Wide Variety of Data Analytics Possibilities: Purchase

There are 7 Key Steps of a Procurement (Purchase) Process

Step 1 – Identify Goods or Services Needed

Step 2 – Consider a List of Suppliers

Step 3 – Negotiate Contract Terms with Selected Supplier.

Step 4 – Finalise the Purchase Order

Step 5 – Receive Invoice and Process Payment

Step 6 – Delivery and Audit of the Order

Step 7 – Maintain Accurate Record of Invoices

 

Procurement analytics is the process of collecting and analyzing procurement data to form meaningful insights and aid effective business decision making. Examples range from simple spend analysis reports in Excel to predictive analytics software.

Procurement analysis typically involves collecting data from several different source systems, classifying data to standard or use-case specific taxonomies, and displaying data in a visualization dashboard or within business intelligence tools.

The need for procurement analytics has developed from many organizations’ desire to get a consolidated view on procurement spend. Initially offered through one-off projects such as spend cubes, procurement analytics has evolved to encompass a number of specialized solutions, dashboards and types of automation software.

For many businesses, analytics is about much more than data visualization. One way to think about procurement analytics is that it’s like refining oil. It’s about collecting, cleansing and enriching large amounts of data from disparate systems to create business value. In procurement analytics, value comes from more timely, accurate and actionable insights, and the ability to measure procurement’s contribution to the bottom line.

Procurement organizations can utilize analytics to describe, predict or improve business performance. When utilized effectively, procurement analytics can enable data-driven decision making, where purchasing decisions and supplier relationships are managed more effectively.

 

Types of Procurement Analysis

The field of procurement analytics has emerged from the need to understand past procurement performance and guide future decision making. Common types of analysis in procurement include:

1. Descriptive Analytics – where procurement data is analyzed to describe what has happened in the past.
2. Diagnostic Analytics – where procurement data is interpreted to understand why something has happened in the past.
3. Predictive Analytics – where trends and patterns in data are used to forecast future procurement performance.
4. Prescriptive Analytics – where predictive models based on procurement data aid decision making.

 

A.  Commodity Analytics

For commodity organizations, advanced analytics ties directly to two major corporate objectives:

·         REVENUE: When implemented, organizations can use advanced analytics to drive revenue growth by directly applying advanced analytics to corporate strategy, assets, and operations.

·         COST: Organizations utilizing advanced analytics can drive earnings growth by directly applying advanced analytics to all financial and operational decisions

 

How Can Our Organization Utilize Commodity Analytics?

        I.            Optimization

      II.            Valuation

    III.           Digitalization

    IV.            Visualization and Reporting

      V.            Machine Learning and Artificial Intelligence

 

1.       Optimization

Advanced commodity analytics directly drives four major optimization initiatives:

a)      Trade and Deal Optimization

b)      Portfolio Optimization

c)      Logistics Optimization

d)      Asset Optimization

a)      Trade and Deal Optimization

Two major trade and deal initiatives

              i.            PRE-TRADE ANALYSIS Using advanced analytics, organizations get increased profit per trade, an increased number of profitable trades executed, and a positive impact on recruiting and retention of top traders.

             ii.            STRUCTURED VALUATION Organizations utilize advanced analytics for accurate pricing and valuation of complex trades, creating a margin between market price and true value

 

b)      Portfolio Optimization

Using advanced analytics, organizations identify opportunities for trading profits, hedging and limiting risk, and creating innovation options for new initiatives.

 

c)      Logistics Optimization

With advanced analytics, logistics professionals within your organization will be able to utilize mode and route optimization to drive maximum profitability for your business. Schedulers and logistics professionals will have a massive amount of data and optimized decisions at their fingertips.

Mode Optimization Using advanced analytics, organizations can achieve maximum profitability by scheduling the right shipment within the right mode — regardless of mode type — all in real time.

Route optimization Organizations can also utilize advanced analytics to decrease costs by scheduling the most profitable movements from beginning to end.

