Netsuite states these new capabilities deliver the insight, interaction and automation customers need to grow their business, adding growth cannot be achieved through backwards-looking data, even with modern business intelligence systems.
Evan Goldberg, NetSuite co-founder, and executive vice-president of Development for Oracle NetSuite, took to the stage at SuiteWorld 2018 in Las Vegas today to launch the new functionality.
Machine learning is a field of artificial intelligence where systems learn from data and improve with experience.
The algorithms may be very complex mathematical applications such as logistic regression, neural networks, centroid clustering or other such lofty terms. Companies that wished to apply machine learning would previously need data scientists on the payroll because it is they who had the knowledge of which type of algorithm made sense for a particular problem.
NetSuite’s new intelligence seeks to simplify this for business users, doing all the work for them. For NetSuite, it’s all about the data – NetSuite has rich data across your organisation for all modules where you use it, and this is magnified over 40,000 customers providing anonymised data about all sorts of industries.
The new intelligent features will not need NetSuite users to know the algorithms being applied, or even that an algorithm is in use. All the user needs to know is NetSuite will begin making recommendations or delivering search results in new ways, or other things that give helpful results based on rich analysis of existing data.
Goldberg explains intelligence will be applied across NetSuite in three ways.
- Intelligent insights – identifying non-obvious correlations that might happen next. For example, NetSuite will warn you a customer payment is likely to be overdue based on past history with that customer, or it may advise a project is over-budget.
- Intelligent automation – learn what people do, and do it for them. This means NetSuite can start approving things that are always approved, or similar actions. It will present unusual cases, allowing users to focus on the things that need their time and attention.
- Intelligent interaction – forms will prompt if an input seems unusual, for example advising you have set the default order priority to one for 87% of wholesale customers with spending over $50,000 but on this customer, you have not.
An example in practice is “searchandising”, an e-Commerce capability to curate results for maximum impact. Goldberg explains a retail clothing store may sell jackets. It wants people to search for jackets on their online retail storefront, and it wants the system to present results which people are most likely to buy. Simple keyword searching won’t cut it; instead, best results are achieved by learning and continuously improving to ensure the results are ordered according to what will sell.
NetSuite will achieve this, Goldberg says, by leveraging all the commerce data in the suite, including search behaviour and ordering history, to predict what people are likely to buy. Everything the shopper does becomes a signal back into the system, continually optimising search results. The shopper may simply skip over a recommendation which is a negative indicator. They may view it but not buy it which provides a small positive indicator, or they may buy it which is a strong positive indicator.
“A pioneer in the cloud, NetSuite has long been a leader in providing powerful business intelligence across its suite,” said Jim McGeever, executive vice-president, Oracle NetSuite. “With new artificial intelligence and machine learning capabilities within NetSuite, we’re equipping our customers to understand not only what’s happened with their business, but what will happen in the future and how they can stay ahead.”
Other examples include
- Finance and procurement professionals: AI and machine learning enable financial professionals to improve audit risk analysis, analyze past payment history with vendors and customers and enhance cash flow predictions, a key pain point for growing businesses.
- HR professionals: AI and machine learning enable HR professionals to create profiles of the best candidate based on existing top performers, predict high performers who might be a flight risk and better automate employee self-service by identifying what questions employees might have based on role, time of year or other factors.
- Supply chain professionals: AI and machine learning offer supply chain professionals the potential to not only identify risks or potential upcoming problems in the supply chain but, as it learns, provides potential solutions.Manufacturing Professionals: AI and machine learning help manufacturing professionals optimize labour schedules in the warehouse based on past performance or predicted demand and identify machinery in need of maintenance before it becomes a problem.
- Commerce professionals: AI and machine learning help commerce professionals significantly boost searchandising and improve online sales and conversions by serving up products customers are more likely to buy based on key indicators such as past purchases, search history and results of similar buyers.
- Customer services professionals: AI and machine learning provide customer service professionals with more accurate results around total customer lifetime value by using predictions of costs to the support organization, anticipated satisfaction and information on a customer’s likelihood of renewal.
- Marketing professionals: AI and machine learning help marketers improve campaign optimisation by identifying what type of campaigns lead to a conversion with what frequency and what type of sale based on demographics, a profile within the customer base, and activity on the website, at events or other available data points.
- Sales professionals: AI and machine learning for sales teams support intelligent interactions guiding agents through the sales process, personalising it for the prospect, the product and upsell, and cross-sell opportunities.
The writer is attending SuiteWorld18 as a guest of the company.