Many organizations struggle with both identifying and prioritizing what sales leads to pursue. Where do you start when you have a large stack of leads to go through? What do you when your leads have gone cold?
For Product Leaders, it’s often a challenge to get a broad spectrum of feedback from their customers. How do they know where to focus next? How can they truly confirm their ROI? How can they improve the product with certainty to drive more revenue?
In this post, I’m going to share the details on a very cool pattern that my team and I got hands-on with. This solution pattern leverages many powerful capabilities of the IBM Watson Platform to optimize your product and sales process. In this example, I will show Watson embedded in the CRM platform of Salesforce. Please note that you can implement this same pattern in other CRM platforms, such as Oracle, SAP, and others.
Below is a demonstration of this solution pattern in action:
Here is an end-to-end view of the solution pattern workflow:
Let’s start on the sales side — As a seller, you strive to pursue the highest quality leads, as quickly as possible. Watson Discovery can generate new leads from potentially untapped resources of millions of news articles, updated daily. This allows sellers to access leads on-demand.
Below, you can see Watson embedded in CRM, allowing the seller to specify exactly what types of leads they would like to pursue. A seller can provide any text as their search string and Watson will understand that natural language query. One can also specify company names to filter in/out, select the number of results to return, and choose an industry to target. All these options are made possible by Watson Discovery.
In this example, let us target the retail industry. I am looking for new businesses that are set to open in the city of San Francisco. Once I click on “Generate”, my query is sent to Watson Discovery News.
I selected to return 5 leads, but in my result set below, you will see 4 leads. There is a feature that removes duplicate entries from the result set. You can also view the date the news article posted and a preview of the content. I will select the “Decathlon” lead and import it.
Once you click on import, the Watson news article is automatically converted into a Lead record in CRM. Watson will provide as much information from the lead that is available. Below are some basic data fields derived with Watson AI such as company name, industry and location information.
Watson provides other information that will be helpful to determine if this lead is worth pursuing, such as the article title and description. You can view more information on the lead source by clicking on the article URL provided by Watson.
Yet, there is still plenty of other data that can be analyzed related to this lead that can help Watson’s prediction. For example, you can contact this lead via phone or web survey to better assess their interests and needs. In this example, I have contacted the lead for more feedback. In their response, they provided the job family they belong to, their time frame to make a decision, and if they have an existing solution.
The key data point here is the “Key Objectives Input” field. This gives the customer an opportunity to state what they are looking to achieve. In this pattern, using the Natural Language Classifier service, Watson analyzes the unstructured text and classifies the intent to a structured field. In this case, security is this customer’s main goal.
In this pattern, we have trained a machine learning model using Watson Studio and AutoAI. We deployed the model in the Watson Machine Learning service. The goal is to get a prediction on the likelihood of leads converting to won opportunities. Most of this data you have seen thus far is used by Watson to predict how likely this lead is to close as a win.
Now I can sort my leads by the highest probability to close as wins, thanks to Watson. As a seller, I would start at the top of the list to review and pursue further as an opportunity.
Let’s fast forward the sales cycle where the lead was converted to an opportunity and closed as a win! There are always lessons learned and areas for improvement. After the opportunity is closed, sellers will obtain post-sales feedback. This can be an in-person conversation, a phone call, email, or other. That feedback contains lots of helpful information for various stakeholders in your organization.
Below is an example of some post-sales feedback received that was analyzed by Watson. Using the Natural Language Classifier service, Watson converted unstructured data into structured data. That data can be aggregated into reports and rolled up in dashboards. Here are three examples of the feedback obtained.
Sales Cycle Recommendation — this guides Sales Leaders on improvements they can make to their sales process. In this example, the seller could use some coaching on better communicating with clients.
Product Gaps — this helps Product Leaders understand what areas they need to focus on, to improve their product. In this example, the customer provided feedback that the product pricing is higher than competitors. This could be an indicator that the Product team needs to reassess their pricing strategy.
Top Features — Product Leaders want to be aware of what is going well with their product to justify their ROI. It is also confirmation that the product is heading in the right direction.
So now, what do we do with all this data for each individual opportunity? How do you make it consumable to sales and product leaders? How does one learn from it? How does Watson continue to get better?
All of these closed opportunity records, both wins, and losses, are ingested into Watson Discovery for more AI analysis. Watson Discovery will enrich each opportunity record with its out of the box Natural Language Understanding model. It will also identify more patterns within the unstructured data.
Watson has done all the heavy lifting of analyzing large amounts of data. Those data points now reside in Watson Discovery and can be queried to create reports and dashboards to be shared with stakeholders. Below is a simple dashboard in Salesforce that shows some key areas for the organization to focus on.
As a Sales Leader looking at the “Sales Feedback” report, I can clearly see that I need to provide some coaching to my team to improve their demeanor with customers. Watson’s insights show that this was the biggest factor that led to lost deals. For more detail, I can drill down into my reports to look at individual record data for specifics.
As a Product Manager looking at the “Product Gaps” report, I realize that I need to revisit my User Interface component. In contrast, looking at the “Top Features” report, Watson is showing me that my customers are enjoying the reporting component of my product. That’s a good confirmation of our ROI for that component.
Finally, this pattern implements continuous learning. Once opportunities are closed, lead and opportunity data is automatically fed back into Watson’s machine learning model. This ensures Watson continues to learn to get better. There is nothing required for an end-user to do in this mode of unsupervised learning. Full automation.
Just a reminder that though this example was shown using Salesforce as the CRM platform, you could implement this pattern with other CRM platforms as well. Be sure to use our official IBM Watson SDKs to reduce your development effort.
To learn more about leveraging the IBM Watson platform, contact your IBM sales representative.