 

d)      Asset Optimization

d)Businesses that deploy assets in the commodity value chain struggle to derive maximum profitability from a portfolio of assets, including storage, processing, power generation, transportation, and more. More and more now, energy and commodity firms want joined up decision support covering multiple assets and markets. Understanding and quantifying complex optionality within each asset and trading around the asset is difficult — unless you’re using advanced analytics.

 

 

2.       Valuation

Advanced commodity analytics directly drives three major valuation initiatives

a. Market Exposure/ Position, b. Risk, c. Optionality

Market Exposure/ Position

With advanced analytics, prices, volatilities, correlations, and forward curves are managed in multiple simulation scenarios, with validation and calibration assessments. Market data is calibrated and correlated against industry operational metrics with machine learning techniques, including vector machines, market basket analysis, and Naive Bayes algorithms.

MONTE CARLO AND HISTORICAL SIMULATION VAR For linear and non-linear instruments, organizations gain higher visibility of potential losses through active risk mitigation. They also gain visibility of changes in margin and changes in exposure through Monte Carlo and Simulation VaR losses through active risk mitigation for linear instruments. They also achieve visibility of changes in margin and changes in exposure.

Risk Valuation:

With advanced analytics, organizations will calculate all of their earnings at risk, cash flow at risk, potential future exposure, and credit with radical efficiency. Market, option, and credit risks are evaluated against incremental PFE, VaR, CFaR, EaR metrics, and changes in risk metrics are attributed to transaction and market changes. Commodity risks are mitigated by trade structures based on objectives and key results of profitability, return, and risk.

Two major Risk evaluation done are: Liquidity Exposure, Counterparty Exposure

Optionality:

POSITION VALUATION: Using advanced analytics, organizations achieve an accurate valuation of options.

 CAPITAL EFFICIENCY: The organization’s accurate valuation of options creates a margin between the market price and true value, and execution yields transactional margin.

3.           Digitalization

a.       Connectivity,  b.  Mobile, c.  Automated Value Chain

a. Connectivity:

EXECUTION SPEED AND ACCURATE SETTLEMENT Using advanced analytics, organizations get increased reliability through interface automation.

POSITION VALUATION Organizations utilize advanced analytics for reduced connectivity costs.

b.      Mobile:

Using advanced analytics, organizations get approval control for near-term transactions on mobile

Organizations utilize advanced analytics for increased speed of data capture and measurement accuracy

c.       Automated Value Chain:

With advanced analytics, your organization can align their digitalization initiatives around the entire value chain. Data will seamlessly flow from one section of the value chain to the next, and decisions at each step of that value chain will be automated.

 

4.       Visualization and Reporting

Making this kind of benchmarking available to your organization has the power to change your business for the better. You can spark new, organization-wide insights with your team. You can empower your people with accurate, decision ready data whenever they need it — on any tool. You can transform your organization by making your data easy to use and easy to understand.

With advanced analytics, commodity organizations can:

·         Harness Better Decision Making

·         Access Your Data Anywhere

·         Get More from Your Reporting Tools (Business Intelligence)

 

5.       Machine Learning and Artificial Intelligence

While implementing advanced analytics will help your organization see immediate results, the compounding value comes from data and business processes optimized over time. As advanced analytics and the digital transformation processes your organization sets in place begin to take hold, the value only increases, as machine learning and artificial intelligence begin to recognize patterns throughout your organization and automate elements of your value chain.

With advanced analytics, commodity organizations can:

·         Gather and Draw Insights from a Massive Amount of Data

·         Ensure Regulatory Compliance

 

B.  Sourcing Studies

 

Strategic sourcing is an institutional procurement process that continuously improves and re-evaluates the purchasing activities of a company. In the services industry, strategic sourcing refers to a service solution, sometimes called a strategic partnership, which is specifically customized to meet the client's individual needs. In a production environment, it is often considered one component of supply chain management. Modern supply chain management professionals have placed emphasis on defining the distinct differences between strategic sourcing and procurement. Procurement operations support tactical day-to-day transactions such as issuing purchase orders to suppliers, whereas strategic sourcing represents to strategic planning, supplier development, contract negotiation, supply chain infrastructure, and outsourcing models.

