Tanya Long, COO Argility -retail concepts and resources

Predictive analytics lights the way

Retailers and brands can realise the immense power of data-driven decision-making by using predictive analytics to glean faster insights.

By Tanya Long, CEO – Argility Technology Group

Previous articles published on ITWeb have explored some of the challenges and solutions for retailers’ consideration to limit the impact of load-shedding on operations. The Industry Insights examined how technology, augmented by partnering with trusted suppliers, can be used to help navigate this, but there is more that can be done.

There has been a radical change in production, supply chain and consumer behaviour which cannot be ignored. And here is where we lean on the technology innovations of this century: artificial intelligence (AI), machine learning, predictive analytics and cloud

It is true that predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

To use a casino analogy as an example: Let’s say you play roulette. In years gone by, one would watch the tables, evaluate the trends of each croupier, and then decide at which table you would take your chance with your R100. Imagine if someone had whispered to you – ‘Table 1: 65% probability on red, 35% probability on black’. What decision would you have made? And if an insight like this was given every 15 minutes, would you have been inclined to include this in your next decision for action?

Predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

This is the advantage that predictive analytics can provide. In a nutshell, it is the power of data-driven decision-making.

With machine learning, fluctuating behaviour can continuously be used to understand supply and demand patterns with a speed that is incomprehensible when compared to five or 10 years ago. The traditional software approach was to create the system rules based on the known rules; however, with the increased landscape complexity, an intelligent approach is to use machine learning to identify the patterns and provide much deeper insight.

It’s often said the beauty of AI is that it never sleeps – it works 24/7 – just what is needed to help us in our current economy.

Retailers and brands now have the ability to glean faster insights from multiple inputs relating to stock availability, pricing, placement and customer demand.

Predictive analytics uses the principle of small, compounded gains. For example:

  • What positive impact would be experienced by having a 2% increase in stock-on-shelf availability?
  • What impact would be gained by having a 2% increase in cash flow due to lowering of aged stock surpluses?
  • What would a 1% increase in gross profit mean, if stock prices were able to be speedily and strategically managed by considering pricing appetite and competitor pricing across a basket of products?

Now, I realise all of this takes data, and prevalent discussions in the media has raised the question: how much data do I need? When do I start using analytics? The general consensus is if one hasn’t started yet, you are already backfooted.

The advent of cloud has given the ability to store and process petabytes of data, cost-effectively and without excessive hardware investment. It has also given us the ability to scale easily with increasing/changing needs and to leverage off always-on infrastructure provided by global players.

What problems can predictive analytics solve?

Let’s start with the concept of demand forecasting. In the old days, stock and sales forecasting were generally formulated based on moving averages. Today’s retailers would be ill-advised to continue in the same vein. Predictive analytics can add immense value in this area.

Another area that predictive analytics can play a huge role for retailers is focusing on customer patterns. Not only for personalisation by getting the right product in front of the right customer, at the right time and right price, but also to evaluate the change in shopping behaviours.

Using machine learning for clustering and segmentation, identifying purchase habits and preferred products would streamline a personalised recommendations strategy.

In a credit retail environment, the additional insights on areas such as propensity to buy and customer lifetime value has the potential of elevating prospecting efforts. Anticipating customer needs would definitely not only assist retailers on building customer loyalty, but also decrease the cost of spray-and-pray marketing efforts.

Labour management is another area where data and analytics can play an important role in improving operational efficiencies. One cost pressure which may be within a retailer’s control is looking at optimal workforce levels.

Gathering data on speed and accuracy of staff performance can allow retailers and brands to streamline their operations to efficiently meet fluctuating business demands.

These are by no means where the use-cases end. I suggest commencing in an area where there is a compelling business challenge and in which one can measure and refine the results in order to take appropriate action.

Business and project sponsor support is vital for success, although I believe many stakeholders would rather be proactive and stack the odds in their favour.

Source: IT Web

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Deep dive into impact of IT infrastructure failure

It is important to understand that almost all IT infrastructure is susceptible to failure when there is a sudden loss of power.
By Keenan Naidoo, Argility divisional system manager, ATG

While the obvious impact of load-shedding is literally a matter of keeping the lights on, the ripple effect of the power outages is a deep one for retailers that have incurred:

  • Significant increased costs due to investments in backup generators, fuel and water storage facilities. Research shared by Trade Intelligence states that organisations allocated millions of rands to provide facilities that ensure operational continuity.
  • Increased prices for goods and services being passed back to consumers due to these added costs.
  • Stock shortages and decreased ability to fulfil demand forecasting needs. Disruptions in manufacturing processes results in delays in production and reduced output. Stock outages impact on revenue targets and customer experience.
  • Different shopping habits in certain verticals, specifically in FMCG. According to a survey conducted by Chirp and Trade Intelligence, 70% of shoppers reported their grocery purchasing and food preparation approach has changed.
  • Decreased consumer spending due to increased costs and economic uncertainty.

