The management of customer relationships changed uncertainly compared to a year ago that created unique data-driven challenges for marketers. This digital convenience became a high priority for a rich and safe customer experience on any mobile device. Also, for those who are willing to change the brands and products to get it. The user’s shifted to E-Commerce for everything so that all the transactions can be digital and touch-free. The marketers are now looking at how artificial intelligence and machine learning can improve customer relationship management. Salesforce Research’s Sixty Annual State of Marketing Report says that about 40% of B2B marketing leaders and 38% of the B2C marketers plan to increase the implementation of AI in 2020.
Salesforce surveyed about 6950 full-time marketers from B2B, B2C, and B2B2C companies around the world. It derived that 84% of marketers used artificial intelligence in 2020 which grew from 29 % in 2018.
CMO and their marketing teams battle the challenge to deliver the results that bring revenue as the markets redefine themselves. B2B marketers focus on AI to improve the customer segmentation and lookalike audience modeling according to Drift and the Marketing Artificial Intelligence Institute’s 2020 Marketing Leadership Benchmark Report.
Other high priorities include personalized channel experience, new data insights discovery, and personalization of the overall customer’s journey. It stands to reason that AI-based CRM applications promise to enhance existing process speed and efficiency. They bring more revenue and help find new services that can sell as well. Too often the apps provide more process automation compared to AI-driven results.
Contribution of artificial intelligence in CRM:
The demand for AI-based applications and platforms is significantly growing which has led some CRM vendors to overstate the AI capabilities in their applications. This has created more hype across the CRM landscape. For instance, process automation is sold as artificial intelligence when what it does is that it performs independently simple repetitive actions and tasks. If it cannot learn from the data sets or initiate new tasks, it is not artificial intelligence.
To provide a certain context CRM is a heavy business. Gartner provides the latest market information and forecast peg the worldwide CRM market at $56.5 billion in 2019. It has placed it as the largest segment of the enterprise software market at 11.7% of global software revenue.
One way to examine whether the CRM application uses AI or is just marketing hype is by looking at feature areas individually. Each feature area assigns a grade in the list based on how much value AI delivers to marketers and the companies that rely on these applications.
AI-based sales assistants:
There is an impressive amount of hype in the CRM community regarding AI-based sales assistants that increase revenue, they are often used to automate data entry, task scheduling and fulfill routine sales force automation functions. Sales assistants or boats often rely on CRM data sets that drastically reduce the possible use cases. Any organization that considers these should provide the product area a few generations to take up a more integrated foundation in place.
Configure, price, and quote (CPQ):
As far as artificial intelligence is concerned, CPQ is another overhyped domain. Constraint-based product configuration and process automation engines dominate this domain. CPQ derives the benefits most from AI when it comes to guided selling and optimization of revenue management. For CPQ to provide maximum value it can bring, it needs integration with an ERP and CRM system. The constraint-based configuration has been there for decades, as process automation engines are. It is easier to use product configuration rules from an existing configurator compared to training the configuration models which requires a true AI-based configuration.
Cross-sell and up-sell:
Often sold as an integrated app within a configure, price, and quote (CPQ) or account-based marketing (ABM) system or platform. Cross-sell and upsell apps have progressed from simple apps CE. The apps integrate with the product catalog to advanced rules and constraint-based scenarios. These include AI-based apps that factor in the customer’s personalized preferences.
Cross-sell and upsell apps have become a great option in the ABM system for expanding sales into existing accounts. But they are limited in how much business value they can offer with the implementation of AI. In this area of CRM, process automation-based apps are sold as AI-based.
Data intelligence solutions:
Vendors that provide apps in this category move away from contact and company information towards contextual intelligence using artificial intelligence and machine language. The goals of the vendors who want to transition to contextual intelligence include supporting sales prospects and selling scenarios by using real-time data. Like some of the CRM apps, vendors in this domain do not offer their own data sets. They come with limited expertise in enhancing the data quality to offer increased value out of such an application.
Sales predictive analysis which includes lead scoring:
The impact of artificial intelligence on the improvisation of sales predictive analytics is evident in how effective these applications are in guiding sales representatives, sales leaders, and sales operations decision-making to enhance margins and revenue. The best apps in this category use machine learning to determine new insights in the account, sales, and revenue data.
Sales predictive analytics’ AI-based capabilities can contribute to predictive forecast, pipeline inspection, opportunity, and lead scoring.
AI- and ML-based quota planning apps are sold as a part of the integrated sales performance management (SPM) platform as a stand-alone product. Most of the apps under this category support collaboration and workflows to define accurate optimal sales quota. The best apps under this category support mathematical modeling approaches to assign quotas across the organization.
AI and ML algorithms define optimal quotas distributed across the organization. The factor that limits the AI’s potential to deliver value in CRM is the lack of consistent, high-quality data. The amount of contribution AI-based apps and platforms provide as a part of a CRM system is heavily dependent on the quality and availability of integrated data and less on the features of the app.
Marketing organizations are known for having data quality problems as data governance is not a core strength of the department. There could be data structures, taxonomies, and meta tags that conflict with each other. Also, they show a lack of consistency across all the databases. Yet, it could be a hurdle that is too much for marketers to clear. But each of these areas has the potential to provide increased value in CRM once they overcome the data quality challenges.