 

The steps in a strategic sourcing process:

a)       Assessment of a company's current spending (what is bought, where, at what prices?)

b)      Assessment of the supply market (who offers what?)

c)       Total cost analyses (how much does it cost to provide those goods or services?).

d)      Identification of suitable suppliers

e)      Development of a sourcing strategy (where to purchase, considering demand and supply situations, while minimizing risk and costs)

f)        Negotiation with suppliers (products, service levels, prices, geographical coverage, Payment Terms, etc.)

g)       Implementation of new supply structure

h)      Track results and restart assessment (Continuous cycle)

 

Here are seven steps that will help you transform mere procurement into strategic sourcing:

a.       Start with a big picture analysis. Look at all purchasing data, then break it down into different departments and locations. Analyze different spending categories, as well as patterns in buying. The more granular the data, the more information you have to analyze.

 

b.       Create a strategic procurement plan based on the business' needs. Create forecasts using historic data about what you’ve purchased, when, and for which locations will help guide the strategic planning. For instance, if you’ve negotiated a discount on certain printer cartridges but they’re not being used, you can direct your efforts elsewhere.

 

c.       Analyze the supplier market. What suppliers can provide your business with the Maintenance, Repair and Operations (MRO) products needed to keep buildings running and in repair? Develop portfolios about potential suppliers, including their strengths and weaknesses.

 

d.       Define your supplier criteria. Know what the business needs from suppliers so it's easier to determine who can meet the criteria and who can't. Determine what qualities are most important in your suppliers and engage them accordingly.

 

e.       Negotiate with a list of select suppliers. Reach out to a carefully chosen list and start the negotiation process for products under contract.

 

f.        Incorporate the new suppliers into your procurement program. Make it easy for contractors and employees to order from the new suppliers. This helps eliminate maverick spending and increased costs. An easy-to-use one-click procurement platform can decrease employee reluctance to use traditional (aka cumbersome) ways of buying.

 

g.       Track the performance of all suppliers. Make sure suppliers are adhering to terms and conditions and providing the optimum level of service. Continually analyze their performance, as well. If they aren’t up to par, investigate potential new suppliers who may be better suited to serve the business.

 

4 Metrics to Consider for Strategic Sourcing

Businesses need to understand what data to look at to create a strategic sourcing process. Here are four pieces of must-have data for strategic sourcing managers and why they matter.

a.       SPEND ANALYTICS

This one is crucial. Procurement teams need to know how much is being spent, when, and where. Without the data, procurement is often hit or miss, because it depends on assumptions and biases of the people doing the purchasing and installation of MRO products.

 

b.       PRODUCT QUALITY

How long are products likely to last? While some suppliers may offer lower prices, if the quality of their product isn't good, a business will end up spending much more on it in the long run. The quality impacts the total cost of ownership, and this is an important metric in strategic sourcing. Data on product quality and longevity is vital.

 

c.       PROCUREMENT AVERAGE CYCLE TIME

How long is it taking for products to get to the business from the time an order is placed? Delays from suppliers may inconvenience a building owner or manager, but it could also end up becoming a health and safety issue. Track supply rates and on-time delivery statistics, especially when evaluating whether to re-up on contracts. You can read more about why this metric matters here.

 

d.       SUPPLIER RELIABILITY

This metric is extremely important because it measures how well suppliers are fulfilling the terms and conditions of the contract. Are deliveries timely? Was the right quantity delivered? Did the right items go to the correct location? If the answer to any of these questions is regularly a “no,” you’ll need to enforce terms and conditions and possibly look elsewhere in the future.

 

As you can see, strategic sourcing is fuelled by data. If you don’t have the data you need to do this kind of detailed strategic analysis, you may want to consider a procurement platform that provides you with the information you need to make your business stronger.

For example, when you become a member of Qmerit Marketplace, you receive access to Avendra’s best-in-class contracts for MRO supplies. That collective buying power means you’ll receive greater savings from some of your everyday suppliers as well as free shipping from most suppliers.