Retailers are a resilient bunch, and there is a saying from Brene Brown that vulnerability spurs creativity. Apart from investment in backup power solutions, the following are some of the interesting measures that retailers have taken to cope with the impact of load-shedding:

  • Adjustment of business hours to facilitate trading to times when power is available, thereby reducing reliance on backup power sources.
  • Enhancement of customer in-store experiences due to backup power planning and approach.
  • Change of route to market strategies by streamlining supply chain.
  • Creative approaches in the prevention of passing costs across to the consumer.
  • Adoption of predictive analytics to cater for adjustments of inventory, pricing and promotional strategies to meet the changes in product and shopping patterns.
  • Adoption of cloud always-on technologies like Google Cloud and Google Workspace to limit the impact on systems, payment structures and operational teams, while improving security.
  • Development and focus on business continuity plans to prepare and manage grid impacts.

Let’s take a deep dive into the impact of IT infrastructure failures. These system failures occur when there are either no backup power solutions available or they fail. This situation is usually amplified by the absence of procedural shutdown processes. It is important to understand that where there is a sudden loss of power, almost all IT hardware is susceptible to failure, especially equipment with moving parts.

Hard disks and solid-state drives

Data and its integrity is considered one of the most valuable and important assets within the IT infrastructure; therefore, the failure of storage mediums pose the highest risk to retailers. For example, most furniture retailers run a fully operational back-office which allows them to trade when networking systems fail.

The back-offices provide a full feature set within the store environment if there is no networking, and this is dependent on data being stored on-premises, on servers. The most economical way of storing data is on hard disk drives that utilise spinning magnetic disks.

Peripheral devices can also fail when there is a loss of power, which can sometimes leave the business inoperable.

This is a mature, well-tested approach; however, due to the age of the technology, it is ultimately dependent on the availability of power. When there is a sudden power loss, in-process transactions fail. This can lead to data integrity issues or file system corruption. There are redundancy solutions that can be implemented; however, these are also dependent on power so the solution can update.

In recent years, retailers have been making the switch to solid-state drives (SSD). While still prone to failure due to power outages, the speed at which SSDs process transactions means that in-process transactions are given the best opportunity to finalise.

Component and peripheral failures

Component failures include IT components like motherboards, power supplies, chipsets, etc. When these occur, they can leave the system totally incapacitated; however, the chances of these failures causing a loss of data or corruption are miniscule.

Moreover, retailers usually have agreements in place with vendors to replace broken components as soon as possible. This in turn reduces downtime but of course, as with everything – this comes at a cost.

Peripheral devices can also fail when there is a loss of power, which can sometimes leave the business inoperable. Certain peripherals are cheap enough to keep spares; however, larger items such as enterprise printers are extremely expensive and can cause delays in business operations. These failures have no impact on the loss or corruption of data.

Effects of failures on the retailer

There are numerous costs that retailers face in the event of IT infrastructure failures – these can be described as tangible and intangible.

Tangible costs include:

  • Replacement or repair cost for the equipment.
  • Service provider call-out fees, plus professional time and material costs.
  • Providers’ software restoration fees – again plus time and material costs.
  • Loss of revenue generation activities due to downtime.

Intangible costs include:

Brand damage in the face of retailers with no backup systems in place. This can have serious negative impact on consumer confidence in the brand.

Service providers to retailers must ensure their resources are geared up and adequate enough to cater to the increasing demands placed on them due to failures related to load-shedding.

Retailers seeking technology providers should look for companies with dedicated teams that aim to meet their needs in project management, development management, business and systems analytics, software development, and support specialists.

In our business, we regularly see the rise in disk failures entirely attributable to load-shedding. Retailers need to look to suppliers that have innovative, developed solutions that assist businesses in mitigating the negative effect on their ability to operate. They must seek solutions that aim to drive value by preventing other costs related to failures, which are triggered by load-shedding.

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Marko Salic CEO ATG

Google Cloud helps Argility boost SA retail competitiveness

Issued by DigiCloud

Argility Technology Group, a Digicloud Africa Google Cloud partner, is helping local retailers transform their operations, grow margins and become more competitive using solutions built on the Google Cloud.

