the sales pipeline.</li></ul><p tabindex="0">By implementing SAP Emarsys and Sales Cloud during the Lead to Opportunity stage, Mega Machines can benefit from a streamlined and efficient lead-to-opportunity process. Additionally, the integration of these SAP solutions provides valuable data-driven insights that can help Mega Machines make informed decisions and optimize their sales and marketing strategies for improved business growth and success.</p><p tabindex="0">Learn how Managing Leads can help Mega Machines in this video.</p><div tabindex="0"><h3>Lead to Opportunity in the Sample Business Case</h3></div><div class="formalblock"></div><div class="kalturaVideoLessonWrapper"><div aria-label="" class="kalturaVideoLessonContainer" data-testid="video" id="1_zvb4sqeq"></div></div><div tabindex="0"><h3>Summary</h3></div><div class="formalblock"><p tabindex="0">We have gone over the sample business case during the Lead to Opportunity stage and how SAP Sales can help to manage leads, qualifications, and scores.</p></div></div>

Managing Leads at Mega Machines

Exploring the Lead to Cash Business Process in SAP Customer Experience

<div>Learn how lead scoring in SAP's sales cloud can help sales representatives like Mike Summers increase lead conversion rates. Discover how machine learning predicts lead probability and the impact of lead scores.<br><br># This description was auto-generated #</div>

Lead to Opportunity in the Sample Business Case

Using the example in the previous unit. the Mega Machines Sales Representative. Mike Summers, receives the new lead. He logs into SAP Sales Cloud and navigates to view Leads. There, he can see the All Leads chart, so he checks the distribution on the source of each lead, their percent status, and the lead qualification numbers. Below a listing of leads appears, including all company leads, along with Mike’s team. Using this listing Mike learns that he can increase his chances of lead conversion by choosing better and more suitable leads.  Also, for each lead, Mike sees a lead with a high propensity score. Powered by machine learning, the lead receives a score before it is converted to a Sales opportunity. Lead scoring helps to organize a list of leads by analyzing data across marketing, sales, service, and other third-party data sets. Lead scoring uses the machine learning model to predict the probability of closing the sale. Lead scores receive a number ranging from 0 to 99. If a lead receives a score between 75 to 99, then it is "very likely to close” and is labeled in green. When it is 51 to 75, it's considered as "likely to close" and displayed in yellow. 0 to 50 is categorized as "less likely to close" and is displayed in red. Mike is interested in Pronto Hardware because the lead score is 92. With this "likely to close"  score Mike chooses to learn more about this potential customer. Lead scoring helps to organize a list of leads by analyzing data across marketing, sales, service, and other third-party data sets. Lead scoring uses the machine learning model to predict the probability of closing the sale. Lead scores receive a number ranging from 0 to 99. If a lead receives a score between 75 to 99, then it is very likely to close and is labeled in green. When it is 51 to 75, it's considered as likely to close and displayed in orange. 0 to 50 is categorized as less likely to close and is displayed in red.  Mike is interested in Pronto Hardware, because the lead score is 92. With this likely to close score Mike chooses to learn more about this potential customer.