[0:00]Hello everyone and welcome to Nespon Solutions first ever webinar. We are excited to have you join us today as we explore the world of data and AI. For those who may be new to Nespon, we are proud to be a Salesforce Summit partner, representing the highest level of expertise and achievement within the Salesforce ecosystem. Established in 2008, Nespon Solutions is headquartered in Texas, USA. With a team of over 600 certified professionals and more than 350 consultants, we're highly skilled in delivering both B2B and B2C Salesforce implementation solutions. Leveraging our nearshore and offshore models to fulfill our clients' requirements. Additionally, our expertise various various Salesforce cloud, including data and AI, communications cloud, service cloud, sales cloud, energy and utilities cloud, and field service. We deliver exceptional services globally through our nine centers of excellence that are in USA, Argentina, UAE, India, Pakistan, Mexico, Spain, Uruguay and Colombia. Our focus on delivering outstanding results has made us a trusted partner for businesses worldwide. Today, we're excited to explore the world of data and AI. Our speaker Toki Khan, a senior technical architect, will share his insights on key use cases that demonstrates with the power of these technologies. We're eager to kick off this insightful session. If you have any questions, please hold them until the end of the session and we'll be happy to address them during our Q&A segment. Now, let's give a warm welcome to our speaker Toki, as he takes the stage to share his insights. Thank you, Mama. Hello everyone. This is me Toki Khan. Thank you everyone for joining the webinar. Today, we will take a deep dive into data cloud and Einstein AI, focusing on key use cases and demos developed by the Neponsolutions. This will include Einstein Go Pilot, chat prediction, and a customer 360 view. I am excited to share valuable insights, best practices, and practical tips that Nespon can liberate in your Salesforce journey. So let's get started. So the first question that comes to everyone's mind, what is data cloud? So Salesforce data cloud is a unified data platform that allow you to connect different CRM systems and external platform like Amazon, Snowflake, AWS, and Google Cloud. It allow you to collect, organize, and analyze data from different sources. Data cloud process historical data and it also enable us to leverage past data for the better decision making and predictive analytics, also, it help us to personalize customer campaigns. And again, the next question related to the Einstein, what is Einstein AI? So Einstein AI is a Salesforce AI powered platform that help business, sales and service agents by providing solution, suggestions and automations within the Salesforce platform. It like a having a virtual helper that understand your work and give you the informations or actions you need. We will further deep dive into Einstein AI during the demo. So today's agenda cover the use cases, those Nespon has implemented. Our use case include Nespon AI chain prediction, quick order journey, power AI billing and customer 360 view. So currently, we are considering three rules, like the first one client, the second one agent, and the third one Einstein. So the journey will involve the client calling the agent about the purchase inquiries or service related issues. The agent will get the help from the Einstein AI and respond to the customer. So our first use case is Nespon AI Chan prediction. As we all are already aware that retaining existing customer is most cost effective than acquiring a new ones. Basically, churn prediction identify the customers that are likely to leave. So Nespon configured Einstein churn prediction to help the business retain existing customers. This helped make the customer happier by giving different offers and discounts. Our Nespon solution identify churning score depending on the conditions and identifier explained by the business. As we have multiple features related to the customer chain prediction, we have listed few of them. So basically, it covers targeted retention strategies, improved customer experience, support long-term growth and it can also increase revenue. So the other part is Nessbest action, which is also associated with the chain prediction. So Nespon has set up Nesbest action NBA to provide most suitable offers based on the chain score. So as I mentioned like depending on the chain score, the next step is the Nesbest action. If the score is high or low, depending on the scores, Nesbest action functionality give different offers and promotions to the customer, which can help in both upselling and retaining existing customers. This enhance personalization, improve the customer experience and drive outcomes like increase sales and higher retention. Now let's move toward the demo. So I can show you how chat prediction functionality work. We have created separate list views for high and low churn score customers. So as we already aware like churn prediction help industries to retain their existing customers. So we have created like high churn score and low churn score list views, which help agent to identify the customers. So first of all, we can select the high churn score account. So in this list, we can see like Mike Welch is available. So we can click on the Mike Welch and we can see the detail and we can also see the right channel showing the chain predictions component. So against this Mike Welch, we can see like the score is 96, which is quite high. So this score is depending on this top predictors which are showing in the below section. For example, as we can see, the billing status for this account is pending and the case status of this account is still open. So this are the top predictors that are calculating or that are helping to calculate the scores. So we have other identifier as well as we can see in the data, the since this customer have seven cases from the last six months. So this are all the reasons that are calculating the score and telling the agent like the probability of this customer is very high. So depending on this scores and predictions, we have the next best action available and by following the company policies and churning scores identifiers, it's provide a discount to this customer. So the journey will be agent call to the customer and explain them like we are giving you a 25% discount. If the customer accept this offer and they want to pay the bill, so agent have the option, they can click on apply discount button and the discount will be automatically added to the billing record of the customer. So