Argility CTO Marko Salic says advanced digital technologies are key to enabling large retailers to overcome today’s top challenges. “Local retailers are grappling with supply chain disruptions and increasing supplier costs that erode their margins, so they have to find ways to make their operations more efficient. They also have to overcome the customer loyalty issue – modern consumers switch stores at the click of a button. Retailers know they need to improve customer experience to differentiate in a competitive market, but understanding what that means and actually achieving it can be difficult,” he says.

To help large retailers overcome these challenges, Argility has built software and technology to transform business and increase margins. “A 1% increase in revenue or reduction in costs is significant for retailers turning over billions of rand,” Salic notes. “Our technology is helping retailers achieve improvements of up to 6%.”

Argility solutions help retailers harness predictive and prescriptive analytics to process data at a massive scale, to support better decision-making and make processes more efficient. “It allows large retailers to innovate, become more competitive and improve margins by doing more with the same resources, or the same work with fewer resources,” he says.

Salic outlines Argility solutions designed to make predictive analytics easier for retailers.

PredictRetail is our AI-powered pricing, inventory, customer and sales analytics platform focused on high-value use cases of machine learning and predictive analytics for retailers and brands. Its PredictPrice module uses historical sales data, complemented by scraping and matching competitor prices, to analyse price elasticity of demand and determine optimal pricing strategies. It allows retailers to identify the sweet spot of what customers will be willing to pay,” he explains.

“The PredictInventory module helps forecast and segment more accurately, so retailers can more accurately predict what will sell in which store on which day, predict earlier when stock will run out and forecast which stock won’t be sold at all. The gains depend on the maturity of business, but for the vast majority that still rely on spreadsheets built up over decades, using machine learning algorithms for inventory prediction can improve accuracy by around 10%. PredictInventory can improve accuracy by 2%-5% for those using specialised inventory tools.”

The PredictCustomer module enables personalisation using a recommendation engine offering products customers are likely to want. Salic says recommendations – crucial for the success of retail giants such as Amazon – can be used to improve sales wherever organisations have customer data. “This could be through online shopping or loyalty programmes. With personalisation and recommendations, retailers can significantly enhance sales and the customer experience. In a recent implementation in India, our customer increased sales volumes by 6.3% based on personalisation.”

Argility says the Google Cloud, BigQuery enterprise data warehouse and the Vertex AI machine learning (ML) platform underpin the company’s ability to innovate and achieve faster time to value for its customers.

Salic says: “The entire platform has been developed as an extension of Google Cloud, using their data analytics tools and services. At the platform’s core is BigQuery, which hosts all our input and output data. The BigQuery TCO was lower than its competitors, and because it is a completely managed service, it allows us to focus on business requirements instead of managing infrastructure.”

“BigQuery is incredibly scalable. This is important for us because we work with massive volumes of data – tens of billions of rows per month.”

Another key advantage of BigQuery is that it uses SQL syntax, which meant that Argility’s team could lean on their decades of experience and get straight to work. “They didn’t need to retrain and reskill, they could just hit the ground running,” Salic says.

Argility uses Vertex AI to manage the end-to-end machine learning life cycle. Salic says: “We wanted one platform, a managed service we could deploy, and start implementing the business logic. So by combining these two platforms, we could do the job of 20 people with just 10, and we no longer needed large infrastructure teams. We shut down our two data centres and moved everything to the Google Cloud, resulting in significant savings. This has enabled us to become more competitive, develop and deploy solutions faster, and achieve faster time to value.”

Argility aims to continue harnessing Google innovation to improve its portfolio. For example, Salic says Google’s image recognition models could prove useful for the group’s Smollan business, which focuses on point-of-purchase retail solutions. “For big brands and retailers, merchandising is crucial. To ensure the shelf displays and pricing are correct, merchandisers and shelf packers take photos and feed them back to the servers; however, ensuring that the photos match the planograms can be complex and costly. Integrating this process with a Google model could simplify our processes significantly,” he says.

Source: IT Web

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Data Opportunity_Retail Success Factor

Getting there: Key success factors for retailers

Success Factors: Using data analytics to become truly customer-centric is now within reach for retailers, but they need to adopt a focused approach with a clear plan − or risk failure.

By Tanya Long, Chief Operating Officer, Argility Technology Group

This article builds on the argument made in my two previous articles that data analytics, and especially the emerging capability to perform predictive analytics, offer retailers a genuine opportunity to reinvent themselves as customer-centric organisations, deepening customer loyalty and enhancing profitability in a volatile marketplace.

As always, though, nirvana isn’t reached in a day, and there are numerous pitfalls along the way. In this final article, I want to look at how to successfully make the move to becoming a data-driven organisation, rather than one that relies on intuition.

