Sales and marketing generally finds significant number of startups and solutions coming up every year. Question generally that we encountered is: is there a true case of artificial intelligence in sales and marketing? Here is a quick analysis of status quo.
Analytics driven Use cases in B2b marketing are put in two categories from my side of the world.
1. External Data driven 2. Internal Data Driven and a combination of those two.

External data driven use cases are the category where B2b marketing teams acquire data from third parties or collect them over years. B2b marketing teams are heavily depended on this data source type. External data is costly. It is also unstructured and hence painful to mine. Startups struggle to acquire quality data. Data with high degree of accuracy does not come free or takes time to be mined. This is one of the reason for data platform Quandl’s high usage.  Aggregating various text heavy data sources take significant time, technological energy.

Identifying the right external data is another pain. News, press releases, job postings, list of conferences attendees, stack overflow postings are the current favorite of predictive marketing companies. External data is generally available for large organizations. Small and medium businesses don’t publish news and press releases as frequently as large organizations do.

Text mining algorithms are being applied to news and other data sources of that type. OceanFrogs platform is built on NLP algorithms. This is one of current darling of data scientists trying to find insights for b2b customers. This field of AI has potential and applications useful for marketing field.

Most sensible use case that is not a data consolidation exercise and can be called an AI exercise is the recommendation of product and services to a prospect and/or existing customer. This fits well to B2B and B2C businesses. It has found more traction in B2C businesses because number of prospects there are high. Challenge in achieving success in this use case is – how to gather data (B2C case) , Do we have enough data (B2C)?

Another promising and solved (well) problem is to identify the root cause analysis behind customer wins and losses. One can apply various algorithms there. ROI on applying AI can be found if there are hundreds of accounts or there are good number of accounts with good history. Otherwise, it can be a reporting exercise. Good aspect of this use case is that data can be available from CRM. This use case also fits well to B2C businesses such as banking, telecom (Airtel is calling you based on the results of this use case). You might say this is a classic ‘Customer Churn’ problem. I agree with you. Techniques to solve this problem might have changed over years. Retail, banking, Credit Card Companies have nailed this down years back because they had the data. I am not going too far when i say that election results can also be analyzed using similar AI results.

External data driven use cases are still in data mining stage in my opinion. Advance analytics will come later when there is significant data.

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