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Machine Learning in Sales - What do Dealcode’s models do?

August 16, 2022

AI Guided Selling

Machine Learning in Sales - What do Dealcode’s models do?

If you're not using data to drive your decisions, you're falling behind. Read why data-driven decision-making is essential for businesses of all sizes and which solutions Dealcode provides.

Machine Learning, Artificial Intelligence, AI Guided Selling, AI in Sales, Sales Tech

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, 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.

Machine Learning & Artificial Intelligence in general

AI and machine learning have the ability to reduce the most time-consuming, manual activities that prevent sales teams from spending more time with clients—automating marketing processes, using predictive analytics, forecasting, reporting, and advising, which help clients to upsell. These are all approaches that reduce sales teams of labour. 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).

Caruana and Niculescu-Mizil, 2006, analysed the top machine learning models with higher performance and those that aren't as 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 flourished 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 is an AI Guided Selling Software that extracts data from CRMs, running its patent AI and machine learning model. 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. This predictive analytics is done by analysing sales processes using a patented machine learning model. 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, to saving 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.

Using data from CRMs (Hubspot, Pipedrive - soon, more CRMs to be integrated), Dealcode's models are trained to identify characteristics that lead to a prospect turning into a successful deal. Dealcode's machine learning algorithms are continuously trained to achieve better results with a better and more reliable deal scoring process. This deal scoring system helps your business realise 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.

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.

Would you like to know more about Dealcode Software (Start for free)? Connect with us and learn more about Dealcode (Talk to our Product Expert)? Be a part of the Dealcode community and Friends (Events and Hangouts)?

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