Based on my experience in the industry, the following key success factors need to be integrated into the plan:

Avoid grand projects and keep an open mind. It’s imperative to move away from grandiose projects led by IT, to which some corporates appear addicted. A much better approach is to focus on iterative projects led by the business that are less risky and allow the organisation to learn as it goes. This in turn means being open to changing tack as the data dictates − we have to be open to what the data is telling us, and act accordingly.

Understand data needs. It is also important to recognise that most retailers already have all or most of the data they need − don’t waste time and money trying to get at the data you want (or think you want).

In line with my advice in the first point − take baby steps, survey the existing data and work with that, at least in the beginning. That said, it is vital to take the necessary steps to ensure the data to be used is clean and reliable; the old “garbage in, garbage out” mantra holds good.

Set goals − actionable insights are key. A related point is that it is easy to fall in love with the data and embark on data-related projects that are interesting but that don’t deliver any real benefits. A disciplined approach is vital, and a data project must be aimed at generating insights that are actionable.

Knowing everything there is to know about a customer segment for itself is ultimately counter-productive. A better approach is to identify what information is needed to reach a strategic goal or make a better decision.

A disciplined approach is vital, and a data project must be aimed at generating insights that are actionable.

It may be interesting to know a customer’s preferences in the abstract, but it is only valuable when aiming to leverage that knowledge in order to get the customer to buy while they are in the store or on the website. An important element is speed: the actionable insight needs to be generated rapidly so that action can be taken in real-time.

Change the corporate culture. If the retailer is going to become customer-centric, just acting on insights is not enough. The whole organisation has to change its focus or predisposition − everything everybody in the company does or says must be founded on the customer.

For that to occur, a vital first step is for everybody to understand the direct link between customer-centricity and the bottom line (and thus, in turn, on benefits, job security and the rest of it). Segmenting customers in terms of their lifetime value to the company, and how much it costs to acquire and then service them, will help to make the business case for data projects. It’s particularly important that the CFO is involved so the return on investment for specific data projects is well understood.

Another central part of the new culture is a shift towards making decisions based on evidence only, not on emotion.

Use the growing understanding of the customer intelligently. My main point here is to ensure the customer experience is well designed in light of this knowledge and is constantly being refined via a feedback loop that links into strategy and operations. Every interaction with the customer, including those undertaken by software, must be linked to data.

Leadership must be on board. A profound change like this will not succeed without strong leadership. It is a vital step to get the leadership team on board and motivated.

Pay due attention to talent management. The customer-centric retailer needs staff who have the right skills. Specifically, this means access to specialists like data scientists but generally an ability to solve problems and follow logic becomes critical.

Make sure the technology is in place. As should be clear by now, technology is not a silver bullet, but it does need to be in place. Storing, processing and analysing fantastically large amounts of data depends on technology, and the building blocks must be solid.

Develop a pilot to demonstrate the value of predictive analytics to the organisation. There’s nothing quite so powerful as a successful project. It remains important to make the case for data’s role in helping the company to become more customer-centric and why that would be beneficial but give the project the best chance of succeeding by carefully designing a pilot project that can be relatively quickly deployed to show what you mean.

Retailers face a set of tough challenges in the short- to medium-term. Only those that harness the power of data to help them understand the challenge and craft effective responses will survive − as will those that understand this is not a quick dash, but a journey undertaken with an open mind.

Published: IT Web

Tanya Long, Chief operating officer, Argility Technology Group.

Tanya-Long COO ATG

Long has 30 years of industry experience. Her career in the IT sector started in 1988 in IT support for point-of-sale solutions. She moved formally into software development with UCS/Argility in 1989 as a developer and progressed through to team leader, account management, project and development management roles, which led her into various industries and corporations.

In 2017, Long returned to Argility (having previously worked at the company in a technical capacity) as human capital executive, where she reunited her retail, IT, leadership and HR knowledge to drive her zeal for transformation.

In her capacity as COO, Long is responsible for overseeing operations, with a specific focus on human capital, sales and marketing, and ensuring the company culture and vision shows up daily for customers through an engaged technical team of experts.

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Data Opportunity ATG

Using data to discover the future of retail

As business digitalisation continues apace, more and more data is being generated. Retailers need to understand its value and how to leverage it.

By Tanya Long, Chief Operating Officer, Argility Technology Group

Digitalisation has been growing for many years, and the social and business changes driven by the global response to the COVID-19 pandemic have accelerated it.

Hybrid working models that involve increased working from home get a lot of the limelight, but equally significant has been the definitive growth in online retail.

For example, e-commerce in the US increased from $431.6 billion in 2020 to $469.2 billion in 2021, with shopping habits looking like they have permanently changed to include a growing online proportion.