C.   Spend Analytics

 

Spend analysis is the process of identifying, gathering, cleansing, grouping, categorizing and analyzing your organizations spend data with a goal of decreasing procurement costs and improving efficiencies. Using real-time data and analytics will give you the insight you need you to save money and gain efficiency.              

Spend analytics is a process of gathering procurement data and organizing it so that it can be visualized and analyzed. Spend analysis deep dives into procurement and expense data to analyze and understand the spending patterns of any business. It helps organizations understand their supply base, track the metrics of procurement performance and creates actionable insights for vendor consolidation to decrease procurement costs. Often the terms are used together because procurement teams review the analytics to complete their cost analysis.                              

 

What is done under Spend Analytics?

Spend analytics helps optimize the procurement performance of any organization by integrating information from spend data sources, cleaning it for accuracy and categorizing it deeply. The business, operational managers and procurement professionals are able to make better, actionable decisions for their organization using spend analysis software with reliable data with deep classifications. Cost savings are organizations’ main goals in this process. Most organizations are increasing time spent analyzing the process of expense management to better understand their spending patterns and identify opportunities to save in the future.

 

Why is the Spend Analytics important?

Spend analytics improves the following:

a)       Increases procurement teams’ visibility on spending and suppliers.

b)      Eases strategic sourcing.

c)       Mitigates risk management.

d)      Removes obstacles facing business units, increasing procurement functions.

e)      Nurtures and manages effective supplier relationships.

f)        Provides better oversight of supply chain processes.

How will Spend Analytics support Procurement?

There are three core areas to consider.

•             What is the total spending of an organization?

•             With whom is the organization spending money?

•             Is the organization getting what has been promised for the purchase?

Spend analytics, in general, is viewed as a part of the large domain which helps an organization to understand and identify the above-mentioned factors for the betterment of the organization.

A spend category is the logical grouping of items or services at an organizational level. With different analysis dashboards (Ex-here Simfoni’s Spend Category Analysis console) you can easily Analyze major sourcing categories at an in-depth level to monitor compliance and pricing accuracy.

 

 

Implementation of the Spend Analytics

To successfully execute the spend analytics process you need to consider the following steps.

1.            Retrieve spend data.

2.            Verify data accuracy – Clean data.

3.            Deeply classify the data.

4.            Use an analytics solution that provides good visibility and instant insights.

5.            Collaborate with other related departments within your organization to identify potential savings.

To benefit more from spend analysis, you have to spend more time sorting data to identify the flaws in the records. Using an AI-driven platform, such as Simfoni’s Spend Analytics, cleaning and categorization time can decrease while data accuracy increases.

To get better benefits from spend analytics you have to spend more time sorting data to identify the flaws in spending.

AI-driven platforms for Analytics

i.              H2O.ai

ii.             MathWorks’ MATLAB and Simulink

iii.            Databricks Unified Analytics Platform (Use AWS)

iv.           Microsoft’s Azure Machine-learning Studio

v.            Alteryx Analytics

vi.           KNIME Analytics Platforms

 

D. Data analytics in risk management

Following a data-driven approach is the best way of running a successful business. Data collection and analysis applies to many different areas- from understanding customer trends to reducing operational costs. But data also applies to risk management. By using analytics to assess business risk, more secure systems that protect against cyber-attacks can be developed.

Risk assessment is a critical part of implementing a data security plan. However, many companies assess risk by merely referring to previous experiences or industry trends. This approach is less specific and may result in higher costs, non-compliance with established regulations, and vulnerability to potential data breaches.

Luckily, following a data-driven approach will help your business protect customer data and assess the effectiveness of current controls. Risk assessment via data analytics will also result in the implementation of better risk management strategies.

But how can you begin to use data when assessing business risk? The following steps will help you get started.

