The megatrend of machine learning is critical to Salesforce and its ability to compete in the sales and customer service space due to a variety of reasons that ultimately link back to the growth strategy of the company. The bulk of Salesforce’s growth strategy is focused on extending, expanding, and improving on its current positioning in various aspects of the business. Cross selling and upselling, extending existing service offerings, reducing attrition, strengthening their service offerings, and improving go to market capabilities are all integral to the future growth of the company and Salesforce has correctly identified an effective way to execute that vision via the widespread adoption of machine learning processes across their array of products . The ability to improve and personalize their products and services, develop proprietary AI processes and metrics, enhance their customer service capabilities, and utilize this type of analysis is a key skill moving forward in the ecommerce and ecommerce support space where a culture of self-improvement is paramount to staying relevant.
In the short term, Salesforce has already released Einstein, an integrated set of AI technologies, and worked diligently to establish this flagship program as central to the Customer Relationship Management software it sells. It works to analyze and adapt to client behavior over time to provide a better sales platform experience. Additionally, Salesforce has also released many early betas of different extensions of the Einstein platform like Einstein Sentiment, which works to classify the “tone” of text communications like posts or comments to better understand feedback, Einstein Intent, which classifies inbound customer support queries to assist with customer service experiences, and Einstein Object Detection, which helps create applications capable of recognizing images and objects which could aid in the restocking of store shelves . In the medium term, Salesforce is working to extend their vision by continuously improving their ecommerce cloud via machine learning to unify cross channel operations. Salesforce believes that 20% of future jobs will be within the Salesforce economy and platform and in order to establish that dominance, they are figuring out betters ways to embed machine learning algorithms in their processes to personalize the shopping experience for their client’s customers . Additionally, they are capitalizing on their current momentum by extending long term relationships with key opinion leaders like Dell to overhaul their customer interactions and make them more predictive. This also has the added benefit of creating multi-channel experiences for Dell and emphasizes the inherent strengths of Salesforce’s architecture .
To further improve upon this success, I recommend Salesforce constantly reassess the ability to extend their newfound machine learning strengths into the other elements of their growth strategy. The solutions pursued currently all make good business sense, but in a constantly changing competitive environment new opportunities can arise quickly and Salesforce needs to be ready to execute immediately. Targeting specific vertical industries is a natural way of pre-planning specific machine learning strategies. A solution for financial services or healthcare on the shelf, but “ready-to-go“, when the chance appears could be the difference between a secured or missed contract. These processes can also be extended to aiding not just Salesforce’s customers, but to their own business tactics as well. Reducing customer attrition is a key goal of Salesforce’s growth strategy and the proprietary machine learning processes can be applied to their own client base to increase that retention in a meaningful and organic way. Finally, the risk factor of AI solutions from startup and established companies is a point well taken by Salesforce . They understand the danger that more effective algorithms pose to their platform and should adjust their M&A strategy accordingly. Without a consistent effort to find the best budding CRM technologies, acquire them, and integrate the best practices into the Salesforce environment, the company runs a massive risk of losing their standing in the long term. This also reinforces their strengths as they implement their growth strategy in the coming years.
In the context of Salesforce there are an array of further questions that merit consideration when discussing the future and relevance of the company. Most are very tactical about the specific methodology the organization should pursue while building these algorithmic processes, but the biggest two are broader in scope. First, what should Salesforce establish as the goal of their research departments moving forward-incremental improvements or great leaps forward? Second, can a bet on developing innovative predictive solutions be consistently successful for year after year?
 Salesforce FY18 Annual Report, https://s1.q4cdn.com/454432842/files/doc_financials/2018/Salesforce-FY18-Annual-Report.pdf
 Salesforce Extends Einstein Machine Learning Features for Developers, By: Needle, David, eWeek, 15306283, 6/28/2017, http://web.b.ebscohost.com.ezp-prod1.hul.harvard.edu/ehost/detail/detail?vid=7&sid=7e0c174a-9d6b-4f2f-88e9-b3a887b51ab4%40pdc-v-sessmgr01&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=123921761&db=bth
 Salesforce Extends Commerce Cloud Einstein, By: Ghosh, Sudipto, MarTechSeries, 5/17/2017, https://martechseries.com/sales-marketing/marketing-clouds/salesforce-extends-commerce-cloud-einstein-delivering-personalized-ai-powered-shopping-experiences/
 Salesforce’s Einstein AI to Power Dell’s Customer Interactions, By: Shaikh, Nahida, MarTechSeries, 5/25/2017, https://martechseries.com/predictive-ai/ai-platforms-machine-learning/salesforces-einstein-ai-power-dells-customer-interactions/