The move towards digitalisation across all industries has resulted in record levels of IT spend. Gartner forecasts that worldwide IT spending will jump by 5.5%, its biggest increase in more than a decade, to reach $4.5 trillion in 2022.

Unsurprisingly, given that data is the major by-product of digitalisation, a hefty proportion of that spending is going on big data and business analytics. Investment was projected to reach $215.7 billion at the end of 2021, with a compound annual growth of 12.8% until 2025.

These are all big numbers but, as always, much of that spend does not necessarily result in bottom-line benefits. There’s a lot of focus on collecting data but turning it into insights that generate business benefits is rather more hit and miss.

Effecting that transformation requires not only a profound understanding of the technology involved but, even more critically, how the particular industry sector works.

Goldilocks technology

It seems as though we’ve been talking about data warehouses and business intelligence forever, and business is littered with projects that never really lived up to expectations.

What’s changed is the emergence of new technologies that make it possible not only to collect and store vast amounts of data, but also to process it to extract valuable information.

Thus far, predictive analytics is not a crystal ball, but if one believes half the hype one reads, that could happen in the distant future.

Perhaps the most important of these is the cloud itself, because it provides a way for companies to access the plentiful and cost-effective storage they need to deposit huge and increasing amounts of data, and also the sheer computing power needed to process and analyse it − think artificial intelligence (AI) and machine learning (ML), both used to turn data into information.

As always, though, technology is only part of the story − and not necessarily even the most important part. The real point is that the cloud’s resources make it possible to mine data for patterns, deploy sophisticated statistical models and create models on which to base decisions.

In short, the real story is data and what it can be used to do. There are basically three ways in which data can be used, namely: to describe, to diagnose and to predict. The first two are backward looking: using data to establish what happened (descriptive), and then to understand why it happened (diagnostic). Both are obviously very valuable and can be used profitably to improve future performance.

However, the real benefits come when one starts to be able to use data to look to the future − what is usually called predictive analytics. It uses AI, ML, data modelling and the rest of them to analyse current data and make predictions about the future.

Of course, one has to understand this approach is predicated on the assumption that the future derives from the past, and thus that the past contains the seeds of the future.

Predictive analytics thus becomes less useful when change is highly disruptive, and also the further into the future one goes. The further forward in time one goes, the greater the tendency to regress to the mean, to become more and more generalised.

Clear benefits

Thus far, predictive analytics is not a crystal ball, but if one believes half the hype one reads, that could happen in the distant future.

But it does offer an evidence-based − or data-driven, to quote the industry jargon − way to identify how the near future is likely to unfold, and what actions a specific business should be taking to defend its current position, or to take advantage of new opportunities.

In this way, it can help an organisation to move away from the challenge of choosing between a large number of 50% probabilities, to choosing between a small number of high probabilities.

This is an extremely valuable strategic tool given the growing complexity of the business environment, and the intense competition fuelled by the globalisation of markets and a volatile economic environment.

All of these combine to create what is known as a VUCA world − one that is volatile, uncertain, complex and ambiguous − and so the company that is able to develop a likely-to-succeed strategy based on evidence rather than intuition is already better positioned than competitors.

In certain environments, organisations have experienced that business intelligence delivers a return on investment of 80%, which just goes to show the importance of data-driven decision-making − but the return can shoot up to 250% when predictive analytics is used.

The reason for this startling increase is that decisions are taken based on the rigorous analysis of fact and not on intuition.

The frustration for CFOs when considering the investment in AI and ML is that actual guarantees on ROI is not set in stone, the statistics come from passageway talk about successful projects. But there is no doubt the success is there and that the benefits abound.

Retail is a particularly VUCA sector because it straddles both the real and digital worlds, and consumers are growing ever-more demanding. For them, as I will explore in my next article, predictive analytics is especially attractive.

Source: IT Web

Tanya Long, Chief operating officer, Argility Technology Group.

Tanya-Long COO ATG

Long has 30 years of industry experience. Her career in the IT sector started in 1988 in IT support for point-of-sale solutions. She moved formally into software development with UCS/Argility in 1989 as a developer and progressed through to team leader, account management, project and development management roles, which led her into various industries and corporations.

In 2017, Long returned to Argility (having previously worked at the company in a technical capacity) as human capital executive, where she reunited her retail, IT, leadership and HR knowledge to drive her zeal for transformation.

In her capacity as COO, Long is responsible for overseeing operations, with a specific focus on human capital, sales and marketing, and ensuring the company culture and vision shows up daily for customers through an engaged technical team of experts.

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