 

1. Determine the potential risks that your business faces

Any risk assessment process begins with determining the specific risks that your company might face. Defining the scope of risks will help you follow a more targeted approach to risk management. Many businesses use regulatory requirements as a guide for setting the scope of risk analysis. You may also refer to the goals and objectives of your business to help guide this decision.

For example, if you’re handling healthcare-related information, HIPAA standards will apply to your company’s risk assessment plan. You may then choose to expand your scope based on the internal objectives that your company has set.

When setting a scope for your risk assessment plan, determine the type of data that will be affected, where it’s stored, who has access, and how it’s handled. Make sure you approach each data category individually- so that you can focus enough attention and resources to any potential risks.

 

2. Identify your key risk indicators

Similar to how KPIs help you determine performance levels, KRIs (Key Risk Indicators) assist in evaluating how specific risks can occur. In other words, KRIs point to the areas that you’ll need to focus on when analyzing data for risk assessment. They help you follow a more targeted approach that applies to compliance requirements and your overall business goals.

Identifying KRIs begins during the data analysis phase. This phase involves outlining all associated data elements- so that specific data flows can be mapped out according to the needs of your business. For example, data analysis will involve categorizing the types of data that you handle (which may include PHI- personal health information, credit card numbers, names, and addresses, among others). After categorization, relevant KRIs can be identified from your company’s assessment matrix, systems, and workflows.

 

3. Classify data accordingly

After categorizing your data and developing KRIs, the next step is to classify this data according to its level of sensitivity. Data classification helps you determine the level of impact that a potential breach might cause, and to align your data analysis accordingly.

Some of the most common categories that are used for data classification include public data, private data, and restricted data. Public data is information that is available to the general public -including first and last names, social media handles, press releases, etc. Private data is your more secure information- such as business purchase orders or company presentations.

Finally, restricted data is information that is only available to specific people. This includes social security numbers, addresses, bank account numbers, etc. Unauthorized access to restricted data will often result in a significant level of risk.

 

4. Carrying out the actual risk assessment

With the previous steps in place, you may now proceed to carry out the actual data-driven risk assessment. This process involves developing algorithms and using tools that will sift through data based on your established parameters.

As the data is being automatically scanned, all relevant discoveries will be identified for use in your final risk report. For example, your scans may uncover that PCI data isn’t in its correct place, or unauthorized parties within the business are accessing personal health information.

When developing an algorithm for data scans and analysis, make sure that it has the following essential elements:

•  Automation to allow for large volume analyses

•  Artificial intelligence that uses past events to prevent future outcomes

•  IoT capabilities so that multiple devices can be assessed

 

5. Preparing a risk report

Once you’ve identified where your sensitive data lies and pinpointed all potential security issues, you can proceed to prepare a report that outlines your findings. But a risk assessment report goes further than just listing out data analysis results, security gaps, and policy violations. The report should also provide recommendations that will assist with overall risk management.

Some of these recommendations may include proposals for handling data, encrypting sensitive information, and installing protective software.

 

Analytics turn your data from useful to extremely effective and allow you to make changes that will benefit your organization as a whole. A survey by Deloitte found that 55% of organizations believe that analysis improves the organization's competitive position and 96% agree that it will continue to become more important over the next three years. Risk managers should utilize data analytics as they allow you to:

5 Reasons All Risk Managers Should Use Data Analytics

1. Prevent repetitive losses

Incidents are the number one predictor of claims.

For example, somebody trips on a faulty floor mat in your organization's entrance. They choose not to file a claim, but a few weeks later, somebody else trips, falls, and wants to bring action against you. If you had taken action upon the first occurrence, the claim could have been mitigated.

Analytics allow you to identify trends and red flags that could become issues and implement strategies to stop them before they cost you money or someone becomes injured. You can also recognize if a certain area, department, or season has a particularly high claim occurrence and run a root-cause analysis to understand what’s going wrong and how you can fix it in the future.

This prevents you from regularly spending money on the same types of claims and will improve the safety and efficiency of the workplace.

2. Improve insurance premiums

Insurance providers are a business like any other — the end goal is to be profitable. To do this, they want to insure organizations that have “good” risk: those that are likely to pay more in premiums than they require for loss coverage.