we land on the billing record without any manual step, as we can see like the amount is 9500 dollar. After given 25% discount, the amount reduced to 7125. So now customer is happy to continue with the organizations and with the help of chain prediction and NBA, we are able to successfully retain this customer. So this is the first journey related to the high turning score. The other journey we already have like related to low churn score. So we can open the low churn score account. In the low churn account, we have Karen Bell, but in this scenario, we have a customer which have the low churn score. And we can consider this as a loyal customer. If we see the prediction section, so the billing status of this account is paid. The case status is closed, so means there is no issue with this customer. He is happy with the organizations, with the business. And if we also see the history of this customer, so they don't have any case open. So depending all this like factors and score and we offered a special gold package which include TV plus internet. And the actual cost of this package is 60 dollars, but we are offering a personalized discount to this customer, so this offer, this package we are offering in a 40 dollar. So now the agent call to the customer and explain them like we are offering you a gold package which can tell TV plus internet and the cost is like 40 dollar. If customer accept this offer, so again, customer click on this interested button. So once customer, once agent click on this interested button, so the product that contain this bundle will be added to the order product page of the customer. So now we can see like once we click on the button, so it navigate us to the order product button, where we can see the product those we have added with the help of NBA. So TV basic and internet plan and we can also see the cost as well like the unit price for each product is 20 dollars, which the total cost of the bundle is 40. So this is an example how Einstein chain prediction and the NBA help agent to retain an existing customer and up sale the loyal customer. Our next use case and demo related to quick order using Nespon Einstein CoPilot feature.
[10:33]So our next use case is Nespon AI quick order, as we already mentioned like we have configured or we have customized the Einstein Co-Pilot. With the help of this Einstein Co-Pilot, less experience can also generate the order. So usually, the telecommunication products and services are address specified. So agent need to verify whether the products or services are available in their region. So Nespon has developed an Einstein Copilot feature that enables agent to check the availability of the product or services against the specific address. And once the customer has selected the product, the agent can easily create order with the help of Einstein Copilot. Even less experienced agent can also create order in real time. Typically, customer call the agent and inquire about the internet products and services in their region. Now, we can show you how it works.
[11:45]So in this scenario, the agent use Einstein CoPilot to obtain a list of the services or product against the customer address. So as you can see like the Einstein CoPilot available in this order, so agent click on this icon, Einstein icon and a windows will be pop up. The journey will be same like agent call to the customer and ask about the technology type. So agent just provide the instructions like show me the technology type for the address. So agent prompted a response like please provide the address against which you want to know the technology. So now agent shared the address, which shared by the customer. So now we can see like agent review the system and provide the technology type for the customer address and the type, the the technology type which is available is fiber. Now the next question from the customer, what is what are the products available against the address? So again, agent add the question and Einstein analyze the request and check the system and provide the available product for this selected area. So now we can see like Einstein retrieve the products, those are available against the address. So the first product is Revolution internet fiber, which include multiple child products like internet and mobile bundles. So this is the bundle product, so that's the reason it includes multiple child product. So this is the first product and if we scroll down, so we can also see like it's also grab the Revolution 2 gig internet as well. So now we are assuming customer has selected the Revolution internet. Now they want to know like what are the additional products available for this Revolution internet. So now again, Einstein review the system and check the available product for this selected area. So now agent will provide the product like Revolution internet and hit the submit button. So now again, it analyze the request and from a message, please provide the required fields to create an account. So as we can see like the first name, last name, email and mobile are required to create an account. So we can provide the we can get the information from the customer like the last name is Johnson, the mobile number and the first name is like Louis. And in the same manner, we can provide the email address like Johnson Luis@gmail.com. So after providing this information, we can hit the submit button. So with the help of Einstein, we are able to successfully generate the account. So as you can see like the Luis Johnson account has been created without any issue. Now, the next step is to place an order with the help of Einstein. Again, agent provide the instructions like please create an account for the Louis Johnson. So now whatever the product customer selected, we need to provide the product information. As we already aware like the additional product selected is Revolution Security System and the main product is the Revolution internet. So once we hit the submit button, so a successful order will be generated without any error or issues. So as we can see like the order 0000338 has been created and the account is Louis Johnson, the order type is new install and we can also see the product those we selected during the journey. So from the same Einstein window, we can reach out to the order detail page where we can see the order information and the product information. So as we can see like all the information has been successfully inserted and less experience agents are successfully able to create the order with the help of Einstein Co-Pilot. Now, let's move toward the next use case, which is billing inquiry.
