Retail Archives - Argility Technology Group

Google Cloud helps Argility boost SA retail competitiveness

ajustus Nov 6, 2023 BigQuery, DigiCloud, Google Cloud, Partnership, Retail

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

Getting there: Key success factors for retailers

ajustus Jun 10, 2022 Data Analytics, PredictRetail, Retail

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.

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.

Using data to discover the future of retail

ajustus May 19, 2022 Artificial Intelligence, Data, Digital Transformation, PredictRetail, 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.