If you can show an insurance company the data analysis and resulting mitigation strategies your organization uses, they will want insure you and may offer a more competitive rate. Being able to prevent repetitive losses as described above can also lower premiums.

3. Improve reporting

With more in-depth data and analytics, you will be able to report on any relevant factor in your organization or industry. Analytics help diagnose the issues in your organization and how to fix them. Data will be actionable, easy for anyone to understand, and able to support any business idea or mitigation strategy.

4. Monitor performance

Regular and consistent analytics will allow you to constantly understand how your organization is doing. Managers will be able to hold units or departments accountable for exceeding or failing to meet goals, recognize red flags that may indicate something needs to be changed, or discover why a business strategy isn’t working out as well as planned.

With this in-depth understanding, you’ll enable growth while meeting goals and avoiding overly risky scenarios. An individual department will be more likely to work on issues if they are shown to be underperforming.

5. Forecasting and decision making

Comprehension of what went wrong historically lets you prepare for potential incidents. Of course, there’s always the chance of completely unexpected events, but a thorough risk plan based on analysis will have you ready for almost anything. Analytics also allow you to understand your growth and performance, which is key for setting goals or budgets.

Without analytics, it’s difficult for risk managers to learn from the past or prepare for the future. They make it simple to improve the efficiency and effectiveness of any business.

 

 

F. Data analytics in cost modelling

Cost modelling and cost benefit analysis help to determine and understand the incurred costs and the value derived from a proposed activity, acquisition or investment. Once conducted, they allow organizations or their financiers to investigate different scenarios and determine the most cost-effective way forward.

Cost Optimization using Data Analytics

·      Consistent data management can help minimize last minute complexities and the costs involved

·      The generated data can be specific to internal employee, processes, operations or about customer experience, partner interactions, etc. Monitoring the trend of such data using Data Analytics will help to streamline overall process while increasing ROI and optimizing spends wherever avoidable.

·      Analytics help in shortening testing cycles which can optimize time and money invested.

·      Using Predictive Analytics for proper decision making, businesses can provide a customized experience to each client or solve complex problems with relative ease.

How Data Analytics is helping the following industries optimize cost

Modern businesses driven by technology generate high volumes of data that is usually difficult to manage. Therefore, enterprises need to find ways to use data in a meaningful manner to get better insights.

For instance, data collected from social media when scrutinized using data analytics technique helps us to understand customer preferences better. Listening to the voice of customer can help to accelerate the product design process, reduce cost and time-to-market.

On the other hand, in the manufacturing industry, inventory management is crucial to regulate stock and supply chain. However, huge amounts of data generated and collected from inventory management process can be used effectively for demand forecasting. Data analytics gives an opportunity to analyze the inventory stock better and reduce cost of storing significant amounts of unwanted products.

The implementation of predictive analytics can give you complex view of the manufacturing operations such as overproduction, idle time, logistics, inventory, raw material availability, etc. This helps in cost optimization and increase in company’s ROI.

Predictive Analytics is a set of statistical techniques that includes data mining, predictive modelling and machine learning to analyze current and historical data to make predictions about future or unknown events.

Recently, Cygnet Infotech provided an AI-powered Predictive Demand Planning Solution for Transport Services in the Middle East. The solution reduces the operational cost and helps identifying taxi demand by employing time-series techniques on a real-world data set.

Procurement functions generate more data than any one employee can track and manage. At one midsize manufacturing company with approximately $2 billion in annual revenue, for example, procurement had data on more than 20,000 transactions for a single category, each with four to five statistically significant drivers of price.

But the models used by most procurement functions dramatically simplify the available data to make it easier for purchasers to handle. A lot of potentially valuable insights get lost along the way. Take the cost curve. This widely used modeling approach provides an overview of the average price paid to range of vendors over a year. The model is appealingly simple, but averaging prices can obscure the most critical aspects of vendor performance. For, say, an agricultural product with significant seasonal price variation, the highest-price vendor may be the only one that can supply the product only during the high-cost season, giving it no incentive to match others' prices.