[16:53]So this is another like the third one use case, in which we have also configured the power billing inquiry capabilities. So in this journey, Nespon has configured AI powered billing inquiry to help agent identify the root cause of billing problems and provide customers with clear explanations or solutions. Generally, most of industries are using external billing systems means like a lot of different organizations we work with them. So normally they are using the custom systems to store the billing information. So Nespon can integrate their billing systems with this AI powered feature with some customized solutions. So we will now present the demo for this.
[18:04]So the customer Austin contacted the agent and want to know the reason for the high bill. In the right panel of the page layout, we have added the Einstein AI billing component that helps agents to inquire about high bill, charge inquiry and the charge dispute. So this are the three things available. For the current use case, we received a query from the customer about the high bill. So the agent click on the preview button against the billing record. And it will provide us the summary and transaction detail of the record. As we can see in the summary section, customer met multiple international calls to Singapore. We can also see the top five charges, those are responsible for this high score. So the customer met international call on 6th August and the call duration is 16.89 and the amount is 30.23 dollar. And we can also view the multiple customer transactions detail as well. So agent can describe this detail to the customer so they can easily understand like what are the reasons he received the high bill. So in the below section, we can also see the recommendations that help customers to reduce the high bill. So customer can use like one of the recommendations like customer can use some internet packages or some they can also purchase some international plan, calling plan, which can reduce also this bill. So there are few recommendations, those added by the Einstein like they can schedule some call during the off peaks, so this are the recommendations like agent can share with the customer. Usually, telecommunication industries are using as I mentioned, they're using some customized system. So we at this point, this AI powered feature retrieving the data from the Salesforce. But if someone is using some external systems like Snowflake or any other system, so we have the capabilities like we can integrate that system with this AI powered billing system. So they can also their agents can also grab the information of the billing and share with the customers and explain the reasons to receiving the high bills. So this use case has been completed, so we can move to the other one.
[21:16]So now this use case is related to the data cloud. So basically, we are discussing here about the customer 360 view. So Nespon has integrated CRM Salesforce and external systems like Snowflake in the data cloud. It also bring different sets of data into one place and provide a 360 view of the customers. This enable companies to better understand your customers and personalize their experience. It also unify and segment the data for better insights. Additionally, Nespon has the capability to integrate any external systems and CRM by using data cloud. So this is the architecture diagram of our solutions that shows how external systems and CRM connected in data cloud to provide a 360 degree view of the customer. Let's move again towards the demo.
