Businesses today can gain a competitive advantage by informing their decisions using data analytics. Unfortunately most domain experts are not well equipped to do data analytics themselves due to a lack of the skills required (programming, advanced statistical techniques, machine learning, etc). Consequently, most businesses hire “data scientists” at a high cost in order to extract value from their data.
Since it’s establishment in 2006 the Early Debit Order (EDO) system has become integral to South African credit providers’ collections strategies. The system currently offers Non-Authenticated (NAEDO) and Authenticated (AEDO) collections mechanisms. The two methods differ in the way in which the client gives the mandate to debit his or her account. NAEDO uses verbal or written agreement while AEDO requires that the client use his or her debit card and PIN to confirm the agreement. Importantly both of these methods offer the option of credit tracking over periods from 1 day to 32 days. The balance of the relevant account is tracked for a specified period and the debit occurs only if the balance exceeds the debit amount. This allows credit providers to collect more effectively.
Documentation is an important part of any non-trivial technical platform. Right from the beginning it has been important to us that we provide accurate documentation for the BusinessOptics platform, all the way from modelling reference documentation through to tutorials to easily on board new users through to more advanced exercises to help intermediate level users extend their skill sets and get more value from the system.
Recently we centralised and updated our documentation. Our new documentation can be found at https://docs.businessoptics.biz. Currently our documentation includes:
Modelling and usage tutorials
More advanced modelling exercises
Best practices and guidelines
Expression language and operator reference
API Reference for developers and integrators
In the last few articles we investigated the different types of analytics and focused on prescriptive analytics. At this stage you might still be wondering “what will the benefits of prescriptive analytics be in my business?”
As stated in a previous post, prescriptive analytics is more advanced than predictive analytics both in terms of complexity and business value it delivers. The main purpose of it being able to prescribe certain actions that are expected to maximize key business metrics. Essentially Prescriptive Analytics is a tool to optimize and streamline your business and its associated processes.
The benefits of prescriptive analytics have already become apparent in a number fields including but not limited to healthcare, supply chains, insurance, financial risk management (specifically credit risk management) as well as sales and marketing operations.
Being client centric and focusing on strong customer relationships are key to every business as this allows you to maximize the customer lifetime value from your existing client base. To enhance customer relationships and drive product interest and purchases, the correct products need to be recommended at the right time.
Cross-selling includes the practice of selling additional products or services to existing customers. The aim of cross-selling is to increase the profit derived from customers and also to keep the customer satisfied such that their needs are met.
Cross-selling is desirable for most businesses as selling an additional product to an existing customer is cheaper than the cost of selling the same product to a new customer. However unlike the acquiring of new business, cross-selling involves an element of risk that existing relationships with the customer could be weakened. For that reason, it is important to ensure that the additional product or service being sold to the customer closely meet their needs.
Companies around the world rely on strong supply chain management to ensure profitable and sustainable businesses. Their practices help reduce order-to-delivery time, improve sales and operations (S&OP) planning, improve financial performance and build trust among suppliers, amongst myriad other benefits. There are, however, many factors that can negatively impact the efficacy of supply chains. Examples of such factors include:
Our customers are continuously dealing with larger and larger data. As data sizes have increased so have the sizes of results from BusinessOptics models. Over a certain size investigating the results of models on a row level basis becomes impractical and the modeller needs a quick and flexible way to investigate them at a more…
On the 8th of July 2015 the Judge Siraj Desai (of the Western Cape High Court) handed down an important judgement regarding the legality of the way in which Emoluments Attachment Orders (EAOs) are currently being administered. The ruling draws attention to the abuse of the administrative processes surrounding EAOs by unscrupulous collection agencies. It further finds that certain sections of the administrative law around EAOs is unconstitutional. This ruling will almost certainly result in changes to the way that EAOs must be used by debt-collectors. These changes are likely to impose a greater burden on the collection agency, possibly even making the orders unusable as a debt-collection mechanism.
Businesses face complex problems which can be addressed by taking one of many courses of actions. While predictive analytics helps model the likely future impact of an action, prescriptive analytics aims to show users how different actions will affect business performance and points them toward the optimal choice.
Typically, prescriptive analytics establishes a comprehensive description of the business process, models selected process, and specifies the business outcomes to be analysed.
Prescriptive analytics incorporates a variety of components to optimize business outcomes. Such outcomes are achieved by combining an organization’s historical data, machine learning algorithms, business rules and the business process model. Prescriptive analytics models can be used to inform individual manual decision making, or be embedded into operational systems to support automated real-time decisions.
How Sensors, When Combined With Prescriptive Analytics, Can Assist In Planning For Perishable Goods Supply Chains?
Supply chains of perishable goods are becoming increasingly complex due to competition and broad consumer advocacy about what goes into the production of food (organic vs non organic, use of artificial flavourants and trans fats). Companies are striving to provide high quality products at competitive prices. These companies need to optimize their supply chain to minimize the production cost, delivery cost and the amount of spoiled inventory whilst ensuring various quality standards are met. Risks threatening the supply chain business objectives can be mitigated by collecting data using various sensors at the different stages of the supply chain, analyzing the collected data and using this as input into sophisticated analytically based planning in order to make smarter decisions.