The advantages of advanced analytics

Advanced-analytics techniques use algorithms to recognize patterns in complex data sets, allowing procurement analysts to query all their data, determine the statistically significant drivers of price, and cluster the data according to those drivers. The resulting clusters represent a set of purchases without significant differences in cost drivers and thus reveal the real differences in vendor performance.

A crucial advantage is that unlike people, advanced-analytics systems don't bias their decisions based on gut feeling, or place undue weight on outliers in the data. The systems also enable the testing of thousands of permutations very quickly to determine which statistical clusters fit the data best. In the agriculture example, the algorithm would not need to be told that seasonality is a driving force. It will make this determination from the data. If logistics costs also have a big impact, the algorithm will reveal the distance brackets that make a statistically significant difference.

But advanced-analytics systems can do more than quantify the cost drivers that procurement teams already know about: they can uncover entirely new insights. A recent analysis of vendors revealed one among 400 that was clearly acting as a broker and applying a markup to every sale. With so many transactions, each with four to five significant drivers of price, these subtle trends are nearly impossible to isolate and act on without advanced analytics.

Advantages of price optimization with Machine Learning

In addition to automation and speed, there are several advantages to using Machine Learning to optimize prices.

First, Machine Learning models can consider a huge number of products and optimize prices globally. The number and nature of parameters and their multiple sources and channels allow them to make decisions using fine criteria. This is a daunting task if retailers try to do it manually, or even using basic software.

For example, it is known that changing the price of a product often impacts the sales of other products in ways that are very hard to predict for a human. In most cases, the accuracy of a Machine Learning solution will be significantly higher than that of a human. In addition, retailers can modify the KPI and immediately see how the models recalculate prices for the new goals.

Second, by analyzing a large amount of past and current data, a Machine Learning can anticipate trends early enough. This is a key issue that allows retailers to make appropriate decisions to adjust prices.

Finally, in the case of a competitive pricing strategy, Machine Learning solutions can continuously crawl the web and social media to gather valuable information about prices of competitors for the same or similar products, what customers say about products and competitors, considering hot deals, as well as the price history over the last number of days or weeks.

A system that can learn most of what is happening in the market allows retailers to have more information than their competitors in order to make better decisions.

Price optimization for brick-and-mortar and e-commerce retailers

While it may seem more natural to apply Machine Learning in the case of e-commerce retailers, brick-and-mortar retailers can perfectly take advantage from this technology.

In fact, price changes are less often performed in brick-and-mortar retailers and thus, having more room to improve and adjust to current demand.

Digital price tags are enabling brick-and-mortar retailers to do as many price changes as e-commerce sites. However, even without digital price tags, weekly or monthly price changes can be performed in order to match the current demand and maximize profit.

We have proven our approach with one of the largest travel retailers in the world with over 400 stores across the globe and over 160 million clients per year. We helped them boost gross margin by 28% performing weekly price changes in-store.

Nowadays the world is moving towards changing prices more often and using state-of-the-art data driven pricing strategies is a must. In a study performed by Bain & Company they show that top performers across industries are nearly twice as likely to price dynamically.

Whether it’s about an e-commerce marketplace or a brick-and-mortar retail store, both are embracing the benefits of dynamic pricing and price optimization.

Price optimization helps retailers understand how customers will react to different price strategies for products and services, and set the best prices. Machine Learning models can take key pricing variables into account (e.g. purchase histories, season, inventory, competitors’ pricing), to find the best prices, even for vast catalogs of products or services, that can achieve the set KPIs.

These models don’t have to be programmed. They learn patterns from data and are capable of adapting themselves to new data. They allow retailers to quickly test different hypotheses and make the best decision.

What is probably most important to keep in mind is that the use of Machine Learning in the retail world keeps widening, and all signs point to the fact that this trend will continue in the coming years.

The question is no longer whether to apply dynamic pricing or not. But the question is how to do so in order to remain profitable.