[22:38]So we are taking the example of one of the telecommunication customer who is using Snowflake to capture the billing data. However, it became challenging for the agent to retrieve this billing information due to complexity of access different systems. So as you can see like the customer is storing their billing data like about the total amount, the paid amount and the pending amount. All the billing informations related to the orders are capturing in this snowflake. But the other data related to the account, order, and the other sales related data is captured in the CRM. So basically, we have already created a relationship between this data and CRM data. So now to capture the Snowflake data in data cloud, we have created a data stream. And in the data stream, we need to build the connection with the Snowflake. And once the connection has been established, so we have the options like we can grab or we can map the data of the Snowflake with our data cloud objects. So this is the first part like we have built the connection of the Snowflake. Now the other journey, we also need to bring the data of the account and order from the CRM. So again, we also, we have already built the data stream for the account object and also for the order object. And once the stream has been like connected, so we retrieve the object and map with our data cloud object. So now the other part is like we have the data, but we need to merge or unify the data in a single place. So we can show our client like in the form of dashboard. So basically, we worked on this data, we are create some identity resolutions by which we are applying some matching rules and some mapping between the fields and then we are successfully able to create this dashboard. So in this dashboard, the agent can see a unified view of the customers. So as you can see, we have several customers available on this like dashboard. So if we take the example of Green field, so they purchased super bundle and the cost is like 25k. So they have paid the 10k amount and the pending amount is 15k. So the data is on fingertips of the agent. So now, with the help of this data clock feature, agent can explain any query or solve any query of the customers. So the other thing is like we can also summarize the data like how many orders amount we have and how many customer paid and how many amount is pending. So this all are the data, we can also grab it in the data cloud with the help of this feature and share with the executive staff.
[26:27]So basically, this are the things like this are the connection that we explained about our use case, but we have the other option as well in the data cloud. As you can see, if a customer have multiple Salesforce, so with the help of this data stream, we can connect multiple ors and organize the data and share with their management. So this is the one feature, but let's say if someone is using some external systems like as I already told you about the Snowflake, Amazon, Google Cloud, so there are multiple connectors available in data cloud, which help us to connect their external systems without any integration. And then we can harmonize the data and unify the data and share with them with the agents and the management. And the other question that that's come up in everyone mind like if some system connectors are not available in this layout, so how we can connect it? So Nespon has the solution like we can build a customized solution for them. We can use some Salesforce core API and we can also use Mulesoft that enable their external systems to connect with the data cloud and after connecting the system, we can harmonize the data and we can unify the data and send their data for the marketing purpose or different purposes. So again, the this is the first part that I explained related to retrieve data from different systems, but we have also option. We can send data to different system as well from the data cloud. So with the help of this data action, we have the functionality like we can harmonize and unify the data and segment the data. Once the data has been segmented, so we can send this data to the CRM as well as in the marketing cloud. So once uh like the connection has been established, so we can send data to marketing cloud and that data, that segmented data can use for the personalized compa. We have covered several data and AI use cases and demos and our team is fully equipped to develop any business requirements related to data cloud and AI. By this, we have provided multiple solutions for our customer. That's all from my side about to you Mama.
[29:47]Thank you so much Toki for the informative session. Let's move towards the Q&A segment. Please everyone note down your questions on the Q&A bar. Okay. So the first question is yes. So if some organization is using external system to maintain the product, how Einstein co-pilot help them to retrieve it? Okay. So as I mentioned, we have the capabilities, we can integrate our system. So in the Einstein Co-Pilot, we have the ability, we can use our Apex classes and rows that help us to integrate and retrieve the data from the external system and we can show in real time to the agents. Okay. So, is it possible to send the data from data cloud to an external system? Yeah, exactly. We have like different options for the CRM and the marketing cloud, we have the out of the box feature available. But for the external systems like Amazon or any other systems like Google Cloud, so we can build Salesforce core API or the other option is Mulesoft. So with the help of this Mulesoft or Core API, we can build a customized solution that send data to the external systems.
[31:18]Okay. So the next question Toki is can Salesforce data cloud integrate with non Salesforce system?
[31:31]Can you repeat again, please? Can Salesforce data cloud integrate with non Salesforce system? Yeah, exactly. Like as I mentioned, we have multiple options available. First of all, we can look for the out of the box connectors. Otherwise, we can go with the customized solutions like Mulesoft or Salesforce for API.
[32:01]Okay. So next question is, can Einstein Co-Pilot be used to draft promotional email and send them to the appropriate customer? Yeah, basically, we have the historical data of the customer. So depending on the historical data, we need to educate the Einstein like adding some questions. So like it recognized the client like if the promotion match with the customer history, so definitely, it will draft the email and send to the customer. Okay. Thank you so much for the answers, Toki. And thank you so much for taking your time. I know we are running a bit late, but thank you so much for such an informative session. A special thanks to everyone who joined and stayed through the entire session. We hope you found the informative information valuable as you explore the world of data and AI. Wishing you all the best. Thank you.
