Machine learning is transforming the sales industry by enabling data-driven decision-making. According to a study by Harvard Business Review, businesses perform better when they use data-driven decision-making. Data-driven decision-making is the practice of basing decisions on data analysis rather than intuition. These data-driven companies that have pioneered the use of AI in sales are raving about the results. Results which include a more than 50% increase in leads and appointments, cost reductions of 40%-60%, and call time reductions of 60%-70%. Add the value provided by letting sales professionals spend more time completing transactions, and AI's allure gets even more robust.
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Machine Learning & Artificial Intelligence, in general
AI and ML have the ability to reduce the most time-consuming, manual activities. These are activities that prevent sales teams from spending more time with clients. Additionally, automating marketing processes using predictive analytics, forecasting, reporting, and advising. Therefore, helping clients to upsell. These are all approaches that reduce sales teams of labor. So, by using data-driven decision-making, businesses in the top third of their industry are, on average, 5% more productive and 6% more profitable than their competitors (McAfee and Brynjolfsson, 2012).
Top-performing Machine Learning Models
Caruana and Niculescu-Mizil, 2006, analyzed the top machine learning models with higher performance and also those less significant models commonly used in sales. This research shows the top-performing machine learning (ML) models, including random forests, boosting, support vector machines (SVM), and neural networks. These models achieve significantly better predictive performance than simple, interpretable models such as decision trees, naive Bayes, or decision rules. Companies that do not use the above-open-sourced models use their own patent AI and ML models.
The race for AI and machine learning patents streamlining selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The US National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting.
The study finds that patenting in machine learning has seen exponential growth since 2010, and Microsoft had the most significant number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and IP search, IAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075. The study relied on PatentSight's Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects the competition for patents and intellectual property. (Dec 26, 2018 - Forbes)
Machine Learning & Artificial Intelligence at Dealcode
Dealcode's patent AI and machine learning model extract data from CRMs, powering its AI Guided Selling Software. It is a predictive analytics tool that determines the winning probability of prospects and risks in the selling pipeline. It provides sales teams with up-to-date information on what deals they should focus on and who to talk to urgently. The patented machine learning model performs predictive analytics by analyzing sales processes. Dealcode determines individual factors that contribute to the success or failure of a sales team. As a result, it makes sales measurably more effective. In addition, it saves cost-intensive resources for complex data analyses.
What does Dealcode provide?
Using past data/ information, it estimates deal scoring and nurturing based on its AI and machine learning algorithm. This software supports sales reps in turning prospects into deals and strengthening their sales pipelines. Prospects and Deals vary from each other, be it the client's need, engagement, behaviour, or even their demography, and all these factors affect the end results. Therefore with this in mind, each prospect should be looked at individually when working on deal scoring.
AI-Based Deal Scoring
Dealcode trains its models to identify prospect characteristics that lead to successful deals by using data from integrated CRMs such as Hubspot and Pipedrive. The machine learning algorithms are continually improved to achieve better results, resulting in a more reliable deal-scoring process. This deal scoring system helps your business realize its likelihood of closing deals and the risks involved in the deal. The system can predict the results of a deal in question, ensuring maximum accuracy as it is solely data-based. The AI-Based deal scoring capability is used by companies using Dealcode's software to rank leads based on their closing success potential, placing them ahead of their competitors.
Predictive deal scoring is helpful and a must-have capability for positioning your organisation to be sustainable. It has done most of the heavy lifting regarding data analysis and selecting which prospects are most likely to convert. Focusing on the essential prospects allows sales reps to close more deals in less time while also developing stronger client relationships.
Smart To-Dos
The smart To-Dos that Dealcode provides by using its ML algorithm improve the sales team's productivity by analysing the most effective actions and behaviour that lead to more successfully closed deals. These actions and behaviours are then suggested to the users, ensuring a higher success rate. The data-driven sales contact and customer predictive analytics consider all contact sources with customers and determine the most effective action. Knowing which actions and behaviours are correlated with the highest close rates, sales reps use these insights to scale their sales teams to higher performance.
Are you unsure which offers to follow? Would you like to save time as a sales rep? CRM connectivity, deal manager (showing deals at risk, deals to watch, new deals, open and closed deals), smart ToDos, and AI analytics (deal winning probability over time), among other features, help you save time. To summarise, Dealcode provides outstanding capabilities that organisations can use to increase revenue and improve their financial situation while saving time.
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